A Choice Too Far: Transit Difficulty and Early High School Transfer

Abstract

The challenge of a long and difficult commute to school each day is likely to wear on students, leading some to change schools. We used administrative data from approximately 3,900 students in the Baltimore City Public School System in 2014–2015 to estimate the relationship between travel time on public transportation and school transfer during the ninth grade. We show that students who have relatively more difficult commutes are more likely to transfer than peers in the same school with less difficult commutes. Moreover, we found that when these students change schools, their newly enrolled school is substantially closer to home, requires fewer vehicle transfers, and is less likely to have been included among their initial set of school choices.

Keywords

educational policy high schools regression analyses urban education

School transfer has been shown to have adverse effects on individual student achievement test scores (Reynolds et al., 2009), school discipline (Engec, 2006), and dropout (Rumberger & Larson, 1998). Schools and teachers find mobile students—especially those who change schools during the school year—to be challenging to integrate and instruct (Lash & Kirkpatrick, 1994). The consequences of mobility, however, may depend on when and why students change schools (Grigg, 2012).

Students change schools for a variety of reasons. The literature on why students transfer proposes three general types of school changes: structural moves, school-related moves, and home-related moves (Welsh, 2017). Structural moves are required by the organization of the school or the district—the school they are in offers a limited grade span, closes, or is rezoned. School-related moves occur when a family decides that a different school would provide a better social or academic fit for their student. These moves may result in improvement in academic outcomes for students in the long run (Hanushek et al., 2004; Schwartz et al., 2017). Home-related moves are thought to be made in reaction to some (often unmeasured) stressor that is unrelated to school quality or preference, such as an eviction, job loss, or family structure change.

In this study, we propose an additional reason that students may change schools midyear: commuting stress. Among adults, a long and difficult commute can encourage an employee to reduce transit strain by seeking out a different workplace (Koslowsky et al., 1995). The challenge of getting to school each day is likely to wear on students in much the same way, leading some to change schools. Using administrative data from approximately 3,900 students in the Baltimore City Public School System (BCPS) in 2014–2015, we show that students who have relatively longer commutes to high school on public transportation are more likely to transfer schools during their freshman year than peers in the same school with less difficult commutes. Moreover, we found that when these students change schools, their newly enrolled school is substantially closer to home, requires fewer vehicles/transfers, and is less likely to have been included among their initial set of school choices.

Background

School Choice and Transportation

Research on school choice describes enrollment preferences as embedded in complex, multidimensional, and potentially overwhelming decisions (Lareau & Goyette, 2014). On the one hand, parents repeatedly say that they want a “high quality” school for their children and that they place a high value on academic performance (Hastings & Weinstein, 2008; Holme, 2002; Stein et al., 2011). On the other hand, parents and students prefer options that are convenient and easy to get to (see Bell, 2009; Glazerman & Dotter, 2016; Harris & Larsen, 2015). The highly segregated nature of cities along with the uneven geographic distribution of school quality means that for some students, particularly those from disadvantaged neighborhoods, it can be difficult to satisfy both of these preferences simultaneously (Rhodes & Warkentien, 2017).

The exact trade-offs between transportation and quality likely depend on a number of district-, school-, and student-level factors. Cities and districts differ on the degree to which they provide free direct transportation to and from schools for all students as well as the quality and reliability of their underlying public transportation systems (Blagg, Chingos, et al., 2018). In districts with more robust transportation systems and policies, parents report being more satisfied with their available schooling options (Jochim et al., 2014), and students often use district-provided transportation to attend higher quality schools than their zoned school (Cordes & Schwartz, 2019). Charter schools are often less likely to provide transportation than traditional schools despite drawing from larger geographic areas. In New Orleans, transportation and convenience were frequently stated reasons for choosing traditional public schools, whereas academic quality was more likely to be listed by charter attendees (Steele et al., 2011).

