NYC High School Dropout Rates
Dijana & Ritasha
May 12, 2025
Introduction
High school dropout rates serve as a critical indicator of educational system performance and equity. This report investigates trends in dropout rates across New York City using publicly available data collected by the Department of Education (DOE). The dataset follows New York State’s cohort-based calculation method, tracking students who entered 9th grade in a specific academic year. To comply with the Family Educational Rights and Privacy Act (FERPA), rows with fewer than five students are suppressed, and as of 2014, the GED was replaced by the TASC exam for high school equivalency certification.
This study seeks to answer the following questions:
How do high school dropout rates vary across the boroughs of New York City?
How have dropout rates changed over time within and across boroughs?
Do dropout rates differ significantly between different cohort types (e.g., “4 year” vs “5 year”)?
Existing research provides essential context for this investigation. Over the past two decades, graduation rates in New York City have improved significantly, reaching record highs before the COVID-19 pandemic. Reports from the New York State Education Department (“NYC Students Achieve All-Time Record High School Graduation Rates in 2012-2013 School Year” 2013a)) and NY Daily News(“Two Decades of Change in Federal and State Higher Education Funding” 2019a) highlight this progress. However, these trends have not been uniform or without controversy. During the pandemic, policies such as relaxed grading and reduced assessments may have inflated graduation figures (“How Have NYC’s High School Graduation and College Enrollment Rates Changed Over Time? | NYU Steinhardt,” n.d.), and recent data show a slight dip in graduation rates post-pandemic (“NYC Graduation Rates Remained Essentially Flat in 2023” 2024), suggesting lasting disruptions in student engagement.
Furthermore, disparities persist across boroughs and demographic groups. Research from NYU’s Steinhardt School shows that improvements have not been equally distributed, particularly affecting students in the Bronx and Brooklyn. Earlier education reforms under Mayor Bloomberg emphasized accountability and data transparency, contributing to prior improvements (“NYC Students Achieve All-Time Record High School Graduation Rates in 2012-2013 School Year” 2013b). Additionally, broader financial factors, such as shifts in federal and state education funding, have influenced outcomes across the nation (“Two Decades of Change in Federal and State Higher Education Funding” 2019b).
Peer-reviewed studies deepen the understanding of dropout behavior. Hughes et al. (“State Education Department Releases 2017 Cohort High School Graduation Rates,” n.d.) found that students’ sense of belonging and teacher support significantly predicted academic persistence. Mistry et al.(Fall and Roberts 2012) highlighted the role of family economic stress in driving educational disengagement, especially in under-resourced communities.
Together, these findings underscore the importance of monitoring dropout rates with a focus on borough-level disparities and long-term trends. By grounding this analysis in existing research, this report aims to identify not only how dropout rates have changed, but also why these patterns matter for the future of educational equity in New York City.
Research Method
This study adopts a quantitative research approach using a publicly available cohort-based dataset from the NYC Department of Education. The dataset tracks students who entered 9th grade in a given academic year from 2001 to 2015 and records high school outcomes, including dropout rates.
The key variables analyzed in this study are:
Borough – the geographical location of the school (Bronx, Brooklyn, Manhattan, Queens, Staten Island)
Cohort Year – the academic year in which students entered 9th grade
Cohort Type – the length of time over which the cohort was expected to graduate (4-year June, 5-year August, 6-year June)
Dropout Percentage – the proportion of the cohort who dropped out of school without completing high school or earning a High School Equivalency diploma
The research follows this analysis process:
Descriptive Statistics: Summary statistics are computed to explore the distribution of dropout rates by borough, cohort type, and cohort year. These include means, standard deviations, and sample sizes, presented in tables.
Data Visualization: Line graphs are used to track how dropout rates have changed over time by borough. Box plots are used to show the distribution of dropout percentages across boroughs and cohort types.
Analysis of Variance (ANOVA): Separate one-way ANOVA tests are conducted to determine whether there are statistically significant differences in dropout rates across boroughs and across cohort types.
Tukey’s HSD Post-Hoc Tests: When ANOVA results indicate significant group differences, Tukey’s Honest Significant Difference test is used to identify which specific pairs of boroughs or cohort types differ.
Linear Regression: Multiple linear regression models are used to examine how dropout rates are associated with borough, cohort type, and cohort year. An interaction model is also included to evaluate how the relationship between cohort year and dropout rates may differ across boroughs.
