UNLOCKING OPPORTUNITIES: THREE ESSAYS ON STATE POLICIES SUPPORTING ENGLISH LEARNERS’ SUCCESS By Caroline Bartlett A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of Education Policy – Doctor of Philosophy K-12 Educational Administration – Dual Major 2025 ABSTRACT Our nation's nearly five million English learner-classified (EL)1 students, comprising nearly 10 percent of the US K-12 student population, face significant educational disparities resulting from inadequate attention to their needs within current policies and practices, placing them at a distinct educational disadvantage compared to their non-EL peers. While a large body of research highlights inequities ELs face in their schools and classrooms, less has focused on the ways broader education policies exacerbate or ameliorate educational inequities for ELs. Research in this area can offer thoughtful analysis to policymakers and education leaders as they work to expand equity for this growing and diversifying subgroup of students. This three-paper dissertation explores the role of state policy in shaping ELs’ educational opportunities in Michigan. Michigan offers a useful context for study as it is a new immigrant diaspora state with a fast-growing EL population, similar to many other US states. The first paper employs a difference-in-regression discontinuities design to assess the impact of shifting reclassification responsibility from school districts to the state, highlighting the ability of default policies to standardize EL reclassification processes. The second paper leverages interview data with school district leaders to better understand how school districts interpret and implement state funding policy to provide EL services. The findings highlight areas in which policy can better support districts to provide effective, high-quality English language development services. The third paper couples administrative and interview data to document changes in recent immigrant, or “newcomer,” student populations, and explores school districts’ responses to such 1 In this study, an English learner (EL) is any multilingual K-12 student in US public schools who has been identified by educators as requiring language assistance services to fully access academic content in English. While I recognize and appreciate more asset-based terms used to describe this student group, including multilingual learner (MLL) and emergent bilingual (EB), I use the term EL in this dissertation as it is consistent with the term used in federal policy (ESSA, 2015) and many state policies to identify this growing population. changes. Findings highlight areas in which state policy can support school districts navigating shifting immigration patterns. Together, these papers examine how systems-level education policies—such as reclassification procedures and funding distribution—shape ELs’ educational opportunities, offering evidence to inform more equitable education policies in increasingly diverse and politically dynamic contexts. ACKNOWLEDGEMENTS I would like to acknowledge the generous financial support provided by a University Distinguished Fellowship, a MITTENSS/Institute for Education Sciences Predoctoral Fellowship, and a National Academy of Education/Spencer Dissertation Fellowship. In addition to their financial support, I am grateful to the people working at the Graduate School, my MITTENSS the faculty and staff who made MITTENSS possible, as well as the staff at the Spencer Foundation. I am also grateful to my colleagues at MITTENSS and Spencer for pushing my thinking and taking the time to engage with my work. I am thankful to Dr. Madeline Mavrogordato, who has been a fantastic mentor both professionally and personally. Maddy, you have helped shape my research questions and trusted me with your research projects from the beginning. You have also helped me develop confidence as a researcher and person. I have never doubted that MSU was the right choice for me, and that is largely due to your mentoring and support. I would also like to thank Dr. Josh Cowen for being a wonderful committee member. I appreciate your kindness, willingness to discuss project ideas at any time, and your advocacy as I navigated the program. Thank you to Dr. Rebecca Callahan for encouraging me to pursue the research projects presented in this dissertation and for always making time for me. Thank you to Dr. Joseph Cimpian for your guidance in quantitative methods and the many enjoyable Zoom calls over the past few years. Thank you to Drs. Scott Imberman and Gerardo Lopez for being excellent committee members and providing thoughtful feedback on this dissertation. I am also indebted to Kelly Alvarez and Michelle Williams at MDE for trusting and encouraging me to ask and answer policy-relevant research questions. iv Next, I would like to extend my heartfelt thanks to the many people within and outside the program who have made Lansing a home. First and foremost, thank you, Matthew. I can’t imagine the last four years without you. I have never met someone as hardworking, brilliant, and thoughtful as you. This dissertation would have been impossible without you, both because you read and edited so many drafts of each paper and because you were always there to chat. You make everyone around you smarter and kinder, and I hope to extend the same support to you. And thank you for the pink Stata code. I don’t think anyone else would—or could—make a gift like that. Keri, I’m so glad MiLife brought us together. You are the biggest Alpha Female I know, and our OTF classes, Whole Foods treat runs, and typical Lansing Thursday nights at Green Door have kept me going. I am very excited to imprint on bbg and watch you become a mom. Andrew, you’ve been a listening ear when I needed it most. I’d like to express sincere thanks for your thoughtfulness and sense of humor. I look forward to all of the future fights we will have about card games and sports and your Ivy League taste in food. Annie, you’ve been the best cohort mate and friend I could ask for. I thank you for your kindness as I’ve navigated personal and professional challenges, your perspective on my research, and the many fun nights (and misadventures) we’ve had in Lansing, Detroit, and Greece. You are a true friend, and I’m thrilled that I’ll live closer to you soon. And to the rest of my amazing support system—Amy, Nadim, Mary Elmore, Danielle, Brie, Lauren, Mikey, Cole, Mary Rose, Ryan, Shane—thank you endlessly for always being there. I am so lucky to have been raised by strong and resilient women. First, to Mama, you have been the perfect balance of kind, fun, and strong. I’m so grateful for our relationship. You are my best friend, and you mean the world to me. Joanie, thank you for always making me v laugh. Miss Mare, you have shown me unconditional love. You have overcome so much in your life, and with each setback, you have found a way to stand back up. Your strength and faith are truly inspiring. You have all shown me how to stand on my own two feet, and I couldn’t have asked for more perfect role models. Thank you to my dad for being such a kind and accepting person. Our trips to concerts have been the breaks from graduate school that I looked forward to the most. I’m thankful that you’ve raised me to be open-minded and shaped my sense of humor, and I strive to be as gracious to others as you are. To Warren, you’ve been a fantastic uncle. Thank you for also shaping my music taste and humor. Your texts are always my favorite to receive. And to Hayes, you’re the best brother I could ask for. Thank you for always letting me put on my reality TV show (and for your commentary about it). Finally, to Pops, I would not be here without your support. Thank you for enabling me to go to Arizona. I think of you every time my alarm goes off at 5AM, although most of the things I wake up for are less fun than our horse rides together. I miss you, and I’m so grateful to have spent so much time with you. Finally, thank you to My Responsibilities, Winnie and Luna. Winnie is the best dog I could ask for. Our daily walks have brought me so much joy, and I wouldn’t feel like I was at home if her lethal tail wasn’t knocking some wine glass or decoration off of a shelf. I wouldn’t drive 18 hours to Wyoming to let a dog run free on a ranch…or pay for a monthly Prozac prescription for a dog…or buy the filtered water at the dog hotel…if it weren’t for Winnie. And Luna has been a constant companion. She has moved across three states with me (and been very quiet in the car every time—thank you), kept me company through (many) ups and downs, and been a very good foster sister to all of the cats and dogs I have brought home over the years. I’m very happy that I have both of them in my life. vi PREFACE This research result used data structured and maintained by the MERI-Michigan Education Data Center (MEDC). MEDC data is modified for analysis purposes using rules governed by MEDC and are not identical to those data collected and maintained by the Michigan Department of Education (MDE) and/or Michigan’s Center for Educational Performance and Information (CEPI). Results, information, and opinions solely represent the analysis, information, and opinions of the author(s) and are not endorsed by, or reflect the views or positions of, grantors, MDE, and CEPI or any employee thereof. The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Grants R305B200009 and R305H190004 to Michigan State University. The opinions expressed are those of the authors and do not represent views of the Institute or the U.S. Department of Education. vii TABLE OF CONTENTS INTRODUCTION .......................................................................................................................... 1 PAPER 1: LEVELING THE PLAYING FIELD: DEFAULT POLICY AND ITS EFFECTS ON ENGLISH LEARNER RECLASSIFICATION RATES ................................................................. 7 PAPER 2: DISTRICT IMPLEMENTATION OF ENGLISH LEARNER FUNDING POLICY: EVIDENCE FROM MICHIGAN ................................................................................................. 56 PAPER 3: THE RELATIONSHIP BETWEEN IMMIGRATION CHANGES AND NEWCOMER ENGLISH LEARNER EDUCATION: EVIDENCE FROM MICHIGAN ................................................................................................. 97 CONCLUSION ........................................................................................................................... 134 BIBLIOGRAPHY ....................................................................................................................... 136 APPENDIX A: SUPPLEMENT TO PAPER 1 ........................................................................... 147 APPENDIX B: SUPPLEMENT TO PAPER 2 ........................................................................... 176 viii INTRODUCTION Our nation's nearly five million English learner-classified (EL)2 students, comprising nearly 10 percent of the US K-12 student population, face significant educational disparities resulting from inadequate attention to their needs within current policies and practices, placing them at a distinct educational disadvantage compared to their non-EL peers. Historically, federal and state policies have aimed to enhance ELs' access and educational opportunities (Mavrogordato, 2012). However, despite contributing rich cultural and linguistic assets to their schools, ELs continue to be underserved within classrooms and by educational policies that do not prioritize their best interests. This disparity is evident in their limited access to rigorous academic content and certified teachers, which their non-EL peers routinely access (e.g., Callahan et al., 2010; Estrada, 2014; Thompson, 2017). At the school level, many educators lack specialized training to support ELs effectively and often approach EL instruction from a deficit perspective, undermining ELs' potential for academic success (Dabach et al., 2018; Kanno, 2018). Consequently, ELs may be under- identified for gifted and talented services (Sanchez, 2016) and inaccurately identified for special education services (Zacarian, 2011). Furthermore, while bilingual instruction has successfully improved both academic and social outcomes for ELs (e.g., Bibler, 2021; Steele et al., 2017), many ELs primarily receive instruction in English (Sugarman, 2018). Finally, while schools are legally required to provide ELs with linguistic support services, many schools receive inadequate 2 In this study, an English learner (EL) is any multilingual K-12 student in US public schools who has been identified by educators as requiring language assistance services to fully access academic content in English. While I recognize and appreciate more asset-based terms used to describe this student group, including multilingual learner (MLL) and emergent bilingual (EB), I use the term EL in this dissertation as it is consistent with the term used in federal policy (ESSA, 2015) and many state policies to identify this growing population. 1 funding to implement policies and practices that expand ELs' opportunities (Sugarman, 2021), limiting their access to rigorous coursework and opportunities. Implementation of many federal and state education policies often works against ELs, failing to consider their unique strengths and needs. While a large body of research highlights inequities ELs face in their schools and classrooms, less has focused on the ways broader education policies exacerbate or ameliorate educational inequities for ELs. Research in this area can offer thoughtful analysis to policymakers and education leaders as they work to expand equity for this growing and diversifying subgroup of students. Further, more nuanced research focused on intersectionality (e.g., the effects of poverty and racial discrimination against ELs) and heterogeneity in the EL population (e.g., students' time classified as ELs, racial and linguistic subgroups of ELs) can help identify policies that support or constrain EL student success as the population continues to grow and diversify. My three-paper dissertation addresses these issues by exploring how states and school districts can use policy inputs, such as reclassification processes and school finance policy, to support school districts in expanding educational opportunities for EL students. The first study, co-authored with Drs. Joseph Cimpian and Madeline Mavrogordato, evaluates the impact of shifting EL “reclassification” procedures from school districts to state responsibility. EL students qualify to reclassify upon demonstrating proficiency on a state English proficiency assessment. Once EL students qualify to reclassify, reclassification becomes an administrative task carried out by either state data systems or school district leaders. Timely reclassification enhances eligible ELs’ educational opportunities and long-term outcomes by granting students access to the full range of academic coursework often unavailable to EL-classified students. Nevertheless, many EL students who qualify to reclassify are not formally reclassified. Factors unrelated to 2 students’ English proficiency levels, such as educators’ subjective judgment and implementation of state reclassification policy, may influence the likelihood that an eligible student is reclassified (e.g., Estrada & Wang, 2018; Mavrogordato & White, 2017; Robinson-Cimpian & Thompson, 2016; Umansky et al., 2020). In this study, my co-authors and I implement a difference-in-regression discontinuities approach, which combines regression discontinuity estimates with difference-in-difference methods to provide the first quasi-experimental evidence that shifting reclassification responsibility to the state can meaningfully affect reclassification rates and reduce linguistic disparities in the reclassification process. We find that shifting reclassification responsibility from districts to the state increases eligible students’ likelihood of reclassifying by nearly 36 percentage points, with larger effects for Spanish speakers. In Michigan, this shift both increases reclassification rates and significantly reduces linguistic disparities in reclassification rates among eligible students, improving students’ access to rigorous learning environments. Given that reclassification procedures rely on student test score measures of English proficiency growth, my second and third papers explore the factors that enable schools districts to provide high quality English language development services to ELs. One such component explored in the second paper is Michigan’s approach to funding EL education. Federal law and policy (e.g., Bilingual Education Act, 1968; Every Student Succeeds Act, 2015) require schools to provide ELs with supplemental supports so that they can meaningfully access academic content in English. However, school districts nationwide report insufficient funding to meet ELs’ needs (Sugarman, 2016), and there is little research or policy consensus on the best ways to allocate funds for EL education (Verstegen, 2017). Thus, two important challenges for 3 researchers and policymakers are determining how much funding is necessary to enhance ELs’ educational opportunities and deciding the best way to allocate that funding. This study examines how school district leaders in Michigan interpret and implement the state’s tiered EL funding policy, which allocates resources based on students’ English proficiency levels. Drawing on interviews with 17 leaders across 10 demographically diverse districts, I find that while the tiered approach aims to reflect ELs’ varying needs, funding levels are widely viewed as inadequate, especially in low-incidence districts with limited capacity and expertise. In contrast, higher-incidence districts often have more robust infrastructures due to experienced leadership and prior interaction with legal oversight, yet they still face challenges related to funding sustainability and enrollment volatility. The study highlights a lack of clear policy implementation guidance and disparities in district capacity as major barriers to effective implementation. Ultimately, the findings suggest that without stronger policy infrastructure and more stable, adequate funding, tiered funding systems may fall short of ensuring equitable access to services for EL students, leaving district leaders to shoulder the responsibility of meeting legal obligations amid resource constraints. Whereas the first paper focuses on the aggregate impacts on ELs from reclassification and English language instructional policies, this paper aims to determine how state policy can better enable education leaders to support ELs. Further, this paper highlights tangible policy inputs that school districts can use to increase ELs’ access to high-quality English support services, rigorous coursework, and reclassification. In addition to financial factors that enable school districts to provide services to all ELs, my third paper examines the implementation of policy specifically supporting EL students identified as “newcomers,” or students who have immigrated to the US in the past three years. Since 2001, changes in immigration law and enforcement have shifted U.S. migration patterns, 4 contributing to a growing population of newcomer students. These students arrive under varied circumstances, including refugee resettlement, family reunification, and work-related moves. They bring diverse linguistic, academic, and social-emotional assets and needs which are, in turn, shaped by immigration policies and public rhetoric. Schools and educators play a crucial role in supporting them, but programming varies widely based on local capacity and policy guidance. While school-level practices like trauma-informed care and inclusive climates are known to foster belonging, less is understood about how state and district leaders adapt newcomer services in response to demographic and political shifts. This mixed-methods study addresses that gap by examining Michigan’s growing newcomer EL population and exploring how state and local education leaders respond to changes in enrollment and political climate through resource allocation and policy adaptation. Findings offer insights relevant to other states navigating similar challenges in creating equitable newcomer support systems. As the EL population continues to shift nationwide, these three essays contribute evidence on systems-level policies that affect EL students' short- and long-term outcomes, identifying policy levers states and school districts can use to expand educational opportunities for their EL students. By examining the effects of shifting reclassification procedures, exploring the affordances and constraints of funding distribution, and analyzing how school district leaders respond to newcomer enrollment in politically dynamic contexts, this dissertation provides a multidimensional understanding of how education policy can better serve EL students. As states and districts grapple with increasing linguistic diversity, fluctuating immigration patterns, and shifting political contexts, data-informed policies can empower education leaders to meet EL students’ needs. Ultimately, these studies aim to contribute to the growing body of work aimed at 5 dismantling systemic barriers and informing policies that provide ELs with adequate educational opportunities. 6 LEVELING THE PLAYING FIELD: DEFAULT POLICY AND ITS EFFECTS ON PAPER 1: ENGLISH LEARNER RECLASSIFICATION RATES Caroline Bartlett, Michigan State University Joseph Cimpian, New York University Madeline Mavrogordato, Michigan State University Equal access to educational opportunities and the equitable application of policy are fundamental principles in modern education policy, as exemplified by the Every Student Succeeds Act of 2015 (ESSA). Implemented in the 2016-17 school year, ESSA represents a shift towards standardizing student benchmarks and away from local discretion. Understanding the effects of policy decisions aimed at increasing standardization and uniformity in processes is critical for assessing if this trend leads to more equitable decision-making. On the one hand, discretionary policies can allow for flexibility and nuanced decision-making regarding the education of individual students. On the other hand, they can also allow for differential treatment of similarly situated students, which may reflect biases against particular groups of students. The trend toward education policy standardization is particularly true for multilingual students classified as English learners (ELs), who make up nearly ten percent of the US K-12 student population and attend schools in almost every district (National Center for Education Statistics, 2023). While classified as ELs, students are legally entitled to linguistic support services and additional resources to help them meet academic content standards as they develop English proficiency (ESSA, 2015). EL classification also often results in students being educated 7 in different settings or receiving different educational services than non-ELs. Once ELs demonstrate English proficiency1, they qualify to exit EL services through a process called “reclassification.” Reclassification is significant because it ends schools’ legal obligation to provide linguistic support services and routes students into mainstream academic coursework that is often unavailable to students classified as ELs. If timed appropriately, reclassification should result in a smooth transition to mainstream academic content and ensure students receive access to a developmentally appropriate educational setting (Robinson, 2011). Once an EL student qualifies for reclassification, their state or school district must administratively formalize their exit from EL status using either a manual or automatic reclassification process. Many states require districts to identify and manually reclassify students who have met reclassification criteria. Manual procedures often result in many eligible students remaining classified as ELs (e.g., Cimpian et al., 2017; Estrada & Wang, 2018; Mavrogordato & White, 2017). Further, reclassification under manual procedures is uneven, with eligible Spanish speakers being notably less likely to reclassify than students speaking other home languages (Mavrogordato & White, 2017; Umansky et al., 2020). This can lead to restricted opportunities to learn in academically rigorous settings and, in many cases, to students being denied access to mainstream core coursework (Estrada & Wang, 2018). A handful of states implement automatic reclassification procedures, in which students who meet reclassification criteria are automatically reclassified via state administrative data systems, thus removing the manual determination by districts. However, the ability of automatic procedures to (1) increase reclassification rates of eligible students and (2) alleviate linguistic biases in reclassification decision-making processes remains unexplored. 8 A state's choice to implement a manual or automatic reclassification procedure has meaningful consequences because it sets the default approach to reclassification. In general, automatic processes assume that the qualifying student will reclassify unless an extenuating circumstance indicates the student is not ready. Manual procedures, on the other hand, require districts to decide to reclassify eligible students actively. In other words, automatic reclassification procedures generally require districts to "opt out" of reclassifying students, while manual procedures require districts to "opt in." Default policies may increase uptake of a desired outcome (Jachimowicz et al., 2019), making them potentially powerful tools in states new to serving EL students, especially in contexts where educators have less experience identifying whether students will benefit from reclassification or in contexts where capacity is limited. In light of ESSA’s (2015) call for standardization of reclassification, identifying processes that effectively reclassify eligible students is vital for policymakers to facilitate equitable reclassification rates for ELs. To better understand the efficacy of different reclassification procedures for reclassifying eligible students, we present estimates of the effect of shifting statewide reclassification policy in Michigan. Michigan is a new immigrant diaspora state and, like most other states, serves a rapidly growing EL population. In the 2019-20 school year, Michigan shifted from a manual (school district responsibility) to an automatic (state responsibility) reclassification process. We use a difference-in-regression discontinuities design to estimate (1) how shifting the default reclassification procedure in Michigan impacts eligible EL students’ likelihood of reclassifying and (2) how the magnitude of impact varies across subgroups of ELs. We find that shifting from manual to automatic reclassification procedures in Michigan results in significant and meaningful default effects on reclassification rates for eligible students. 9 In Michigan, eligible students are over 35 percentage points more likely to formally reclassify under automatic reclassification than manual reclassification. We also find preliminary evidence of larger effect sizes for ELs reporting Spanish as a home language, suggesting that automatic procedures have the potential to close gaps in reclassification rates for groups of eligible students who may experience bias under manual reclassification procedures. Given ESSA’s (2015) call to standardize reclassification policy, state education agencies may look towards procedures like automatic reclassification to facilitate standardization of the reclassification process and equitable reclassification rates for eligible students. Our analysis contributes to the literature on default policies and EL education policy. First, we present what we believe to be the first evaluation of a default policy in K-12 education. The increasing prevalence of administrative data sets and the goal of closing education opportunity gaps have resulted in a rise in default policies in education, particularly around enrolling students in advanced coursework. For example, legislators in Texas recently adopted a policy that requires 5th graders who performed in the top 40 percent on a standardized math assessment to automatically be enrolled in advanced math for sixth grade (Richman, 2023). While researchers have examined the effectiveness of default policies in other fields (e.g., automatic voter registration [Garnett, 2022] and automatic enrollment into retirement savings [Madrian & Shea, 2001]), we have little information on how default policies function in K-12 education. Additionally, we contribute to the literature on EL reclassification by evaluating the ability of two commonly used reclassification procedures to reclassify eligible students. Prior research identifies reclassification as a critical but elusive juncture in EL students’ educational trajectory, with a substantial population of reclassification-eligible students remaining classified 10 as ELs and lacking access to mainstream coursework. However, no studies have investigated whether shifting to an automatic reclassification process leads to more students who meet reclassification criteria exiting EL status. Moreover, existing research on EL reclassification uses data from before ESSA’s (2015) implementation, which included a push for greater standardization of state reclassification procedures. This study uses post-ESSA data, which may apply to states’ current reclassification contexts. Background In what follows, we discuss the prior literature on the significance of timely EL reclassification and the factors influencing reclassification, as well as the role of default policies. This review will examine the adverse effects of prolonged EL status for students demonstrating English proficiency, the variability in state and district reclassification policy implementation, and the complexities of implementing manual reclassification procedures. We then discuss the impact of default policies across various contexts and highlight their potential as effective policy tools in education, particularly for improving EL reclassification outcomes. English Learner Reclassification Timely reclassification is vital as premature reclassification may lead students to struggle in mainstream coursework without linguistic scaffolds, and prolonged EL status may inadvertently lead to adverse social and academic consequences for students. While classified as ELs, students are legally entitled to linguistic supports that allow them to access academic content meaningfully (ESSA, 2015). Premature reclassification may negatively impact ELs’ outcomes in the mainstream classroom by providing fewer opportunities for English language development and limited linguistic scaffolding to access mainstream content (e.g., Cummins, 1980; de Jong, 2004). For those with relatively advanced English proficiency, the EL label itself 11 and resulting barriers to core academic coursework can adversely affect academic achievement (Umansky, 2016). EL classification has also been linked to restricted access to core academic content and limited opportunities to interact with non-EL peers (Umansky, 2018), limited access to honors and college preparatory coursework at the secondary level, higher dropout rates, and decreased rates of college enrollment (e.g., Carlson & Knowles, 2016). Ultimately, EL services are intended to benefit students as they develop English proficiency, and reclassification upon demonstrating English proficiency ensures students have equitable access to challenging and appropriate coursework. State-specific reclassification criteria and processes determine whether and when EL students reclassify (Morales & Lepper, 2024). ESSA (2015) requires states to determine reclassification criteria which are to be implemented evenly across districts in the state. Criteria must include an English language proficiency test score but can also include other objective or subjective criteria at the state’s discretion, such as standardized test scores or teacher recommendations. Determining appropriately rigorous reclassification criteria are paramount to ELs’ long-term success. For example, if policymakers set the reclassification criteria too low or high, students may experience adverse effects of reclassification or prolonged tenure in EL status, respectively. Although states establish standardized criteria for reclassification eligibility, meeting reclassification criteria does not guarantee that a student will reclassify. Existing studies have found that in many districts, a substantial number of eligible students are not reclassified due to variations in local policy interpretation and implementation (Cimpian et al., 2017; Estrada & Wang, 2018; Mavrogordato & White, 2020). For example, in a mixed-methods case study of reclassification procedures and outcomes in two California school districts, Estrada and Wang 12 (2018) report that while one district reclassified nearly all students who met the criteria, another district reclassified only 67% of eligible students. Ultimately, the authors conclude that several factors drive differences in reclassification likelihood for eligible students, including excessive administrative burden on school district leaders to formally reclassify a student (e.g., requiring signature forms from parents, errors in applying criteria, lack of district monitoring of students and procedures) and staff perceptions of the benefits or drawbacks of reclassification compared to remaining EL-classified for specific students. To better understand between-district variation in reclassification outcomes