Most of the existing literature on choice preferences relies on ranked preferences in unified enrollment systems and researchers’ calculations of straight-line distance or travel time. It should be noted, however, that people are notoriously bad at estimating travel times, even on familiar routes (Parthasarathi et al., 2013). This means that at the time that students fill out their choice forms, they may not have properly accounted for the actual time it will take them to get to their different options. They may only fully understand the costs of traveling to a specific school after repeated experience with their daily commute. This study adds to what is known about the relationship between transportation and choice by examining when initial choices fail and students decide they would prefer a different option.

Commuting and School Transfer

Students are not the only ones to commute daily. There is a large body of literature that examines how adults respond to the stress of their daily travel to and from work. Commuting difficulty for adults (e.g., long commute time, multiple transfers, vehicle delays, crowding, poor ventilation) is related to increased physiological and psychological stress (Koslowsky et al., 1995). The accumulation of commuting stressors can induce work and workplace withdrawal behaviors that include lateness, absenteeism, and seeking different employment (Novaco et al., 1990; Steinmetz et al., 2014).

Recent work has begun to explore the extent to which student commuters and school disengagement is analogous to adult workers. Support for the hypothesized relationship between commuting difficulty and student disengagement from school in the form of increased absenteeism has been shown in the transition from middle to high school in Baltimore City (Stein & Grigg, 2019) and Washington, D.C. (Blagg, Rosenboom, & Chingos, 2018). Absenteeism has also been shown to be related to commuting difficulty in the form of increased exposure to violent crime along the route to school (Burdick-Will et al., 2019; Einhorn, 2016). In this study, we further this line of research by examining school transfer as the student analogue of adult workplace withdrawal through changing employment.

Baltimore Context

Baltimore City is a good place to study the relationship between commuting stress and school transfer for three reasons. First, Baltimore City public high schools are assigned via an open enrollment system. All potential ninth graders complete a choice form in which they rank their top five high school choices (out of 37). Most schools are filled by a lottery if oversubscribed, but there are seven selective enrollment schools—three career and technology education (CTE) and four college-prep schools—that admit only the highest achieving applicants based on a combination of middle school grades, attendance, and standardized test scores. There is one arts-focused school that admits students based on an audition performance. Students may transfer high schools midyear by petitioning the district. Homelessness or medical or safety concerns are the only officially listed reasons for a transfer (BCPS 2020), but it is possible that a long commute might in and of itself pose a safety concern if it requires getting up before dark or traveling through dangerous neighborhoods (Einhorn, 2016).

Second, high school students are required to find their own way to school. Public transportation passes, subsidized by the state of Maryland, are available to any student who lives more than 1.5 miles from their enrolled school and can be used on the Baltimore City bus, metro, and light rail systems (BCPS, 2018). The exact number of Baltimore students who walk or take public transportation is unknown, but a survey of BCPS students suggests that less than one third of high school students use a car as their primary mode of transportation (Stein et al., 2017).

Finally, both the public transportation and road network in Baltimore City contribute to long and unpredictable commutes. Despite its small size, average commute time in Baltimore is longer than Los Angeles and comparable to New York or Washington, D.C. (Campbell, 2018). Busses are notoriously unreliable and often pass stops with large numbers of waiting students when overcrowded during school commute times (Glenn, 2013; McKone, 2015). Given heavy traffic and unreliability, it is likely that the stressors involved in a particular route to school may not be fully appreciated by students during the school selection process. Many may find that their trip across town is more taxing than they anticipated.

Data and Measures

Student data came from BCPS administrative records archived at the Baltimore Education Research Consortium. We focused on ninth graders in the 2014–2015 academic year. Records included the student’s address and schools attended as well as race, ethnicity, gender, and participation in the free and reduced meals (FARM), English language learner (EL), and special education programs. The vast majority of Baltimore public school students reported their race as Black or White, and no students who reported another race changed schools. Therefore, we created a single dummy variable for Black versus not Black. We created a separate indicator for Hispanic regardless of reported race. We included a count of the days suspended at a student’s first or only school of enrollment to capture student disciplinary problems and adjust for any informal counseling out that might lead students to transfer involuntarily.