All statistical analyses are performed using the R programming language, ensuring a reproducible and statistically rigorous examination of dropout trends in New York City public high schools.
Results
| Borough | Mean_Dropout | SD_Dropout | N |
|---|---|---|---|
| Bronx | 18.50 | 4.43 | 62 |
| Brooklyn | 14.05 | 5.31 | 62 |
| Manhattan | 12.79 | 4.07 | 62 |
| Queens | 13.07 | 4.80 | 62 |
| Staten Island | 10.35 | 3.13 | 62 |
Table 1 shows the average dropout rates by borough. The Bronx had the highest average dropout rate at 18.5%, followed by Brooklyn (14.05%), Queens (13.07%), and Manhattan (12.79%). Staten Island had the lowest average at 10.35%. Each borough had 62 cohorts of students in the dataset.
| Cohort_type | Mean_Dropout | SD_Dropout | N |
|---|---|---|---|
| 4 year August | 9.63 | 2.83 | 55 |
| 4 year June | 10.91 | 3.60 | 75 |
| 5 year August | 14.04 | 3.78 | 45 |
| 5 year June | 16.09 | 4.65 | 70 |
| 6 year June | 17.80 | 5.05 | 65 |
Table 2 shows the average dropout rates by cohort type. The 4-year August cohort had the lowest average dropout rate at 9.63%, followed by the 4-year June cohort at 10.91%. The rates increased with longer cohort durations: 5-year August (14.04%), 5-year June (16.09%), and 6-year June (17.80%). The number of cohorts for each cohort type ranged from 45 to 75.
| Cohort_year | Mean_Dropout | SD_Dropout |
|---|---|---|
| 2001 | 21.69 | 5.84 |
| 2002 | 19.13 | 5.05 |
| 2003 | 18.51 | 4.75 |
| 2004 | 16.53 | 3.77 |
| 2005 | 14.60 | 4.20 |
| 2006 | 15.08 | 3.86 |
| 2007 | 15.53 | 4.49 |
| 2008 | 14.03 | 3.75 |
| 2009 | 13.28 | 3.70 |
| 2010 | 12.28 | 3.69 |
| 2011 | 11.57 | 3.56 |
| 2012 | 10.55 | 3.32 |
| 2013 | 10.14 | 3.34 |
| 2014 | 9.74 | 3.84 |
| 2015 | 7.66 | 2.80 |
Table 3 shows the average dropout rates by cohort year from 2001 to 2015. The highest average dropout rate was in 2001 at 21.69%, and the rates generally decreased over time. By 2015, the average dropout rate had dropped to 7.66%.
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
Figure 1 shows dropout trends by borough from 2001 to 2015. All five boroughs saw a steady decline in dropout percentages during this period. The Bronx consistently had the highest dropout rates, while Staten Island had the lowest. By 2015, the rates across boroughs became more similar, although the gap between the Bronx and Staten Island remained persistent.
Figure 2 presents a boxplot showing dropout percentages by borough. The Bronx has the highest median dropout rate and the widest range, indicating more variation in outcomes. Staten Island has the lowest median rate and the most consistent distribution. Manhattan, Queens, and Brooklyn fall in between, with similar patterns. These results reflect clear differences in dropout rates across boroughs.
Figure 3 shows a boxplot of dropout percentages by cohort type. Dropout rates increase as cohort length increases. The 4-year cohorts have the lowest medians and the most consistent results. The 5-year and 6-year cohorts show higher medians and more variability, with the 6-year June cohort having the highest dropout rates. This pattern suggests that longer enrollment is linked to higher dropout risk.
| term | df | sumsq | meansq | statistic | p.value |
|---|---|---|---|---|---|
| Borough | 4 | 2204.647 | 551.16182 | 28.34331 | 0 |
| Residuals | 305 | 5931.007 | 19.44593 | NA | NA |
Table 4 shows the results of an ANOVA test examining differences in dropout percentages across boroughs. The analysis found a statistically significant effect of borough on dropout rates. This suggests that average dropout percentages vary significantly between boroughs.