for students who meet reclassification criteria, Cimpian and colleagues (2017) compare two states using a regression discontinuity design. Across both states, the authors report substantial between-district and between-grade variation in how meeting test-based reclassification criteria predicts a student's likelihood of reclassifying. For example, among districts with below-average reclassification rates in one state, meeting the reclassification criteria did not influence a student's likelihood of reclassifying. In contrast, meeting the same criteria in districts with above-average reclassification rates significantly increased students' likelihood of reclassifying. These studies suggest a complex interplay between reclassification criteria and outcomes, and they indicate that several factors, including district characteristics, district-level policies, procedural burden, and staff attitudes or knowledge, contribute to variation in reclassification likelihood among students who meet the established criteria. Other studies identify heterogeneity in eligible students' likelihood of reclassification based on their grade level. For example, Robinson (2011) finds that as EL students in California progress through school, they are less likely to reclassify upon meeting the criteria. Specifically, the reclassification rate for fourth graders meeting all criteria was 91%, compared to 64% for 13 tenth graders, signifying greater teacher discretion in the reclassification process in high school. In contrast, using data from one large California school district, Umansky and Reardon (2014) report that 12% of ELs who meet test-based reclassification criteria do not reclassify in 5th grade. However, more ELs reclassify in 11th grade than qualify, suggesting educators are more likely to perceive an urgent need to reclassify students in later grades (Umansky & Reardon, 2014). Finally, educators' perceptions of EL students from different racial or ethnic backgrounds may also inform their reclassification decisions. For example, Umansky and colleagues (2020) report a higher likelihood of reclassification among Chinese-origin than Latinx ELs, even when Chinese-origin ELs do not meet reclassification criteria. Furthermore, Mavrogordato and White (2017) find that reclassification-eligible students who speak languages other than Spanish are five percentage points more likely to be reclassified than their Spanish-speaking peers. In summary, findings regarding heterogeneity in the reclassification rates of eligible students suggest that the reclassification processes can be influenced by various factors, including grade level, teacher discretion, and perceived urgency, highlighting the complexity of manual reclassification decisions. Given these collective findings, researchers suggest that reliance on more objective and standardized reclassification policies can improve discrepancies in reclassification for eligible students (Estrada & Wang, 2018; Okhremtchouk et al., 2018). Default Policies An automatic or default policy is a standard or predetermined choice automatically applied to an action if no alternative option is chosen (Herd et al., 2013). Policymakers establish defaults that reflect their preferred choice, and individuals must take deliberate action to opt for something different. Ultimately, policymakers implement these preselected choices because they 14 are a subtle but powerful way to influence decisions and increase the uptake of a preferred option (Jachimowicz et al., 2019). In practice, default policy options provide straightforward means of implementation. For example, a well-known default policy is organ donation registration. This policy is often implemented by stating, "You are currently registered as an organ donor. Do you not want to be an organ donor?" By default, individuals are enrolled to be organ donors and must take deliberate action if they wish to opt out (Johnson & Goldstein, 2003). Despite their simplicity, default policies substantially influence individuals' decision-making. Defaults have proven to be practical policy tools across a wide range of social issues, increasing organ donation rates (e.g., Johnson & Goldstein, 2003), voter registration (e.g., Garnett, 2022), and retirement savings (e.g., Madrian & Shea, 2001). Generally, the literature on default effects finds that decision-makers are likelier to choose the default option than an alternative (Jachimowicz et al., 2019). Research studying the efficacy of default effects in education policy is small but growing and, thus far, has focused on higher education rather than K-12 education policy. Behlen and colleagues (2023) investigate the impact of defaults on universities’ final exam sign-up procedures in contexts that require students to register for final exams. They find that under default enrollment in final exams, students are likelier to participate in and succeed in final exams, underscoring the effectiveness of defaults in education. Additionally, Cox and colleagues (2020) provide insights into the factors that affect student loan borrowers’ decisions to opt for income-driven repayment plans instead of other loan repayment plans that may increase their chances of defaulting. Their findings highlight the importance of defaults and information provision in decision-making processes in this area and demonstrate that implementing income- driven repayment plans as the default can substantially decrease students’ likelihood of choosing 15 riskier repayment plans. These studies indicate that defaults can be promising policy levers to improve students’ short- and long-term outcomes. Specific to EL reclassification policy, automatic procedures require districts to opt out students who meet reclassification criteria, while manual procedures require districts to actively opt in students to be reclassified. Many states currently employ a manual reclassification procedure in which school districts are responsible for identifying and reclassifying eligible students in district data systems. A handful of states have moved to an automatic reclassification procedure, which leverages state administrative data systems to automatically identify and reclassify eligible students upon receiving standardized test scores. Automatic procedures eliminate the burden on districts to identify and complete reclassification paperwork for eligible students, factors that contribute to disparities in the reclassification of eligible students (Estrada & Wang, 2018). In addition, they implicitly convey to districts that reclassification is the status quo for students who meet the criteria. It is also important to note that automatic reclassification policies do not imply that districts lose agency in determining which students reclassify. Exiting an EL student who has met eligibility criteria is the default, but automatic policies can be written such that districts can intervene and opt out of reclassification for individual students if they have reasons to believe continuing to receive English language development services would benefit the student. Whether a manual or an automatic reclassification procedure leads to more equitable decision-making remains an open question. On one hand, manual policies allow for flexibility and nuance at the local level. These policies implicitly encourage local discretion by requiring district personnel to reclassify eligible students individually. For example, educators may identify students who meet test-based reclassification criteria but would benefit from continued 16 EL services and retain that student in services. On the other hand, manual policies allow for differential treatment of similarly situated students. For example, Mavrogordato and White (2017) report that under manual reclassification procedures, educators at times rely on data unrelated to English proficiency, such as students’ personality traits and behavior. In these cases, a student may remain classified and receive EL services when they are in need of other, unrelated supports. In this case, automatic policies may result in more equitable decision-making because they remove the manual determination by districts. Consistent with existing literature examining default policy effects, we hypothesize that an automatic reclassification policy will increase reclassification rates among eligible students compared to a manual reclassification policy. Additionally, because automatic reclassification is based solely on students’ test scores and completed via state data systems, we anticipate that automatic policy will close gaps in reclassification rates across subgroups of eligible EL students whom prior research has identified as less likely to reclassify when eligible, particularly Spanish speakers (Mavrogordato & White, 2017; Umansky et al., 2020). Michigan Policy Context Michigan serves a linguistically diverse and growing number of EL students. In the past ten years, the Michigan EL population has nearly doubled, and in 2023, ELs comprised 98,771 students, roughly 6.9% of Michigan’s K-12 student population. Following federal requirements that states annually assess ELs’ English proficiency growth, Michigan and 40 other states use the WIDA ACCESS 2.0 (hereafter, WIDA) English proficiency assessment to evaluate ELs. All Michigan ELs take the WIDA assessment and other statewide standardized tests each spring. WIDA consists of four domains (listening, speaking, reading, and writing). Students receive a scale score for each domain and an overall scale score 17 ranging from 100 (lowest score) to 600 (highest score; WIDA, 2024). Ultimately, scale scores correspond to an interpretive “proficiency level” score ranging from 1.0 (low) to 6.0 (high; WIDA, 2024). Scale score interpretations vary across grades, while proficiency levels can be compared across grades. We detail the history of Michigan’s reclassification criteria and procedures below. Michigan districts serving ELs are legally obligated to provide students with language support services that enable them to access academic content in English. To meet this goal, Michigan provides supplemental per-pupil funding to districts serving ELs. This tiered funding system provides more funding to students with beginning English proficiency levels and less to those with more advanced English proficiency. Students with WIDA scores above 4.0 do not generate additional funding for their districts (Michigan Department of Education, 2023), so we do not anticipate financial incentives to keep students classified as ELs after they have attained English proficiency. Manual Reclassification, 2016-17 through 2018-19 Before the 2019-20 school year, Michigan required students to meet multiple test-based criteria to qualify for reclassification. In addition, districts were responsible for manually reclassifying students in the state data reporting system. To qualify for reclassification, students needed to attain (1) a WIDA overall score of 4.5, (2) a WIDA reading domain score of 4.0, (3) a WIDA writing domain score of 4.0, and (4) score “proficient” in a locally chosen reading assessment. Regarding the locally chosen reading assessment criterion, districts were permitted to choose from several pre-approved options (e.g., NWEA, AIMSWeb, DIBELS Next, iReady Diagnostic, Star Early Literacy, and the state standardized reading assessment, M-STEP) and define the minimum score required to be considered “proficient,” creating variation across 18 districts in the score needed to demonstrate proficiency and the overall difficulty of the chosen assessment (Personal communication with MDE, 2023). For example, some districts set their proficiency threshold at the 25th percentile of their locally chosen assessment, while others set it at the 75th percentile (Personal communication with MDE, 2023). Districts were not required to submit proficiency thresholds or student performance data on locally chosen reading assessments to the state. However, the proficiency threshold was generally lower on locally chosen reading assessments than on statewide standardized ELA assessments (Personal communication with MDE, 2023). During the manual reclassification process, school district administrators identified and reclassified EL students who met state reclassification criteria using a multi-step process depicted in Figure 1. 19 Figure 1 Manual Reclassification Procedure in Michigan, 2016-17 through 2018-19 Source: Personal Communication with MDE, 2023 20 Automatic Reclassification, 2019-20 through 2021-22 Acknowledging disparities between the number of students meeting reclassification criteria and those actually reclassifying, Michigan shifted from a manual to an automatic reclassification policy in the fall of 2019. Changes to the reclassification protocol in the fall of 2019 applied to the 2019-20 school year2 and beyond. In shifting to an automatic process, MDE assumes responsibility for reclassifying students through state administrative data systems when the state receives annual WIDA scores from the WIDA consortium. While districts have the opportunity to review students identified for reclassification and can override reclassification decisions if they feel a student is unprepared to reclassify despite meeting criteria, the procedure automatically reclassifies eligible students. Importantly, this process removes all responsibility from districts to reclassify students in district and state data systems. Beyond the shift to automatic reclassification, MDE made two significant changes to the reclassification criteria. First, MDE simplified the criteria to require only one assessment score as evidence of English proficiency. The revised reclassification criteria eliminated the WIDA reading, WIDA writing, and local ELA assessment proficiency thresholds. Second, the state raised the overall WIDA performance level required to qualify for reclassification from 4.5 to 4.8 out of 6.0. Table 1outlines the differences in each period's reclassification criteria and procedures. 21 Table 1 Reclassification Criteria and Procedures by Policy Period Criteria Manual Reclassification (2016-17 through 2018-19) • Overall WIDA performance level of Automatic Reclassification (2019-20 through 2021-22) • Overall WIDA performance level 4.5 or greater of 4.8 or greater • WIDA Reading Performance level of 4.0 or greater • WIDA writing performance level of 4.0 or greater • “Proficient” in a locally chosen reading assessment Procedure • District convenes a team and completes a nine-step reclassification process for qualified students • State data system automatically reclassifies qualified students • Districts have the option to override automatic reclassification Data The data for our analyses come from the Michigan Education Data Center and include observations for all 3rd through 8th grade EL students with valid WIDA scores between academic years 2016-2017 through 2022-2023 (N = 345,044). We restrict our sample to include only EL students who met at least a 4.0 reading and 4.0 writing performance level on the WIDA assessment and have a valid state standardized ELA test score (N = 59,045). We make this restriction to facilitate comparisons across policy periods, as these students would have been relatively close to the reclassification cutoff in both the manual and automatic reclassification periods. In addition, this restriction accounts for students needing to meet a 4.0 reading and 4.0 writing WIDA performance level and score proficient on a reading assessment to qualify for reclassification in the manual reclassification period. Table 2 presents summary statistics for the primary analytic sample by policy period (manual reclassification = pre-period; automatic reclassification = post-period). About 35 and 26 22 percent of students reported speaking Spanish or Arabic as their home language, respectively. Approximately 4 percent of students were also classified as students with disabilities (SWDs), and roughly 72 percent were identified as low-income. About 77 percent of students in the sample were enrolled in elementary grades (third through fifth), and about 23 percent were in middle grades (sixth through eighth). Students’ overall, reading, and writing performance level scores were similar across policy periods. This provides some evidence that students in our sample were similar academically before and after the policy change. WIDA overall and subdomain performance levels are similar across policy periods. To compare scale scores across grades in our analysis, we recenter the scale scores around 0, and 0 represents the minimum scale score required to qualify for reclassification in a given grade. As an example, a third-grade student who attained an overall scale score of 357 in the manual reclassification period would have a value of 1 (the reclassification threshold is 356 for third graders), and a fourth-grade student who attained an overall scale score of 367 in the manual reclassification period would also have a value of 1 (the reclassification threshold is 366 for fourth graders). Recentering the standardized scale scores allows us to estimate the effects of changing policy procedures for the total sample of students. Recentering within grades also facilitates comparisons between the manual and automatic reclassification policy periods. MDE increased the overall WIDA score needed to qualify for reclassification between periods. To facilitate comparisons across policy periods, we recenter students’ scores around the post-period reclassification threshold. On average, ELs in our sample scored 2.18 points above the post-period reclassification threshold. To address changes made to the overall scale score reclassification threshold, we simulate raising the reclassification threshold in the pre-period to match the post-period threshold in our primary analysis. We 23 discuss methods used to simulate raising the threshold in detail in the section titled “Endogeneity Issues.” Of note, the state did not collect data on locally chosen reading assessments, which comprised one additional component of reclassification criteria during the manual reclassification period. To account for this, we use standardized, statewide ELA assessments (M- STEP ELA or PSAT Reading) as a proxy for students’ ELA proficiency on locally chosen assessments. Statewide ELA assessments were likely to be as difficult or more difficult than locally chosen reading assessments, so they should serve as a strong indicator for students’ performance on local reading assessments (Personal Communication with MDE, 2023). Due to the COVID-19 Pandemic, statewide standardized ELA assessments were not administered during the 2019-20 school year. As a result, we do not include observations from 2019-20 in our main sample3. We consider students “qualified for reclassification” if they meet the WIDA overall performance thresholds. The sample in Table 2 reflects the set of students who met WIDA reading and writing performance thresholds of 4.0 (criteria for reclassification eligibility in the manual reclassification period). The manual reclassification period shows large gaps between the number of students eligible for reclassification and those who were reclassified (80 percent versus 51 percent). In contrast, we see similar percentages of students qualifying and reclassifying in the automatic reclassification period (54 percent versus 53 percent). The overall percent of students qualifying for reclassification likely shrunk between policy periods due to raising the overall score needed to qualify for reclassification and an overall decrease in student performance in Michigan after the COVID-19 pandemic (Kilbride et al., 2024). We discuss how we account for raising the reclassification threshold in the “Endogeneity Issues” section of this 24 paper. Although students’ performance declined in Michigan following the pandemic, student performance showed signs of progress beginning in the 2022-23 school year, which we incorporate into our post-period analytic sample. Our primary outcome of interest is whether an EL student reclassifies upon meeting reclassification criteria. Table 2 displays the percentage of students in our main sample who met each reclassification threshold under manual and automatic reclassification policies. 25 Table 2 Descriptive Statistics by Policy Period Covariate means (% of sample) Female Primary Language: Spanish Primary Language: Arabic Primary Language: Other SWD Low-income Grade (% of sample) 3 4 5 6 7 8 Test Scores Reclassification Period Manual Period (2016-17 through 2018-19) Automatic Period (2019-20 through 2022-23) Full Sample 54 36 25 37 4 72 19 28 25 9 9 9 55 31 27 42 4 71 18 37 30 5 5 6 54 35 26 39 4 72 19 31 27 7 8 8 WIDA Overall Scale Score Recentered Around Post-Period Reclassification Threshold 2.19 (15.49) 2.17 (15.68) 2.18 (15.56) WIDA Overall Performance Level WIDA Reading Performance Level 4.87 (0.52) 4.87 (0.52) 4.87 (0.52) 5.58 (0.56) 5.51 (0.59) 5.56 (0.57) 26 Table 2 (cont’d) WIDA Writing Performance Level Reclassification Qualified for Reclassification Reclassified 4.35 (0.33) 4.36 (0.39) 4.35 (0.35) 80% 51% 54% 53% 70% 52% N Note: Data for this analysis come from the Michigan Education Data Center. EL, English learner; SWD, student with disabilities. WIDA scale scores are recentered around their respective grade-level reclassification threshold for interpretation across grades (e.g., a recentered scale score of -1 can be interpreted as meaning the student missed the reclassification threshold in their grade and policy period by 1 point). The WIDA assessment reports scale scores between 100-600, and performance level scores are reported on a scale of 1.0-6.0. State ELA assessment = M-STEP ELA or PSAT reading. A student is considered “qualified for reclassification” in the pre-period if they met the grade-level WIDA overall scale score threshold for reclassification in the pre-period, and in the post-period if they met if they met the grade-level WIDA overall scale score threshold for reclassification in the post-period. 50,221 32,124 18,097 27 Endogeneity Issues Our estimation strategy faces two primary endogeneity threats. First, when MDE shifted from manual to automatic reclassification, they also eliminated three components of the reclassification criteria (see Table 1). Second, MDE increased the overall WIDA score needed to qualify for reclassification upon shifting to an automatic reclassification process. This section discusses how our analysis accounts for these endogeneity threats. Accounting for Eliminating Reclassification Criteria To address the removal of WIDA reading, writing, and local reading assessment scores from state reclassification criteria, we apply a “frontier RD” approach (Reardon & Robinson, 2012). Frontier RD models subset the sample of students used in the analysis to those with scores above or below the cutoff score on all dimensions but one, then model the RD along only one cutoff score (Reardon & Robinson, 2012). Here, we subset the sample to students with at least a 4.0 performance level on the WIDA reading and writing subdomains required to qualify for reclassification during the pre-period. We also include a covariate for students’ recentered and standardized M-STEP ELA or PSAT reading scores in equations (1a) and (1b) as a proxy for proficiency on local reading assessments. The resulting frontier RD sample produces results that are easily interpretable, reduces a multidimensional problem (shifting multiple reclassification criteria simultaneously) to a single dimension (only estimating the effect of shifting manual to automatic reclassification policies by accounting for other factors through sample selection), and isolates the effect of shifting from manual to automatic reclassification (e.g., Reardon & Robinson, 2012). 28 Accounting for Raising the Reclassification Eligibility Threshold The second endogeneity threat concerns the change to the reclassification eligibility threshold for students’ overall WIDA scale scores. MDE increased the overall scale score required to qualify for reclassification across policy periods. Students were eligible for reclassification if they achieved a WIDA overall performance level of 4.5 during the pre-period and 4.8 during the post-period. To ensure comparisons between similar students who would have met reclassification criteria in either period, we simulate raising the reclassification threshold in the pre-period to match the post-period threshold. To create a sharp RD cut point at the higher reclassification threshold in the pre-period models, we assume that all students who did not meet the post-period reclassification threshold were not reclassified. In reality, some of the students below the simulated threshold were reclassified (as demonstrated in Figure 2, Upper Bound ITT Estimate). As a result, our simulated sample (Figure 2, Lower Bound ITT Estimate) will provide a lower-bound estimate of the effect of shifting reclassification procedures because the simulated ITT effect in the pre-period is larger than in reality. Formally, this implies that we inflate the pre- period ITT effect estimate. This inflated estimate is subtracted from the post-period estimate such that 𝐷𝑖𝑅𝐷 = 𝛽! "#$% − 𝛽! "&’. We report results for estimates using the simulated 4.8 reclassification threshold for comparison across policy periods. Table 3 provides information on analytic sample selection and reclassification eligibility and rates by year based on the post- period reclassification cutoff score. 29 Figure 2 Upper- and Lower-Bound Pre-Period ITT Estimates 30 Table 3 Analytic Sample Selection and Classification Rates by Year Year 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 Total Students (N) 9,376 10,801 11,947 4,370 7,087 6,640 Eligible to Reclassify Based on Post- Period Cutoff (N) 5,130 6,192 6,017 Eligible to Reclassify Based on Post- Period Cutoff (%) 55 57 50 Qualified to Reclassify and Reclassified (N) Reclassification Rate of Eligible Students Based on Post-Period Threshold 3,154 4,460 4,369 61 72 73 99 98 99 Omitted due to COVID-19 Pandemic 2,174 3,979 3,637 50 56 55 2,144 3,904 3,596 31 Research Methods We use two approaches to estimate the effect of qualifying for reclassification on reclassifying during the manual versus automatic reclassification periods. First, we use a sharp RD analysis to estimate the intent-to-treat (ITT) effect of qualifying for reclassification on reclassifying during each of the two policy periods. The ITT effect estimates the impact of meeting or exceeding the reclassification threshold (recentered WIDA scale score) on the outcome (reclassification). Then, we use a DiRD approach to compare the two ITT effect estimates (e.g., Robinson-Cimpian & Thompson, 2016). The difference obtained from the DiRD framework provides a plausibly causal estimate of the effect of shifting from a manual to an automatic reclassification process. Substantial research indicates that meeting reclassification criteria differentially impacts students’ likelihood of reclassifying based on their grade level (e.g., Robinson, 2011; Umansky & Reardon, 2014). To address this, we estimate all RD models separately for grade-level subsets. We present results for grade-level subsamples of students as well as a weighted average effect of the policy for all students in the sample. Sharp RD Estimates We first estimate the ITT effect of meeting the overall WIDA reclassification threshold in the “pre” period (manual reclassification) for student i in grade g: "&’ = 𝛽* 𝑌() "&’ + 𝛽! "&’𝐶() + 𝑓(𝑀())"&’ [+𝑋()𝛽"&’] + 𝑣() "&’ (1a) This RD model predicts student i’s likelihood of reclassifying Y as a function f 4 of their recentered and standardized overall WIDA scale score M, an indicator for whether or not that score is above the recentered reclassification threshold C, and in some specifications, a vector X 32 of additional covariates (recent immigrant status, special education status, low-income status, gender, home language, prior year overall WIDA score). We restrict the bandwidth of WIDA scale scores used in the analysis to limit the influence of outlier students with very high or very low WIDA scores using the rdrobust command, which implements a data-driven process to determine an optimal bandwidth and estimates bias- corrected coefficients and robust standard errors (Calonico et al., 2014). We report results from the optimal bandwidths chosen by the rdrobust command for each grade but also report results for ½ and twice the size of the optimal bandwidths as robustness checks. Our preferred model uses a triangular kernel, but we also report results using a uniform kernel as a robustness check. For all RD models, we cluster standard errors at the school district level because districts were responsible for manually reclassifying students in the pre-period and overriding automatic reclassification in the post-period. The rdrobust command also adjusts for mass points in determining the bandwidth, meaning that it accounts for the running variable being less than fully continuous, as is the case with most education studies. In equation (1a), 𝛽! "&’ represents the ITT effect of just barely qualifying for reclassification on reclassifying in the pre-period (manual reclassification). Equation (1b) is a corresponding analysis for the “post,” automatic reclassification period: "#$% = 𝛽* 𝑌() "#$% + 𝛽! "#$%𝐶() + 𝑓(𝑀())"#$% [+𝑋(𝑔𝛽"#$%] + 𝑣() "#$% (1b) The ITT estimates for each policy period and grade-level subsample give the impact of qualifying for reclassification on reclassifying in each policy period. Difference-in-Regression Discontinuities Next, we use a DiRD approach in an attempt to estimate the impact of shifting from manual to automatic reclassification on eligible students’ likelihood of reclassifying. The DiRD 33 approach will estimate the difference in ITT effects from equations (1a) and (1b). This estimate can inform policy by indicating whether the change altered eligible students’ likelihoods of reclassifying. Equation (1c) estimates the DiRD separately for each grade level using the sample of students included in the optimal bandwidths5. Estimates can be interpreted as the causal effect of shifting from a manual to an automatic reclassification process if there are no other confounding factors and are obtained by subtracting the post- and pre-period ITT effects: 𝐷𝑖𝑅𝐷 = 𝛽! "#$% − 𝛽! "&’ (1c) Where 𝐷𝑖𝑅𝐷 is the coefficient estimate of the difference between ITT estimates (𝛽! "#$%, 𝛽! "&’) in the post- and pre-periods. Although we estimate 𝐷𝑖𝑅𝐷 separately for each grade level, we also report estimated effects for the full sample of students. These estimates are precision-weighted by grade level estimate. To do this, we calculate a weighted average estimate inversely proportional to the standard error of each grade-level estimate. Equation (2) calculates the weighted average estimate of the effect of qualifying for reclassification on reclassifying6: 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑖𝑅𝐷 = +!( " #$%(’!(’)* ×.&/0’12’3’4 6$%(7/%’!) +! " #$%(’!(’)* (2) The weighted average DiRD is the combined estimate for all grades with weights inversely proportional to the variance; 𝑣𝑎𝑟(𝐷𝑖𝑅𝐷)( represents the variance of the estimate for grade level g; 𝐺𝑟𝑎𝑑𝑒 − 𝐿𝑒𝑣𝑒𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒( is the estimate for grade level g. The weighted average effect provides an overall estimate of the impact of the policy change on all 3rd to 8th-grade students (e.g., a weighted average effect of 0.26 corresponds to a 26 percentage-point increase in an eligible student's likelihood of reclassifying after the policy change). 