These records also include codes that identify the type of withdrawal or entrance for every school that a student attended (e.g., within-district transfer, transfer to a school in another district, withdrawal from school). We used these codes to identify students who transferred within BCPS in the middle of their freshman year. We focused on these early within-year moves because they are the most problematic for students and schools and because interdistrict transfers by definition require a residential move and these students were lost from our data.

We supplemented these records with ones from the 2013–2014 school year to create measures of percentage days absent during eighth grade and an indicator for whether a student changed schools in the eighth grade. We used 2013–2014 choice forms that identify the rank order of the schools that each student listed in his or her high school application and to create an indicator for whether the student submitted a choice ranking form. Highly disengaged students and late enrollees to the district may have been assigned a high school without submitting any preferences. These students would be limited to relatively unpopular schools that still had open slots after all other students were assigned. We also created a dummy variable for whether the student was enrolled at the beginning of the ninth grade in his or her first-choice high school. Although these variables cannot account for all the unobserved heterogeneity between students with regard to school preferences and engagement, they likely reduce this bias if present.

Following the same methodology outlined in our previous research (see Burdick-Will et al., 2019; Stein & Grigg, 2019), we estimated routes from home to school for each student in the data set using the Network Analyst suite in ArcGIS (Esri, 2017). We calculated the most efficient route from the residential address of record to the enrolled school with an arrival time set to the start of school. Travel time is the total estimated time required for the commute and includes time spent walking, waiting, and riding on transit vehicles. Vehicles is the estimated total number of transit vehicles required and captures a measure of the complexity of a given route given that more vehicles would require more connections on the way to school.

It must be noted that these are estimated public transportation travel times and not measures of actual commuting behavior. It is possible that some students with long public transit travel times can shorten their commutes by using a private car. For others, the estimated times are going to be overly optimistic because they do not consider schedule reliability. Nevertheless, the estimated routes do provide our best guess at relative differences in commuting experiences across the district.

We created two peer measures to capture a student’s social connections at school, which may be related to decisions to transfer. First, we counted the number of students from each neighborhood attending each school. This indicates how many co-commuters a student might have as well as the student’s local social connections at school. Second, we calculated the percentage of students from the ninth-grade class that attended the same middle school. This indicates the degree to which students’ social worlds remain the same during the transition to high school (see Grigg, 2014).

Finally, we included measures of the academic quality at students’ initial high school enrollment. We included indicators for whether the school was a CTE academy or a selective enrollment college-prep school. From the Maryland State Department of Education (2019), we used school average algebra score, the percentage of students in special education, the percentage of FARM students, and the percentage of students who were chronically absent.

Analytic Sample

We began building the analytic sample by identifying first-time ninth-grade students enrolled in regular, nonspecialized population BCPS high schools in 2014–2015. We then restricted the sample to students who also had a stable residential address record during the ninth grade. We did this to avoid confounding residential moves with school changes and to ensure that our estimates of travel time are accurate. Of the 5,934 ninth graders enrolled in BCPS in 2014–2015, 4,538 were first-time freshmen, and 3,989 of those had stable residential addresses. The analytic sample was further refined to include only those students who remained within BCPS for the entire school year. This step excluded 45 students who transferred out of the district or to an alternative school, students who were institutionalized, and students who formally withdrew from the school system. The final analytic data set includes 3,944 students.

Excluded students are somewhat more likely to be White, Hispanic, male, and ELs. They are overwhelmingly more likely to not have participated in the eighth-grade choice system (73% vs. 6%). They also have higher mobility rates in eighth and ninth grades and higher eighth-grade absence rates. However, their travel times are slightly shorter on average (33 vs. 35 minutes and 1.95 vs. 2.1 vehicles). (For more detail, see Supplementary Table S1 available on the journal website.)

