Across the country, school systems he spent the past decade tightening their focus on metrics that signal student success. From third-grade reading benchmarks1 to college- and career-readiness indicators,2 these metrics shape everything from public perceptions of school quality to the flow of state dollars. But while much of this infrastructure was designed to monitor and reward students’ and schools’ progress, few systems were ready to deal with the sudden drop in an important indicator of learning: student presence.
The COVID-19 pandemic not only disrupted instruction but also fundamentally reshaped student attendance patterns. In 2023, over one in four students were chronically absent, a rate that more than doubled compared with pre-pandemic levels.3 Chronic absenteeism rates he remained alarmingly high, straining efforts to close achievement gaps, reengage students, and use new federal and state investments in academic recovery.4 Yet despite the scale of the problem and the clear shift in how students engage with school, most states and districts still rely on metrics that were not designed to capture today’s attendance dynamics. As a result, these methods may miss where and how student attendance has changed over the past several years.
This report takes a deeper look at student attendance patterns after COVID-19 and compares these trends with those observed before and during the pandemic. Using statewide longitudinal student-level data from North Carolina, Rhode Island, Texas, and Virginia, I examine not only how attendance and chronic absenteeism rates he changed but also how the distribution of absences has shifted within and across schools. In doing so, I aim to answer a pressing question for school leaders and policymakers: Are the students who are missing school today the same ones who missed before, or has absenteeism become a broader, more systemic issue?
This report brings new descriptive evidence to bear on that question and offers the latest data to support better policy responses. I begin by analyzing trends in chronic absenteeism and daily attendance alongside the full distribution of student absences from 2018 to 2024. I then explore how student absence patterns shifted during the pandemic, how those patterns he persisted or evolved in the years since, and what those shifts reveal about broader changes in attendance behior. In the final section, I outline key takeaways for education leaders and policymakers and implications for ongoing efforts to improve student engagement and success.
Rethinking Attendance in the Wake of DisruptionLong before the COVID-19 pandemic, being present in school was widely recognized as a fundamental condition for learning. A growing body of research has documented the strong relationship between school attendance and academic outcomes, including achievement, grade progression, and high school graduation.5 Chronic absenteeism, typically defined as missing 10 percent or more of the school year, emerged as a nonachievement-based early-warning indicator and was incorporated into federal accountability frameworks under the Every Student Succeeds Act. In response, many states and districts invested in attendance-monitoring systems and intervention programs designed to flag and support students missing a significant amount of school.6
Despite these efforts, attendance was often treated as a secondary measure of student academic success that could be managed through compliance strategies rather than meaningfully integrated into school improvement or instructional planning.7 The pandemic shattered that assumption. School closures, illness, transportation disruptions, and shifting family circumstances caused attendance rates to plummet nationwide. In the years since, what was once viewed as a family, individual, or behioral problem has become a system-wide challenge. While erage chronic absenteeism spiked across the country, in some districts, the share of chronically absent students now exceeds 70 percent,8 raising serious concerns about instructional loss and disengagement.
At the same time, most existing attendance metrics, especially those used in accountability and funding formulas, he not evolved to capture the dramatic shift in attendance patterns across the country. Measures like erage daily attendance obscure meaningful variation by compressing attendance into a school-wide erage, while chronic absenteeism rates alone focus on only a threshold without capturing broader shifts in student behior. These measures may miss how attendance patterns are changing across the entire student body, limiting school leaders’ and policymakers’ ability to identify emerging trends, allocate resources, and evaluate the success of recovery initiatives.