| Comparison | Difference | Lower_CI | Upper_CI | P_Value |
|---|---|---|---|---|
| Brooklyn-Bronx | -4.4532258 | -6.626648 | -2.2798040 | 0.0000004 |
| Manhattan-Bronx | -5.7080645 | -7.881486 | -3.5346427 | 0.0000000 |
| Queens-Bronx | -5.4306452 | -7.604067 | -3.2572234 | 0.0000000 |
| Staten Island-Bronx | -8.1451613 | -10.318583 | -5.9717395 | 0.0000000 |
| Manhattan-Brooklyn | -1.2548387 | -3.428260 | 0.9185831 | 0.5085718 |
| Queens-Brooklyn | -0.9774194 | -3.150841 | 1.1960024 | 0.7314306 |
| Staten Island-Brooklyn | -3.6919355 | -5.865357 | -1.5185137 | 0.0000460 |
| Queens-Manhattan | 0.2774194 | -1.896002 | 2.4508411 | 0.9967598 |
| Staten Island-Manhattan | -2.4370968 | -4.610519 | -0.2636750 | 0.0192084 |
| Staten Island-Queens | -2.7145161 | -4.887938 | -0.5410944 | 0.0061953 |
Table 5 presents the results of the Tukey HSD post-hoc test for pairwise comparisons of dropout rates between boroughs. Significant differences were found between the Bronx and all other boroughs, with the Bronx having higher dropout rates. Notably, Staten Island had significantly lower dropout rates than the Bronx, Brooklyn, and Manhattan, while there were no significant differences between Brooklyn, Manhattan, and Queens. These results highlight the significant disparities in dropout rates, particularly between the Bronx and the other boroughs.
| (1) | |
|---|---|
| (Intercept) | 18.498 |
| (0.560) | |
| BoroughBrooklyn | −4.453 |
| (0.792) | |
| BoroughManhattan | −5.708 |
| (0.792) | |
| BoroughQueens | −5.431 |
| (0.792) | |
| BoroughStaten Island | −8.145 |
| (0.792) | |
| Num.Obs. | 310 |
| R2 | 0.271 |
| R2 Adj. | 0.261 |
| F | 28.343 |
Table 6 presents the results of a linear regression analysis showing how dropout rates differ by borough. The intercept of 18.498 represents the average dropout rate for the Bronx, which serves as the baseline. The negative values for the other boroughs show how much lower their dropout rates are compared to the Bronx: Brooklyn’s rate is 4.453% lower, Manhattan’s is 5.708% lower, Queens’ is 5.431% lower, and Staten Island’s is 8.145% lower. The model explains about 27% of the variation in dropout rates across boroughs, and the F-statistic indicates that the model is statistically significant, meaning the differences in dropout rates between boroughs are not random.
| term | df | sumsq | meansq | statistic | p.value |
|---|---|---|---|---|---|
| Cohort_type | 4 | 2995.249 | 748.81216 | 44.4299 | 0 |
| Residuals | 305 | 5140.406 | 16.85379 | NA | NA |
Table 7 shows the results of an ANOVA test examining the effect of cohort type on dropout rates. The analysis found a statistically significant effect of cohort type on dropout rates. This indicates that dropout percentages vary significantly across different cohort types.
| Comparison | Difference | Lower_CI | Upper_CI | P_Value |
|---|---|---|---|---|
| 4 year June-4 year August | 1.283879 | -0.7160708 | 3.283828 | 0.3980333 |
| 5 year August-4 year August | 4.414546 | 2.1500485 | 6.679042 | 0.0000017 |
| 5 year June-4 year August | 6.463117 | 4.4331732 | 8.493061 | 0.0000000 |
| 6 year June-4 year August | 8.177622 | 6.1136117 | 10.241633 | 0.0000000 |
| 5 year August-4 year June | 3.130667 | 1.0063802 | 5.254953 | 0.0006328 |
| 5 year June-4 year June | 5.179238 | 3.3069887 | 7.051488 | 0.0000000 |
| 6 year June-4 year June | 6.893744 | 4.9846112 | 8.802876 | 0.0000000 |
| 5 year June-5 year August | 2.048571 | -0.1039773 | 4.201120 | 0.0707413 |
| 6 year June-5 year August | 3.763077 | 1.5783722 | 5.947782 | 0.0000342 |
| 6 year June-5 year June | 1.714506 | -0.2260252 | 3.655036 | 0.1115563 |
Table 8 presents the results of the Tukey HSD post-hoc test for pairwise comparisons of dropout rates by cohort type. Significant differences were found between several cohort types. For example, the 5-year August, 5-year June, and 6-year June cohorts all showed significantly higher dropout rates than the 4-year August and 4-year June cohorts. Notably, the 6-year June cohort had the highest dropout rate compared to 4-year August, 5-year August, and 4-year June. In contrast, there were no significant differences between the 5-year August and 5-year June cohorts. These results highlight that longer cohort durations are associated with higher dropout rates.