34 Subgroup Analyses We next use a difference in DiRD (DiDiRD) framework to evaluate differential changes in reclassification rates related to the policy change for subgroups of ELs reporting different home languages. The estimate obtained from the subgroup analyses provides preliminary evidence of the ability of the policy change to ameliorate differential reclassification outcomes unrelated to English proficiency level. We begin by subsetting our sample to students in three subgroups based on their reported home language (Spanish, Arabic, and Other Home Language) and re-estimating Equations (1a) through (1c) and Equation (2). Then, Equation (3) estimates the DiDiRD separately for each grade level subgroup of students7. Estimates can be interpreted as the effect of shifting from a manual to an automatic reclassification process for a given subgroup of students and are obtained by subtracting the DiRD estimates for subgroups of students: 𝐷𝑖𝐷𝑖𝑅𝐷 = 𝐷𝑖𝑅𝐷$9:) " !,) − 𝐷𝑖𝑅𝐷$9:) " <,) (3) Where 𝐷𝑖𝐷𝑖𝑅𝐷 is the estimate of the difference in DiRD estimates for a subgroup of students in grade 𝑔. For example, if testing for a differential effect of the policy across home language, we would subtract the DiRD estimate for Spanish speakers from that of Arabic speakers in grade 𝑔. Internal Validity of Estimates Our DiRD design relies on several assumptions to produce a causal estimate of the effect of shifting from manual to automatic reclassification. First, we assume that the running variable is not manipulated at the cutoff. Because educators and EL students know the cutoff score required to qualify for reclassification, they may act to manipulate student scores to either retain or reclassify students from EL status, potentially threatening the validity of our estimates. We test the assumption that the running variable is not manipulated at the cutoff using a McCrary test. McCrary (2008) suggests there should be no discontinuity in observations at the cutoff for this 35 assumption to hold. A spike in observations on either side of the cutoff may indicate score manipulation. We report results from McCrary tests in Online Appendix Table A1 and Online Appendix Figures A1 and A2 to demonstrate no discontinuities in recentered scale scores at the cutoff using triangular and uniform kernels, confirmed using the rddensity command in Stata. Next, the RD assumes that only treatment and outcomes change discontinuously at the cutoff. In other words, although treatment status should change at the cutoff, students must be otherwise similar on either side of the cutoff. If this assumption holds, the RD design produces causal estimates of the effect of the policy change as the groups of students on either side can be used as counterfactuals for one another. Although we cannot test this assumption for unobservable student characteristics, we conduct tests for observable factors (such as gender, special education status, low-income status, and home language). We tested these factors by running separate RDs by grade level and policy period for the analytic sample, each time substituting a different variable as the outcome of interest. Results indicate that no observable student characteristics vary discontinuously at the cutoff other than reclassification likelihood. Online Appendix Table A2 displays the results of these tests. Finally, for the DiRD to be interpreted as a causal effect, there must be no other cooccurring change—other than those already addressed above, such as the shift in the threshold—that changed and could account for the manual-to-automatic reclassification effect estimate. Ideally, we could address this assumption through the use of an unaffected comparison group via a DiDiRD approach, but there is no unaffected group (e.g., never-ELs) who take the WIDA assessment and are not affected by either one of these policies. As such, there is no comparison that can be used to remove any secular trend from the reclassification policy change. While noting this caveat, it is also worth noting that raw reclassification rates based on the 36 WIDA overall cutoff score for each respective policy period are fairly stable from year to year in Michigan, as we demonstrate in Table 4. Additionally, there are no other policy changes that we are aware of that could produce a discontinuous change in reclassification rates at the threshold in either the pre- or post-period. Thus, a sizable change from the pre- to post-period in the DIRD is plausibly attributable to the change from manual to automatic reclassification. 37 Table 4 Raw Reclassification Rates for Eligible ELs by Year Year 2016-17 2017-18 2018-19 2019-20 2020-21 2021-22 2022-23 Students Eligible to Reclassify (N) 7,560 8,818 9,165 5,104 2,174 3,979 3,637 Students Reclassified (N) 4,235 5,811 6,120 4,936 2,144 3,904 3,596 Percentage of Eligible Students Reclassified 56 66 67 97 99 98 98 Note: Data come from the Michigan Education Data Center. Automatic reclassification was implemented in the 2019-20 school year. Between 2016-17 and 2018-19, students in the sample were eligible to reclassify if they attained a 4.5 overall WIDA performance level. Between 2019-20 and 2022-23, students in the sample were eligible to reclassify if they attained a 4.8 overall WIDA performance level. 38 Effects of Automatic Policy on the Likelihood of Reclassification Results In this section, we will focus on the results of our lower-bound estimates, which provide the strongest test of the policy change and most directly address credible threats to internal validity of the design. We note that the patterns of significant effect estimates reported here for the lower-bound estimates also—and unsurprisingly—hold for the upper-bound estimates of the effect. See Online Appendix Table A3 for the upper-bound estimates. We now focus on our preferred model, which we argued yields a lower-bound estimate of the effect. Table 5 presents the results from this preferred model for reclassification likelihood by grade and policy period for the main model specification and an overall effect of the policy change by grade. Online Appendix Table A4 displays results by alternative bandwidth and kernel specifications. Overall, we estimate a significant discontinuity (p < 0.001) in students’ likelihood of reclassifying upon meeting the reclassification threshold in both the pre- and post-period. This finding holds for all grade levels and weighted average effects in each policy period. Of note, the magnitude of the jump in eligible students’ likelihood of reclassifying varies by grade level. For example, during manual reclassification, eligible third graders were the most likely to reclassify "&’= 0.806, p < 0.001). In other words, a third grader just above the reclassification threshold (𝛽! experienced an 80.6 percentage-point increase in their likelihood of reclassifying compared to a peer just below the threshold. In contrast, eligible fifth graders were least likely to reclassify "&’= 0.393 [39.3 percentage points], p < 0.001). Although a fifth grader just above the (𝛽! reclassification threshold was more likely to reclassify than a peer just below the threshold, the effect of meeting the reclassification threshold was substantially smaller than in other grades. During manual reclassification, we estimate a weighted average effect of 0.634 (p < 0.001), 39 meaning, on average, meeting the reclassification threshold increased a student’s likelihood of reclassifying by 63.4 percentage points. During the automatic reclassification period, students just above the reclassification threshold experienced much greater reclassification likelihood than those just below. On average, students just above the cut point were 98.0 percentage points more likely to reclassify than those just below. Further, there is less grade-level variation in eligible students’ likelihood of reclassifying during the automatic period. For example, eligible middle schoolers are the most likely to reclassify of all grade levels (𝛽! "#$%= .985 [98.5 percentage points], p < 0.001), and eligible fifth graders are the least likely to reclassify (𝛽! "#$%= .972 [97.2 percentage points], p < 0 .001). Estimated ITT effects across policy periods imply that manual and automatic reclassification features differentially impacted eligible students’ likelihood of reclassifying. Across all grade levels, we find a statistically significant DiRD estimate. The DiRD estimate is largest in fifth grade (𝐷𝑖𝑅𝐷 = .579, p < 0.001), where shifting from manual to automatic reclassification increased eligible students’ likelihood of reclassifying by 57.9 percentage points. This finding suggests that of all grade levels, automatic reclassification “leveled the playing field” the most for fifth graders, who were the least likely of all grade levels to reclassify in the pre-period. In contrast, the DiRD effect is smallest in the third grade (𝐷𝑖𝑅𝐷 = .172, p < 0.001), meaning shifting from manual to automatic reclassification increased eligible third graders’ likelihood of reclassifying by roughly 17.2 percentage points. Although the DiRD estimate is the smallest in third grade, the policy change still resulted in a statistically significant increase in eligible students’ likelihood of reclassifying. The weighted average effect of the policy change is .356 for all students. On average, the policy change across the sample resulted in a 35.6 40 percentage point increase in any third through eighth-grade student’s likelihood of reclassifying upon meeting state reclassification criteria. 41 Table 5 Lower-Bound Estimated Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Periods Manual Reclassification Automatic Reclassification Policy Change 3 SE BW N 4 SE BW N 5 SE BW N 6-8 SE BW N Weighted Average SE Standardized ELA Score Control Local Polynomial Bandwidth 2016-17 through 2018-19 0.806*** 2019-20 through 2021-22 0.979*** (0.023) [-50, 18] 5566 0.561*** (0.036) [-48, 25] 7978 0.393*** (0.035) [-48, 26] 7281 0.580*** (0.026) [-57, 18] 8276 0.634*** (0.014) (0.013) [-11, 8] 1453 0.981*** (0.007) [-14, 24] 5154 0.972*** (0.013) [-10, 24] 3957 0.985*** (0.011) [-10, 9] 1327 0.980*** (0.005) X 1 Optimal (DiRD) 0.172*** (0.026) 0.420*** (0.036) 0.579*** (0.038) 0.405*** (0.028) 0.356*** (0.016) Kernel Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. We restrict the bandwidth of WIDA scale scores used in the analysis to limit the influence of outlier students with very high or very low WIDA scores using the rdrobust command, which implements a data- driven process to determine an optimal bandwidth and estimates bias-corrected coefficients and robust standard errors (Calonico et al., 2014). Triangular 42 Differences in Policy Change Effects Across Language Subgroups of ELs Prior research has found that eligible students’ likelihood of reclassifying varies based on students’ racial and linguistic backgrounds (e.g., Mavrogordato & White, 2017; Umansky et al., 2020). In other areas of education policy, automatic procedures are being implemented to ensure greater racial equity in service provision (e.g., automatic advanced course enrollment for students who score at the top of a standardized test distribution, Berg & Plucker, 2023). Where sample sizes allow, we evaluate the effect of automatic reclassification policy across subgroups of ELs to test its ability to increase standardization in reclassification rates using a DiDiRD framework. We find that the shift to automatic policy had a larger effect on students reporting Spanish as a primary language compared to students reporting other primary languages. This implies that under manual reclassification, eligible Spanish speakers were less likely to reclassify than students speaking other primary languages, and automatic reclassification ameliorated some of the difference in reclassification rates for students reporting different home languages. Among students near the reclassification threshold, we estimate that shifting from a manual to an automatic reclassification policy affected Spanish speakers more than students reporting another home language. Table 6 presents the DiRD point estimates of shifting from manual to automatic reclassification for students reporting Spanish and Arabic as their home languages. We compare the effects of the policy change for Spanish and Arabic speakers because these are the two most commonly spoken languages among Michigan ELs. We report comparisons to students reporting another primary language in Online Appendix Table A5. Results are consistent with those reported here. 43 Table 6 DiRD Estimates Across Language Subgroups of ELs 3 SE Bandwidth N 4 SE Bandwidth N 5 SE Bandwidth N 6-8 SE Bandwidth N Weighted Average SE Standardized ELA Score Control Primary Language: Spanish Primary Language: Arabic Manual Reclassification 2016-17 through 2018-19 0.663*** (0.064) [-50, 8] 1283 0.527*** (0.053) [-40, 19] 2765 0.358*** (0.045) [-48, 21] 2886 0.575*** (0.040) [-52, 13] 3299 0.515*** (0.024) Automatic Reclassification 2019-20 through 2021-22 0.964*** (0.330) [-11, 9] 342 0.972*** (0.020) [-42, 13] 1485 0.956*** (0.032) [-55, 19] 1683 0.953*** (0.029) [-8, 10] 443 0.964*** (0.014) Policy Change (DiRD) 0.300*** (0.072) 0.445*** (0.057) 0.598*** (0.055) 0.377*** (0.050) 0.440*** (0.028) Manual Reclassification 2016-17 through 2018-19 0.844*** (0.029) [-44, 9] 1274 0.543*** (0.048) [-42, 19] 1978 0.396*** (0.064) [-44, 23] 1864 0.596*** (0.039) [-51, 14] 1933 0.684*** (0.020) Automatic Reclassification 2019-20 through 2021-22 1.000*** 0.000 [-41, 3] 637 0.992*** (0.007) [-6, 14] 955 0.975*** (0.020) [-7, 12] 790 1.001*** (0.015) (-6, 13) 248 1.000*** 0.000 Policy Change (DiRD) 0.156*** (0.029) 0.450*** (0.049) 0.579*** (0.067) 0.405*** (0.042) 0.350*** (0.020) X X 44 1 Optimal Triangular Table 6 (cont’d) Local Polynomial Bandwidth Kernel Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. We restrict the bandwidth of WIDA scale scores used in the analysis to limit the influence of outlier students with very high or very low WIDA scores using the rdrobust command, which implements a data-driven process to determine an optimal bandwidth and estimates bias-corrected coefficients and robust standard errors (Calonico et al., 2014). When point estimate and standard error equal 1 and 0, respectively, estimates indicate that every student who qualified for reclassification was reclassified. This could be interpreted as a sharp RD. Because the third-grade post period point estimate for Arabic speakers is interpreted as a sharp RD, the precision weighted average is exactly the value of the sharp RD. 1 Optimal Triangular 45 Results from the weighted average effects indicate that during manual reclassification, Spanish speakers just above the reclassification threshold were roughly 17 percentage points less likely to reclassify than Arabic speakers (.515 vs. .684). In the automatic reclassification period, the weighted average effects of qualifying for reclassification on reclassifying are more similar across subgroups, with (.964 for Spanish speakers vs. 1.000 for Arabic speakers). Overall, leveling out across subgroups’ likelihood of reclassifying under automatic reclassification implies that the policy had a greater impact on Spanish speakers (.440) than Arabic speakers (.350). Table 7 presents DiDiRD estimates of the effect of the policy change for Spanish and Arabic speakers. The DiDiRD estimates of the policy change confirm this finding, with the shift to automatic policy having a 9.0 percentage-point greater impact on Spanish speakers than on Arabic speakers. We report DiDiRD comparisons to students reporting another primary language in Online Appendix Table A6. Table 7 DiDiRD Estimates Across Language Subgroups of ELs Weighted Average Estimate SE DiRD: Spanish DiRD: Arabic DiDiRD 0.440*** (0.028) 0.350*** 0.090*** (0.020) (0.034) Notably, the difference in DiRD estimate appears to be primarily driven by differences in third-grade Spanish reclassification rates compared to Arabic. During manual reclassification, third-grade Spanish speakers just above the reclassification threshold experienced a roughly 66.3 percentage-point increase in their likelihood of reclassifying. In contrast, similar Arabic speakers experienced a roughly 84.4 percentage-point increase in reclassification likelihood. Other grade- level ITT effect estimates are similar across these linguistic subgroups during manual reclassification. For example, eligible fifth graders reporting Spanish as a home language 46 experience a nearly 35.8 percentage point increase in likelihood of reclassification, compared to an increase of nearly 39.6 percentage points at the threshold for students reporting Arabic as a home language. Weighted average effects during the automatic reclassification period suggest that eligible Spanish speakers (.964) continue to experience a lower likelihood of reclassification than Arabic speakers (1.000). However, the difference in weighted average effects across groups is much smaller than under manual reclassification. Robustness Checks We conduct several robustness checks to estimate the ITT effect of qualifying for reclassification on reclassifying across grade levels and policy periods. We test the sensitivity of our main analytic models to different models, including alternative kernels, bandwidths, and clustered standard errors. We also estimate each model with and without the inclusion of covariates. Additionally, our main models include a control for standardized ELA scores as a proxy for achievement on a local ELA assessment (one component of reclassification criteria during manual reclassification). This excludes data from 2019-20, as standardized tests were not administered during the COVID-19 Pandemic. In some alternative models, we exclude the standardized ELA score control and include reclassification data from 2019-20. Our estimations are also robust to models excluding data from both 2019-20 and 2020-21, given that both years were directly impacted by the COVID-19 Pandemic (Online Appendix Table A7). Given that we achieve balance on all covariates, we estimate a single OLS model in Online Appendix A8. Results are robust to alternative estimation strategies, including logistic regression. We report point estimates for logistic regression estimations in Online Appendix A9. 47 We also explored whether the policy change led to reductions in the variance of reclassification effects within and between school districts by estimating multilevel models where level 1 is students and level 2 is school districts, including random effects for the level-2 intercept and all slopes in Online Appendix A10. Importantly, we find that the variance in the between-district random effects in the slope coefficient associated with attaining the threshold decreases from the pre- to post-automatic reclassification period. For example, the variance on the district-level random slope for scoring above the cutoff (labeled “Above Cutoff (slope)” in Appendix A10) for third graders in the pre-period was 0.095, and it was substantially smaller at 0.015 in the post-period. Results from the robustness checks are comparable to results presented in the main findings and indicate that shifting to automatic reclassification had a large, positive effect on reclassification rates among eligible students and reduced variation in reclassification outcomes within and between districts. Conclusion and Policy Implications Using administrative data from Michigan, this study finds that automatic or default procedures can (1) substantially increase adherence to statewide standardized EL reclassification policy and (2) reduce linguistic or other disparities in access to reclassification compared to manual procedures. We find statistically significant, substantial effects of shifting from a manual to an automatic reclassification policy on reclassification rates of eligible EL students. The effects of shifting to an automatic reclassification policy are larger for specific subgroups of ELs, namely Spanish speakers. While these findings do not speak to students’ outcomes following reclassification, which depend upon both reclassification criteria and procedures, they have implications for EL policy in light of ESSA’s (2015) mandate that states establish standardized EL reclassification protocol. 48 Findings also have implications for education policy more broadly as states and school districts look to increase equity in students’ access to specialized programs, such as advanced coursework. Future research should consider how the application of default policies and selection of enrollment and reclassification criteria influences students’ access to specialized coursework and outcomes following enrollment in specialized coursework. Implications for EL Policy From an EL policy perspective, these findings corroborate earlier pre-ESSA research that finds substantial discrepancies between the population of students qualifying for reclassification and those reclassifying (Cimpian et al., 2017; Estrada & Wang, 2018). We extend this research base by highlighting automatic reclassification procedures as a mechanism to reduce these discrepancies. Recognizing that many eligible ELs were not reclassifying on time under manual procedures, Michigan implemented an automatic reclassification policy. Under manual reclassification, we confirm significant disparities in reclassification rates of eligible students. This suggests that reclassification decisions may have been based on factors other than reclassification criteria. Prior literature highlights several features of manual reclassification procedures that may contribute to disparities in reclassification rates, including excessive administrative burden on school districts, educators’ beliefs about the merits of reclassification, and EL students themselves, differences in state reclassification policy interpretation (Estrada & Wang, 2018; Mavrogordato & White, 2017), and variation in policy implementation across districts (Cimpian et al., 2017). This study provides causal evidence that shifting to an automatic procedure can create much greater parity in reclassification rates of eligible students. Our findings and discussion raise two important caveats worth noting. First, some eligible students still do not reclassify under automatic procedures. This is because districts can override 49 or opt out of automatic reclassification, a key feature of default policies. This feature allows for local discretion, particularly if a student meets test-based reclassification criteria, but educators feel the student could benefit from further linguistic support for other reasons. Second, reclassification does not in itself imply positive outcomes for students. Whether a student benefits from reclassification depends on the state's reclassification criteria (e.g., Robinson, 2011). For example, if the reclassification criteria are set too low, students may reclassify too soon and struggle without linguistic support. If the criteria are set too high, students may remain in EL status when they would benefit from mainstream academic coursework. Assuming a state implements appropriately rigorous reclassification criteria, automatic procedures may be an effective policy for ensuring students reclassify when they demonstrate English proficiency. The combination of effective reclassification criteria and procedures can provide students with access to developmentally appropriate coursework. As the EL population continues to grow and diversify rapidly, it is vital to consider how reclassification policies impact students within the EL subgroup differently. We provide the first causal evidence of the effect of a state’s choice of reclassification procedures on reclassification likelihood for ELs as a whole and among subgroups. Overall, we find that manual procedures impact subgroups of ELs differently. First, we find that eligible ELs are more or less likely to reclassify based on their grade. For example, under manual reclassification, roughly 20 percent of third-grade students eligible for reclassification did not reclassify. This research parallels Umansky and Reardon’s (2014) conclusion that in early grades, more students meet reclassification criteria than reclassify. However, Umansky and Reardon (2014) find that this trend reverses in middle school, with more students reclassifying than meeting eligibility criteria. In contrast, we find that under manual procedures, a significant proportion of 6th through 8th 50 grade students who met reclassification criteria were not reclassified. Under automatic reclassification, these gaps close, and nearly all eligible 3rd through 8th grade students reclassify. Taken together, these findings suggest that between-grade variation in eligible students’ reclassification likelihood exists under manual procedures. Automatic procedures may be more effective at standardizing reclassification rates for eligible students, a key goal of ESSA (2015). In addition, research has identified subgroups of ELs that are less likely to reclassify upon meeting reclassification criteria, particularly ELs reporting Spanish as a home language (Mavrogordato & White, 2017; Umansky et al., 2020). Our estimates align with this research. Under manual reclassification procedures in which districts are responsible for reclassifying ELs, we find that eligible ELs who report Spanish as a home language are substantially less likely to reclassify than ELs who report other home languages. However, this discrepancy largely dissipates upon shifting to automatic reclassification, in which state data systems reclassify eligible ELs. This finding suggests potential bias against Spanish speakers under manual reclassification procedures. Implications for Education Policy Nationwide, state education agencies are grappling with the most effective ways to increase representation and enrollment in specialized educational services such as advanced coursework and gifted education (Blad, 2020). Many state education agencies are moving towards automatic enrollment to ensure students are served in a developmentally appropriate environment (Plucker, 2021). In light of this movement, rigorous causal evidence is needed to evaluate the ability of automatic policy to increase equity and representation in educational service enrollment. The present study offers the first evaluation of the effects of automatic policy in K-12 educational settings, finding it can increase students’ likelihood of being served in a 51 developmentally appropriate environment (e.g., by reclassifying upon demonstrating English proficiency). This study faces several notable limitations. First, reclassification often entails a significant change in students’ instructional environment and has important implications for their short- and long-term outcomes. The present study focuses on evaluating the efficacy of automatic procedures in increasing adherence to state policy and ESSA guidance rather than assessing the effects of reclassification on students’ outcomes. Future research may explore outcomes for “compliers,” or eligible students who would reclassify under automatic procedures and not under manual procedures, to determine whether shifting the policy had positive or negative effects on student outcomes. Moreover, this study does not identify the mechanisms that caused lower reclassification of eligible students under manual reclassification. Future qualitative research may explore why manual reclassification procedures resulted in a substantially lower likelihood of reclassification among eligible students than automatic procedures. Finally, although this analysis presents the first examination of automatic policies in K-12 education and incorporates post-ESSA data to examine effective EL reclassification procedures, our analysis has two contextual limitations. First, the policy change occurred one year before the COVID-19 Pandemic. We acknowledge that the lingering effects of the pandemic, including disruptions to instruction, assessment, and student English proficiency growth patterns may also influence our findings in unobserved ways. Second, reclassification eligibility was based on multiple test scores under manual reclassification procedures but only a single test score under automatic reclassification procedures. The change in reclassification criteria may have led to a shifting of effort in which teachers or students focus more on attaining the single cutoff score 52 (overall WIDA English proficiency score), whereas under manual procedures, they would have been focused on developing proficiency along multiple dimensions (overall WIDA English proficiency score and WIDA reading and writing subscores). However, we believe this shifting of effort is unlikely because the WIDA overall score is a weighted composite of students’ WIDA reading, writing, listening, and speaking subscores, meaning students need to attain high scores on each subdomain to meet the WIDA overall reclassification cutoff score. In addition, there are several limitations of our DiRD design worth noting. The generalizability of our estimates is restricted to students just above or below the reclassification threshold, and this limits the applicability of our conclusions. Finally, these findings will not be generalizable to all states. Many states include subjective measures in their reclassification criteria (e.g., teacher recommendation, student grades), and subjective criteria are not collected by state data systems. As such, results and implications should be considered in a state with reclassification criteria captured by state administrative data systems. Rigorous research is needed to examine the ways policy can expand or constrain educational opportunities for the growing and diversifying EL population in US schools. Reclassification is one mechanism through which ELs gain access to the full range of academic coursework, and thus, policymakers should prioritize reclassifying students who demonstrate eligibility by meeting state reclassification criteria. This study offers one potential mechanism, automatic policy, that policymakers may consider to ensure greater equity in reclassification decisions among eligible students. Notes 1. Although ESSA (2015) requires states to establish standardized within-state EL exit criteria, states have discretion to determine their exit criteria. Reclassification criteria 53 vary across states and may include student grades, teacher recommendations, standardized test scores, and other factors, but all states must include an assessment- based measure of English proficiency set by policymakers within that state (Linquanti & Cook, 2015). 2. Schools first closed for the COVID-19 Pandemic in spring 2019. The window of time in which schools administered WIDA assessments occurred largely before school closures, unlike many standardized assessments. Therefore, we consider the first year of automatic policy implementation to be unaffected by the COVID-19 Pandemic. Further, although fewer students participated in WIDA assessments during COVID-19 and virtual learning (2019-20; 2020-21), we find no substantive differences in WIDA performance between the pre- and post-periods, suggesting that schools did not systematically test higher performing students during virtual learning. 3. Further, fewer assessments than usual were administered during the 2020-21 school year because many students attended virtual school due to the COVID-19 Pandemic and thus did not participate in standardized testing. We pool observations across policy periods for our main analyses. Because 2021-22 was also an abnormal school year in the sense that many students were returning from virtual or hybrid instruction to in-person learning, we have included Online Appendix Table A7 as a robustness check to our main analyses, in which we exclude data from 2019-20 and 2020-21 from our analytic sample. 4. Our preferred model uses a first-order polynomial because we anticipate that a student’s WIDA score has a linear relationship to their likelihood of reclassifying (students with a perfect score being most likely to reclassify under manual reclassification procedures). Our results are robust to higher-order polynomials. 54 5. After obtaining a DiRD estimate for each grade level, we compute standard errors for each grade-level DiRD estimate: 𝑆𝐸(𝐷𝑖𝑅𝐷) = D𝑣𝑎𝑟(𝛽! "&’) + 𝑣𝑎𝑟(𝛽! "#$%) Where 𝑆𝐸(𝐷𝑖𝑅𝐷) represents the standard error of the DiRD estimates. 𝑣𝑎𝑟(𝛽! "&’) and 𝑣𝑎𝑟(𝛽! "#$%) represent the variance of the estimated treatment effects obtained separately for the manual and automatic reclassification periods. This formula accounts for uncertainty in both pre- and post-period effect estimates when calculating the standard error of 𝐷𝑖𝑅𝐷. We assume covariance between 𝛽! "&’ and 𝛽! "#$% is zero or positive, thus if covariance is included in this equation, 𝑆𝐸(𝐷𝑖𝑅𝐷) will be smaller than is presented in the results. 6. The variance estimate of the precision-weighted average effect is computed using the following formula: 𝑉𝑎𝑟(𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑖𝑅𝐷) = 1 1 𝑣𝑎𝑟(𝐷𝑖𝑅𝐷)( Σ The corresponding standard error is calculated using the following equation: 𝑆𝐸=’()>%’0 ?3’&/)’ @(A@ = H𝑣𝑎𝑟(𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑖𝑅𝐷) 7. After obtaining a difference in DiRD estimate for each grade level, we compute standard errors for each grade-level DiRD estimate: 𝑆𝐸(𝐷𝑖𝐷𝑖𝑅𝐷) = D𝑣𝑎𝑟(𝐷𝑖𝑅𝐷$9:) " !,)) + 𝑣𝑎𝑟(𝐷𝑖𝑅𝐷$9:) " <,)) Where 𝑆𝐸(𝐷𝑖𝐷𝑖𝑅𝐷) represents the standard error of the difference in DiRD estimates. 55 DISTRICT IMPLEMENTATION OF ENGLISH LEARNER FUNDING POLICY: PAPER 2: EVIDENCE FROM MICHIGAN Caroline Bartlett, Michigan State University As a result of their diverse language backgrounds and experiences with education, English learner (EL) students require additional resources in schools to access academic content (Gándara & Rumberger, 2008). ELs speak a language other than English upon entering school and have been identified as requiring additional linguistic supports to meaningfully access academic content. These supports may include additional time to learn (e.g., summer school or extended day programs), technology to improve English proficiency, and resources in their native languages to support academic development. Federal law and policy requires schools to provide these services (e.g., Bilingual Education Act, 1968; Every Student Succeeds Act, 2015). US public schools serve a growing population of English learner (EL) students. In the 2021-22 school year, over 5.2 million K-12 students, or 10.6 percent of students in the country, were classified as ELs, up from 4.6 million (9.4%) in 2011-12 (National Center for Education Statistics, 2023, Table 204.20). Such growth implies that schools will need to allocate more supports to this student group in the future. The extra supports ELs require include additional funding for human and material resources above and beyond the general education funds provided to all students. To that end, the federal government provides over $800 million in Title III grant funds to supplement EL education (US Department of Education [USED], n.d.), and 49 states additionally supplement ELs through their state school finance formulas. 