Variation in Post-COVID Absenteeism TrendsChronic absenteeism surged to unprecedented levels during the pandemic and, even after a slight retreat from the pandemic peak, remains at 23–25 percent nationally, more than 50 percent higher than pre-pandemic baselines.9 Below those high erages, rates differ sharply across student subgroups, schools, and districts, underscoring an uneven impact. As a result, researchers he begun employing new metrics that reflect the persistence and severity of absences among certain students, as well as overall rates.10
In the 2022 school year, two-thirds of US students were enrolled in a school with high or extreme levels of chronic absence.11 The next year, 20 states reported that more than 30 percent of their K–12 students had missed at least three weeks of school.12 Some of the states with the highest absenteeism rates were those that had extended periods of remote learning or severe COVID-19 outbreaks. Studies suggest that instructional modality during the pandemic influenced patterns of absenteeism, with one examining over 11,000 districts finding that those with 100 percent virtual instruction in 2021 saw chronic absenteeism rates 6.9 percentage points higher in 2022 compared with districts that stayed fully in person.13 That gap was even wider in high-poverty districts that went virtual. This prompts a need to examine the patterns underlying the chronic absenteeism threshold.
Binary flags such as “chronically absent” are useful metrics, especially for flagging extreme cases, but understanding how absenteeism has changed and for whom is essential for an effective policy response. Flattening attendance data into broad erages or single cutoffs can obscure meaningful differences with real potential consequences: misguided academic recovery efforts and resource allocations and distorted accountability metrics and attendance-related funding.
Without clear insight into which students are disengaging, how attendance trajectories he shifted over time, and the cumulative intensity of students’ absences, schools risk intervening too late, in the wrong places, or for the wrong students. In short, absenteeism poorly measured will be poorly addressed.
DataThis study draws on longitudinal, student-level administrative data from North Carolina, Rhode Island, Texas, and Virginia from 2018 to 2024.14 The analytic sample includes students in grades three through 11 for North Carolina and prekindergarten through grade 12 for Rhode Island, Texas, and Virginia. Data from the 2020 school year were excluded due to pandemic-related disruptions that affected the consistency and reliability of attendance reporting. Data for North Carolina and Virginia are unailable for the 2024 school year.
Table 1 presents annual measures of student absenteeism and attendance from the 2018 to the 2024 academic years. See Table A1 for descriptive statistics of students and a brief discussion of demographic differences by state. In all four states, the data include the number of absences, the number of days attended, and the number of days enrolled. This allows me to calculate multiple measures of attendance.
Before the pandemic, chronic absenteeism rates were much higher in Rhode Island (20 percent) than in North Carolina, Texas, and Virginia (12–14 percent). Mean and median absence rates, which reflect the proportion of instructional days missed per student, were generally consistent across states during the pre-pandemic years, ranging from 5 to 8 percent on erage and around 3–4 percent at the median. Average daily attendance, by contrast, remained relatively stable across states, generally within 2 to 3 percentage points of its pre-pandemic level (e.g., 95 percent in Texas in 2018 versus 93 percent in 2024).
This stability, however, masks the sharp rise in absences revealed by other measures and underscores why the analysis that follows concentrates on levels of absence rather than erage daily attendance. These pre-pandemic figures therefore serve as the reference point for the next section, which examines the magnitude of post-pandemic disruptions and the degree to which attendance patterns he shifted in recent years.
Measures of Absenteeism: Do They Tell the Same Story?
Figures 1–4 present trends in the four attendance metrics from 2018 to 2023 for North Carolina and Virginia and from 2018 to 2024 for Rhode Island and Texas. These figures include (1) the chronic absenteeism rate, defined as the percentage of students who miss 10 percent or more of the school year; (2) the mean number of days absent per student; (3) the median number of days absent per student; and (4) erage daily attendance. Each measure captures a different dimension of student attendance.
Chronic absenteeism identifies students with sustained disengagement across the year, while the mean absence rate reflects the overall level of missed instruction across the student population. The median absence rate offers insight into the typical student’s experience, unaffected by a small number of students with extremely high absences.
Average daily attendance, by contrast, captures the proportion of students present on a given day and is often used in school funding and operational decisions. To facilitate comparison across states and over time, all values in Figures 1–4 are indexed to their respective 2018 levels. This means that a value of 125 percent in a given year reflects a 25 percent increase relative to that state’s 2018 level, while a value of 90 percent reflects a 10 percent decrease from 2018.