Method

To estimate the relationship between transit difficulty and early high school transfer, we specified two logistic regression models. The first model predicts transfer as a function of our transit variables, student individual characteristics, eighth-grade attendance, eighth-grade school mobility, choice and peer measures, and school quality. However, the relationship between transit difficulty and decisions to transfer high schools may be confounded by unobserved characteristics of specific schools, such as school climate, academic rigor, or safety. To adjust for any unobserved differences that are constant across students enrolled in the same high school, our second model adds high school fixed effects for the initial high school to Model 1.¹

The inclusion of school fixed effects in Model 2 resulted in the loss of 345 observations in four schools from which no students transferred during the school year. For comparability, we have estimated Model 1 on the sample of students represented in the estimation of Model 2 (n = 3,530). Estimates on the full analytic sample for Model 1 are substantively equivalent and are available in Supplemental Table S2 available on the journal website.

Results

Table 1 presents descriptive statistics for the analytic sample. Approximately 6% of the analytic sample transferred from their original ninth-grade high school to another BCPS high school during the 2014–2015 school year. Compared to their nontransfer peers, transfer students were significantly more likely to be Black/African American, less likely to be White, more likely to be receiving special education services, and were suspended more often. They were also twice as likely to have transferred in eighth grade and were absent for a larger percentage of school days in eighth grade. Transfer students also attended lower quality schools with fewer of their neighbors and middle school classmates. They were less likely to have been enrolled in their first-choice school and were more likely to not participate in the choice process at all. Students who transferred also had more difficult commutes than their peers who did not transfer (39.6 vs. 35.4 minutes). The 4.2-minute longer travel time for subsequent transfer students is approximately one third of a standard deviation. Although the mean number of vehicles is similar for the two groups, a higher percentage of transfer students than nontransfer students required at least one transfer (76.4% vs. 64.2%). The difference in travel times between nontransfer and transfer students at their first school can also been seen in the travel density plot in Figure 1.

Table 1

Descriptive Statistics of Transfer and Nontransfer Students in Analytic Sample

	Nontransfer	Transfer
Black	87.9	94.5
White	10.1	5.5
Other	2.0	0.0
Hispanic	4.1	1.8
Male	48.7	54.4
FARM	84.2	90.8
Special education	21.3	29.5
EL	1.7	1.8
School transfer, 8th grade	5.0	10.1
Not in choice data	5.5	10.1
High school is first choice	51.3	34.6
Career and technical academy	17.6	7.8
Selective enrollment	28.7	5.1
Travel time	35.4 (14.6)	39.6 (15.7)
Vehicles	2.1 (1.2)	2.4 (1.3)
% Absent in 8th grade	6.3 (9.9)	10.6 (12.6)
Days suspended	0.5 (3.4)	1.5 (9.3)
Number of neighbors	5.3 (6.2)	4.0 (5.0
% Middle school peers	6.1 (5.3)	3.9 (4.3)
School math score (standardized)	0.6 (1.2)	−0.2 (0.7)
% Special education	15.1 (9.3)	21.5 (6.5)
% FARM	76.0 (12.1)	82.0 (7.8)
% Chronic absent	31.0 (17.6)	43.9 (17.1)
N	3,727	217

Note. Standard deviation in parentheses. FARM = student on free and reduced meals program; EL = English language learner.

Figure 1.

Travel time density comparison between transfer and nontransfer students.

Table 2 presents the results of our logistic regressions. Coefficients are presented in log-odds. The estimated coefficient on travel time in Model 1 is .16 (SE = .07), indicating that a 10-minute increase in travel time is associated with a 17% (e^.16 = 1.17) increase in the odds of transfer, all else equal. The estimate on vehicles required is .09 (SE = .08) and not statistically significant. The only other statistically significant predictors in the model of early high school transfer are being enrolled in the student’s first-choice school, attending high school with more middle school peers, school math achievement, and attending a CTE center.