56 Despite receiving supplemental funds, school districts nationwide report insufficient resources specific to ELs’ needs (Sugarman, 2016). Researchers estimate that supporting an EL could cost anywhere between 39% and 200% more than supporting a general education student (Jimenez-Castellanos & Topper, 2012). Further, there is little consensus on the best way to allocate funding for ELs. Nearly every state provides supplemental funding for ELs, but the amounts and methods of funding vary widely across states (Verstegen, 2017). Two important challenges for researchers and policymakers are determining how much funding is necessary to enhance ELs’ educational opportunities and deciding the best way to allocate that funding. While researchers and policymakers have identified EL finance as a growing issue given the growth in the EL population, very little research comprehensively examines effective EL funding policies. Existing research has explored how federal and state governments fund EL education and the school and student characteristics that influence the cost of educating ELs. While this research provides some insight into effective funding mechanisms for EL services, further research can identify specific features of funding policies that ensure equitable access to educational resources that support ELs’ academic and linguistic development. In this interview-based study, I explore school district EL leaders’ and school business officials’ perspectives of the affordances and constraints of Michigan’s EL funding policy. Michigan provides students with varying levels of funding based on their English proficiency level, a strategy being adopted by at least seven other states (Griffith & Burns, 2025). I ask: 1. Is Michigan’s tiered EL funding policy aligned with districts’ perceptions of ELs’ needs and efforts to serve ELs? o How do district leaders interact with the policy? 2. How can state EL funding better support districts to provide ELs with resources? 57 This study makes three main contributions to the literature on EL education policy and school funding. First, it is the first study to provide insights into how school district leaders experience and respond to differentiated EL funding mechanisms. Second, it identifies specific policy design features that either enable or constrain the provision of effective EL services, offering practical recommendations to policymakers. Finally, it contributes to the broader literature about educational equity by examining how targeted funding structures can better support diverse EL populations in achieving academic and English language proficiency. Legal Context Guiding EL Education Background Under the Bilingual Education Act (BEA, 1968), the federal government first mandated that schools support ELs through English language development (ELD) programs. ELD programs are specialized instructional programs designed to support ELs in acquiring academic English skills that enable them to access grade-level curriculum in English. These programs aim to promote EL reclassification to general education status, at which point ELs no longer need ELD services to engage with academic content. Despite implementing a mandate, the BEA itself provided limited funding, minimal support, and vague guidance to schools, and these drawbacks limited its effectiveness (Ruiz, 1984). To address these limitations, the No Child Left Behind Act (2001) expanded protections for ELs and required schools to promote both English acquisition and academic achievement. Further, the Every Student Succeeds Act (ESSA, 2015) also increased state responsibility by incorporating EL proficiency into Title I accountability systems and standardizing EL identification and reclassification processes. 58 In addition to these federal mandates, major court cases have also clarified schools’ responsibilities to support ELs. Most notably, Lau v. Nichols (1974) established that denying students English language support violates their civil rights under Title VI of the Civil Rights Act, and Castañeda v. Pickard (1981) created a three-part test requiring ELD programs to be theoretically sound, properly resourced, and regularly evaluated. While these laws and court mandates plainly state that schools have a responsibility to support ELs through ELD services, they provide little to no guidance regarding the policies or services states, districts, and schools should provide to better support ELs. For example, ESSA called on states to standardize EL identification and exit processes, but did not specify best practices or what standard states should use to accomplish this. Similarly, while Castañeda v. Pickard (1981) created a straightforward test to determine whether ELD programs were sufficient, scholars have criticized its vague standards for enabling minimally inclusive ELD programs (Hakuta, 2020). This legal context provides a broad and ambiguous federal framework that leaves significant discretion to school districts to resource and implement ELD programs.3 EL Cost and Funding Adequacy Although school districts are required to provide additional services to ELs, school district leaders consistently report insufficient resources to support ELs (Sugarman, 2016). In general, some of the primary resources EL students need to be successful are additional time to learn (e.g., summer school or extended day programs), technology to improve English proficiency, books and resources in their native language to support academic development while 3 In addition to ambiguous legal guidance on how to support ELs, the definition of an adequate education for ELs also varies across states. States generally define an adequate education for ELs as at least attaining English proficiency and meeting academic performance standards, and this definition supports EL reclassification (Gándara & Rumberger, 2008). However, researchers and advocates have suggested that an adequate education or ELs should also include the possibility for students to develop biliteracy (e.g., Gándara & Rumberger, 2008). This goal of EL education could require substantively different programming and support for ELs as well. 59 learning English, school communication with parents, professional development for teachers tailored to EL needs, and teachers and leaders that respect and are knowledgeable about ELs (Gandara & Rumberger, 2008). Researchers have concluded that providing these and other resources to EL students requires anywhere from 39 percent to 200 percent more funding than that for native English speakers (Baker, 2005; Jimenez-Castellanos & Topper, 2012). While these studies endeavor to provide specific dollar values to states, they also report substantial heterogeneity in ELs’ needs and the costs associated with supporting them. Factors that influence the cost of EL education include characteristics of students, school districts, and state policies for ELs. Student characteristics that influence cost are students’ initial levels of English proficiency, whether ELs are also classified as low-income or special education students, students’ backgrounds with formal education and their proficiency in their home language (Gándara & Rumberger, 2008; Imazeki, 2008; Knight et al., 2017). Different student characteristics may compound to increase the duration and intensity of instruction necessary to engage with appropriate grade-level curriculum, and these can also increase costs for schools. In addition to student characteristics, characteristics of school districts also impact the cost of supporting ELs. For example, schools in Texas that serve an extremely low or an extremely high number of ELs paradoxically pay more per EL student than schools that serve an average number of ELs (Taylor et al., 2021). This suggests that there are some returns to scale of serving ELs, but only up to a certain level of EL enrollment. Additionally, the characteristics of other students in the class or school can also influence the cost of EL education. For example, Imazeki (2008) reports that in California, schools serving Spanish-speaking ELs may face lower per-pupil costs than schools serving ELs with other home langauges, which may reflect economies of scale associated with higher proportions of EL students spaking the same language. 60 Features of state policy for EL education can also heavily influence the cost of supporting ELs. States provide varying levels of guidance to school districts regarding how to support ELs. In addition, state refugee resettlement agencies may move large groups of immigrants to cities with limited communication to school districts. These policy decisions may not involve any school district-level input, and this can create large and sudden changes in the types of ELD services that schools need to provide. Federal and State EL Funding Given the legal mandates and substantial costs associated with providing ELD services, federal and state governments provide additional funding to support these programs. The federal government supports EL education through Title III grants administered under ESSA (2015). Title III funds are allocated to state education agencies through grants administered by the Office of Elementary and Secondary Education and the U.S. Department of Education (National Clearinghouse for English Language Acquisition, n.d.). States must set and meet accountability goals for ELs’ academic achievement and progression in English proficiency to receive Title III grants (Gándara, 2015). These typically comprise long-term goals such as increasing the percentage of students who will reach English proficiency within a specified timeline, the percentage of ELs who pass standardized assessments in core subject areas, or the graduation rates of EL students. In addition, state goals may also include short-term benchmarks to track progress towards longer-term goals (Pompa & Villegas, 2017). In the 2023-2024 school year, Title III funds totaled $890 million and were distributed to states based on the number of ELs in each state (USED, n.d.). The federal government considers Title III funds supplemental, meaning schools may use these funds to support ELs’ educational needs beyond services that are legally required to be 61 provided by states and districts without additional funds. Districts commonly report using Title III funds to provide ELs with additional educational support services (e.g., tutoring or additional instructional materials), to offer professional development for teachers (e.g., offering bilingual or English as a Second Language certification), and to facilitate parent engagement (e.g., translation services), although there is substantial variation in how these funds are used (Office of Elementary and Secondary Education, n.d.).4 Allowable expenses under Title III vary by district based on the district’s specific civil rights obligations to ELs, as some districts have consent decrees with the U.S. Department of Justice because of civil rights complaints or lawsuits (Sugarman, 2021). Therefore, what the federal government considers to be an allowable use of Title III funds in one district may be unallowable in another. In addition to federal support for EL education, 49 states and the District of Columbia provide funding to support districts to serve ELs. Among states that provide supplemental EL funding, there is wide variation in eligibility criteria for funding, method of funding allocation, and total funding amount provided to EL students (Verstegen, 2017). In addition, while states follow general principles to provide funds, no two states distribute EL-specific funds in the exact same way (Verstegen, 2017). Common approaches to providing funding include a flat weight funding formula, a categorical funding approach, and reimbursement. 21 states provide a flat weight in their school finance formula for EL-identified students (Evans et al., 2020; Verstegen, 2017). This means that the state sets a baseline amount of funding they provide for every student in the state (ELs and non-ELs), and EL students receive an 4 As these are supplemental, Title III funds cannot be used to provide core educational services for ELs because they would legally need to be provided regardless of funding level (Sugarman, 2021). Core educational services include (1) high-quality language instruction programs that can be proven effective at increasing ELs’ English proficiency and academic achievement and (2) high-quality professional development that supports improving instruction and assessment for ELs and enhances teachers’ ability to support ELs (Michigan Department of Education, n.d.). 62 additional “weight” above and beyond the baseline funding amount. Weights are included as part of the state’s primary funding formula and because they are defined as some percentage above and beyond the base funding, the amount of money provided to ELs through the weight will vary as the baseline per pupil funding amount varies. Weights for EL education vary widely across states. In Verstegen (2017)’s 50-state survey of state finance policies for EL students, the average pupil weight provided to EL students was .54 among states using pupil weight to fund EL services. This implies that on average, ELs receive 54 percent more funding than the base amount in their state when their state uses a flat weight (Verstegen, 2017). Other common approaches states use to fund ELs include categorical funding and reimbursement (Freemire et al., 2020). States that use categorical funding provide funding to school districts to be used for a specific purpose and with restrictions on how the funds may be used. States that use reimbursements require districts to submit expenditures to the state after they have made purchases, and the state provides reimbursements to districts after reviewing their expenses. In addition to these approaches, several states have recently implemented innovative tiers in their EL finance policies so that these policies adapt to support ELs’ heterogeneous needs. For example, in 2019, Texas legislators restructured the state’s weighted funding formula by providing a greater weight to ELs enrolled in dual language programs, a highly effective instructional approach to serving ELs (e.g., Umansky & Reardon, 2014). Texas adopted this approach to incentivize schools to implement dual language programs (Texas Education Agency, 2019). 63 Like Texas, other states have also adopted funding policies that account for heterogeneity within the EL student population. As of the 2024 school year, Hawaii, New Jersey, North Dakota, Ohio, Minnesota, Michigan, and Tennessee provide tiered funding systems based on ELs’ English proficiency levels, their immigration background, or the concentration of ELs in a school district (Mavrogordato & Bartlett, 2024; Villegas, 2025). These policies prioritize equity in resource distribution for ELs. Critical Resource Theory and EL Funding Policy Implementation Critical resource theory (Kaplan & Owings, 2022) and the education policy implementation literature (e.g., Honig, 2006) inform my examination of how state EL funding policy influences district-level capacity and decision-making. Critical resource theory, rooted in the foundations of critical theory, examines how power inequities embedded in public funding systems advantage those with institutional and political influence at the expense of marginalized populations, particularly students from linguistically and culturally diverse backgrounds (Owings et al., 2022). Critical resource theory offers a pragmatic, policy-relevant lens to study how school funding policies reflect deeper racial, economic, and linguistic biases in education. Critical resource theory describes the societal context in which those with power influence the distribution of resources to students, the inequities in school funding policies across contexts, and the history of structural racism and wealth inequities in shaping resource allocation in US schools (Owings et al., 2022). Within this framework, state funding policies and policy implementation decisions can be understood not as neutral distributions of dollars and actions but as expressions of institutional priorities that either perpetuate or disrupt systemic inequities. This framework is particularly relevant to EL education, where funding policies are often fragmented and insufficient to support the broad scope of ELD services that districts are legally 64 obligated to provide (Weddle et al., 2024). While ELs are guaranteed civil rights protections under federal law, the degree to which those protections translate into tangible support likely varies depending on how states structure funding policies and how districts respond to those policies. To understand the practical implications of state funding policies, I also draw on education policy implementation theory, particularly Honig’s (2006) concept of policy implementation as a negotiated process involving actors at multiple levels. From this perspective, local implementation of state EL funding policy is shaped not only by federal and state mandates but by district leaders’ interpretation of policy, their internal capacity to serve ELs, and their responsiveness to the communities they serve. For example, state funding policy may result in different ELD programmatic decisions depending on whether a district has experienced EL staff or a strong advocacy base within their community. Combining critical resource and policy implementation theory allows me to analyze how the design and delivery of EL funding affect leaders’ perceptions of equity and adequacy across district contexts. They help explain both the structural forces that constrain or enable local district capacity to serve ELs and the localized practices through which policy is implemented. These frameworks guide my inquiry into how state EL funding can better support districts in delivering high-quality ELD services across contexts. Study Context Michigan serves a linguistically diverse and growing population of EL students. In the 2023-24 school year, over 98,000 K-12 students, or six percent of students in the state, were classified as ELs, up from 77,000 in 2013-14 (Michigan Department of Education, n.d.). While the majority of Michigan ELs speak Spanish as a home language, Michigan also serves the 65 second-largest population of Arabic-speaking ELs in the country (Moslimani, 2023), and in 2023 was in the top five refugee receiving US states (Ward & Batalova, 2023). As a result of its status as a top refugee resettlement destination, Michigan serves a large population of newcomer students, or students who have immigrated to the US in the past three years. Many newcomer students are also classified as ELs, meaning they require ELD services and services to support their acculturation to the US. More broadly, Michigan ELs vary in their English proficiency levels. For example, in the 2023-24 school year, seven percent of ELs were proficient in English as measured by state English proficiency assessments, and the mean proficiency level score was a 3.3 out of 6, with 6 being the highest English proficiency level possible (MDE, n.d.). Michigan provides supplemental per-pupil funding for school districts serving ELs based on their English proficiency levels through a competitive grant known as Section 41 of the State School Aid Act. Districts may use funds to purchase “direct instruction by ESL- or bilingual education-endorsed staff, professional learning for EL staff and co-teaching content area teams, computer-assisted instruction, family engagement, the purchase of English Language Development instructional materials…transportation to support extended learning and community activities…[and] summer school EL programs” (MDE, 2023). If districts intend to use Section 41 funding to hire staff, the staff must be supplemental to their core English language development program, meaning that they must use their general fund to pay for at least enough staff members to provide ½ hour of daily English language instruction to EL students (MDE, 2023a). Section 41 is a competitive grant intended to provide greater support to students with lower levels of English proficiency (MDE, 2023a). Districts apply for Section 41 funding annually between October and January (MDE, 2023a). The Section 41 application requires 66 districts to report student counts auto-populated by the state, the number of teachers in the district with bilingual and English as a Second Language endorsements, and a program plan including a budget summary, budget items, the current year budget for English language development services, and any documents that support the districts’ application (e.g., estimates for large line-item requests, EL staffing plans; MDE, 2023b) MDE approves applications in January and releases information on final allocations for the following school year in March. By July 15th, school districts must submit a report that demonstrates the amount of Section 41 funds that were spent, and bi-annually, districts must submit an evaluation of the effectiveness of their spending to meet outcomes identified in their Section 41 application (MDE, 2023a). If a school district’s Section 41 grant application is successful, then each of its EL students receives a supplemental funding amount based on the student’s prior year score on the WIDA English proficiency assessment. WIDA assessments are administered annually, and students receive a score ranging from 1.0 to 6.0. In the 2023-24 school year, students reporting WIDA scores of 1.0-1.9 (the earliest level of English proficiency) received an additional $1,476 of Section 41 funding, scores 2.0-2.9 received $1,019, and scores 3.0-3.9 received $167. These dollar amounts correspond to 15%, 11%, and 2% more funding than the state’s foundation base for general education students (Mavrogordato & Bartlett, 2024). Notably, students remain classified as ELs until they score 4.8 on the WIDA assessment. Students with WIDA scores between 4.0 and 4.7 do not qualify to receive Section 41 funding. In one sense, Michigan’s approach is innovative: it attempts to address disparities in student needs by providing greater support for students with the lowest English proficiency levels (and highest linguistic needs). However, it does not offer any additional funding for ELs at 67 higher levels of English proficiency, even if these students still require linguistic supports to be successful in school. Data and Methods I used a qualitative interview approach to explore school district leaders’ perspectives on whether and how Michigan’s tiered funding policy aligns with their perceptions of ELs’ needs and efforts to serve students (Brinkmann & Kvale, 2015; Creswell et al., 2007). This design allowed me to gather multiple perspectives about Michigan’s funding policy in various contexts and collect information about contexts in which state funding policy aligned or converged with districts’ needs. Data and Sample My data sources include semi-structured interviews conducted with 17 school district leaders (e.g., Superintendents, Directors of EL Education, Directors of Finance) across 10 school districts in spring 2023. I worked with two additional researchers to recruit participants using a purposeful sampling approach. We sought to interview leaders in districts representative of Michigan’s statewide demographic profile. Accordingly, we recruited participants from diverse localities, districts with varying EL concentrations, and districts with varying concentrations of low-income students (as many ELs in the state also qualify as low-income and, therefore, receive additional federal and state funding for this designation). All districts contacted for interviews agreed to participate. Given that EL services and school finance and budgeting fall under different offices in school districts, we requested to interview two individuals from each participating district (one leader who specialized in EL education and another with expertise in district finances). We gave participants the option to conduct interviews in pairs given that some participants only had expertise in one area (EL 68 education or finance). Joint interviews allowed us to engage both leaders in a conversation about their respective expertise and discuss the extent to which they collaborate when determining how to fund and provide EL services. We interviewed both leaders jointly in six districts, and in four districts, we spoke only with a leader who specialized in EL education. In total, we interviewed participants from five urban, three suburban, and two rural districts. Four districts served a low concentration of ELs (<10% of district population), four districts served a moderate concentration of ELs (10.1-20%), and two districts served a high concentration of ELs (>20.1%). Table 8 describes my sample. 69 Table 8 Sample Information District # Interviewee # Role 1 1 2 2 3 4 4 5 6 6 7 8 8 8 9 10 10 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 Director of EL and Comensatory Education Director of Business Services and Operations Director of Finance Director of English Language Development, Federal Grants, and State Assessments Executive Director of Special Populations and Community Outreach Business Manager Superintendent Director of English Language Development, State and Federal Programs Contract Accountant Superintendent English Learner Consultant Multilingual Director Assistant Superintendent of Business Supervisor of Purchasing Director of English Learner and Bilingual Programs Director of Finance Supervisor of Instruction Percent EL Enrollment in Entity >20 >20 10-20 10-20 10-20 <10 <10 >20 <10 <10 >20 <10 <10 <10 10-20 <10 <10 Locale City City City City City Rural Rural City Rural Rural City Suburb Suburb Suburb Suburb Suburb Suburb 70 All three researchers conducted interviews. I was present in all interviews, and either the second or the third researcher joined me in seven of the interviews. Our interview protocol was designed to elicit leaders’ perspectives on Michigan’s tiered funding policy, ELs’ needs in their districts, and the state funding policy’s ability to support their efforts to serve ELs. We initially asked open-ended questions about EL services, school budgeting, and the state funding policy. For example, we asked, “What are the key state and district policies that guide how your district funds EL services?” and “Can you describe the process for developing budgets for EL programming in your district? Do you work together to develop that budget?” Appendix B contains the full interview protocol. We then conducted an activity with participants to gather more direct information about their budgeting process and planning for EL services by asking them to list the human and material resources that they believed would constitute an adequate education for ELs in their district. We then asked the leaders to sort these materials into three categories: Purchased with state EL funding, purchased with funding from another source, and unable to purchase. As leaders sorted the resources into categories, we asked open-ended questions to probe their rationale for sorting each resource. Our full interview protocol is listed in Appendix B. Interviews ranged from 30 to 75 minutes and were conducted on Zoom. Each interview was transcribed verbatim for analysis. Analytic Approach I aimed to understand how school district leaders engage with state EL funding policy to support ELs. Following my conceptual framework, I created a coding framework based on existing literature on critical resource theory (Owings et al., 2022) and policy implementation (Honig, 2006). First, I coded transcripts for evidence of perceptions of state funding systems for 71 ELs, local policy implementation and leaders’ beliefs about ELs, and the broader policy environment and interpretation. For example, I coded transcripts for evidence of leaders’ beliefs about ELs’ resource needs (e.g., “…it costs more to educate [ELs]…there’s a progression of English language development…it’s not [just] about reading level or ability to write or speak…it’s all things combined”). I also attended to societal factors that may uniquely shape ELs’ access to funding and resources (“There’s a very real racial component [to EL funding policy]…many of the EL parents in my community may not be legal…they may not want to draw attention to themselves…[advocacy for greater funding is] a white middle-class avenue not available to people that are not white or middle class”). Next, I categorized my codes based on whether they facilitated or hindered district leaders' efforts to meet ELs' needs under state funding policy. Using a constant comparative approach (Patton, 2002), I coded interview transcripts and developed matrices to track coding across interviews (Glaser & Strauss, 1967). This allowed me to identify connections and organize codes into emerging findings. To establish trustworthiness, an external colleague coded three interview transcripts using my coding scheme. We met to reach consensus regarding codes and discussed connections between codes and their alignment to emerging findings. Codes and representative examples of each code are displayed in Table 9. Finally, I grouped codes into emergent findings. I discussed emergent findings with the external colleague who coded three interview transcripts to ensure my emergent findings were consistent with my coding scheme. 72 Table 9 Detailed Coding Scheme Parent Code Child Code Description Emergent Finding Representative Quote Beliefs About and Prioritization of ELs EL Needs Vague policy oversight; leaders building district capacity to serve ELs Leader's perception of ELs' needs in their district and what would constitute an equitable or adequate educational experience for EL students Challenges forecasting fiscal needs for ELs Political Climate Prioritization of ELs How broader political climate surrounding immigration and marginalized populations informs prioritization of ELs in the local district Local community and school district commitment to ELs' educational opportunities "A lot of our special ed families know that they can have an advocate, that they that they should be expecting certain services, that there's IEPs that should be followed. You know, a lot of our ELs that are not born here, their families don't know the school culture or they don't want to get on like somebody's radar if they are feeling like they're unsafe or they don't have that sense of community. So I feel like, you know, they don't necessarily advocate for themselves. So it's up to us to be advocates for them. And I think it's important for people to realize the value that comes in in providing good service and quality service. I also think that a lot of times people don't realize that. If you're not taking an asset minded approach to it and you're looking at where there's curriculum gaps or there's holes, a lot of times our systems create the gaps that some of our students have." "[During the first Trump Administration Travel Ban] we did not get Title III Immigrant [grant funding] because we never met the criteria for the formula that was created, even though we were very high numbers of English learners. Based on the criteria and the politics at the time, we were never really averaging the number we needed to continue that funding." Districts’ approaches to navigating state funding policy "We have a school district with a Board of Education that is very supportive and understanding of the population that we serve. And therefore, it comes from the top of the board and the superintendent that they understand that the needs for [EL] kids are specific. They're special. They demand a little bit more money. And that's so it makes it easier for us as we create programs that we know that we have to support from the top to ensure that these programs get funded. And that in itself is a great benefit and asset that we have." 73 Table 9 (cont’d) Policy Interpretation Litigation and Legal Pressure Influence of court cases and legal intervention on EL funding practices in the district Vague policy oversight; leaders building district capacity to serve ELs Monitoring and Guidance Influence of state monitoring and policy guidance on EL funding practices in the district Vague policy oversight State Policy Awareness Understanding of state Section 41 funding policy Leaders building district capacity to serve ELs Funding Systems and Decision Making Insufficient EL Funding Evidence of insufficient funding for EL services (e.g., encroachment, unmet needs) Mechanisms by which insufficient funding affects planning for EL services "The [district's] agreement with Office of Civil Rights...was actually a blessing in disguise because it really did help map out what is expected for students, what type of services [to provide students]....