Chronic Absenteeism
Shown by blue lines in this report’s figures, chronic absenteeism rates surged dramatically across North Carolina, Rhode Island, Texas, and Virginia during the pandemic, though states show differences in the severity of disruption and the pace of recovery. North Carolina and Texas experienced the sharpest increase (Figures 1 and 3), with 2022 chronic absenteeism more than double their 2018 rates. Rhode Island and Virginia followed a similar trajectory with a more tempered rise, peaking at 175 percent of the pre-pandemic baseline (Figures 2 and 4). By 2024, Rhode Island’s chronic absenteeism rate had declined to 130 percent of pre-pandemic levels, whereas in 2023 North Carolina remained at more than double its pre-pandemic chronic absence rate. In the latest year of ailable data, Texas and Virginia he chronic absence rates of 163 percent and 148 percent of baseline, respectively. The consistency of the 2022 peak across the four states suggests common pandemic-era drivers, but the variation in recovery points could point to state-specific conditions that may either mitigate or exacerbate the lingering effects of pandemic-related disruptions.
Mean Absence RatesWhile simply expressing the inverse of erage daily attendance, examining the percentage change of the erage number of days missed per student paints a clearer picture of the magnitude of the overall change in attendance behior. Shown by the green lines in the figures, the data demonstrate that mean absence rates rose sharply during the pandemic and remain elevated. In North Carolina, the mean rate climbed steadily, reaching a peak of around 160 percent of baseline in 2022 and settling at 152 percent in 2024. Rhode Island, Texas, and Virginia exhibited a similar pattern, although rates diminish modestly in the latest year of data compared with North Carolina. Mean absence rates peak in 2022 followed by a partial decline, with the 2024 values still 122 to 136 percent higher than in 2018.
Median Absence RatesThe median student absence rate represents the middle value in the distribution of student absences, where half of all students missed fewer days than the median and half missed more. The median absence rate offers a clearer picture of typical student behior and is less influenced by outliers with exceptionally high or low absence totals. In North Carolina, the median number of days missed plummeted during the hybrid-learning year of 2021 but surged the following year to about 160 percent of the 2018 rate, where it largely remained in 2023 (Figure 1). In Rhode Island, Texas, and Virginia, the median absence rates also spiked in 2022 and remain 131 to 138 percent above pre-pandemic levels (Figures 2–4).
Why These Differences MatterWhen compared with erage daily attendance, these three metrics together offer a more nuanced and complete picture of how attendance patterns he changed since the pandemic. While all four measures capture a valid dimension of school attendance, they differ in scope, sensitivity, and implication for policy and interventions.
The contrast between chronic absenteeism and the median absence rate is especially informative. In North Carolina, the chronic absenteeism rate remains twice as high as it was before the pandemic, and the other three states also show significantly higher levels of chronic absenteeism. This suggests that there is a persistent proportion of highly disengaged students. Yet at the same time, the erage (median) student is missing 31 percent (Virginia) to 55 percent (North Carolina) more school than before. That distinction matters. If chronic absenteeism alone guides policy, it may imply that elevated absence rates are concentrated in a small subset of students. But the elevated median rate demonstrates that absenteeism has become a more generalized behior across the student body.
Mean absence rates add yet another layer to this picture by capturing the overall volume of missed school. Mean absences surged at the height of the pandemic and remain well above pre-pandemic baselines. Because the mean is sensitive to both moderate and extreme absences, it is particularly useful for assessing the system-wide instructional burden caused by increased absenteeism. Even as the pandemic’s most acute disruptions he subsided, the fact that mean rates remain 122 percent (Rhode Island) to 152 percent (North Carolina) above 2018 levels suggests that students across the distribution are missing more school than they did before COVID-19.
Yet, erage daily attendance appears relatively stable, declining only slightly across the years. This relative stability, however, reflects a structural limitation of the measure rather than evidence of recovery. Because erage daily attendance is bounded and tends to cluster near the upper end of the scale (typically around 95 percent nationwide), it is less responsive to modest increases in student absences unless those increases are large and widespread. As a result, erage daily attendance may understate the magnitude of post-pandemic changes and obscure the growing prevalence of moderate to severe attendance issues among students. While erage daily attendance remains a measure for school funding and operational planning, it is less effective in diagnosing the current shifts in student engagement.