| (1) | |
|---|---|
| (Intercept) | 9.625 |
| (0.554) | |
| Cohort_type4 year June | 1.284 |
| (0.729) | |
| Cohort_type5 year August | 4.415 |
| (0.825) | |
| Cohort_type5 year June | 6.463 |
| (0.740) | |
| Cohort_type6 year June | 8.178 |
| (0.752) | |
| Num.Obs. | 310 |
| R2 | 0.368 |
| R2 Adj. | 0.360 |
| F | 44.430 |
Table 9 presents the results of a linear regression analysis examining the relationship between dropout rates and cohort type. The intercept of 9.625 represents the average dropout rate for the 4-year August cohort, which serves as the baseline. The coefficients for the other cohort types show how much higher their dropout rates are compared to the 4-year August cohort: the 4-year June cohort has a dropout rate 1.284% higher, the 5-year August cohort is 4.415% higher, the 5-year June cohort is 6.463% higher, and the 6-year June cohort is 8.178% higher. The model explains about 36.8% of the variation in dropout rates across cohort types, and the F-statistic of 44.43 indicates that the model is statistically significant, meaning the differences in dropout rates across cohort types are not random. These results show that longer cohort durations are associated with higher dropout rates.
| (1) | |
|---|---|
| (Intercept) | 26.867 |
| (1.784) | |
| Cohort_year2002 | −3.567 |
| (2.523) | |
| Cohort_year2003 | −4.500 |
| (2.523) | |
| Cohort_year2004 | −7.167 |
| (2.523) | |
| Cohort_year2005 | −9.517 |
| (2.360) | |
| Cohort_year2006 | −7.827 |
| (2.257) | |
| Cohort_year2007 | −6.087 |
| (2.257) | |
| Cohort_year2008 | −7.687 |
| (2.257) | |
| Cohort_year2009 | −8.307 |
| (2.257) | |
| Cohort_year2010 | −9.167 |
| (2.257) | |
| Cohort_year2011 | −10.107 |
| (2.257) | |
| Cohort_year2012 | −11.167 |
| (2.257) | |
| Cohort_year2013 | −11.847 |
| (2.257) | |
| Cohort_year2014 | −11.467 |
| (2.360) | |
| Cohort_year2015 | −14.117 |
| (2.821) | |
| BoroughBrooklyn | −2.333 |
| (2.523) | |
| BoroughManhattan | −6.933 |
| (2.523) | |
| BoroughQueens | −5.200 |
| (2.523) | |
| BoroughStaten Island | −11.400 |
| (2.523) | |
| Cohort_year2002 × BoroughBrooklyn | 0.567 |
| (3.569) | |
| Cohort_year2003 × BoroughBrooklyn | 0.400 |
| (3.569) | |
| Cohort_year2004 × BoroughBrooklyn | 0.200 |
| (3.569) | |
| Cohort_year2005 × BoroughBrooklyn | 0.608 |
| (3.338) | |
| Cohort_year2006 × BoroughBrooklyn | −0.547 |
| (3.192) | |
| Cohort_year2007 × BoroughBrooklyn | −1.967 |
| (3.192) | |
| Cohort_year2008 × BoroughBrooklyn | −3.047 |
| (3.192) | |
| Cohort_year2009 × BoroughBrooklyn | −3.387 |
| (3.192) | |
| Cohort_year2010 × BoroughBrooklyn | −3.727 |
| (3.192) | |
| Cohort_year2011 × BoroughBrooklyn | −3.567 |
| (3.192) | |
| Cohort_year2012 × BoroughBrooklyn | −3.427 |
| (3.192) | |
| Cohort_year2013 × BoroughBrooklyn | −3.167 |
| (3.192) | |
| Cohort_year2014 × BoroughBrooklyn | −4.167 |
| (3.338) | |
| Cohort_year2015 × BoroughBrooklyn | −3.267 |
| (3.990) | |
| Cohort_year2002 × BoroughManhattan | 0.767 |
| (3.569) | |
| Cohort_year2003 × BoroughManhattan | 2.133 |
| (3.569) | |
| Cohort_year2004 × BoroughManhattan | 3.200 |
| (3.569) | |
| Cohort_year2005 × BoroughManhattan | 3.033 |
| (3.338) | |
| Cohort_year2006 × BoroughManhattan | 1.713 |
| (3.192) | |
| Cohort_year2007 × BoroughManhattan | 0.893 |
| (3.192) | |
| Cohort_year2008 × BoroughManhattan | 1.453 |
| (3.192) | |
| Cohort_year2009 × BoroughManhattan | 0.693 |
| (3.192) | |