Table 2

Estimated Log-Odds of Transfer by Transit Characteristics

	Model 1	Model 2
Travel time	0.16*	0.21**
	(0.07)	(0.07)
Vehicles	0.07	0.03
	(0.08)	(0.08)
White	0.17	0.27
	(0.39)	(0.41)
Hispanic	−0.69	−0.56
	(0.67)	(0.69)
Male	0.02	−0.10
	(0.15)	(0.16)
FARM	0.14	0.18
	(0.25)	(0.25)
Special education	−0.08	−0.10
	(0.17)	(0.18)
EL	0.54	0.37
	(0.77)	(0.78)
% Absence 8th grade	0.01	0.01
	(0.01)	(0.01)
School transfer, 8th grade	0.03	0.02
	(0.31)	(0.32)
Days suspended	0.02	0.02
	(0.01)	(0.01)
Not in choice data	0.14	0.20
	(0.27)	(0.28)
HS is first choice	−0.38*	−0.35*
	(0.17)	(0.18)
Neighbors in school	−0.02	−0.02
	(0.02)	(0.02)
% 8th-grade peers	−0.05*	−0.06**
	(0.02)	(0.02)
School average math score (standardized)	−0.38**
	(0.14)
% Special education	0.01
	(0.02)
% FARM	0.02
	(0.01)
% Chronic absent	0.01
	(0.01)
Career and technical academy	−0.84**
	(0.30)
Selective enrollment	−0.15
	(0.64)
Constant	−4.33***	−3.09***
	(1.23)	(0.91)
School fixed effects	No	Yes
Observations	3,599	3,599
Likelihood ratio χ²	175.5	242.7
Pseudo R²	.12	.15

Note. Robust standard errors in parentheses. FARM = student on free and reduced meals program; EL = English language learner.

p < .05. **p < .01. ***p < .001.

The inclusion of high school fixed effects (Model 2, Table 2) produced a somewhat stronger relationship between commute time and transfer (.21, SE = .07). This indicates that the bias from unobserved school characteristics was negative, perhaps because students who tend to go to closer schools attend schools with high overall transfer rates. Comparing only students who attend the same school and holding all else constant, a 10-minute increase in commute time predicts a 23% (e^.21 = 1.23) increase in the odds of transfer.²

Figure 2 graphs the predicted probability of transfer from Model 2 across the range of travel times holding all other characteristics at their mean. The slope of the line increases rapidly as travel time increases, with a short commute (10 minutes) predicting an approximately 3% chance of changing schools versus an approximately 6% chance of transferring for students with an average commute (35 minutes) and around a 10% chance of transferring for students who commute for an hour or more.

Figure 2.

Predicted probability of school transfer by travel time.

Table 3 compares the characteristics of transfer students’ first and second schools. Travel times for transfer students change from 39.6 minutes on average at their first school to 31.6 minutes on average at their second (see also Figure 1, Transfer [1st Sch.] vs. Transfer [2nd Sch.]). The difference of 8 minutes represents a little more than one half of a standard deviation in travel times. The average number of vehicles required is also reduced from 2.4 to 1.8. Although there is no significant change in objective school quality after changing schools, students do end up in schools that they were less interested in during the choice lottery. Before they transferred, 34.6% of these students attended their first-choice school compared to only 10.6% after they changed schools. Students are substantially more likely to attend a school that they did not even consider in their top five during the initial high school selection process (70.0% posttransfer vs. 27.2% pretransfer). Based on our calculations (not presented in Table 3), nearly half of transfer students ended up in a school that was both closer and lower ranked than their initial enrollment. Only 16% transferred to a school that was farther and higher ranked. Twenty-two percent ended up in a school that was lower ranked but farther and 13% in a school that was higher ranked and closer. This suggests that at least some students regret their initial trade-off between distance and preferred school characteristics and adjust their enrollments so that they are enrolled in closer but less desirable schools.

Table 3

First and Second School Comparison for Transfer Students

	First School	Second School
First choice	34.6	10.6
Not in choice set	27.2	70.0
Career and technical academy	7.8	7.8
Selective enrollment	5.1	4.6
Travel time	39.6 (15.7)	31.6 (15.8)
Vehicles	2.4 (1.3)	1.8 (1.3)
School math score	−0.2 (0.7)	−0.2 (0.7)
% Special education	21.5 (6.5)	21.7 (7.0)
% FARM	82.0 (7.8)	82.3 (7.0)
% Chronic absent	43.9 (17.1)	43.4 (16.3)

Note. Standard deviation in parentheses. FARM = student on free and reduced meals program.