The large differences in these measures over time show that attendance patterns he not returned to pre-pandemic norms, and in many cases, they he stabilized at substantially worse levels. The elevation in the median and mean absence rates and chronic absenteeism rates points to a recalibration of what constitutes “typical” attendance. It shows that while the most severe absenteeism has not subsided, absences remain elevated even among students who previously demonstrated consistent attendance. This motivates the next set of analyses examining the distribution of absences across students.
Shifts in the Distribution of Absenteeism Before, During, and After the PandemicTo examine how the distribution of student absences changed over time, Figures 5 through 8 present density curves of absence rates for the states. These plots show the proportion of students by absence rate as a percentage of enrolled days for three periods: the 2018 and 2019 academic years (before the pandemic, in blue), the 2021 academic year (during the pandemic, in yellow), and the 2022 and 2023 academic years (after the pandemic, in green). For reference, the vertical dashed line in each figure marks the chronic absenteeism threshold at 10 percent. Although some discussions of attendance accountability he raised concerns about potential “stacking” just below the chronic absenteeism threshold, these data allow us to test whether such gaming behior is visible in the distributions.
All four states show a striking shift where the post-pandemic distributions are flatter and more skewed to the right, with a heier tail extending beyond the chronic absenteeism threshold. This indicates that not only did more students begin missing school, but the students who were already frequently absent became even more so.
In each state, the pre-pandemic distribution was concentrated well below the chronic absenteeism threshold, with a modest right tail. In contrast, in 2022 and 2023, the density curve flattens significantly and the right tail thickens, meaning a larger share had absence rates above 10, 15, and 20 percent. The sharp spike at low absence rates in 2021, followed by the drop-off, likely reflects hybrid or remote learning environments that disrupted traditional attendance monitoring. The post-pandemic curve also shows a notable decline in students with very low absence rates, indicating a general shift away from near-perfect attendance in all four states.
Taken together, these distributional shifts signal fundamental attendance changes. Rather than a temporary, concentrated surge in absenteeism, the post-pandemic period has ushered in a durable and broader pattern of disengagement. The flattening and rightward skew of the absence-rate distributions across the three states indicate that absenteeism has intensified both at the margins and throughout the student body. Fewer students he very low attendance, most students are missing school at higher rates, and the most-absent students are absent more often.
This pattern complicates traditional intervention models developed using clearly defined thresholds or narrowly focused on high-risk students. A system that once treated chronic absenteeism as the proportion of students missing far too much school has run into a new phenomenon. Absenteeism is more widespread, less predictable, and more deeply embedded in student behior.
Finally, while we expected to observe signs of “stacking” just below the chronic absenteeism threshold of 10 percent—a pattern that would suggest manipulation of absences to game accountability measures—the distributions show no such clustering. Instead, absence rates remain smooth around this threshold, reinforcing the conclusion that the rise in absenteeism reflects genuine shifts in student behior rather than strategic reporting. This section has illustrated how absenteeism has become more widespread across the student population, and the following section examines the extent to which absences remain concentrated among a small group of students and shows a stable, underlying pattern that has persisted despite broader shifts.
Absenteeism Remains Concentrated Among a Small Share of StudentsWhile the data show absenteeism has become more widespread across the student population, this broader shift has not altered a long-standing pattern: Most absences continue to be heily concentrated among a relatively small share of students. Even as most students are missing more school days, most missed school days come from the most chronically and severely absent students.
Figure 9 displays the cumulative distribution of student absences across deciles in the four states for the 2023 school year. Students are ranked from those with the most absences (left) to those with the fewest (right), allowing for the assessment of how heily absences are concentrated among a subset of students.
Absenteeism is disproportionately driven by the most consistently absent students, and this is remarkably consistent across all four states. The top 10 percent of students by absence count are responsible for roughly 35 percent of all absences, and the top 20 percent account for roughly half, in all three states. By contrast, the bottom 50 percent of students collectively contribute around 20 percent of total absences. These patterns are nearly identical to those observed in 2018 (not shown because the lines would be nearly indistinguishable), indicating that the distribution of absences has remained remarkably stable, even as overall absence rates he shifted in the wake of the pandemic.