[and defined] a teacher who is highly qualified...it really breaks down what happens when you're a WIDA [level] 1, a 2 or 3 or 4. And that's kind of what we do. That's kind of our Bible, so to speak, as far as when we're looking at what type of services to provide." "I know some states have mandated [teacher to student] ratios. And because Michigan doesn't, it leaves us up to this interpretation gray area where [ELD] needs to be based on sound research or theory, and then it needs to be shown that it's effective. Well, if you don't have staff that knows the research and theory and you don't have the evaluation tools or aren't using the tools necessary, it's really hard to get to a place where we're actually enacting that." "When I came [to the district], there were certainly a lot of misunderstandings of what our obligations are [to ELs] and what those funding sources can be [to meet those obligations]. And so, um, to be very honest, when I came in, we had very few of our certified teachers funded out of general funds, and it was truly because, um, they were all good intentions. They just didn't know. So like, when you know better, you do better." "We don't get any pressure within our community. Um, but we the tensions are more around the finances, right? So like, we know, you know, as a, as a school district, what these kids need, we understand that they're coming to us, um, with. The language barriers that absolutely should be addressed in order for them to be successful here in school. And the tension is building what we know they need with zero money. Like, yeah, hey, we would like to build a wonderful house for all of you, but we have no materials, resources or people, right? Um, so that's the tension is how do you how do you build it without the things you need?" 74 Table 9 (cont’d) Unpredictable Enrollment Influence of unpredictable enrollment on Section 41 and budgeting processes Challenges forecasting fiscal needs for ELs Use of Additional Funding or Connections to Meet EL Needs Seeking out additional funding or resources beyond earmarked EL funding sources. Collaborating with other districts or external partners to meet EL needs. Seeking external funding from community relationships; collaboration to budget for ELD services “Federal and state formulas are often written on head count of who's here. And when they're here, they use census data, they use school pupil accounting data. But that that system doesn't account for kids who come and go. And that system doesn't account for people who don't want to be found. Non-native speakers, families coming and going with English as a second language are very much prone to this sort of structural bias. Right? Like this is this is an intrinsic form of, you know, of what's going to happen when people who have these characteristics of how they're accounted for within a system like a public school, it'll never fund what they are and what they need in the way that it's designed." “I have gotten different grants through a local community foundation. Um, also we have the Outdoor Discovery Center, another nonprofit in the area, and they've gotten other big grants for us to support after school programming, some summer programming. We made immigrant welcome kits with one of those grants. Um, and so we do, we do seek out additional funding because we know that we can always do a little bit more. And honestly, I think that's one of the my favorite things about this community is we have so many people looking to support these students. And so really, if I have a need that's not met with [Section 41] dollars, there's just such a huge network. Um, our migrant program in the summer, for example, we had honestly just community partners, businesses like general donations without even me asking or trying to seek this funding, people coming out of the woodworks, recognizing that we have this population that needs that." 75 Limitations My findings provide insights into how school district leaders interact with Michigan’s EL funding policies. I acknowledge several limitations of this study that impact the interpretation and generalizability of my findings. First, findings are situated within the context of one state. Existing research highlights that no two states use the same funding mechanism or policy to provide funding for EL services (Verstegen, 2017). Given limited research on funding for EL students specifically, findings still provide insights into the ways school leaders engage with state EL funding policy. However, findings should be interpreted with caution with respect to other state contexts. Findings Discussions with school district leaders revealed several trends in response to my guiding questions. In this section, I present findings in three areas. First, leaders’ experiences with Section 41 indicated that the policy was not well aligned with their perceptions of ELs’ needs and efforts to serve ELs. Second, leaders with explicit training in EL education and/or experience teaching ELs felt more confident interacting with Section 41 and meeting ELs’ needs. Finally, participants discussed suggestions for better aligning tiered funding policies with ELs’ needs. State Funding Policy Lacks Alignment with Districts’ Perceived Needs and Goals for EL Education Vague Policy Oversight District leaders consistently described how policies guiding EL services are far less prescriptive than those for other student groups. The vague nature of federal and state EL policy created uncertainty about how much districts should invest in EL education programs to expand 76 students’ educational opportunities and created challenges for district EL leaders to advocate for additional funding and resources for ELs. Five participants directly compared the requirements for districts receiving Section 41 funding to implement EL education programs with the more stringent requirements for programs for students with disabilities. As one district EL leader described: “There isn’t really a whole lot of policy from the state and federal side, or district policy, that says ‘you have to do this,’ or ‘you don’t have to do that.’ The guidelines that we run on are the ones that are written directly into the grant funding process…it’s not ideal...other federally or state-mandated programs are run on policy and very clear state law. [With Section 41], we’re not obligated to do [anything]. It’s just like, ‘If you take these funds, then here are your boundaries.’ If this was special education, you might find some structures that are mandated to school districts so as not to violate the rights of the individual student…there is nothing specific enough that would say, ‘Here’s this mandate of what has to be offered for [ELs].” (District 6, Superintendent) This leader described how the vague nature of EL policy creates a disconnect for districts seeking to provide ELs with high-quality ELD services. This vague policy created substantial variation for districts regarding how they provided ELD services. In some cases, district EL leaders were informed and proactive about using policy creatively to advocate for smaller caseloads for EL teachers: “[The state policy] is not necessarily what’s necessary to move the needle for our students. We looked at the guidance. I don’t know if it’s helpful or hurtful because it’s so vague that it’s very hard to use as an advocacy tool…it leaves so much loose to interpretation. We went back to the Castañeda three-prong test, Lau v. Nichols, and 77 compared what we’re doing funding-wise and staffing level-wise. It’s in our strategic plan now to get to a ratio of 1 to 32.” (District 9, EL Leader) This leader had extensive experience working as an EL teacher and a doctorate with an emphasis on teaching ELs. Highlighting the disconnect between their perception of what is needed to improve outcomes for ELs and state funding policy, this turned to federal civil rights precedents, specifically Castañeda v. Pickard and Lau v. Nichols, to evaluate whether their staffing and funding structures were adequate. In cases where district leaders were not explicitly trained in ELs’ educational rights or were newer to serving ELs, there was greater confusion regarding leaders’ perceptions of the resources and funding needed for EL services. In one interview, a Director of Finance and Supervisor of Curriculum described their process for funding EL services: “I don’t know…I do the accounting side. The [Supervisor of Curriculum] makes sure that the funds are being utilized in the best manner according to legal guidance. I move funds around as needed.” (District 10, Director of Finance) “[Responding to Director of Finance] Yeah, there isn’t a policy so as to, like, so many students equal so many staff based on a certain fund allocation. It’s more of we use the funds as responsibly and appropriately as we see fit.” (District 10, Supervisor of Curriculum) This exchange reveals that in some districts, there is no formal policy linking the number of EL students to specific staffing levels. Instead, decisions about allocating funds for EL services are made locally and based on professional judgment and broad legal guidance. This underscores the discretionary and decentralized nature of EL resource allocation under vague policy directives. 78 Vague and nonbinding policy guidance surrounding the use of EL funds leaves significant discretion to local leaders and results in wide variation in how EL services are staffed and the corresponding level to which they are funded. Compared to more carefully regulated programs like special education, EL programs lack mandated caseloads or service requirements. While some well-informed and experienced leaders proactively referred to federal civil rights laws to guide service provision, this level of advocacy depended heavily on individual expertise rather than statewide policy expectations. In districts where leaders lacked formal training or experience with EL policy and civil rights, the absence of concrete guidance often led to ad hoc decision-making and potential underinvestment in budgeting and resourcing for EL services. Challenges Forecasting Fiscal Needs for ELs In addition to challenges with vague policy guidance, participants expressed challenges predicting the amount of Section 41 funding they would receive from year to year based on the quality of services provided to students and unpredictable enrollment. Leaders perceived that this unpredictability hindered their ability to provide high-quality ELD services and constrained their ability to use Section 41 effectively. Five leaders expressed how unpredictable EL enrollment changes, particularly among migrant and immigrant students who are also classified as ELs, influenced their ability to use Section 41. Although most ELs are born in the United States, Michigan is a top refugee resettlement state (Ward & Batalova, 2023), and, as a result, some Michigan districts serve a disproportionate number of immigrant EL students. Changes to federal immigration policy that would influence the number of immigrant EL students entering Michigan schools, such as the Travel Ban instituted by the first Trump Administration, the COVID-19 Pandemic, and the 79 withdrawal of US troops from Afghanistan, posed challenges for district leaders trying to forecast Section 41 funding and plan for sustainable ELD programs. For districts serving large shares of immigrant EL students, this problem was compounded by declining enrollment statewide (Smith & Levin, 2025), resulting in budgetary challenges for districts seeking to maintain their current educational services. One Director of Finance described the fiscal impact of declining enrollment on their district as “a decrease in 700 students…it’s about [a] $7 million [loss] for us over the last two or three years” (District 2). Another Director of Business Services working in a district with a large population of immigrant students emphasized this point. He specified that a decline in immigrant students specifically caused the district to “take a look at staffing [for ELD] because you still have to pay the utilities, buy gas for the buses” (District 1). The leader further explained how the district handled the declining enrollment by “approach[ing the district EL leader] saying, ‘we’re looking at this kind of a deficit. Do you have recommendations that we could implement to reduce staffing and wait for the funding to kind of come back?” These accounts illustrate how broader enrollment declines, especially among immigrant EL populations, forced district leaders to make difficult trade-offs between maintaining essential ELD services for students who remained in school and addressing overall budget shortfalls. Without stable or protected funding streams, even districts committed to EL students faced pressures to scale back staffing and programming, jeopardizing well-established ELD programs and underscoring the vulnerability of EL services in financially constrained environments. In addition to declining enrollment and enrollment fluctuations following changes to immigration policy, participants discussed challenges to budgeting and planning for ELD services for migrant students classified as ELs. Migrant students are children of migratory 80 workers, especially farmworkers and fishers (Migrant Education Program, n.d.). Leaders of smaller, rural districts expressed the limitations of Section 41 to effectively meet the needs of migrant EL students. One Superintendent recounted their experiences serving a migrant student who: “…had zero English language. He came to us unannounced with needs that we were unprepared to meet because we don’t have a day-to-day [ELD] teacher. He was with us for about two and a half months with almost no plan or paperwork other than he was here…If you think about how a school district operates, we had no allocation of funding, no teacher hired, no classroom to send him to, no resources and materials.” (District 6, Superintendent) This story highlights the difficulties small, rural districts face in responding to the unpredictable arrival of migrant EL students, especially under constrained resources and staffing. With no dedicated ELD funding, the district could not maintain a teacher or instructional infrastructure and ultimately, the district was unprepared to meet the student’s needs. In addition to Section 41, migrant and immigrant EL students qualify to receive federal funding through the Migrant Education Program and federal Title III immigrant subgrants. Beyond Section 41, federal grant eligibility further constrained districts’ abilities to obtain and use earmarked EL funding. Title III immigrant subgrants are reserved for districts that “experienced a significant increase in the percentage or number of immigrant children and youth, as compared to the two preceding fiscal years” (USED, 2016, p. 35). This stipulation created budgetary challenges for districts serving large immigrant student populations, especially following major political events impacting immigration patterns. 81 One EL leader working in a district with a high concentration of immigrant EL students lamented, “How can a district that goes from 9 to 10 [immigrant] kids get funding, but a district that has hundreds of [immigrant] kids do not get funding because they have one less than the year before?” (District 2). This leader further explained that they were working with MDE to create an “over-time formula” allowing districts to receive grant funding based on a multi-year running average of the number of immigrant students served. They expressed gratitude for this progress, stating, “Because a whole bunch of districts aren’t growing [in immigrant students], which is the political climate, we were going to lose our funds?” While this example focused on federal grants, it illustrates how rigid eligibility criteria for EL funding can unintentionally disadvantage districts. In contrast, more flexible, multi-year funding formulas may better reflect districts’ evolving needs. Beyond variable enrollment caused by fluctuations in the immigrant population in Michigan, enrollment changes were also driven by reclassification policies. Most ELD programs aim to help students develop proficiency in English and formally exit EL services (commonly known as reclassification). Therefore, EL classification is much different than other student classifications that generate funding for districts (e.g., students with disabilities and economically disadvantaged students). When EL students become proficient in English, they no longer generate additional revenue for the district. This makes sense because districts should use supplemental EL funding to provide language support services only for as long as students require such services, but district leaders reported challenges sustaining high-quality EL programs when funding was tied to individual students’ EL classifications. One EL leader described this tension: 82 “We were getting a lot of Section 41 funding…we were getting really good at exiting kids [because] we had cleaned up our [ELD] programming. The system to exiting students was improving all these things that made our total number of ELs decrease. There was fear that we would not have enough grant funds to continue doing some of the extra work that we were having because we had less kids…there was kind of this uneasiness.” (District 7, EL Leader) This quote highlights the tension between improved program effectiveness and funding sustainability. Because Section 41 funding is tied to the number of ELs served, improving ELD programs can potentially result in losing EL funds. This example further highlights how Section 41’s per-pupil funding structures can create perverse incentives for program success and generate uncertainty about maintaining financial support for EL initiatives even as student outcomes improve. Mechanisms by Which Insufficient Funding Affects Planning for EL Services Insufficient funding limited leaders' abilities to provide high-quality ELD services, particularly for small districts or districts with low concentrations of EL students. Given their low EL populations, these districts received a limited allocation from Section 41’s per-pupil tiered system. Despite receiving limited funds, the districts still faced legal obligations to provide ELs with ELD services and access to certified staff. One superintendent lamented, “there’s nowhere near enough money in Title III or Section 41 to do anything of substance other than buy a workbook or two” (District 4). Another superintendent echoed a similar sentiment when discussing challenges associated with providing ELs with the services they need to be successful when resources are constrained, “[A teacher would service] one, two, or ten [ELs]…no more than that in the district. A [salaried teacher] would cost us around $130,000…Section 41 is 83 $15,000” (District 6). Although Section 41 is intended to supplement, rather than supplant core ELD programs, in practice, the limited funds provided little to students. In some low-incidence districts, this meant that EL students went without robust, dedicated ELD services, revealing how limited EL funding leaves students in low-incidence districts with limited support: “We know, as a school district, what [ELs] need, we understand that…the language barriers should absolutely be addressed in order for them to be successful here in school. The tension is building what we know they need with zero money. Like, hey, we would like to build a wonderful house for you, but we have no materials, resources, or people, right?” (District 6, Superintendent) Higher-incidence districts also emphasized that Section 41 funding, even at larger allocation levels, was insufficient to meet the needs of their EL populations. One Director of Finance underscored the scale of the mismatch by describing their district’s core ELD program as costing $3.7 million while Section 41 provided only about $250,000, stating, “If the intent of [Section 41] is to supplement the education that our ELD students are receiving, certainly, the funding for it isn’t supplemental” (District 2). In another large district, the EL Leader described the difficulty of securing adequate resources despite being actively involved in budget discussions: “Do I get everything I want [for the ELD program]? Not always. I go in there, like, ‘I need this.’ And [the Finance Director is] like, ‘Nope.’ The funding sources are the funding sources” (District 1). These accounts demonstrate that even in districts with dedicated infrastructure and high numbers of EL students, leaders face persistent resource constraints. The scale of their ELD programs far exceeds what current Section 41 allocations can support, even in a supplemental way, limiting districts’ abilities to enhance or expand services beyond the bare minimum. 84 In over half of the districts, leaders reported that meeting ELs’ needs resulted in encroachment on the district’s general fund. Rather than being fully supported by targeted grants, supplemental services for ELs were routinely subsidized using funds intended for general education services. A Director of Business Services and Operations explained, “The grants aren’t enough to pay for the ELD program. So a lot of it is funded by the general fund,” noting that programmatic decisions became “competitive” given the absence of additional EL funding resources and the large EL population in the district (District 1). Another Director of Finance emphasized the broader tradeoffs of limited Section 41 funding, stating, “we’re eating into just shy of $10,000 [general fund per pupil to fund ELD programs]. So we’re not necessarily funding the best basic general education program—from a public education standpoint—that we could do” (District 2). This practice not only constrained district resources for both ELs and non-ELs but also created structural tensions between general education and supplemental EL programming. One EL leader described a conversation with their finance director, who asked, “If [ELD] is truly a general fund obligation, how do we make sure that schools with a lot of [ELs] are given adequate funding to be able to do the things that are required?” (District 9). Without additional targeted resources, funding obligations for ELs may reduce districts’ capacities to fully support both general education and ELD. Districts’ Approaches to Navigating State Funding Policy Despite the limitations of Section 41 funding, leaders described several strategies they used to interact with the policy in a way that expanded opportunities for ELs. In this section, I describe how leaders developed district capacity to serve ELs, sought external funding to 85 supplement Section 41, and developed cross-district collaborations to provide essential ELD services with limited funding. Leaders Building District Capacity to Serve ELs Almost all participants described deliberate, long-term efforts to build internal capacity to support ELs through investing in certified staff, aligning leadership priorities to ELs’ needs, and cultivating a district-wide commitment to serving ELs. In several high-incidence districts, leaders emphasized that their ELD programs resulted from internal decisions to allocate general fund resources beyond those provided to EL students to ELD programs. As one Director of Business Services and Operations leader explained, “We’ve already made the commitment [to ELs]. You can see from [EL leader], the number of staff she has, we understand the need for that and we put our general fund money into that to support those students” (District 1). Leaders also highlighted the importance of leadership, particularly from superintendents who prioritized EL services in district strategic plans and hiring decisions. One EL leader recalled, “Our superintendent is very supportive. They created my position because they had recognized the need for many, many years…They knew we needed to do things differently” (District 9). Other districts established new roles to build ELD programs from the ground up with support from their district central office. One EL leader described this process: “They aligned funds for me from the get-go…now we have a full EL team” (District 8). In addition to benefiting from organizational commitment to serving ELs, leaders emphasized that deep knowledge of schools’ legal obligations to ELs and civil rights laws significantly improved their ability to use Section 41 and secure appropriate services for ELs. One EL leader noted that misunderstandings about schools’ obligations to ELs were common before their arrival to the district, saying, “They were all good intentions. They just didn’t 86 know…when you know better, you do better” (District 9). Upon joining the district, this leader audited staffing allocations and funding sources using federal guidance, including a 2015 Dear Colleague letter from the US Department of Education and Office of Civil Rights documentation, to clarify what sufficient staffing and service provision should look like in the district. Rather than relying solely on local judgment, this EL leader presented case law and federal guidelines to their district finance team, explaining, “Those things are pretty black and white. There was no mal intent, he just truly didn’t know [what was required to serve ELs]” (District 9). In another district, earlier civil rights complaints prompted more formal compliance mechanisms, including intervention from the Office of Civil Rights. The EL and finance leader in this district described their Office of Civil Rights resolution as a “Bible” that clearly defined staffing and service expectations for ELs based on their English proficiency, grade level, and teacher qualifications. The EL leader noted, “The agreement with [The Office of Civil Rights] was a blessing in disguise…it really did help map out what is expected for students” (District 1). This clear legal framework guided staffing and budgeting decisions. When discussing budgeting processes, the Director of Business Services and Operations explained, “We have to have a certain part of our [teaching] population that is endorsed [to work with ELs] so that way they can address the population better” (District 1), referencing legal precedent. By leveraging legal expertise and clearly articulating compliance standards, these leaders reframed Section 41 not simply as a grant program, but as a civil rights obligation that required systemic planning, sustained and additional funding, and district-wide accountability. 87 Seeking External Funding from Community Relationships Across the board, participants emphasized that Section 41 funding alone is insufficient to deliver high-quality ELD programs. To meet students’ needs, EL leaders often relied on external funding, inter-agency partnerships, and community advocacy. In areas with a high concentration of immigrant families, refugee resettlement organizations and local nonprofits filled funding and resource gaps. One EL leader working in this context explained, “you’ve got [social services nonprofit]…[refugee resettlement nonprofit]…[resource collaborative for immigrants]…they help keep an eye on school systems to ensure that kids are being properly given the best possible services” (District 3). In other districts, leaders developed or leveraged existing partnerships to provide resources when funding was unavailable. Notably, rural and low-incidence districts lacked access to these partnerships and sources of support, disadvantaging ELs in those contexts. An EL leader in a high-incidence district expressed the challenges facing rural districts: “You go to little towns where the English learner’s population is not made of refugees and possibly is made of labor children of Central America or Mexico that are coming into the country to do some of the labors that nobody else wants to do. And they come into the small communities. They don't have an agency behind them. So they can't push for these kids, and the parents don't understand the system, so they can fall behind the cracks.” (District 3) In these settings, districts relied on the goodwill of community members to fill critical services for ELs. One district superintendent in a low-incidence, rural context described having a “parent liaison [for ELs]. But we don’t pay her. She volunteers her time” (District 4). These efforts demonstrate both the deep commitment of district leaders and community members to meet ELs’ 88 needs but also the structural limitations of limited funding and unclear policy guidance to dictate students’ legal obligations and create unequal opportunities available to ELs across districts. Collaboration to Budget for ELD Services District leaders described navigating Section 41 policy most effectively when they engaged in sustained collaboration across administrative departments or with peers in similarly situated districts. In high-incidence districts, leaders emphasized the importance of collaboration across administrative leaders when developing a budget. One EL leader explained that decisions around staffing for ELD programs were made collaboratively with “myself, [the finance director], the superintendent, and the Executive Director of Special Populations…through communication, collaboration, and conversations about where your numbers are and how many certified teachers do you need to meet the needs of your kids…it really comes out” (District 1). In low-incidence contexts, cross-district collaborations helped build capacity and use Section 41 funds effectively. One EL leader recalled their time working in a low-incidence district. Their district, and several others, could not afford adequate translation and interpretation services. To address this, they established a regional contract with a translation service and each district subsidized a share of the service based on their EL enrollment: “I think [translation services were not provided] because [the districts] did not understand the true obligation, what the law was with that. And it was so expensive. They didn’t know where to start. I set up a regional contract [with a translation service]. The [district collaboration] uses their general fund to provide a certain dollar amount for each district. The dollar amount varies on your [EL] population. Once [districts] want to go above and beyond that, then it’s on [the districts] to use their general fund for translation and interpretation.” (District 7) 89 Finally, state-level leadership supported districts by creating a responsive, trust-based environment that encouraged questions and adapted to districts’ needs. One EL leader described an MDE leader who supports Section 41 as “really want[ing] what’s best for the kids. [State leader’s] system for monitoring is there to support the students and staff…it’s not like, an out-to- get-you type situation” (District 7). When discussing partnerships and collaborations, participants emphasized that Section 41 policy implementation was most impactful when district and state leaders worked collectively to align funding, staffing, and programming with ELs’ needs. Leaders’ Perspectives on Improving EL Funding Policy District leaders expressed a clear need for more predictable and intensive funding structures to better support ELs, noting that current Section 41 policy carries significant logistical and structural limitations. These leaders asserted that EL services are often left to district discretion and do not have enforceable guidelines. One EL leader explained, “with EL, it’s up to districts to create an equitable [funding] formula. There isn’t anything out there that will put a number, like, this is your max [teacher to student ratio]. That’s something the state is working towards, but it doesn’t exist” (District 7). This lack of structural guidance contributes to wide variability in district capacity and EL policy implementation. In addition to structural reforms, district leaders called for restructuring how EL funding is distributed and used. Many expressed frustration with supplemental funding streams like Section 41 and Title III, which are constrained by vague guidance, unpredictable enrollment changes, and limited funding. Several leaders suggested that the state should integrate EL dollars categorically into the general per-pupil allocation instead of expanding funds, allowing for greater alignment with local budgeting practices and more flexible spending. Another leader 90 advocated for more concrete state guidance, including exemplar budgets for initiatives like EL summer school, and recommendations for staffing based on EL enrollment. As one EL leader explained, “There are so many pieces [to providing summer school for ELs], and I feel like it’s overwhelming…exemplar models of what a program could look like and cost would be really valuable” (District 7). These suggestions were underscored by the belief that EL services would benefit from dedicated, earmarked funding within a well-supported general fund structure. In sum, district leaders advocated for Section 41 to be guided by clear standards and grounded in equitable investment. Discussion Findings from this study offer insights into how district leaders navigate a categorical, tiered funding system for ELs, and how they perceive its effectiveness in meeting students’ needs. Across districts, leaders voiced concerns about the challenges of implementing EL services under the current policy framework—especially in low-incidence and under-resourced contexts. While higher-incidence districts were more likely to describe meeting their legal obligations to ELs, they often attributed their perceived success not to the state funding system itself, but to the presence of knowledgeable and persistent EL leaders, organizational knowledge of ELs, and external oversight from federal enforcement agencies like the Office for Civil Rights. By contrast, lower-incidence districts reported lacking both the internal infrastructure and the external guidance needed to adequately serve their EL populations. These districts were less likely to have staff with formal EL training, and more likely to express confusion about allowable uses of funds, best practices, and how to meet compliance requirements. Ultimately, all districts expressed a need for enhanced support, resources, and guidance to effectively meet the needs of their EL populations and ensure their equitable access to education. 