Discussion and Conclusion

The results of this study show that travel time is a strong predictor of early high school transfer for students in Baltimore City. Holding all else constant, including the specific high school, students whose estimated travel time is around an hour are approximately three times as likely to transfer as those with very short commutes (10 minutes or less). Moreover, students who do change schools, on average, attend new schools that are closer to home but less likely to have been ranked highly in their initial choice application. It is possible that these associations could be even stronger if we had measures of actual commuting behavior and not just travel estimates.

This suggests a real trade-off between distance and preferred school characteristics for at least some students. It also means that some students will get that calculation wrong and need to change schools midyear despite the potential academic costs of those moves. The findings, therefore, highlight the evolving preferences and information updating that naturally take place for students throughout their education careers but are often considered constant by those who design open enrollment programs (Abdulkadiroğlu & Sönmez, 2003). Given the limitations of administrative data, further qualitative research examining these trade-offs is warranted and could provide deeper insights on preference formation and evolution.

The results also represent a substantial and as of yet unrecognized source of instability for urban students in open enrollment contexts. Overall, within-year mobility rates are relatively low in our analytic sample, but even a few new students can cause serious problems for instruction at the classroom level (Lash & Kirkpatrick, 1994). To date, most of the student mobility literature has focused on a relatively narrow set of acute stressors, such as eviction or job loss, to understand why students change school. These findings suggest that daily stressors, such as commuting stress, also weigh on students and can build over time and increase the likelihood of transfer. They also suggest that providing faster and more reliable transportation could be a way to keep students enrolled in their most preferred schools (i.e., Ely & Teske, 2014).

Finally, students in areas that are poorly served by transportation and whose closest schools are not a good fit academically are at a real disadvantage in an open enrollment system. They may technically have access to any school in the district, but enrolling across town has a cost. First, they must endure the long commute and the potential stress that it entails. Second, they risk not optimizing their options if they initially stretch geographically for a better academic option only to find that situation unsustainable and subsequently transfer to a less optimal option. If and when they do decide to transfer, it may not be possible to get into any of their other preferred schools, leaving them worse off academically than if they had stayed closer to home in the first place. In a broader sense, these findings remind us of the importance of the built environment and urban infrastructure in school inequality even when the enrollment system is explicitly designed to limit its influence.

Although Baltimore as a focus of this study presents a specific case with regard to the details of its school choice and public transit system, it is not unique among other large urban school districts in that it allows for substantial out-of-neighborhood enrollment and relies on local public transit systems to transport high school students across the city to schools of choice (e.g., Chicago, Washington, D.C., Philadelphia, Cleveland, Detroit). Given this, we expect that our results would generally hold across other districts and cities. It is unknown, however, the extent to which features of public transit systems that decrease unreliability and overall travel times that are not present in Baltimore, such as a fully developed mass rapid transit (e.g., subways, light rail) or bus rapid transit system, would have on early high school transfers. Similarly, features of school choice systems that improve students’ ability to understand commuting costs as part of the school choice process prior to making choices or limit the extent to which students can choose options that are far from home may also affect early high school transfers. The next step in further understanding the effects of public transit commuting on student outcomes will be to field cross-city studies that can leverage variation in public transit and school choice systems and qualitative studies that can more clearly explicate student school preference formation and change with respect to transit difficulty.

Supplemental Material

EdR949504_supplemental_tables – Supplemental material for A Choice Too Far: Transit Difficulty and Early High School Transfer

Supplemental material, EdR949504_supplemental_tables for A Choice Too Far: Transit Difficulty and Early High School Transfer by Marc L. Stein, Julia Burdick-Will and Jeffrey Grigg in Educational Researcher

Footnotes

ORCID iDs

Marc L. Stein

Julia Burdick-Will

Jeffrey Grigg

Notes

Authors

MARC L. STEIN, PhD, is an associate professor of education at Johns Hopkins University, 307F Seton, 2800 N. Charles St., Baltimore, MD 21218; m.stein@jhu.edu . His research focuses on the intersection between the social contexts of schools and schooling and education policy, and he is using geospatial data to examine educational opportunity and access across social groups.