At first glance, the lack of change in the cumulative distribution may appear at odds with the large increase in the overall rates and distributions of absences, where absenteeism became more widespread across the student population. On the one hand, the post-pandemic period saw a clear broadening of absenteeism, with more students missing more school and a visible rightward shift in the distribution of absences. On the other hand, the cumulative distribution shows that a small proportion of students continue to account for a disproportionately large share of all missed instructional time, a pattern that has remained stable since before the pandemic. Rather than being contradictory, these findings reflect two distinct but co-occurring dynamics in the post-pandemic attendance landscape.
The pandemic elevated absences across the general student population and intensified absenteeism among students already at the highest risk of disengagement. In other words, the distribution of absences has shifted systematically, where the erage student is missing more school than before. Yet it remains structurally skewed, with a small subset of students still accounting for the majority of missed days. This duality underscores the importance of designing interventions that are both universal and targeted,15 with universal approaches to reestablish school attendance as a norm across the broader population and targeted strategies that directly address the barriers facing students in the highest absence deciles. Interventions aimed at less would not address the full change that occurred over the pandemic.
Post-Pandemic Absenteeism Is Higher, Stickier, and More UnequalTo examine how student absenteeism has shifted since the pandemic, I present Sankey diagrams16 for North Carolina, Rhode Island, Texas, and Virginia that follow the same students over time. Figure 10 shows the Sankey diagram for North Carolina. Figures A1– A3 present data for Rhode Island, Texas, and Virginia. These figures trace the movement of individual students across absence quintiles—from academic years 2018 (far left), 2019 (middle left), and 2022 (middle right) through 2023 for North Carolina and Virginia and 2024 for Rhode Island and Texas (far right).
What Do the Quintiles Represent?
To account for grade-level differences in absence rates, students are grouped into quintiles of absences within their grade level. That is, a student in third grade in 2018 is compared with only other third graders in terms of absences, a seventh grader is compared with only other seventh graders, and so on. Each quintile represents 20 percent of students in that grade, with the first quintile (Q1) being the least-absent students and the fifth quintile (Q5) being the most-absent students. Importantly, these quintile thresholds are based on students’ 2018 absence ranks and remain fixed in all subsequent years of the analysis.
By anchoring to 2018 thresholds, I hold constant what was previously considered as high or low absence in each grade. This allows me to answer the question, How would a student’s absences in 2022 and 2024 compare with those in 2018, using the same yardstick? This strategy oids the misleading impression that absence patterns he stayed the same just because the population distribution has shifted. It also ensures that normal age-related trends (e.g., kids being more absent in high school than in elementary school) do not inflate the appearance of change.
Only students who were enrolled in the same state for all years, 2018 through 2023 or 2024, are included. These are not cross-sectional samples. Rather, the Sankey diagrams follow the same students over time as they advance in grade level. Because I assigned quintiles by grade level, a student is always being compared with their current-grade peers in each year, but the thresholds used to assign quintiles remain frozen at their 2018 values.
Each colored band in the Sankey diagrams represents a flow of students from one quintile of absenteeism to another using 2018 benchmarks. If students had similar absence patterns over time, we would expect most bands to run horizontally. However, upward movement (e.g., from Q2 to Q4 or Q5) indicates students who became more absent relative to the 2018 standard. Downward movement suggests improved attendance.
Upward Migration into Higher Absence Quintiles, Especially Q5The data clearly show structural shifts in attendance behior that are not visible when comparing simply population-wide means or medians. While 2018 to 2019 shows relatively minor reordering reflecting expected shifts between quintiles around the margins, dramatic, asymmetric changes emerge beginning in 2022. A substantial proportion of students who were in middle quintiles (Q2–Q4) in 2018 and 2019 moved into Q5 by 2022 and remained there in 2023–24. This is the most striking pattern. This movement of students is large and primarily unidirectional, indicating that the pandemic catalyzed sustained movement into the higher absence thresholds.