91 These findings align with prior research, which suggests that concrete state policy monitoring and guidance interact with local leaders’ knowledge to shape policy implementation for ELs (Bartlett et al., 2024). Insights gained from this overarching finding speak to EL education policy and contribute to our understanding of critical resource theory more broadly. Implications for Policy and Practice Ultimately, all districts expressed a need for enhanced support, resources, and guidance to effectively meet the needs of their EL populations and ensure their equitable access to education. These findings align with prior research, which suggests that concrete state policy monitoring and guidance interact with local leaders’ knowledge to shape policy implementation for ELs (Bartlett et al., 2024). While states with a long history of serving EL and immigrant students tend to have leaders with more experience serving ELs and understanding their needs, clear, concrete policy guidance can improve understanding of how to best serve ELs in new immigrant destination states (Bartlett et al., 2024). In this study, participants who felt prepared to meet ELs’ needs and effectively use Section 41 dollars often used concrete policy guidance from sources such as the Office for Civil Rights to ensure ELs had access to adequate services. Participants who felt unprepared to meet ELs needs using Section 41 funding described weak or nonexistent guidance as failing to motivate change when their district had less experience serving ELs or a smaller population of ELs. In addition, this aligns with school finance research, which recommends that supplemental EL funding include requirements that funds be used specifically for EL students and research-backed ELD programming-to-azeki et al., 2018). T and better support local districts’ accessibility and use of EL funds, states may consider clearly defining adequate teacher to student ratios for ELs, developing technical assistance documents that school districts can use 92 to examine research-backed uses of their funds (Umansky & Porter, 2020). As one district EL Consultant shared, states could publish “exemplar models of what [funding different effective ELD programs] could look like, knowing that there are limits to what funding source you're using to pay for that program” (District 7). Importantly, while most ELs nationwide are US-born, Michigan also serves a substantial population of immigrant EL students. These students (and the districts serving them) are especially vulnerable to fluctuations in enrollment caused by shifting federal immigration policy and rhetoric (e.g., Dellinger, 2025). Although tiered, per-pupil funding systems are a promising way to differentiate funding based on ELs’ varying needs (Umansky & Porter, 2020), participants expressed that these sytems introduced unpredictability into districts’ budgeting processes. This volatility disproportionately affects districts serving large proportions of immigrant and migrant students, who tend to require more intensive supports. One potential solution would be a “hold harmless” provision that calculates districts’ EL funding allocation based on a multi-year average of EL enrollment, allowihng districts to maintain consistent services throughout enrollment shifts. In addition, integrating EL funding into the state’s general per-pupil allocation may allow districts to plan and staff ELD programs consistently from year to year rather than relying solely on restricted supplemental programs (Title III and Section 41). Finally, in the absence of greater policy oversight and sufficient funding to provide EL services, district leaders determined locally whether and how to financially prioritize ELs. In districts with experienced EL leaders, this sometimes resulted in robust ELD programs that adhered closely to federal mandates and best practices. In districts without experienced EL leaders, however, ELs were often deprioritized in funding discussions. This finding is troubling because there is a nationwide shortage of teachers and school leaders with formal training to 93 meet ELs’ needs (Umansky & Porter, 2020) and a lack of funding to incentivize teacher preparation programs to offer EL-specific coursework (Gándara & Maxwell-Jolly, 2006). Yet under federal law (ESSA, 2015), ELs are entitled to high-quality ELD services. This legal requirement obligates districts to plan for and deliver resources, staffing, and instruction that allow ELs to meaningfully access academic opportunities. Without clearer state guidance and stronger funding mechanisms, local districts may be unable to recognize and address these needs, raising both moral and legal concerns. Implications for Scholarship and Theory Critical resource theory draws on broader critical theories to analyze how power, race, class, and policy structures influence the distribution of resources across school districts and among students. This theory challenges the notion that school finance policies are allocated in a neutral fashion based on student needs. Instead, it highlights how school finance policies often reflect and reproduce existing social hierarchies, particularly based on race, income, and immigration status. Furthermore, critical resource theory recognizes resource distribution as a politically and racially charged process. Existing research applying critical resource theory uses primarily quantitative data and finds evidence of racial and income disparities in access to equitable funding (CITE). In this paper, I extend the critical resource framework to include non- financial resource streams, aiming to provide a more holistic understanding of how funding policies contribute to the uneven distribution of resources. The findings from this study provide evidence that limited financial resources, lack of concrete guidance, and limited professional experience serving ELs jointly contribute to the lack of equitable service provision for ELs. Existing cost studies of Michigan’s school funding formula find that ELs require substantially more funding than they currently receive through 94 Section 41 to access adequate educational opportunities (CITE SFRC). However, the extent to which funding policies expand or limit opportunities for ELs depends not only on adequate funding but also on the presence of appropriate capacity to meet ELs’ needs. Findings suggest that capacity can be developed through guidance in state policy or school district leader and staff expertise in serving ELs. For example, participants with less experience serving ELs requested examples of how to best spend Section 41 funding. Prior research corroborates this finding, with experienced EL leaders in new immigrant destination states requesting more concrete policy guidance due to the relatively recent influx of EL students statewide (Bartlett et al., 2024). Participants expressed that concrete policy implementation guidance could support Section 41 in meeting ELs’ needs, while nonexistent guidance failed to support districts in using Section 41 to develop English language support programs. Ultimately, ELs were disadvantaged by both the level of funding and unclear policy guidance under Section 41. Conclusion This study highlights the challenges Michigan school districts face in implementing effective services for ELs under the state’s current tiered funding system, a system that at least seven other states have also implemented. While some higher-incidence districts have developed the capacity to meet EL students’ needs, often due to strong local leadership and external oversight, many lower-incidence and under-resourced districts lack the infrastructure, training, and guidance to do so. Leaders perceived inconsistent funding, minimal policy oversight, and a lack of accountability mechanisms as producing uneven support for a highly vulnerable student population. Without more equitable and predictable funding structures, coupled with stronger state-level guidance and capacity-building efforts, Michigan risks perpetuating educational 95 disparities for ELs and incurring greater long-term costs. Addressing these systemic gaps is not only a matter of policy effectiveness but of educational equity and legal compliance. 96 PAPER 3: THE RELATIONSHIP BETWEEN IMMIGRATION CHANGES AND NEWCOMER ENGLISH LEARNER EDUCATION: EVIDENCE FROM MICHIGAN Caroline Bartlett Since 2001, changes in immigration law, enforcement practices, and refugee resettlement policy have contributed to shifting migration patterns, altering the demographics of schools and communities across the country (U.S. Citizenship and Immigration Services, 2025). The population of foreign-born US citizens has grown by more than 20 million since 2000, and this growth was primarily driven by individuals from Latin and South America (Center for Immigration Studies, 2025). These migration shifts coincided with a rise in the number of newcomer students, defined as multilingual students classified as English Learners (ELs) who immigrated and enrolled in US schools three or fewer years prior. The number of newcomer students in the U.S. increased by almost 300,000 between 2014 and 2021 (Sugarman, 2023). Newcomers represent a distinct subgroup of students whose experiences are heavily shaped by immigration policy. Newcomers arrive under a wide range of circumstances, including refugee resettlement, family reunification, and employment- or education-based visas (Suárez- Orozco et al., 2008; Takanishi & Le Menestrel, 2017). These varied pathways influence students’ linguistic, academic, and social-emotional needs. Schools often support newcomers by offering specialized language development, acculturation, and academic content programming. However, the form and availability of these programs vary across districts and are heavily influenced by 97 local capacity, community context, and state policy guidance (Short & Boyson, 2012; U.S. Department of Education, n.d.). Given the many circumstances under which newcomers arrive in the US, educators and school leaders often play key roles in supporting these students. Research indicates that increased immigration enforcement and anti-immigrant discourse negatively affect immigrant-origin students’ academic outcomes, attendance, and well-being—regardless of their legal status (Dee & Murphy, 2020; Kirksey et al., 2020; Sugarman, 2019). Newcomer students must navigate the psychological and material consequences of shifting immigration enforcement and public rhetoric while adjusting to US schools and academic content (US Department of Education, n.d.a). As a result, school-level actions, including adopting trauma-informed practices and creating inclusive school climates, can strongly contribute to newcomers’ sense of belonging in their new communities (Crawford, 2017; Jaffe-Walter et al., 2019; Lowenhaupt et al., 2021). School leaders contribute to newcomers’ opportunities and success by setting an inclusive school climate, facilitating culturally responsive professional development, and building relationships with newcomers and their families (e.g., Guo-Brennan & Guo-Brennan, 2021). While substantial research has explored school leaders’ central roles in shaping newcomers’ educational opportunities, relatively little research has examined how state and district-level leaders adapt newcomer education policies in response to broader immigration trends and political contexts. This study addresses that gap by focusing on Michigan, a top refugee resettlement state with a fast-growing newcomer EL population. Using a mixed methods approach, I consider whether and how shifts in newcomer enrollment, district resource allocation strategies, and schools’ approaches to supporting newcomers have changed over time. This study offers insights into how education leaders navigate changing demographic and political 98 landscapes. Understanding how education leaders navigate changing demographic and political landscapes is important because it sheds light on how they respond to shifts that directly impact student needs, resource allocation, and policy implementation. As student populations become more diverse and immigration policy climates evolve, school leaders play a critical role in ensuring schools remain equitable, inclusive, and effective for all students. Their decisions influence regarding funding, curriculum, and staffing for EL and newcomer students. Exploring their perspectives on services for newcomer students amid shifting political climates helps identify strategies that promote educational equity and resilience in dynamic immigration policy contexts. Specifically, I ask: 1. How have newcomer enrollment patterns changed over time in Michigan, especially following major changes to federal immigration policy? 2. How do state and local education leaders describe the relationship between shifting political contexts of reception and newcomer enrollment trends? 3. How do these leaders adapt newcomer policies and programming in response to changes in enrollment and political climate? By centering the perspectives of state and local education leaders, this study contributes to a deeper understanding of how education systems respond to the complexities of immigration policy and demographic change. The findings from Michigan have broader relevance for other states facing similar challenges and seeking to design responsive and equitable newcomer education policies in dynamic and uncertain policy environments. 99 Serving Newcomer Students Literature Review The US newcomer population comprises a diverse group, including students with various linguistic backgrounds, educational experiences, and immigration histories. Newcomer students—those who have lived in the US for three or fewer—represent a distinct subgroup of ELs with unique educational and social-emotional needs. Unlike most ELs born in the US, newcomers immigrate to the US with disparate experiences with formal education, home language literacy, and English proficiency (Suárez-Orozco, 2010; Umansky et al., 2022). Some arrive as refugees fleeing war or persecution. In contrast, others immigrate through employment or student visas (Suárez-Orozco et al., 2008), and this wide range of experiences creates highly variable needs for this population. These varied migration pathways influence students’ educational needs in schools. In general, newcomers require additional services to adjust to US society and the US school system, including wraparound services to support acculturation to the US (Brenner & Kia-Keating, 2016). For instance, refugee students often require additional services in school to support their psychosocial well-being (McBrien, 2005). Other newcomers who immigrated to the US with a family member on a work or student visa may be less likely to require such supports. These supports are provided in addition to more standardized English language development (ELD) services that are provided to all ELs. In addition to academic and linguistic challenges associated with integrating into US schools, newcomer students may also experience added stress related to immigration enforcement and rhetoric, which contributes to adverse academic and social outcomes. Enhanced immigration enforcement has been linked to declining student enrollment among Hispanic 100 students, regardless of immigration status (Dee & Murphy, 2019). In addition, the expansion of deportation efforts and anti-immigrant rhetoric heightens stress and uncertainty for EL and immigrant families, with research documenting negative effects on academic achievement and attendance (Kirksey et al., 2020; Sugarman, 2019). Because the newcomer population is marked by diversity in age, initial English language proficiency, reason for immigration, and background with formal education, there is not one standard newcomer program implemented across all schools and contexts. Schools typically offer specialized programming for newcomer students, including intensive English language development programs, programs that support acculturation to the US, and academic programming (USED, n.d.). Many newcomer programs provide intensive English instruction for six months to one year (Morales & Mogollon, 2024), while others integrate US cultural orientation into mainstream academic content or use counseling or refugee and immigrant support programs to aid in students’ acculturation (Bridging Refugee Youth & Children’s Services, n.d.). These services are intended to support newcomers’ language skills as well their unique needs.5 Education Leadership and Policy Implementation Federal policies governing EL and newcomer education are broad, and state and local education leaders need to interpret these policies into practice (Garver, 2022). For example, the Every Student Succeeds Act (ESSA, 2015) requires state education agencies to determine outcome expectancies for EL students. States also determine the amount of supplemental funding 5 Despite variety in schools’ approaches to newcomer programming, experts recommend such programs include flexible course scheduling, intentional staffing and professional development targeted to newcomers’ needs, basic literacy and content area instruction, extended instructional time (e.g., summer school programs), connections with community organizations to support families, monitoring of student data, and support for newcomers’ transition out of the program and into mainstream academic coursework (Short & Boyson, 2012). 101 provided to school districts to implement English language development programming (Verstegen, 2017), and school districts determine how they will serve newcomers. As such, state and local education leaders are central in designing and sustaining newcomer programs. Substantial existing research considers how teachers and school leaders respond to shifting immigration policies and enforcement practices in their communities. Research suggests that educators employ a variety of strategies to support immigrant-origin students in response to broader socio-political contexts. Using survey data from six school districts, Lowenhaupt and colleagues (2021) found that educators supported students by signaling affirmation for students and their families, building knowledge and capacity among educators and their families, finding resources for students, and facilitating conversations about immigration. Regarding school leaders, Crawford (2017) highlights how school leaders and teachers navigate the presence of Immigration and Customs Enforcement in their communities, demonstrating that educators seek to protect undocumented students’ rights to public education by leveraging their knowledge of students’ legal protections. Similarly, Jaffe-Walter and colleagues (2019) examined how school leaders engaged with immigration discourse in the wake of heightened national attention to the Obama-era “Dreamer” movement and the 2017 inauguration of Donald Trump. Taken together, this research highlights the critical role of educators and school leaders in responding to shifting immigration landscapes. While existing research has primarily focused on educators, less is known about how state and district-level leaders shape newcomer policy and practice in response to federal immigration policy changes. State and local education agency leaders are central in interpreting federal and state policies into actionable guidance for schools, shaping the resources and structures available to students. State immigration policies have been shown to influence states’ 102 approaches to EL education (Callahan et al., 2022). More broadly, state and local education agency leaders must navigate institutional and political constraints in shaping policy implementation and determining the resources available to EL students (e.g., Bartlett et al., 2024; Golash-Boza, 2018; Hopkins et al., 2022; Rigby et al., 2016). Understanding how these leaders respond to federal policy changes is critical for supporting newcomer students’ needs. Conceptual Framework: Contexts of Reception and Newcomer Enrollment and Services Contexts of reception refer to the social, political, and economic environments that shape how immigrant populations are received in the United States, including the resources and opportunities available to them and the attitudes of host communities (Portes & Rumbaut, 2006). These contexts are produced through dynamic interactions across multiple policy levels and institutional actors and can shift rapidly in response to changes in immigration policy, public perceptions, and resource availability. For newcomer students enrolling in United States schools for the first time, contexts of reception influence both their immediate educational environment and the newcomer-specific support systems schools can provide for them. Federal immigration policy structures the overarching political and legal environment for newcomer reception by shaping who arrives in the United States, under what conditions, and with what protections or constraints. Policies such as Deferred Action for Childhood Arrivals, immigration bans on specific countries, and enforcement priorities signal broader national attitudes toward immigrants and have direct implications for school enrollment and participation. For example, increased federal immigration enforcement has been associated with declines in Hispanic student enrollment (Dee & Murphy, 2019) and negative educational outcomes for immigrant-origin students (Ee & Gándara, 2020; Kirksey et al., 2020). 103 At the state level, education finance policies and refugee resettlement decisions shape how school districts are resourced and guided in response to immigration. State legislators determine whether and how much supplemental funding is available to schools serving EL and newcomer students. These allocations may lag behind demographic shifts, leaving districts responsible for serving rapidly growing newcomer populations without proportional increases in funding or staffing capability. For example, although Michigan is a top refugee resettlement state (Ward & Batalova, 2023), most newcomers are resettled within a small number of communities in the state rather than evenly distributed across districts (Rahman, 2024). This pattern can increase local experience and capacity to support recent immigrant communities over time, but can also create localized surges in enrollment that are not immediately met with corresponding resources or staffing pipelines. Locally, district and school leaders must make sense of and respond to shifting contexts of reception. This includes balancing compliance with legal mandates, student needs, and the availability of staff, funding, and community support for immigrant populations. Local school district leaders play a central role in shaping the lived experiences of newcomer students through their decisions about newcomer program design, partnerships with community organizations, and professional development for staff. For example, partnerships with local refugee-serving nonprofits can increase a district’s capacity to meet newcomers’ needs (Hopkins et al., 2021), while staffing and course placement decisions affect newcomers’ access to meaningful content (Dabach, 2015; Callahan et al., 2010). In this study, I apply a contexts of reception lens to explore how state and local leaders navigate changing immigration and policy environments to serve newcomer students. In contexts where newcomer enrollment is highly responsive to changes in federal immigration policy and 104 sentiment, and where funding and staffing are impacted by these changes, leaders must develop systems that are both adaptable and resilient. This framework highlights the need to attend not only to changes in systems-level policy changes, but also to how school district leaders interpret and respond to these changes in their local political and demographic environments. By analyzing the interplay between policy shifts and local responses, I aim to capture the complex challenge of providing newcomer services in politically dynamic settings. Michigan as a Context for Examining Newcomer Enrollment and Policy Implementation Michigan presents a compelling context for studying the intersection of newcomer enrollment trends, shifting contexts of reception, and policy implementation. Michigan has observed an increase in its newcomer population over the past decade despite an overall decline in student enrollment, potentially due to its history as a top US refugee resettlement destination (Kallick, 2023). Between the 2013-14 and 2023-24 school years, the proportion of Michigan ELs classified as newcomers grew from 12% to 18%, and among US states, Michigan is a top refugee resettlement destination (Ward & Batalova, 2023). This growth reflects broader national immigration trends and Michigan’s role as a prominent refugee resettlement state (Ward & Batalova, 2023).6 The changes in Michigan’s newcomer population are illustrated in Figure 3. Changes have been driven by federal, state, and local policy changes. Michigan serves the highest concentration of Arabic-speaking EL students in the country (Office of English Language Acquisition, 2024). Immigration shifts following the 2017 Executive Order No. 13769, more 6 Refugee resettlement patterns contribute significantly to Michigan’s EL population as newcomers enroll in schools upon arrival to the US. Resettlement is not uniform across the state. Historically, refugees tended to be placed in select metropolitan areas where resettlement agencies and support networks are concentrated. Following the Pandemic, refugees began to be resettled in new areas within the state. This geographic variation results in differing district-level capacities, experiences, and policies for supporting newcomers. 105 commonly known as President Trump’s “Muslim Ban,” had an immediate impact on visa and green card seekers from Muslim-majority countries hoping to immigrate to the US and espoused Islamophobia across the country (Ayoub & Beydoun, 2017), creating uncertainty for districts serving Arabic-speaking newcomers. This executive order was aligned with a broader trend of isolationism that exhibited similar effects on potential entrance to the US from other language groups along the southern US border (e.g., American Immigration Council, 2025). In addition, following a slowdown in international travel during the COVID-19 Pandemic, the state experienced a temporary and disproportionate decline in newcomer enrollment (Altavena, 2021). Most recently, after the US military’s withdrawal from Afghanistan, five Michigan counties welcomed over 1,700 Afghan refugees who had assisted the US military in Afghanistan (Michigan Department of Labor and Economic Opportunity, 2021). These policy changes have created large swings in the number of newcomer students in Michigan, and school districts have been responsible for providing newcomer services as this population has changed. 106 Figure 3 Total Newcomer Enrollment by Fall Year Despite this growth in newcomer students, Michigan provides limited funding to support EL and newcomer programs (Mavrogordato & Bartlett, 2024). In 2024, the state received roughly $11 million in Title III funding to support EL education and supplemented this funding with $39.8 million in state grant funding to districts (Mavrogordato & Bartlett, 2024). State grant funding via State Aid Section 41 is allocated to districts on a per-pupil basis, with students demonstrating lower English proficiency levels receiving more funding. As of 2024, Michigan provides newcomer ELs with roughly $1,476, or 15%, more funding than the baseline (Mavrogordato & Bartlett, 2024). This financial landscape provides an opportunity to examine how districts navigate resource constraints while serving a rapidly changing newcomer population. 107 Methods This sequential explanatory mixed-methods study (Ivankova et al., 2006) leverages administrative data from the Michigan Education Data Center and semi-structured interviews with 10 state and school district EL education leaders to explore how changes in newcomer enrollment following major political events relate to planning for newcomer and English language development services. First, I estimate interrupted time series (ITS) models to understand how the composition of newcomer entries to Michigan schools changes over time following major political events impacting immigration. Next, I use qualitative interview data to explore whether and how those changes affect school districts’ resource allocation and staffing for EL and newcomer programs. ITS estimates provide insights into broad, statewide trends in newcomer enrollment, and interviews contextualize the quantitative findings, providing a deeper perspective of the practical ways in which newcomer enrollment changes affect EL programming. Interrupted Time Series (ITS) Approach The data for my analyses come from the Michigan Education Data Center and include student, school, and district demographic information for traditional public and charter schools in Michigan between academic years 2013-2014 and 2023-2024 (N = 16,101,541). These data include information on students’ newcomer and EL classification. I use this information to identify the first year a student was classified as a newcomer and/or EL. The main outcome of interest in my analysis is a binary indicator for the first year that students are classified as newcomers in Michigan7 and receive English language development 7 Students may be classified as ELs at two points in time. Many are classified upon entering school for the first time. However, students may also be classified as ELs after attending school for at least one year as a non-EL. 108 services.8 I am able to identify newcomers through the 2023-2024 school year, which allows me to capture changes in identification rates before, during, and after two major events impacting immigration: the 2017 Trump travel ban and the COVID-19 Pandemic. While enrollment changes following the 2017 Trump travel ban have not been examined, the pandemic led to declines in public school enrollment nationwide (Dee & Murphy, 2021). Therefore, I report trends in newcomer identification as a proportion of the overall student population in any given year. This allows me to assess newcomer identification trends relative to the overall student population rather than independently of changes to overall K-12 student enrollment. I use these trends as a proxy to estimate demographic changes related to immigration across years. To identify whether there were shifts in newcomer enrollment from year to year following major political events affecting immigration, I estimate the following model: 𝑦(% = 𝛽* + 𝛽!𝑇𝑟𝑒𝑛𝑑% + 𝛽<2017% + 𝛽B2018% + 𝛽C2019% + 𝛽D2020% + 𝛽E2022% + 𝛽F2023% + 𝑋(%Ω + 𝜆0 + 𝜀(% This model predicts whether student i in year t is newly classified as an EL. 𝑇𝑟𝑒𝑛𝑑% represents a linear time trend centered at 2013-14, the base year of the sample. The variables 2017%, 2018%, and 2019% represent indicators for fall school years directly impacted by the 2017 Trump travel ban, and 2020%, 2021%, 2022%, and 2023% are indicators for fall school years impacted by the COVID-19 pandemic. 𝑋(%Ω represents a vector of student and school characteristics. 𝜆0 is a district fixed effect. 𝛽!, or the coefficient on 𝑇𝑟𝑒𝑛𝑑%, represents the change in newcomer enrollment over time. For each year-specific indicator, 𝛽<, 𝛽B, 𝛽C, 𝛽D, 𝛽E, 𝑎𝑛𝑑 𝛽F indicate the year- specific deviation in newcomer entrance rates relative to the long-term predicted trend. 8 I group newly identified newcomers into two categories: those identified for English language development services in their first year attending Michigan schools and those identified after attending Michigan schools for at least one year. In 2023-24, most newcomers (86%) were identified for ELD services in their first year in US schools. 109 Given existing evidence that immigration policy changes adversely affect EL and newcomer students (e.g., Callahan et al., 2020), I extend my ITS model from equation (1) to include year interactions with an indicator for newcomer students. This allows me to explore whether newcomers to Michigan schools were differentially affected by federal immigration policy changes during the 2017 Travel Ban and Pandemic. This model takes the following form: 𝑦(% = 𝛽* + 𝛽!𝑇𝑟𝑒𝑛𝑑% + 𝛽<2017% + 𝛽B2018% + 𝛽C2019% + 𝛽D2020% + 𝛽E2022% + 𝛽F2023% E + U 𝛽% % G H (2017% × 𝑁𝑒𝑤𝑐𝑜𝑚𝑒𝑟(%) + 𝑋(%Ω + 𝜆0 + 𝜀(% Where 𝑁𝑒𝑤𝑐𝑜𝑚𝑒𝑟(% is an indicator equal to 1 if a student was classified as a newcomer in a given year. In this model, the coefficients on 𝛽<, 𝛽B, 𝛽C, 𝛽D, 𝛽E, 𝑎𝑛𝑑 𝛽F represent the change in entries in Michigan schools following the 2017 Travel Ban and Pandemic for non-newcomer students relative to the underlying linear time trend, while 𝛽H, 𝛽I, 𝛽!*, 𝛽!!, 𝛽!<, 𝑎𝑛𝑑 𝛽!B represent the increase or decrease from trend in newcomer entries relative to non-newcomers. Semi-Structured Interviews In addition to my ITS approach, I conducted semi-structured interviews in the spring of 2025 with 9 state and local education agency EL leaders. I purposefully selected interview participants who could speak to changes in Michigan’s newcomer demographics and service provision over time. I invited leaders with at least five years of experience working in EL education in Michigan, as they could speak to the state and districts’ responses to federal immigration policy changes. In total, I invited 11 educational leaders to participate, and 9 leaders agreed. In total, I interviewed one state education agency leader and eight local education agency leaders from eight school districts. Statewide, newcomers represented 1 percent of the Michigan student population, and district leaders served in districts with between 1 and 7 percent 110 newcomers. Leaders served in districts ranging from Table 10 describes my sample. Interviews were conducted and audio recorded on Zoom, lasting between 30 minutes and one hour, and were later transcribed for analysis. 