JULIA BURDICK-WILL, PhD, is an assistant professor of sociology and education at Johns Hopkins University, 533 Mergenthaler, 3400 N. Charles St., Baltimore, MD 21218; jburdickwill@jhu.edu . Her research combines the sociology of education and urban sociology to study the roots of educational inequality and examine the dynamic connections between communities and schools that shape opportunities to learn.

JEFFREY GRIGG, PhD, was an assistant professor at the Johns Hopkins University School of Education, 2800 N. Charles St., Baltimore, MD 21218. His research focused on student mobility and transitions, intergenerational inequality, student noncognitive skills, teacher professional development, and causal inference in real-world settings. Sadly, he passed away in 2019.

References

Abdulkadiroğlu

Sönmez

(2003). School choice: A mechanism design approach. American Economic Review, 93(3), 729−747.

Baltimore City Public Schools. (2018). Transportation. https://www.baltimorecityschools.org/transportation

Baltimore City Public Schools. (2020). Transfer requests. https://www.baltimorecityschools.org/transfer-requests

Bell

(2009). Geography in parental choice. American Journal of Education, 115(4), 493−521.

Blagg

Chingos

Corcoran

S. P.

Cordes

S. A.

Cowen

Denice

Gross

Lincove

J. A.

Sattin-Bajaj

Schwartz

A. E.

Valant

(2018). The road to school: How far students travel to school in the choice-rich cities of Denver, Detroit, New Orleans, New York City, and Washington, DC. Urban Institute.

Blagg

Rosenboom

Chingos

M. M.

(2018). The extra mile: Time to school and student outcomes in Washington, DC. Urban Institute.

Burdick-Will

Stein

M. L.

Grigg

(2019). Danger on the way to school: Exposure to violent crime, public transportation, and absenteeism. Sociological Science, 6(5), 118−142.

Campbell

(2018, June 18). “The traffic is horrible”: Baltimore commute ranks among nation’s longest. The Baltimore Sun. https://www.baltimoresun.com/maryland/baltimore-city/bs-md-commute-times-20180510-story.html

Cordes

S. A.

Schwartz

A. E.

(2019). Does pupil transportation close the school quality gap? Evidence from New York City. Urban Institute.

10.

Einhorn

(2016, April 8). Six hours, eight buses: The extreme sacrifice Detroit parents make to access better schools. Detroit: Chalkbeat. https://detroit.chalkbeat.org/posts/detroit/2016/04/08/six-hours-eight-buses-the-extreme-sacrifice-detroit-parents-make-to-access-better-schools/

11.

Ely

Teske

(2014). Success express: Transportation innovation in Denver Public Schools. Center for Education Policy Analysis.

12.

Engec

(2006). Relationship between mobility and student performance and behavior. The Journal of Educational Research, 99(3), 167−178.

13.

Esri. (2017). Add GTFS to a network dataset [Computer software]. Esri.

14.

Glazerman

Dotter

(2016). Market signals: How do DC parents rank schools, and what does it mean for policy? Mathematica Policy Research, Inc.

15.

Glenn

(2013, May 17). Getting to school is harder than you think. WYPR. http://news.wypr.org/post/getting-school-harder-you-think#stream/0

16.

Grigg

(2012). School enrollment changes and student achievement growth: A case study in educational disruption and continuity. Sociology of Education, 85(4), 388−404.

17.

Grigg

(2014). Student mobility, identity, and attitudes: The social and academic consequences of changing schools [Unpublished doctoral dissertation], The University of Wisconsin-Madison.

18.

Hanushek

E. A.

Kain

J. F.

Rivkin

S. G.

(2004). Disruption versus tiebout improvement: The costs and benefits of switching schools. Journal of Public Economics, 88(9−10), 1721−1746.

19.

Harris

D. N.

Larsen

(2015). What schools do families want (and why). Education Research Alliance for New Orleans.