Persistence in Q5: Higher Absenteeism Is the New BaselineThe most concerning pattern is the persistence of students in Q5. Students who reach the highest absence quintile in 2022 largely remain there in 2023–24. The width of the rightmost Q5 bars reflects students who were chronically absent before the pandemic and those who became highly absent during it. There is very little return flow out of Q5, suggesting that high absenteeism has become a sticky new baseline for a significant segment of students.
Relative Stability at the Bottom and Instability in the MiddleWhile a significant proportion of students moved into Q5, there is more stability at the lower end of the absence distribution. Students with the lowest absence rates in 2018 were the most likely to remain at similar absence rates after the pandemic. In contrast, students with more moderate levels of absences (Q2, Q3, and Q4) were more likely to experience significant upticks in missing school. Many moved upward into Q4 and Q5, and a much smaller proportion moved downward. This asymmetry implies that the post-pandemic shift disproportionately affected students who were moderately absent before COVID, pushing them into higher levels of absenteeism. Meanwhile, students with historically strong attendance he largely sustained it.
Absence Disparities He GrownThe overall takeaway is that absence inequality has been exacerbated. The middle quintiles he hollowed out as students he moved upward into Q5, while downward mobility into lower absence categories is much less common. This has resulted in a growing concentration of students with higher rates of absenteeism, which indicates there are more students who may require more intensive, targeted interventions than what pre-pandemic systems were designed to deliver.
DiscussionThis new evidence demonstrates that the post-pandemic absenteeism crisis is not only persistent but also more complex than a single attendance measure can capture. While chronic absenteeism rates remain elevated, the most striking finding is the distributional shift: More students are missing more school across the board, not just historically higher-risk students. This broad increase complicates traditional assumptions about which students require the most support and should inform how attendance interventions are designed and deployed.
The findings also suggest that school systems now face a fundamentally different attendance landscape. Research increasingly shows that the detrimental effects of absenteeism extend across the full distribution of missed days—not just those who cross the chronic absenteeism threshold.17 The sharp increase in moderate absences may not trigger intervention under most district policies, yet these students account for a significant share of missed instructional time. While the chronic absenteeism threshold offers a clear way to flag at-risk students, it understates the extent of the post-pandemic attendance challenge, how absences are distributed across students, and how moderate absenteeism may drive learning loss at scale. States’ recovery efforts should supplement these binary thresholds with measures that capture the severity and breadth of missed school days.
Researchers, too, should also consider how metrics reflect absences across the entire student population. Conventional attendance indicators that rely on erages or thresholds limit their utility for targeting support to the most urgent needs. The index-based measures used here offer one example for providing a clearer view into the concentration or diffusion of absences and may provide a clearer basis for targeted policy responses and aligned interventions.
Beyond measurement, these findings raise questions about the adequacy of existing interventions. Most current strategies are designed to identify and respond to students who cross a fixed threshold, typically those deemed chronically absent. While clear and administratively convenient, these metrics will miss the many students now falling behind due to moderate but sustained absences, leing substantial need unaddressed.
Furthermore, the findings challenge school systems’ resources and personnel allocation. Attendance teams, interventionists, and support staff are often deployed using potentially outdated pre-pandemic models of risk. A tiered approach may be more effective when moderate absenteeism is the primary issue, because it could identify emerging attendance issues before they escalate.
Policymakers, too, should grapple with how accountability systems’ attendance metrics reflect— or fail to reflect—the current scale and shape of the problem. Capable metrics would better guide funds and interventions where they are most likely to mitigate instructional loss. Without such adjustments, school systems may remain stuck in reactive cycles that only address absences at crisis levels.
This report underscores the importance of investing in high-quality attendance data infrastructure. With measures of only chronic absenteeism and erage daily attendance, there is little incentive to delve into more granular data. Districts and states should employ tools that support real-time monitoring and longitudinal analysis to sharpen policy responses and enhance school leaders’ ability to adapt strategies to shifting attendance patterns.