111 Table 10 Sample of Interview Participants Identifier Level 1 2 3 4 5 6 7 8 9 District District District District District District District District State Current Role Director of School Improvement and Supplemental Programs ESL Teacher and Coordinator Multilingual Consultant ELL Training and Support Coordinator Manager of District Staffing Supervisor of Instruction and Pedagogy Supervisor of Special Populations Director of EL & Bilingual Programs English Learner Consultant Years in Current Role % Newcomers in District, ISD, or State 11 22 5 5 3 10 5 5 8 5 4 3 1 7 4 2 4 1 112 I designed my interview protocol to elicit participants’ perspectives on changes in newcomer enrollment and demographics following significant federal immigration policy changes in their local context. I began interviews by sharing ITS findings with the participants and asking whether the findings matched their experiences serving ELs and newcomers. I then asked questions designed to elicit leaders’ perspectives on how changes to the federal context of reception shaped resource allocation and service provision for newcomer students at the state or local levels. For example, I asked, “Let’s step back a few years to January 2017, when President Donald Trump signed an executive order that temporarily banned travelers from seven predominately Muslim countries—Iran, Iraq, Libya, Somalia, Sudan, Syria, and Yemen—from entering the United States for 90 days. The order also suspended the US Refugee Admissions Program for 120 days and indefinitely halted the acceptance of Syrian refugees. Can you describe your professional experiences with this policy?” and, “Did your district respond to this policy change by changing any district-level policies or practices?” In contrast to the ITS findings, which provide a descriptive and statewide perspective of newcomer enrollment changes over time, my analysis of interview data centered on understanding the perspectives of state and local education agency leaders who directly implement education policies and practices for newcomer students. I established a theoretically grounded coding framework based on the literature on contexts of reception (Portes & Rumbaut, 2006) and ecological systems (Bronfenbrenner, 1994). I coded transcripts for evidence of federal, state, and local contexts of reception shaping enrollment and resource allocation for newcomer services, as well as interactions between various levels’ (federal, state, and local) contexts of reception. To establish trustworthiness, an external colleague coded three interview transcripts 113 using my coding scheme. We met to reach consensus regarding codes and discussed connections between codes and their alignment to emerging findings. Limitations My findings provide new insight into how federal immigration policy changes and subsequent shifts in state and local contexts of reception shape districts’ planning for newcomer programs in Michigan. However, I acknowledge several limitations that may impact the generalizability and interpretation of my findings. First, I estimate changes to statewide newcomer enrollment using student enrollment data collected in October, but newcomers arrive in Michigan schools throughout all months of the year. This implies some measurement error in my estimation of annual newcomer enrollment fluctuations. Notably, the state provides funding for newcomer ELs based on fall enrollment counts, meaning districts’ material realities are shaped by this number. A primary limitation of my interview data is the small sample size (N = 10) of leaders interviewed. My small sample size is attributable to purposeful sampling for leaders with extensive experience in EL and newcomer education. While this sampling approach provided an insightful perspective into changes to contexts of reception and newcomer education over time, it may restrict the extent to which findings can be generalized to other contexts where newcomer and EL leaders possess fewer years of experience and have served across fewer changes to the context of reception. Federal Policies Influence Newcomer Enrollment Findings All participants described several major international and federal events that spurred increases in newcomer enrollment in Michigan, including the US military’s withdrawal from 114 Afghanistan in 2021, the war in Ukraine, and expanded humanitarian parole programs for Cuban, Haitian, Nicaraguan, and Venezuelan immigrants. As one state leader described, “2021 was the end of the Afghan War…in November, [the Afghan kids] were released [from military bases they had been evacuated to] to finally start entering schools...after that, we got the Ukrainian kids, and they after that we got the Cuban, Haitian, Nicaraguan, and Venezuelan groups and those have been consistently growing. One of the big Arabic spikes in newcomers was our Lebanese and Syrian kids” (State Leader). These changes in immigration policy are evidenced by descriptive trends in Figure 4. Figure 4 details raw K-12 newcomer identification trends as a share of EL entries. In the first four years of my sample, the newcomer student share of total annual EL entries increased (from 16.77% in 2013-2014 to 31.90% in 2016-2017). In the 2017-2018 school year, the share of EL entries who were also classified as newcomer students began to decrease to 28.16%. This declining trend persisted through the 2020-2021 school year, where newcomer students comprised just 18.44% of total EL entries. In the 2021-2022 school year, the share of EL entries who were classified as newcomers began to “rebound” and reached a peak in the 2023-2024 school year (the last year for which data are available), when newcomers comprised 33.90% of total EL entries. 115 Figure 4 Trends in K-12 Student Entries into Michigan Schools by Newcomer Status, 2013-2014 through 2023-2024 In addition to federal policies aligning with influxes in newcomer enrollment, all participants pointed to restrictive federal immigration policy and anti-immigrant sentiment— particularly throughout the Trump administration—as shaping both actual and anticipated declines in newcomer enrollment. One leader referred to “self-deportation” causing “newcomer numbers to slow” not just due to travel bans or visa restrictions, but because “the anti-immigrant sentiment was strong…even if the bans didn’t pertain to some of our newcomer families” (District Leader 8). Another school district leader described the effect of federal policy changes on newcomer enrollment, stating, “During [the 2017 Trump Travel Ban], I think across the country, there was a drop in newcomers from Arabic-speaking countries. So [our district] was 116 similar to many districts in that we saw a decline [in newcomer enrollment] based on some broader policy outside of the state of Michigan, outside of [the city of the school district], more national, federal policy” (District Leader 5). To illustrate changes in newcomer enrollment associated with the 2017 Trump Travel Ban, Figure 5 reports trends in newcomer EL entries disaggregated by home language. In the 2013-2014 school year, Arabic and other languages were the most commonly reported home languages among newcomer EL entries. The share of newcomer EL entries reporting Arabic as a home language began to decline in the 2017-2018 school year. Newcomer EL entries declined for all home languages in the 2020-2021 school year, with the largest declines for students reporting home languages other than Spanish or Arabic. In the 2023-2024 school year, languages other than Spanish and Arabic were most commonly reported among newcomer EL entries, followed by Spanish. 117 Figure 5 Trends in K-12 Newcomer Student EL Entries into Michigan Schools by Home Language, 2013- 2014 through 2023-2024 5 State Policies Support and Constrain Newcomer Programs While participants expressed that federal policy and immigration rhetoric shape newcomer enrollment patterns, state policy mediates the implementation and sustainability of policies supporting newcomer education. Resources for immigrant and newcomer populations are distributed in a decentralized way through the Office of Global Michigan, which spearheads programs for integration of newcomers, and the Michigan Department of Education, which both facilitates and complicates service provision for newcomer ELs. State funding for newcomers emerged as the most pressing challenge at the state level. First, state funding is insufficient to provide newcomer services. Four interviewees described districts’ efforts to maintain high-quality newcomer services in the face of declining enrollment. Some districts maintained staffing for newcomer programs by diversifying funding sources 118 rather than relying solely on state Section 41 funding. For example, one participant described this process: “If you’ve been solely relying on Section 41 to fund [a newcomer teacher], now you have less newcomers…less Section 41…what are you going to do? MDE has been pushing hard on utilizing general funds to have your basic [newcomer English language development] program, which is federal law, [so] you have to diversify those funds when you see [an enrollment] trend coming” (District Leader 7). Although Section 41 provides roughly $1,476 per EL for students at the earliest English proficiency levels, researchers have estimated that these school districts require an additional $6,862 per pupil to provide newcomers with an adequate education (Augenblick, Palaich and Associates, 2021). In light of insufficient targeted funding for newcomer services, eight participants echoed the importance of diversified funding to maintain continuity in newcomer services. In addition, state categorical funding for ELs (including newcomers) is distributed to districts on a per-pupil basis based on prior-year enrollment. This funding disbursement structure creates challenges for districts receiving unpredictable or rapidly changing numbers of newcomer students. As a result, most participants described using Section 41 for truly supplemental purposes rather than staffing or sustaining core newcomer programs. One school district leader described Section 41 as “Bonus money. Our newcomer summer program is on that. That’s my family liaison money” (District Leader 1). Although this participant described the resources purchased through Section 41 as critical to newcomers’ success, they did not use this funding to purchase resources related to newcomers’ foundational education programs. 119 Another school district leader discussed the ways that limited and unresponsive state funding for newcomer ELs creates differences in districts’ abilities to provide high-quality newcomer services: “Honestly, the rate of newcomers in the past few years has been so fast that it’s been almost impossible for schools to keep up with because we're already behind as far as having sufficient staff and services for the kids we have. Now, we have all these newcomers…as soon as we try to expand our services to make sure every single kid that qualifies for EL is getting the service they need, you see a new influx. And until the funding truly allows schools to provide the services that they want to provide and they know the kids deserve, we just can't keep up. We're in a really lucky spot at [school district], where we live in a community that supports us. And so, like, all of our physical upgrades can be funded from a bond. A lot of schools across the state can't get a bond, and you end up using general fund dollars for projects like that. I think some schools, it's not that they don't want to provide services. They don't have the funding available. You'd have to do other services for other kids.” (District Leader 8) Rapid changes in newcomer populations create challenges for districts trying to forecast budgets for EL programming from year-to-year. Teachers discussed staff as the most difficult resource to maintain under changes to newcomer funding. One school district leader highlighted their experience staffing newcomer programs following major immigration policy changes: “If you’ve been solely relying on Section 41 to fund a teacher, now you have fewer newcomers. You have less Section 41. What are you going to do? Then, you look at cutting a position. You have to diversify those funds, especially when you see the [policy change] coming. You also have to ask, are there cuts that we have to make in terms of 120 hours of the day [students receive newcomer services]? Is there summer school that we need to scale back? The problem is that a lot of our districts don’t anticipate this quickly enough.” (District Leader 7) This leader has a long history serving newcomer students throughout various political changes and knew to plan ahead to provide newcomer services. However, in uncertain political climates, another district leader emphasized that “it’s hard for our districts to keep up and plan for that” (District Leader 6). One school district leader described the process of creating a five-year strategic plan to increase EL-certified staff: “…based on the number of ELs we had five years ago. You can imagine, now, with a lot more newcomers, that goal is not the target anymore. Those newcomers are now second- year immigrant students who still need a lot of support. We’ve had to make some hard decisions, not provide as many supports for other English learners. We were lucky to have that plan in place to grow our staff, but I’m not able to realize the full benefit of that staff because we had to make up for a shifting demographic.” (District Leader 8) Overall, school district leaders felt that Michigan’s state-level agencies strongly supported newcomer programs. As put by one school district leader, “The state has been very, very supportive of our programs…[following President Trump’s second inauguration] the state superintendent immediately started putting out reminders about Plyler v. Doe, about birthright citizenship, that these supports are in the law and that’s still current” (District Leader 7). In Plyler v. Doe (1982) the Supreme Court ruled that states cannot deny undocumented students access to a free public education, and birthright citizenship states that citizenship can be acquired by birth within a US territory. This leader felt that messaging from the state reinforcing these laws supported their ability to provide newcomer services. However, due to uncertain state 121 funding for newcomer programs following enrollment changes, consistent service provision is highly contingent on local capacity and district wealth. Without stable funding and long-term planning structures, many schools implement reactionary policies and programs for newcomers after influxes rather than building sustainable, high-quality systems. Schools and Communities Responding to Immigration Policy Changes All participants discussed the critical roles parent liaisons, teachers, and community organizations play in shaping newcomers’ educational experiences. Participants described EL- certified staff and parent liaisons and strong community partnerships as helping to welcome and support newcomers and their families. Yet these supports were often fragile, contingent on shifting enrollment patterns, grant-based funding for immigrant and EL programming, and staff capacity, which constrained the sustainability of such services in shifting immigration contexts. Family liaisons and community partnerships emerged as vital to supporting newcomers’ transition to US schools. District leaders with multilingual staff and liaisons described these individuals as bridges between schools and newcomers’ families. One district leader shared how their family liaison hosted regular events for newcomer families, such as “a social coffee hour every week…families drop in, and she has a story time via Zoom” (District Leader 1). Another district leader described how their family liaison hosts “cultural nights, where people can bring food and network and be together” (District Leader 2). After receiving an influx of Bosnian students, another school district leader described an “involved and coherent relationship [between the school district and] local mosques…culturally competent resource organizations with food pantries that match religious requirements” (District Leader 7). These organizations served as a point of contact between schools and newly arrived families. 122 Despite the value of these partnerships to schools, participants described these supports as precarious because they rely on federal and state funding for immigration support. Many of these organizations lost some or all of their funding under the first Trump administration (Macchi, 2017). One school leader described the impact of funding loss at a community-based organization that provided critical wraparound services for immigrant families. “When they had their funding or need cut, they lost staff, and we lost that capacity” (District Leader 7). Following these changes, all participants indicated that “more and more of those questions came to the teacher, the school” (District Leader 7). School staff, particularly teachers, were often called upon to fill systemic gaps for newcomers and their families, especially following shifting immigration policy contexts. Pandemic Effects on Newcomer Services The COVID-19 Pandemic and its impact on immigration spurred further enrollment declines and communication challenges between school districts, community organizations, and newcomer families. However, participants also noted that the pandemic forced districts to develop innovative approaches to serving newcomers. First, leaders described newcomer families’ challenges maintaining connections with local refugee and immigrant support agencies during the pandemic. In their place, many educators supported newcomer families during the pandemic by acting as community navigators. Teachers “became basically the lifeline for their students and families” by providing access to accurate information and support when families could not access other services (District Leader 7). Another leader described teachers’ efforts to build relationships with newcomer families throughout the pandemic, describing teachers who “were not just supporting the educational achievement of newcomer students, but also their social, emotional well-being and their families’ well-being outside of schools…making sure that 123 they had food [and] housing security” (District Leader 5). Four participants noted that this relational work, including making home visits and connecting with families on non-academic issues, extended beyond the Pandemic and created a strong culture of care for newcomers and their families. While schools worked to maintain relationships with newcomers throughout the pandemic, leaders also described disproportionate declines in newcomer enrollment in the aftermath of the Pandemic, because “a lot of our immigrant communities were extremely fearful…we saw parents leaving the labor force…kids started working and many had taken on full-time jobs…” which caused the district to “lose a lot of kids. We started seeing less of our ELs because they didn’t come back” (District Leader 7). Enrollment shifts associated with the Pandemic are presented in Figure 6. The panels in Figure 6 illustrate how trends differed across newcomer and non-newcomer student populations. Figure 6, panel (a) shows that at baseline, newcomer students are roughly 31.84 percentage points more likely than non-newcomers to be identified as ELs. For newcomer students, EL identification rates dropped 6.43 percentage points in 2017-2018 and 17.99 percentage points in 2020-2021 (23.02% and 64.92%, respectively). In the 2021-2022 school year, newcomer EL entrances began to increase, or “rebound,” to pre- trend, but remained 3.58 percentage points below pre-trend. In the 2023-2024 school year, newcomer EL entrances exceeded pre-trend. Figure 6, panel (b) shows that while non-newcomer EL entrances declined in the 2016- 2017 through 2020-2021 school years, they did so at a much lower rate than newcomers. The pre-trend suggests that among all students entering Michigan public schools, roughly 1.56% will be identified as ELs. EL identification for non-newcomer students dropped 0.13 percentage points in 2017-2018 and reached its largest decline in 2020-2021 when it dropped 0.26 124 percentage points relative to the pre-trend (8.33% and 16.67%, respectively). In subsequent years, EL entrances began to “catch up” to the pre-trend but grew at a slower rate than newcomer ELs. Figure 6 ITS Estimates of EL Identification Rates, Grades K-12, Newcomer and non-Newcomer 125 Eight participants described challenges in teacher recruitment and retention following declines in newcomer populations. Following the pandemic, another school district leader described how decreases in newcomer enrollment affected staffing and educational opportunities for remaining students, “A school had a newcomer program with three teachers. I had to disband that program because we did not have enough [kids]…” The elimination of this newcomer program had significant negative effects for remaining newcomers, with “kids [not] getting [English language development] services because there were so few, even though we still [legally] have to provide the service” (District Leader 1). Describing rapidly changing immigration contexts, another school district leader lamented: “Recently, we’ve attempted to launch a newcomer center…staffing that building has been incredibly challenging because we don’t want to overstaff it in the sense of having too many people that we can’t afford to keep long-term, but we also don’t want to understaff and not service students…the heavy spikes and then valleys of newcomers makes it really hard to figure out how many different types of teachers, how many different types of assistants you need into the future, and, therefore, students will almost always be underserved. Because, like, districts are going to be unable and unwilling to over-hire because that's really expensive and really hard to do.” (District Leader 5) Participants emphasized that the adverse effects of reduced staffing and resources following declines in immigration extend to students themselves. These changes disproportionately affected newcomers, who “get less support, less robust instruction, less referrals to community agencies, less opportunities” (District Leader 7). Finally, participants felt that the adverse effects of the pandemic on newcomer enrollment extended to non-newcomer ELs and never-EL students. One school district leader described the 126 effect of declining newcomer enrollment on other ELs, emphasizing, “[when newcomers attend school] there’s more of a sense of identity around being a multilingual learner, because there’s a group of them. When we have a very small percentage of ELs, I don’t know that they feel as connected to one another…it’s not a belonging-type feeling” (District Leader 2). This leader explained that when EL programs are larger, they tend to promote a sense of identity and togetherness for all students receiving services. One school district leader described the effect of declining newcomer enrollment in 2017, stating, “whenever we lose multiculturalism in buildings, there’s an impact on [all] programs…the richness that those students and families bring to the communities” (District Leader 6). Community Mindsets Shaping Newcomer Services Finally, the role of local community members, school district leaders, and educators’ mindsets about immigration emerged as powerful forces shaping newcomer programming. In nine interviews, participants underscored how beliefs about newcomer students, and whether they were viewed as a burden or asset to schools shaped the allocation of resources, staff, and instructional attention on newcomer and EL services. In some settings, sharp increases in newcomer student enrollment “transformed the way that all ELs were served,” by prompting district- and school-wide professional development about English language development services (State Leader). These shifts were driven by demographic necessity and by a mindset that viewed newcomers’ presence as an asset for all students. Three participants described working with district and school leaders who committed to meeting newcomers’ needs, insisting on the importance of newcomer programming even when enrollment declined: “We don’t have an administration that says, ‘Oh, an enrollment of four [newcomers], you can’t offer that class.’ Like, they recognize the importance of [newcomer services]” (District Leader 2). This 127 commitment reflected a community belief that newcomers were entitled to the services that would allow them to access academic content and integrate into US culture rather than a focus on efficiency or cost-effectiveness. Conversely, deficit-based mindsets shaped and intensified by anti-immigrant political contexts emerged as significant barriers to newcomers’ educational opportunities, distorting perceptions of immigrant students and undermining their access to services. All participants described a troubling shift in public discourse following anti-immigrant policies and rhetoric, which corresponded with the normalization of discriminatory language and exclusionary beliefs in schools. One leader noted, “The climate [following President Trump’s second inauguration] is such that things you would never say…now people have no problem saying at meetings or within a school building. And it’s a climate that very much targets [newcomers]” (District Leader 7). Participants stated that these mindsets were often echoed in school board meetings and community rhetoric, and framed newcomer students as undeserving of resources, with one school leader recounting the discourse at a school board meeting following an influx of Venezuelan immigrants, “people were just very, very comfortable saying, ‘Oh, that’s not my community. That doesn’t touch my life. Why should I fund that child? My child is struggling. Why should we fund yours?” (District Leader 7). These mindsets had real, emotional consequences for students and families. Participants described fear, isolation, and discrimination as everyday realities for newcomers and their families. These adverse consequences were further amplified by threats of deportation, even among students with legal status. One district leader shared that some students were so fearful, they felt unsafe going about daily routines, stating, “we’ve had issues of [non-newcomer students saying] ‘I’m going to report you and you’re going to get deported and your family is going to get 128 deported’” (District Leader 7). This leader also shared that “we’re seeing fear among our refugee families and students who have permanent citizenship cards, because we’re now seeing examples of people being removed with those” in response to federal actions taken by the Trump Administration in 2025 to detain and deport documented immigrants (District Leader 7). Anti- immigrant policies did not merely reflect ideological divides, they shaped school policies, teacher attitudes, student needs, and community behavior in ways that fundamentally constrained the emotional, academic, and social experiences of newcomer students. Discussion This study aimed to understand how fluctuations in newcomer enrollment following shifts in political and social contexts affected the educational experiences of newcomer ELs and the allocation of resources among school districts in Michigan. I explored how contexts of reception at the federal, state, and local policy levels related to enrollment trends and intersected with school district leaders’ approaches to providing newcomer services. The findings reflect a dynamic relationship between federal immigration policies, state education and immigration policies, and local educational leaders’ actions, which collectively shape the opportunities available to newcomer students. At the macrosystem level, federal policies and political climates played critical roles in shaping enrollment trends and the broader educational experiences of newcomer students. Participants described the negative impact of anti-immigrant policies and rhetoric, particularly during the Trump administration, on both newcomers’ enrollment and educational opportunities. Such policies created fear and uncertainty among immigrant families and also contributed to an exclusionary climate in schools. This is consistent with existing research that highlights the adverse effects of immigration enforcement and anti-immigrant sentiment on EL and immigrant- 129 origin students’ academic and social outcomes (Dee & Murphy, 2019; Kirksey et al., 2020). Further, decreased federal funding for social support services including refugee resettlement agencies exacerbated these challenges. School districts were often asked to fill gaps in services when resettlement agencies lost funding, creating further strain on already limited resources for newcomers and ELs. At the exosystem level, state policies were vital in determining the financial resources allocated to school districts serving newcomers. Michigan’s status as a top refugee resettlement destination for refugees means that shifts in federal immigration policy have a disproportionate and direct impact on the state’s ability to serve newcomer populations. Participants reported that state funding was critical but insufficient to meet districts’ needs in the context of fluctuating newcomer populations. Prior research which finds that state funding mechanisms are insufficient to meet ELs’ educational needs corroborates this finding (Baker, 2005; Jimenez-Castellanos & Topper, 2012). The result of lost funding was that many districts struggled to maintain specialized programs and supports for newcomers in the face of unpredictable, declining enrollment. These findings underscore the importance of stable state-level funding mechanisms that protect districts’ fiscal capacity in supporting educational equity for newcomer students. At the mesosystem level, the varying local contexts of school districts and their leadership significantly influenced how schools responded to changes in newcomer enrollment. District leaders’ commitment to supporting newcomer students, regardless of enrollment trends, was instrumental in maintaining stability in services for this population. In addition, educators’ and community members’ attitudes toward newcomer students were shaped by broader political climates. Some schools and communities responded to challenges posed by shifting enrollment and policy by adopting inclusive and trauma-informed practices, such as increasing home visits 130 and integrating newcomers into mainstream coursework with language supports. This finding aligns with prior research on the role of teachers in promoting inclusive environments for newcomers (Dabach et al., 2018). Conversely, other communities perpetuated exclusionary and racist attitudes. In addition, when districts struggled to meet newcomers’ needs due to lack of resources, students experienced significant barriers to high-quality newcomer services and inclusion in general education courses. These challenges reflect the importance of certified teachers and targeted interventions to support newcomers’ integration into US schools. The role of community partnerships also emerged as a significant factor in supporting newcomer students through shifting immigration contexts. School districts that partnered with community organizations focused on refugee and immigrant populations were better positioned to provide wraparound services that addressed both educational and social-emotional needs. These partnerships included services such as acculturation support, family advocacy, and educated school districts on the cultures and needs of their newcomer populations, helping facilitate smooth transitions into US schools. Strong connections between schools and immigrant-serving community organizations emerged as critical to addressing the holistic needs of newcomer students but tended to weaken following major political events that resulted in declining newcomer populations. Existing research has suggested innovative funding policies for districts serving newcomers, including concentration grants, which would provide additional funding to provide targeted supports for districts serving high populations of newcomers, tiered funding systems where newcomers receive additional funding, or shared staffing to districts serving low concentrations of newcomers (Thompson et al., 2020). While states do receive federal funding through Title III, Part A for districts experiencing rapid growth in newcomer students (USED, 131 n.d.b), Federal Title III officials and state and local educational leaders have expressed that although Title III provides some support for ELs’ resource needs, it is ultimately insufficient to meet states’ predetermined outcome expectancies for ELs (Millard, 2015). One evident reason for this discrepancy is that Title III funding has not kept pace with growth in the EL student population nationwide. For example, Title III provided an average of $169 per EL student during the 2007-2008 school year compared to $147 per EL in the 2017-2018 school year (Sugarman, 2021). Prior to the second Trump administration, congress moved to increase Title III funding levels to keep pace with the growth in the EL student population. However, EL advocates raised concerns that proposed funding increases were still insufficient to meet EL student needs (Sugarman, 2021). In 2025, the second Trump administration moved to abolish the federal Office of English Language Acquisition, creating uncertainty regarding the future of federal Title III funding (Belsha, 2025). In the aftermath of dramatic changes to federal immigration and education policy under the second Trump administration, states may assume a greater financial responsibility for serving newcomer students or experience large declines in immigration. In addition to funding suggestions raised in existing research, such as concentration grants, tiered funding for newcomer students, and shared staffing for districts serving few newcomers (e.g., Thompson et al., 2020), a hold harmless funding policy would protect districts from significant losses in funding due to changes in student enrollment by ensuring that districts receive at least as much Section 41 funding as they did in the prior year, even if their current numbers would warrant less. Without stable Section 41 funding, districts struggle to maintain high-quality programs for newcomers and other ELs who already attend school in the district. All school district leaders 132 interviewed for this study echoed this leader’s sentiment, discussing the need to find external funding to supplement Section 41 to maintain stable EL and newcomer programs. This study also highlighted the critical emotional and psychological consequences of shifting political consequences on state and school district leaders’ responses to, and perceptions of, services schools provide to newcomer students. State and district leaders described fear, isolation, and discrimination among newcomer students and families in more hostile immigration climates. Participants described the pervasive impact of anti-immigrant rhetoric on students’ sense of belonging, which they perceived to negatively affect students’ academic and social- emotional well-being. These findings are consistent with existing research, which shows how heightened immigration enforcement and political rhetoric contribute to stress and anxiety among immigrant student populations (Kirksey et al., 2020; Sugarman, 2019). Addressing these challenges requires comprehensive support systems. Notably, during these climates, school districts received less targeted state funding to support newcomer students. This study aims to contribute to our understanding of the multi-layered influences on newcomer student enrollment trends and the educational opportunities available to newcomer students. Changes to newcomer enrollment occur alongside changes to federal immigration policy. In turn, shifting national political contexts affect state education agencies, which support newcomers’ education via supplemental funding and set statewide goals for newcomer education. Finally, school districts and communities set newcomers’ immediate contexts of reception with their belief systems and ideologies as well as in more tangible ways, including funding staff to support newcomers. Participants underscored the importance of considering how federal, state, and local immigration and education policy contexts collectively shape the opportunities available to newcomers. 