| Cohort_year2010 × BoroughManhattan | 0.313 |
| (3.192) | |
| Cohort_year2011 × BoroughManhattan | 0.393 |
| (3.192) | |
| Cohort_year2012 × BoroughManhattan | 0.833 |
| (3.192) | |
| Cohort_year2013 × BoroughManhattan | 1.753 |
| (3.192) | |
| Cohort_year2014 × BoroughManhattan | 0.683 |
| (3.338) | |
| Cohort_year2015 × BoroughManhattan | 1.283 |
| (3.990) | |
| Cohort_year2002 × BoroughQueens | 2.033 |
| (3.569) | |
| Cohort_year2003 × BoroughQueens | 1.767 |
| (3.569) | |
| Cohort_year2004 × BoroughQueens | 2.500 |
| (3.569) | |
| Cohort_year2005 × BoroughQueens | 3.650 |
| (3.338) | |
| Cohort_year2006 × BoroughQueens | 0.960 |
| (3.192) | |
| Cohort_year2007 × BoroughQueens | −1.620 |
| (3.192) | |
| Cohort_year2008 × BoroughQueens | −1.340 |
| (3.192) | |
| Cohort_year2009 × BoroughQueens | −1.140 |
| (3.192) | |
| Cohort_year2010 × BoroughQueens | −1.000 |
| (3.192) | |
| Cohort_year2011 × BoroughQueens | −0.840 |
| (3.192) | |
| Cohort_year2012 × BoroughQueens | −1.000 |
| (3.192) | |
| Cohort_year2013 × BoroughQueens | −1.280 |
| (3.192) | |
| Cohort_year2014 × BoroughQueens | −2.200 |
| (3.338) | |
| Cohort_year2015 × BoroughQueens | −1.350 |
| (3.990) | |
| Cohort_year2002 × BoroughStaten Island | 1.667 |
| (3.569) | |
| Cohort_year2003 × BoroughStaten Island | 2.267 |
| (3.569) | |
| Cohort_year2004 × BoroughStaten Island | 4.133 |
| (3.569) | |
| Cohort_year2005 × BoroughStaten Island | 4.800 |
| (3.338) | |
| Cohort_year2006 × BoroughStaten Island | 3.920 |
| (3.192) | |
| Cohort_year2007 × BoroughStaten Island | 2.300 |
| (3.192) | |
| Cohort_year2008 × BoroughStaten Island | 3.060 |
| (3.192) | |
| Cohort_year2009 × BoroughStaten Island | 3.300 |
| (3.192) | |
| Cohort_year2010 × BoroughStaten Island | 3.200 |
| (3.192) | |
| Cohort_year2011 × BoroughStaten Island | 3.920 |
| (3.192) | |
| Cohort_year2012 × BoroughStaten Island | 3.700 |
| (3.192) | |
| Cohort_year2013 × BoroughStaten Island | 4.160 |
| (3.192) | |
| Cohort_year2014 × BoroughStaten Island | 3.275 |
| (3.338) | |
| Cohort_year2015 × BoroughStaten Island | 3.750 |
| (3.990) | |
| Num.Obs. | 310 |
| R2 | 0.724 |
| R2 Adj. | 0.637 |
| F | 8.334 |
Table 10 presents the results of a linear regression analysis examining dropout rates by cohort year and borough. The intercept of 26.867 represents the average dropout rate for the Bronx in 2001, which serves as the baseline. The coefficients for each year show how much dropout rates changed from 2001 for each borough. For example, by 2005, dropout rates were 9.517% lower compared to 2001, and this downward trend continued through 2015. The coefficients for boroughs indicate the differences in dropout rates for each borough compared to the Bronx: Brooklyn had a dropout rate 2.333% lower, Manhattan had 6.933% lower, Queens had 5.200% lower, and Staten Island had 11.400% lower. The interaction terms show how the relationship between year and dropout rates varies by borough. These terms provide insight into how the trends over time differ across boroughs. For instance, Brooklyn saw smaller decreases in dropout rates in later years compared to the Bronx, while Staten Island saw more consistent decreases.The model explains 72.4% of the variation in dropout rates, and the F-statistic of 8.334 indicates that the model is statistically significant. This means that both cohort year and borough have a significant impact on dropout rates.