Families and communities may also need clear communication about the consequences of missed instruction, even short of chronic thresholds. Even the most consistent attenders before the pandemic are now missing more school, and unchecked increased absenteeism may become a new social norm that poses serious implications for student achievement and long-term opportunity. Reversing this trend requires more than technical solutions. It requires rebuilding the cultural and institutional importance of regular attendance as nonnegotiable for student success. Schools are contending with not only absenteeism but also an erosion of expectations that requires sharper tools, stronger accountability, and a broader coalition to correct.
About the AuthorJacob Kirksey is an associate professor of education policy in the College of Education at Texas Tech University. His research focuses broadly on the intersection of education and public policy and has examined factors shaping student absenteeism, college and career readiness, and quality of educator labor markets.
Appendix A
Descriptive Statistics
Notable demographic differences across North Carolina, Rhode Island, Texas, and Virginia student bodies include variation by race and ethnicity and the proportions of students identified as English learners, economically disadvantaged, or hing a disability. North Carolina (25 percent) and Virginia (22 percent) he a larger share of black students compared with Rhode Island (9 percent) and Texas (13 percent), while Texas enrolls the highest proportion of Hispanic students (50 percent) and English learners (22 percent). Rhode Island reports the largest share of students with disabilities (17 percent), followed by Virginia (14 percent) and Texas (11 percent). Note that North Carolina’s data capture only students with learning disabilities, yielding a lower estimate (0.13 percent) that is likely not representative of the state’s full population of students with disabilities. Economically disadvantaged students represent a majority of the sample in Texas (62 percent), while they comprise 45 percent in North Carolina, 46 percent in Rhode Island, and 43 percent in Virginia. The analytic dataset includes over 49 million student-by-year observations across the four states.
Cross-State Differences in the Extent of DisruptionLooking across the four Sankey charts, all four states exhibit the same general pattern, though the magnitude of disruption varies. North Carolina displays the most pronounced increase in students moving into the highest absence quintile, especially between 2018 and 2022. The proportions of students moving into the highest absence quintile are more variable across lower quintiles. Rhode Island and Texas also exhibit substantial movement into Q5, but with a somewhat more diffuse flow from across Q2–Q4. Virginia’s patterns are more balanced, with slightly more students staying in Q3 and Q4, yet Q5 still captures a larger share of the population over time. North Carolina shows particularly sharp transitions into Q5 by 2022, especially from Q3 and Q4. Rhode Island, Texas, and Virginia mirror this pattern, with a large influx into Q5 from Q3 and Q4 between 2018 and 2022 and a sustained proportion of students remaining in the high-absence quintile in 2024.
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Data from Rhode Island and Virginia came from each state’’ department of education through a data-sharing agreement with the American Enterprise Institute’s Chronic Absenteeism Research Working Group. Data from Texas came from the University of Houston Education Research Center through an approved research project of the author of this study. Data from North Carolina come from the North Carolina Education Research Data Center, a partnership with the North Carolina Department of Public Instruction, which collects data on all public school students in the state, and are make ailable to researchers who pay a usage fee and satisfy data security requirements.
Attendance Works, “3 Tiers of Intervention,” accessed August 5, 2025, https://www.attendanceworks.org/chronic-absence/addressing-chronic-absence/3-tiers-of-intervention/.
Asjad Naqvi, “SANKEY: Stata Module for Sankey Diagrams,” Boston College, Department of Economics, October 19, 2024, https://ideas.repec.org/c/boc/bocode/s459154.html.
Seth Gershenson et al., “Are Student Absences Worth the Worry in US Primary Schools?,” Education Finance and Policy 12, no. 2 (2017): 137–65, https://docs.iza.org/dp9558.pdf; Todd Rogers and Avi Feller, “Reducing Student Absences at Scale by Targeting Parents’ Misbeliefs,” Nature Human Behiour 2, no. 5 (2018): 335–42, https://scholar.harvard.edu/files/todd_rogers/files/rogers_feller_absenteeism.pdf; and Did Blazar et al., Absences and Achievement After the Pandemic: Evidence from Maryland and North Carolina, American Enterprise Institute, October 6, 2025, https://www.aei.org/research-products/report/absences-and-achievement-after-the-pandemic-evidence-from-maryland-and-north-carolina/.