133 CONCLUSION The EL population continues to grow and diversify across the United States, yet many federal and state education policies work against ELs, failing to consider their unique strengths and needs. A large body of existing research highlights inequities ELs face in their schools and classrooms, but less attention has been focused on the ways broader education policies exacerbate or ameliorate such inequities. This dissertation has contributed to the body of knowledge on education policies that support ELs’ educational opportunities. The insights from these three papers can offer thoughtful analysis to policymakers and education leaders as they work to expand equity for this growing and diversifying student group. Findings from the first paper show that shifting reclassification procedures from districts to the state increases standardization in reclassification rates, particularly for Spanish-speaking ELs. This suggests that centralizing reclassification processes can mitigate local variation and potential bias in reclassification decision-making. Assuming states select appropriately rigorous reclassification criteria, this procedure can improve students’ access to developmentally appropriate coursework. The second paper examines how Michigan’s tiered EL funding policy, which was designed to allocate resources based on ELs’ English proficiency levels, is interpreted and implemented by district leaders across demographically diverse contexts. Drawing on interviews with 17 leaders from 10 districts, the findings suggest that while there is promise in a tiered funding system that reflects variation in student needs, current policies lack guidance on best practices and stability due to shifting student demographics. Districts serving low populations of EL students, in particular, face pronounced challenges using supplemental EL funding due to limited capacity and insufficient funding, while even high-incidence districts struggle with 134 funding instability when supplemental funding is tied closely to student enrollment counts. The findings underscore the importance of sufficient, stable funding and clear implementation guidance to support equitable EL services, particularly in contexts new to serving EL students. The third paper explores how school district and state education agency leaders respond to fluctuating newcomer enrollment amid shifting immigration patterns and political dynamics. Findings reveal that while some districts have developed responsive supports to serving shifting EL and newcomer populations, many face capacity constraints and a clear lack of policy direction. This study emphasizes the need for adaptable, well-resourced EL policies that provide support to districts navigating rapid demographic changes, enabling them to foster inclusive learning environments for newcomer EL students. Collectively, these findings demonstrate that while school districts face challenges to adequately serving ELs, state education agencies can use policy levers to reduce inequities and better support ELs’ academic opportunities. Effective policy must not only provide sufficient resources but also be grounded in an understanding of how local educators implement reforms in complex and constrained environments. 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ACCESS for ELLS: Interpretive guide for score reports, grades K-12, spring 2024. https://wida.wisc.edu/sites/default/files/resource/Interpretive-Guide.pdf 146 APPENDIX A: SUPPLEMENT TO PAPER 1 Table A1 Results of McCrary Tests (1) T p BW N T -1.5958 0.2156 -1.0938 1.7504 -0.5746 1.1646 -1.4601 -0.4814 0.1105 0.8293 0.2741 0.0800 0.5656 0.2442 0.1443 0.6302 [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] 6077 9099 8075 8873 3214 6673 5362 2848 -1.2003 -0.6212 -1.326 1.511 -0.2612 0.6996 -1.6913 -0.3871 Optimal Triangular (2) p 0.2300 0.5344 0.1848 0.1308 0.8288 0.4842 0.0908 0.6987 Optimal Uniform BW [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] [23, 23] Pre-Period 3rd Grade 4th Grade 5th Grade 6, 7, 8th Grade Post-Period 3rd Grade 4th Grade 5th Grade 6, 7, 8th Grade Bandwidth Kernel 147 Figure A1 McCrary Tests: Triangular Kernel 148 Figure A1 (cont’d) 149 Figure A2 McCrary Tests: Uniform Kernel 150 Figure A2 (cont’d) 151 Table A2 Balance Tests Variable Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Grade 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 6, 7, 8 6, 7, 8 Estimate 0.041 -0.005 -0.032 -0.005 -0.011 0.017 -0.014 0.040 -0.003 -0.026 0.003 -0.011 0.010 -0.012 0.040 -0.003 -0.026 0.004 -0.011 0.009 -0.011 0.040 -0.005 Manual (Pre) Reclassification T-Statistic 1.743 -0.787 -0.393 -0.069 -0.077 0.155 -0.622 1.791 -0.451 -0.323 0.043 -0.078 0.095 -0.541 1.798 -0.413 -0.314 0.057 -0.078 0.088 -0.524 1.717 -0.757 BW [-50, 18] [-50, 18] [-50, 18] [-50, 18] [-50, 18] [-50, 18] [-50, 18] [-48, 25] [-48, 25] [-48, 25] [-48, 25] [-48, 25] [-48, 25] [-48, 25] [-48, 26] [-48, 26] [-48, 26] [-48, 26] [-48, 26] [-48, 26] [-48, 26] [-57, 18] [-57, 18] p-value 0.081 0.431 0.694 0.945 0.938 0.877 0.534 0.073 0.652 0.747 0.966 0.938 0.924 0.588 0.072 0.679 0.754 0.954 0.938 0.930 0.600 0.086 0.449 SE (0.023) (0.007) (0.081) (0.066) (0.136) (0.108) (0.022) (0.022) (0.006) (0.082) (0.066) (0.136) (0.107) (0.022) (0.022) (0.006) (0.082) (0.066) (0.136) (0.107) (0.022) (0.023) (0.006) 152 Table A2 (cont’d) Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Variable Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 -0.032 -0.005 -0.012 0.019 -0.014 Grade 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 5 5 Estimate -0.042 0.025 -0.014 -0.002 -0.013 0.015 0.002 -0.028 -0.016 0.024 -0.041 0.042 0.002 -0.020 0.006 0.008 0.042 -0.009 -0.015 0.022 0.012 0.689 0.935 0.931 0.862 0.538 -0.401 (0.081) -0.081 (0.066) -0.086 (0.136) 0.174 (0.108) (0.022) -0.616 Automatic (Post) Reclassification T-Statistic SE -0.836 (0.050) 1.241 (0.020) -0.154 (0.094) -0.027 (0.064) -0.116 (0.117) 0.149 (0.102) 0.060 (0.032) -0.769 (0.037) -1.059 (0.015) 0.489 (0.048) -0.522 (0.079) 0.331 (0.126) 0.018 (0.090) -0.939 (0.021) 0.165 (0.037) 0.532 (0.015) 0.844 (0.050) -0.090 (0.095) -0.116 (0.127) 0.289 (0.075) 0.437 (0.028) p-value 0.604 0.214 0.878 0.978 0.908 0.881 0.952 0.442 0.290 0.625 0.602 0.741 0.985 0.348 0.869 0.595 0.399 0.929 0.908 0.773 0.662 153 [-57, 18] [-57, 18] [-57, 18] [-57, 18] [-57, 18] BW [-11, 8] [-11, 8] [-11, 8] [-11, 8] [-11, 8] [-11, 8] [-11, 8] [-14, 24] [-14, 24] [-14, 24] [-14, 24] [-14, 24] [-14, 24] [-14, 24] [-10, 24] [-10, 24] [-10, 24] [-10, 24] [-10, 24] [-10, 24] [-10, 24] -0.424 -1.117 -0.837 0.013 -0.245 0.285 -0.659 [-10, 9] [-10, 9] [-10, 9] [-10, 9] [-10, 9] [-10, 9] [-10, 9] Table A2 (cont’d) Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Variable Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 Grade 3 3 3 3 3 3 3 4 4 4 4 4 4 4 5 5 5 5 5 -0.023 -0.025 -0.061 0.001 -0.023 0.024 -0.032 (0.055) (0.022) (0.073) (0.092) (0.093) (0.084) (0.048) Policy Change 0.671 0.264 0.403 0.989 0.806 0.775 0.510 Estimate -0.083 0.030 0.017 0.007 -0.003 0.003 0.016 -0.068 -0.013 0.050 -0.034 0.050 -0.007 -0.008 -0.034 0.010 0.068 -0.002 -0.006 SE (0.055) (0.021) (0.124) (0.077) (0.146) (0.126) (0.039) (0.043) (0.016) (0.095) (0.091) (0.154) (0.115) (0.030) (0.043) (0.016) (0.096) (0.104) (0.155) T-Statistic -1.495 1.424 0.140 0.096 -0.018 0.025 0.400 -1.589 -0.813 0.527 -0.377 0.327 -0.062 -0.275 -0.788 0.650 0.707 -0.015 -0.039 154 Table A2 (cont’d) Primary Language: Other Newcomer Student Female Special Education Economically Disadvantaged Primary Language: Spanish Primary Language: Arabic Primary Language: Other Newcomer Student 5 5 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 6, 7, 8 0.014 0.024 -0.063 -0.020 -0.029 0.012 -0.011 0.010 -0.018 (0.105) (0.036) (0.060) (0.023) (0.109) (0.102) (0.128) (0.111) (0.053) 0.131 0.665 -1.056 -0.860 -0.264 0.113 -0.090 0.093 -0.341 155 Table A3 Upper-Bound Estimated Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Periods 3 SE Bandwidth N 4 SE Bandwidth N 5 SE Bandwidth N 6-8 SE Bandwidth N Weighted Average SE Standardized ELA Score Control Local Polynomial Bandwidth Kernel Manual Reclassification 2016-17 through 2018-19 0.619*** (0.050) [-12, 13] 3353 0.364*** (0.041) [-14, 27] 6692 0.234*** (0.038) [-8, 26] 5737 0.403*** (0.035) [-9, 21] 6503 0.381*** (0.020) Automatic Reclassification 2019-20 through 2021-22 0.979*** (0.013) [-11, 8] 1453 0.981*** (0.007) [-14, 24] 5154 0.972*** (0.013) [-10, 24] 3957 0.985*** (0.011) [-10, 9] 1327 0.980*** (0.021) Policy Change (DiRD) 0.360*** (0.051) 0.617*** (0.042) 0.738*** (0.040) 0.583*** (0.037) 0.597*** (0.021) X 1 Optimal Triangular 156 Table A3 (cont’d) Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. 157 Table A4 Lower-Bound Estimated Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Periods, by Alternative Model Specifications Manual Reclassification 3rd Grade SE Bandwidth N 4th Grade SE Bandwidth N 5th Grade SE Bandwidth N 6, 7, 8th Grade SE Bandwidth N Weighted Average Effect SE Automatic Reclassification 3rd Grade SE (1) (2) (3) (4) (5) (6) 0.806*** (0.023) [-50, 18] 5566 0.561*** (0.036) [-48, 25] 7978 0.393*** (0.035) [-48, 26] 7281 0.580*** (0.026) [-57, 18] 8276 0.634*** (0.014) 0.807*** (0.022) [-100, 36] 6064 0.578*** (0.036) [-96, 50] 9039 0.403*** (0.036) [-96, 52] 8049 0.589*** (0.028) [-114, 27] 8816 0.650*** (0.014) 0.804*** (0.027) [-25, 9] 4276 0.556*** (0.034) [-24, 13] 5966 0.392*** (0.038) [-24, 13] 5594 0.585*** (0.028) [-29, 9] 7242 0.618*** (0.015) 0.807*** (0.023) [-50, 16] 5318 0.559*** (0.036) [-48, 18] 6946 0.388*** (0.035) [-48, 20] 6699 0.577*** (0.026) [-57, 12] 7687 0.630*** (0.014) 0.809*** (0.021) [-100, 31] 6019 0.576*** (0.037) [-96, 36] 8684 0.406*** (0.036) [-96, 40] 7932 0.587*** (0.028) [-114, 23] 8536 0.654*** (0.014) 0.801*** (0.028) [-25, 8] 4025 0.555*** (0.035) [-24, 9] 5186 0.388*** (0.037) [-24, 10] 5055 0.580*** (0.029) [-29, 6] 6655 0.611*** (0.016) 0.979*** (0.013) 0.987*** (0.008) 0.971*** (0.019) 0.984*** (0.011) 0.989*** (0.007) 0.979*** (0.017) 158 Table A4 (cont’d) Bandwidth N 4th Grade SE Bandwidth N 5th Grade SE Bandwidth N 6, 7, 8th Grade SE Bandwidth N Weighted Average Effect SE Policy Change 3rd Grade SE 4th Grade SE 5th Grade SE 6, 7, 8th Grade SE Weighted Average Effect SE Standardized ELA Score Control [-11, 8] 1453 0.981*** (0.007) [-14, 24] 5154 0.972*** (0.013) [-10, 24] 3957 0.985*** (0.011) [-10, 9] 1327 0.980*** (0.005) 0.172*** (0.026) 0.420*** (0.036) 0.579*** (0.038) 0.405*** (0.028) 0.356*** (0.016) X [-5, 4] 759 0.981*** (0.008) [-7, 12] 2997 0.967*** (0.017) [-5, 12] 2364 0.989*** (0.011) [-5, 4] 659 0.981*** (0.006) 0.166*** (0.033) 0.425*** (0.035) 0.576*** (0.041) 0.404*** (0.030) 0.374*** (0.017) X [-21, 15] 2609 0.982*** (0.006) [-28, 48] 3838 0.975*** (0.011) [-20, 48] 5139 0.971*** (0.015) [-19, 17] 2351 0.981*** (0.004) 0.179*** (0.023) 0.404*** (0.037) 0.572*** (0.037) 0.382*** (0.031) 0.331*** (0.015) X 159 [-9, 7] 1226 0.981*** (0.008) [-12, 18] 4459 0.973*** (0.012) [-10, 18] 3512 0.974*** (0.016) [-9, 9] 1371 0.979*** (0.005) 0.177*** (0.026) 0.422*** (0.037) 0.585*** (0.037) 0.396*** (0.031) 0.354*** (0.016) X [-18, 14] 2311 0.982*** (0.006) [-25, 35] 6323 0.975*** (0.010) [-20, 36] 4980 0.967*** (0.016) [-19, 18] 2315 0.982*** (0.004) 0.180*** (0.022) 0.405*** (0.037) 0.569*** (0.038) 0.380*** (0.032) 0.324*** (0.015) X [-4, 3] 661 0.983*** (0.008) [-6, 9] 2551 0.968*** (0.018) [-5, 9] 2000 0.992*** (0.010) [-5, 5] 659 0.984*** (0.006) 0.178*** (0.032) 0.428*** (0.036) 0.580*** (0.041) 0.412*** (0.031) 0.380*** (0.017) X Table A4 (cont’d) Local Polynomial Bandwidth Kernel Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. 1 Optimal*.5 Triangular 1 Optimal*.5 Uniform 1 Optimal*2 Triangular 1 Optimal*2 Uniform 1 Optimal Triangular 1 Optimal Uniform 160 Table A5 DiRD Estimates for Students Reporting a Primary Language Other than Spanish or Arabic Manual Reclassification Automatic Reclassification Policy Change 3 SE Bandwidth N 4 SE Bandwidth N 5 SE Bandwidth N 6-8 SE Bandwidth N Weighted Average SE Standardized ELA Score Control Local Polynomial Bandwidth 2016-17 through 2018-19 0.853*** 2019-20 through 2021-22 0.977*** (0.026) [-45, 11] 2020 0.628*** (0.044) [-48, 22] 2596 0.458*** (0.046) [-36, 22] 2019 0.598*** (0.041) [-57, 19] 2726 0.703*** (0.018) (0.016) [-9, 9] 719 0.983*** (0.007) [-50, 25] 2292 0.985*** (0.008) [-50, 19] 1527 1.007*** (0.005) [-7, 6] 415 0.994*** (0.004) X 1 Optimal (DiRD) 0.124*** (0.031) 0.355*** (0.045) 0.527*** (0.047) 0.409*** (0.041) 0.304*** (0.020) Kernel Triangular Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. 161 Table A6 DiDiRD Estimates Across Language Subgroups of ELs Weighted Average Estimate SE DiRD: Spanish 0.440*** (0.028) DiRD: Primary Language Other than Spanish or Arabic 0.304*** (0.020) DiDiRD 0.136*** (0.034) 162 Table A7 Estimated Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Periods Excluding Years Directly Impacted by COVID-19 Pandemic Manual Reclassification Automatic Reclassification Policy Change 2016-17 through 2018-19 2019-20 through 2021-22 3 4 5 0.806 (0.023) [-50, 18] 5566 0.561 (0.036) [-48, 25] 7978 0.393 (0.035) [-48, 26] 7281 0.580 (0.026) [-57, 18] 8276 0.634 (0.014) SE Bandwidth N SE Bandwidth N SE Bandwidth N 6, 7, 8 SE Bandwidth N Weighted Average SE Standardized ELA Score Control Local Polynomial Bandwidth X 1 Optimal 163 0.991 (0.010) [-6, 10] 761 0.982 (0.009) [-50, 21] 5154 0.975 (0.013) [-55, 22] 3644 0.978 (0.015) [-11, 9] 1102 0.983 (0.005) (DiRD) 0.185 (0.025) 0.421 (0.037) 0.582 (0.038) 0.398 (0.030) 0.352 (0.016) Table A7 (cont’d) Kernel Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. Estimates exclude 2019-20 and 2020-21 school years. Triangular 164 Appendix A8 Our balance tests of covariates indicate that no observable characteristics change discontinuously at the threshold, so we can model the data jointly. Below, we the data jointly and estimate DiRD directly as a robustness check to our main analyses. The model is as follows: 𝑌() = 𝛾* + 𝛾!𝐴𝑏𝑜𝑣𝑒() "&’ + 𝛾<𝑊𝐼𝐷𝐴() "&’ + 𝛾B^𝐴𝑏𝑜𝑣𝑒() "&’ × 𝑊𝐼𝐷𝐴() "&’_ + 𝛾C𝐴𝑏𝑜𝑣𝑒() "#$% + 𝛾D𝑊𝐼𝐷𝐴() "#$% + 𝛾E^𝐴𝑏𝑜𝑣𝑒() "#$% × 𝑊𝐼𝐷𝐴() "#$%_ [+ 𝑿𝚪] + 𝜀() Where 𝑌(% is a dummy variable that equals 1 if student i in grade g reclassifies, 𝐴𝑏𝑜𝑣𝑒 is an indicator of whether student i attained reclassification criteria in year t, and 𝑊𝐼𝐷𝐴 is a continuous variable that represents the recentered overall scale score for student i in year t. In this regression, 𝛾C provides the estimate of the difference in RD estimates. We expect that 𝛾C will be equivalent to the difference between 𝛽! "#$% and 𝛽! "&’in equations (1a) and (1b) in the “Methods” section of the manuscript. We also interacted each term with students’ grade level, noted in 𝑿𝚪, to account for variation in likelihood of reclassifying upon qualifying based on grade level. Results were comparable to estimates presented in the main analyses. We present results from the above model below. 165 Table A8 OLS Estimates of the Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Periods Manual Reclassification Automatic Reclassification Policy Change 3 SE Bandwidth N 4 SE Bandwidth N 5 SE Bandwidth N 6-8 SE Bandwidth N Weighted Average SE Standardized ELA Score Control Local Polynomial Bandwidth 2016-17 through 2018-19 0.805 2019-20 through 2021-22 0.982 (0.023) [-50, 18] 5566 0.569 (0.035) [-48, 25] 7978 0.399 (0.034) [-48, 26] 7281 0.583 (0.026) [-57, 18] 8276 0.634 (0.014) (0.010) [-11, 8] 1453 0.982 (0.007) [-14, 24] 5154 0.972 (0.012) [-10, 24] 3957 0.975 (0.013) [-10, 9] 1327 0.979 (0.005) X 1 Optimal (DiRD) 0.177 (0.024) 0.406 (0.035) 0.569 (0.035) 0.393 (0.029) 0.344 (0.015) Kernel Triangular Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. BW indicates the bandwidth of students selected using rdrobust for the OLS estimation. 166 Appendix A9 As a robustness check to our main analyses, we estimate logit regression models where the dependent variable is 1 if a student scored above the reclassification threshold and 0 if they did not. The model is estimated using a general logit model that estimates the log odds of reclassifying or not: 𝑙𝑛[𝑜𝑑𝑑𝑠(𝑌() "&’ = 1)] = 𝛽* "&’ + 𝛽! "&’𝑀() + 𝜀() "&’ Where Y for student i in grade g in the pre-period represents the dichotomous outcome reclassified, and M is the student’s recentered and standardized overall WIDA score. In our pre-period estimates, we simulate raising the reclassification threshold by setting all students on the left of the cutoff to 0, or did not reclassify, as discussed in the “Endogeneity Issues” section of the manuscript. Logistic estimations do not support collinearity between the outcome and predictor variable. As a result, we restrict our pre-period sample to only students who exceeded the reclassification threshold and interpret point estimates as the jump from 0 to the point estimate at the cutoff. Since all students in our logit regression sample scored above the cutoff, we exclude the indicator for being above the cutoff from pre-period estimations. As a result, sample sizes are smaller in pre-period logit estimations than those presented in the DiRD analyses. In the post-period, we estimate: 𝑙𝑛[𝑜𝑑𝑑𝑠(𝑌() "#$% = 1)] = 𝛽* "#$% + 𝛽! "#$%𝐶() + 𝛽< "#$%𝑀() + 𝛽B "#$%𝐶() × 𝑀() + 𝜀() "#$% Which includes Y for student i in grade g in the post-period represents the dichotomous outcome reclassified, C is an indicator equal to 1 if a student scored above the reclassification threshold and 0 if they did not, M is the student’s recentered and standardized overall 167 WIDA score, and an interaction term for whether a student scored above the reclassification threshold and their recentered and standardized overall WIDA score. For both policy periods, we obtain the sample of students for our logistic model using the optimal bandwidth computed using rdrobust. Regression discontinuity is based on linear regression models and does not support logistic or probit models. Because our outcome is binary, rdrobust estimates effects using local polynomials, which can be interpreted similarly to a linear probability model with the optimal bandwidth chosen by rdrobust. Formally, this means that the sample for the logistic regression is selected using a linear regression because there is not a way to calculate the optimal bandwidth for the logistic regression. Estimates from the logistic regression are consistent with those presented in the manuscript and can be found in the table below. We calculate the effect of the policy change and weighted averages as described in the manuscript, or by differencing logit estimates from the post- and pre-periods: 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠 = 𝛽! "#$% − 𝛽! "&’ Where 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠 is the coefficient estimate of the difference between logit point estimates (𝛽! "#$%, 𝛽! "&’) in the post- and pre-periods. Although we estimate 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑒𝑡𝑖𝑚𝑎𝑡𝑒𝑠 separately for each grade level, we also report estimated effects for the full sample of students. These estimates are precision-weighted by grade level estimate. To do this, we calculate a weighted average estimate inversely proportional to the standard error of each grade-level estimate. Below, we describe the weighted average estimate of the effect of qualifying for reclassification on reclassifying: 168 𝑊𝑒𝑖𝑔ℎ𝑡𝑒𝑑 𝐴𝑣𝑒𝑟𝑎𝑔𝑒 𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠 = Σ!( 1 𝑣𝑎𝑟(𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠)" × 𝐺𝑟𝑎𝑑𝑒 − 𝐿𝑒𝑣𝑒𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒!) Σ! 1 𝑣𝑎𝑟(𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑛𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑡𝑖𝑎𝑚𝑡𝑒𝑠)" The weighted average difference in logit estimates is the combined estimate for all grades with weights inversely proportional to the variance; 𝑣𝑎𝑟(𝐷𝑖𝑓𝑓𝑒𝑟𝑒𝑐𝑒 𝑖𝑛 𝐿𝑜𝑔𝑖𝑡 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒𝑠)( represents the variance of the estimate for grade level g; 𝐺𝑟𝑎𝑑𝑒 − 𝐿𝑒𝑣𝑒𝑙 𝐸𝑠𝑡𝑖𝑚𝑎𝑡𝑒( is the estimate for grade level g. The weighted average effect provides an overall estimate of the impact of the policy change on all 3rd to 8th-grade students (e.g., a weighted average effect of 0.26 corresponds to a 26 percentage-point increase in an eligible student's likelihood of reclassifying after the policy change). 169 Table A9 Logistic Regression Estimates of the Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Periods 3 SE BW N 4 SE BW N 5 SE BW N 6, 7, 8 SE BW N Automatic Reclassification 2019-20 through 2021-22 Policy Change (DiRD) 3.915*** 0.980*** 2.478*** (0.017) (0.880) (0.868) [-11, 8] 1453 3.877 *** 0.980*** 3.626*** (0.007) (0.390) 0.173*** (0.028) 0.417*** (0.035) 0.974*** (0.011) 4.036*** 0.572*** (0.592) (0.035) Manual Reclassification 2016-17 through 2018-19 1.437*** (0.144) [0, 16] 2270 0.251* (0.140) [0, 25] 4613 0.808*** (0.022) 0.562* (0.035) 0.399*** 0.402*** (0.032) (0.134) [0, 27] 4431 (0.026) (0.107) [0, 19] 2736 (0.364) [-13, 24] 5154 3.639*** (0.454) [-10, 24] 3957 (0.562) [-10, 9] 1327 3.761*** (0.243) 0.338*** 0.584*** 3.606*** 0.974*** 3.268*** (0.014) (0.572) 0.390*** (0.030) Weighted Average 0.553*** 0.632*** SE (0.064) (0.014) 0.978*** (0.005) 3.524*** 0.362*** (0.269) (0.016) Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. We display conversions from log odds to percentage points in the column on the right of each of the log odds point estimates. 170 Appendix A10 As a robustness check to our main analysis, we estimate a multilevel model to account for random intercepts and slopes at the district level in each policy period. We estimate this model as follows: Level 1 (student level): "&’/"#$%,) = 𝛽*0 𝑌(0 "&’/"#$%,) + 𝛽!0 "&’/"#$%,)𝐶(0) + 𝛽<0 "&’/"#$%,)𝑀(0) + 𝛽B0 "&’/"#$%,)𝐶(0) × 𝑀(0) + 𝜀(0 "&’/"#$%,) Level 2 (district level): "&’/"#$%,) = 𝛾** 𝛽*0 "&’/"#$%,) + 𝜐*0 "&’/"#$%,) "&’/"#$%,) = 𝛾*! 𝛽!0 "&’/"#$%,) + 𝜐!0 "&’/"#$%,) "&’/"#$%,) = 𝛾*< 𝛽<0 "&’/"#$%,) + 𝜐<0 "&’/"#$%,) "&’/"#$%,) = 𝛾*B 𝛽B0 "&’/"#$%,) + 𝜐B0 "&’/"#$%,) Which includes Y, the dichotomous outcome variable for reclassification, for student i in district d in either the pre- or the post-period and run separately by grade g. C is an indicator equal to 1 if a student scored above the reclassification threshold and 0 if they did not, M is the student’s recentered and standardized overall WIDA score, and an interaction term for whether a student scored above the reclassification threshold and their recentered and standardized overall WIDA score. We include district-level random effects in this model, which allows for the intercept and all slopes to vary across districts, reflecting our assumption that in the pre-period, there is greater district-level variation in students reclassification outcomes. Of particular interest are the between-district variance 171 components on the slope of the coefficient associated with attaining the threshold (i.e., var^𝜐!0 "&’/"#$%,)_). Estimates presented below are consistent with our main results and highlight that the policy change reduced district and school-level variation in policy implementation, suggesting that automatic reclassification resulted in more standardized reclassification processes. For example, the variance on the district-level random slope for scoring above the cutoff (labeled “Above Cutoff (slope)” in the table below) for third graders in the pre-period was 0.095 (i.e., the estimate of var^𝜐!0 "&’,B_), and was 0.015 in the post-period (i.e., the estimate of var^𝜐!0 "#$%,B_). This would indicate greater between-district variation in reclassification likelihood for students attaining the threshold in the pre-period compared to the post. 172 Table A10 Multilevel Modeling Estimates of the Effect of Qualifying for Reclassification on Reclassifying Across Grades and Policy Period Manual Reclassification Automatic Reclassification Policy Change 3 SE BW N Variance Components School District (Intercept) SE Above Cutoff (Slope) SE WIDA Score (Slope) SE Above Cutoff*WIDA Score (Slope) SE Residual (Variance) SE 4 SE BW N Variance Components School District (Intercept) SE Above Cutoff (Slope) (DiRD) 0.253*** (0.029) 0.406*** (0.024) 2016-17 through 2018-19 2019-20 through 2021-22 0.713*** (0.025) [-50, 18] 5567 1.550*10-11 (0.012) 0.095 (0.012) 5.590*10-14 (6.200*10-14) 0.000 (0.000) 0.041 (0.001) 0.553*** (0.022) [-48, 25] 7979 1.800*10-12 (1.020*10-9) 0.074 173 0.966*** (0.007) [-11, 8] 1453 0.000 (0.000) 0.015 (0.002) 7.15*10-19 (7.770*10-16) 0.001 (0.000) 0.002 (0.000) 0.959*** (0.011) [-14, 24] 5154 0.027 (2.700*10-10) 0.027 Table 10 (cont’d) SE WIDA Score (Slope) SE Above Cutoff*WIDA Score (Slope) SE Residual (Variance) SE 5 SE BW N Variance Components School District (Intercept) SE Above Cutoff (Slope) SE WIDA Score (Slope) SE Above Cutoff*WIDA Score (Slope) SE Residual (Variance) SE 6, 7, 8 SE BW N Variance Components School District (Intercept) (0.009) 6.430*10-15 (3.91*10-12) 0.000 (1.800*10-12) 0.085 (0.001) 0.395*** (0.023) [-48, 26] 7282 2.520*10-17 (2.480*10-14) 0.069 (0.009) 8.230*10-17 (1.190*10-13) 0.000 (0.000) 0.108 (0.002) 0.565*** (0.022) [-57, 18] 8277 2.510*10-15 174 (0.003) 2.700*10-14 (6.94*10-12) 6.420*10-6 (2.200*10-6) 0.004 (0.000) 0.956*** (0.011) [-10, 24] 3957 3.760*10-16 (2.440*10-13) 0.027 (0.003) 1.170*10-17 (1.890*10-6) 1.180*10-16 (1.890*10-6) 0.007 (0.000) 0.982*** (0.014) [-10, 9] 1327 2.230*10-9 0.561*** (0.026) 0.417*** (0.026) Table A10 (cont’d) SE Above Cutoff (Slope) SE WIDA Score (Slope) SE Above Cutoff*WIDA Score (Slope) SE Residual (Variance) SE Weighted Average SE Standardized ELA Score Control Local Polynomial Bandwidth Kernel (4.420*10-15) 0.082 (0.010) 8.430*10-15 (9.510*10-12) 0.000 (0.000) 0.055 (0.001) 0.551*** (0.011) (2.340*10-7) 0.011 (0.002) 2.150*10-11 (3.100*10-9) 1.710*10-18 (2.510*10-15) 0.008 (0.000) 0.965*** (0.005) X 1 Optimal Triangular 0.417*** (0.013) Note: * p < .05, ** p < .01, *** p < .001 Robust standard errors clustered at the school district level appear in parentheses below the point estimates. 175 Interview Protocol APPENDIX B: SUPPLEMENT TO PAPER 2 Thank you for taking the time to talk with us. We really appreciate your willingness to share your perspective on how your district finances EL support services. We have some specific questions that we will ask you, but, in essence, we would like this to be more like a conversation. There are no right or wrong answers. We value your input and ask that you try to be as honest and candid as possible. General Background Information Background information on interviewee(s) 1) Tell us a little bit about your role(s) in the district and your background in education. a) Tenure in this position b) Previous employment at the district level c) Classroom experience d) Have you focused on ELs in any other position? Background on ELs in the state/district 2) Tell us about the EL population in your district (e.g., total number of EL students, number of newcomer/LTEL/general EL/etc.). a) How, if at all, has the EL population shifted over the past several years? General EL Finance Questions 176 As previously mentioned, we are interested in talking with you about your state and district school finance policies for ELs and the ways you see those policies impact EL students in your district. 3) What are the key state policies that guide how your district funds EL services? 4) What are the key district policies that guide how your district funds EL services? 5) When developing budgets, do you face tension between funding programs for ELs and funding programs for general education students? 6) In your opinion, does the state have the authority or power to require districts to fund EL services adequately? a) If yes, how so? Does the state monitor compliance with targeted funding policies? b) If no, what prevents them from having greater authority? 7) Can you describe the process for developing budgets for EL programming? Do you work together to develop that budget? 8) How do you think your district compares to others in terms of budgeting for EL services (similarities, differences)? What do you think explains those similarities and differences? 9) How do you think your district compares to others in terms of serving EL students? In your opinion, what are some strengths and areas for growth in terms of serving ELs? Section 41-Specific Questions As we previously mentioned, we’d like to learn more about how your district uses Section 41 funds, and how effective you perceive those funds to be at meeting student needs in your district. 177 10) Please summarize, in your own words, the ways Title III and Section 41 funds can be used for EL students in your district. a) Are there any differences in the ways these two funding sources are used? 11) In your experience, are Title III and Section 41 funds sufficient to meet the needs for EL programming in your district? a) If no, can you describe some of the unmet needs? b) If you had to estimate, how much additional funding would you need to meet these needs? What would you purchase? 12) Do you pull from additional funding sources beyond Title III and Section 41 to provide EL services in your district? a) If yes: i) What are those funding sources? ii) Approximately what proportion of those funding sources are directed towards EL programming? iii) What do you purchase with those funding sources? 13) How do you collect data related to school funding? a) For school business official: How do you collect data on fiscal and personnel expenditures? How do you categorize expenses related to ELs? b) For EL leader: What types of data inform EL student fiscal and resource needs? For example, home language survey responses, ELP levels, anything else ELs are assessed for that might help you understand their programmatic needs? i) Do you collect data on the kinds of programs ELs are enrolled in and how many ESL/bilingual endorsed teachers you have that work directly with ELs? 178 14) Are you aware of any lawsuits or court cases that have arisen in your district claiming it is not adequately funding EL students? a) If yes: i) Can you tell us about these cases? ii) Did they result in a consent decree or greater monitoring of how EL services are funded? iii) Do you think the case(s) prompted the district to alter the way EL services were funded? If so, how? b) If no: Are you aware aware of any lawsuits or court cases that have arisen in other districts claiming they are/were not adequately funding EL students? i) Can you tell us about these cases? ii) Did they result in a consent decree or greater monitoring of how EL services are funded? iii) Do you think the case(s) prompted the district to alter the way EL services were funded? If so, how? Final Questions 15) A nonpartisan research group conducted a study in 2018 on Michigan school finance and recommended changes to the education budget to meet student needs. The study was updated in 2021. The group, called the School Finance Research Collaborative, most recently recommended the following changes to the state budget related to funding for English Learners (paste bullets in Zoom chat). If this funding were provided, how would that alter how your district budgets for English Learner services? • Base per-pupil amount: $10,421 179 • Weights per English Learner funding to be added to general fund: o WIDA level 1-2: 0.70 o WIDA level 3-4: 0.50 o WIDA level 5-6/FEL: 0.35 16) Is there anything else you would like to share with us about the ways your district funds EL services or how Title III and Section 41 funding affect the way you choose to provide EL services? Thank you for taking the time to speak with us today! 180