Discussion
The goal of this study was to investigate high school dropout rates across New York City’s boroughs, exploring how these rates vary by borough, cohort type, and over time. This analysis highlights significant disparities in dropout rates, as well as the trends in educational outcomes in New York City public schools from 2001 to 2015. The findings reveal important insights into the factors that may influence student retention and graduation, and the need for targeted interventions in specific boroughs and cohort groups.
Borough Disparities
The results of this study highlight notable differences in dropout rates across the five boroughs of New York City. The Bronx consistently exhibited the highest average dropout rate, with an average of 18.5%. This trend is not only significant but persistent, suggesting that the Bronx faces unique challenges that hinder student retention. In contrast, Staten Island showed the lowest dropout rate at 10.35%, with rates in Manhattan, Brooklyn, and Queens falling in between. The disparity is evident not only in the summary statistics but also in the results of the ANOVA and Tukey’s HSD post-hoc tests, which confirmed that the Bronx’s dropout rate was significantly higher than all other boroughs, and Staten Island’s dropout rate was significantly lower than the Bronx, Brooklyn, and Manhattan.
This borough variation suggests that socioeconomic factors, educational resources, and community engagement may play pivotal roles in shaping student outcomes. The Bronx, for example, has historically faced higher levels of poverty, unemployment, and educational inequity, factors that are widely seen as contributors to lower graduation rates. Targeted efforts to address these disparities, such as increased funding for schools, improved student support services, and community-based educational programs, may help reduce the dropout rate in the Bronx and similar communities.
Cohort Type and Duration
The analysis also reveals a clear relationship between cohort type and dropout rates. Longer cohort durations were associated with higher dropout rates, with the 6-year June cohort exhibiting the highest dropout rates (17.8%). These results suggest that students who take longer to graduate are at higher risk of disengaging from the educational system.
The significant differences in dropout rates across cohort types, confirmed through ANOVA and Tukey’s HSD post-hoc tests, further indicate that shorter cohort durations are more successful at retaining students. This insight can inform policies aimed at increasing graduation rates within shorter time frames. By focusing on interventions that help students graduate on time such as academic support, mentoring, and tutoring, schools may be able to reduce the dropout rates among students in extended cohorts.
Trends Over Time
The trend analysis shows a steady decline in dropout rates from 2001 to 2015, with the average dropout rate decreasing from 21.69% in 2001 to 7.66% in 2015. This reduction is consistent across all boroughs, suggesting that citywide efforts to improve graduation rates may have contributed to this positive trend.
The regression analysis, which included interaction terms between cohort year and borough, indicates that the decline in dropout rates was not uniform across boroughs. While boroughs like Brooklyn and Staten Island saw more consistent improvements over time, the Bronx experienced a more uneven decline. This suggests that the Bronx may require additional support to maintain progress in reducing dropout rates.
Implications and Recommendations
The results of this study have significant implications for policymakers and educators in New York City. The persistent dropout rates in the Bronx highlight the need for tailored interventions that address the unique challenges faced by students in this borough. Policymakers should consider additional funding for schools in higher risk areas, with a focus on providing students with the resources and support they need to stay engaged and graduate on time.
The findings related to cohort type suggest that efforts to shorten the time to graduation could help reduce dropout rates. Programs designed to keep students on track for graduation should be expanded, particularly for students who may be at risk of falling behind.
While the overall trend in dropout rates is decreasing, schools should still implement strategies that specifically support students who may be at risk of dropping out or not graduating on time.
Conclusion
In conclusion, this study provides valuable insights into the dropout rates across New York City’s boroughs and cohort types. While the city has made progress in reducing dropout rates over time, significant disparities remain, particularly between boroughs. The Bronx, in particular, continues to face challenges in retaining students, highlighting the need for targeted interventions. Additionally, the relationship between cohort length and dropout rates suggests that policies aimed at promoting on time graduation could be effective in reducing overall dropout rates. Moving forward, policymakers and educators must continue to monitor dropout trends, with particular attention to borough-level disparities. By doing so, New York City can continue its progress toward achieving greater educational equity for all students.