20.

Hastings

J. S.

Weinstein

J. M.

(2008). Information, school choice, and academic achievement: Evidence from two experiments. The Quarterly Journal of Economics, 123(4), 1373−1414.

21.

Holme

J. J.

(2002). Buying homes, buying schools: School choice and the social construction of school quality. Harvard Educational Review, 72(2), 177−206.

22.

Jochim

DeArmond

Gross

Lake

(2014). How parents experience public school choice. Center for Reinventing Public Education.

23.

Koslowsky

Kluger

A. N.

Reich

(1995). Commuting stress: Causes, effects, and methods of coping. Springer Science & Business Media.

24.

Lareau

Goyette

(Eds.). (2014). Choosing homes, choosing schools. Russell Sage Foundation.

25.

Lash

A. A.

Kirkpatrick

S. L.

(1994). Interrupted lessons: Teacher views of transfer student education. American Educational Research Journal, 31(4), 813−843.

26.

Maryland State Department of Education. (2019). Maryland public schools report card. https://reportcard.msde.maryland.gov/

27.

McKone

(2015, August 24). No yellow buses here: One student’s MTA commute. WYPR. https://www.wypr.org/post/no-yellow-buses-here-one-students-mta-commute

28.

Novaco

R. W.

Stokols

Milanesi

(1990). Objective and subjective dimensions of travel impedance as determinants of commuting stress. American Journal of Community Psychology, 18(2), 231−257.

29.

Parthasarathi

Levinson

Hochmair

(2013). Network structure and travel time perception. PLOS One, 8(10), Article e77718. https://doi.org/10.1371/journal.pone.0077718

30.

Reynolds

A. J.

Chen

C. C.

Herbers

J. E.

(2009, June 29−30). School mobility and educational success: A research synthesis and evidence on prevention [Paper presentation]. Workshop on the Impact of Mobility and Change on the Lives of Young Children, Schools, and Neighborhoods, Washington, DC, United States.

31.

Rhodes

Warkentien

(2017). Unwrapping the suburban “package deal”: Race, class, and school access. American Educational Research Journal, 54(1), 168S−189S.

32.

Rumberger

R. W.

Larson

K. A.

(1998). Student mobility and the increased risk of high school dropout. American Journal of Education, 107(1), 1−35.

33.

Schwartz

A. E.

Stiefel

Cordes

S. A.

(2017). Moving matters: The causal effect of moving schools on student performance. Education Finance and Policy, 12(4), 419−446.

34.

Steele

J. L.

Vernez

Gottfried

M. A.

Schwam-Baird

(2011). The transformation of a school system: Principal, teacher, and parent perceptions of charter and traditional schools in post-Katrina New Orleans. RAND Corporation.

35.

Stein

Goldring

Cravens

(2011). Do parents do as they say? Choosing Indianapolis charter schools. In Berends

Goldring

Cannata

(Eds.), School choice and school improvement. Harvard Education Press.

36.

Stein

M. L.

Grigg

J. A.

(2019). Missing bus, missing school: Establishing the relationship between public transit use and student absenteeism. American Educational Research Journal, 56(5), 1834–1860. https://doi.org/10.3102/0002831219833917

37.

Stein

M. L.

Grigg

Cronister

Chavis

Connolly

(2017). Getting to high school in Baltimore: Student commuting and public transportation. Baltimore Education Research Consortium. http://baltimore-berc.org/wp-content/uploads/2017/01/GettingtoHighSchoolinBaltimoreJanuary2017.pdf

38.

Steinmetz

de Vries

D. H.

Tijdens

K. G.

(2014). Should I stay or should I go? The impact of working time and wages on retention in the health workforce. Human Resources for Health, 12(1), 23.

39.

Welsh

R. O.

(2017). School hopscotch: A comprehensive review of K–12 student mobility in the United States. Review of Educational Research, 87(3), 475−511.

Supplementary Material

Please find the following supplemental material available below.

For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.

For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.

0.00 MB

0.02 MB