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Cited by 81Crime Patterns and InterventionsSchool Choice and PerformanceUrban, Neighborhood, and Segregation Studies

Danger on the way to school: exposure to violent crime, public transportation, and absenteeism

Open access
Burdick-Will, Julia, Stein, Marc, Grigg, Jeffrey·2019·Sociological Science
DOI 10.15195/v6.a5
Open this paper in CoChat — free accountOpen PDF

Abstract

In this study, we propose and test a mechanism for the effect of neighborhood of residence on school outcomes: absenteeism that results from exposure to danger on the way to school. We first determine the most efficient route to school using public transportation for 4,200 first-time freshmen in Baltimore City public high schools. Then, we link the specific streets along the most efficient route to incident-level crime data from the Baltimore Police Department. We find that students whose estimated routes require walking along streets with higher violent-crime rates have higher rates of absenteeism throughout the year. We also show that absenteeism is not associated with exposure to dangerous streets while riding on public transit and exposure to property crime.These conclusions hold with and without adjustments for student demographic characteristics, prior school attendance, violent crime around homes and schools, and unobserved differences related to school preference and neighborhood selection.

AI snapshot

Written from the paper’s full text.

TL;DR

This study tests whether exposure to violent crime while walking or waiting for public transit on the way to school predicts absenteeism among Baltimore first-time freshmen. It finds higher walking violence exposure is associated with more absences, while on-bus violent exposure and property-crime exposure are not.

Key findings

  • Among approximately 4,200 first-time freshmen in Baltimore City public high schools in 2014–2015, a 1% increase in walking violence exposure predicted a 0.10% change in attendance in model 1; in the final model with school-choice indicators and neighborhood fixed effects, doubling walking violence exposure predicted about 6% more absences, roughly one additional day at the average absence level of 19.6 days.
  • Exposure to violent crime while riding the bus or train was not associated with attendance (coefficients small and imprecise, e.g., -0.005 and 0.004 in one model), and property-crime exposure was not substantially or precisely related (β = 0.001, s.e. = 0.002; β = 0.009, s.e. = 0.03).
  • Results held after adjusting for demographics, prior attendance, violent crime near home and school, school preferences, and neighborhood fixed effects; a placebo test found no relationship between ninth-grade walking violence exposure and prior-year attendance (β = 0.03, s.e. = 0.03; β = 0.03, s.e. = 0.02).
  • Average estimated commute was about 36 minutes; students missed about 20 of 179 days on average; only about 8% were estimated to walk to school without public transit.

Methods

  • Used administrative data for approximately 4,200 first-time ninth graders in Baltimore City schools, 2014–2015, including home and school addresses, attendance, demographics, and high-school-choice forms.
  • Built a transit network from Maryland Transit Administration GTFS data in ArcGIS 10.4.1 to estimate each student’s most efficient public-transit route to school; linked route street segments and stops to Baltimore Police Department incident-level Part I crime data, separating walking/waiting exposure from on-bus exposure.
  • Estimated regressions of annual absences on inverse hyperbolic sine (IHS)-transformed crime exposure, adjusting for prior absences, commute characteristics, demographics, school-choice indicators, and neighborhood fixed effects; compared violent with property crime and ran robustness checks.

Limitations

  • The paper states the analytic sample was limited to first-time freshmen stably enrolled in one school, excluding higher-absenteeism mobile or retained students, so estimates may understate the relationship; exposure is measured along estimated routes, not actual behavior; annual timing cannot test event-specific effects; unobserved bias cannot be entirely eliminated.
  • Evident from the text: the design is observational and cannot establish causality; route estimates may not match students’ actual routes; crime data include all times and days and do not confirm student awareness or witnessing; findings are specific to Baltimore’s 2014–2015 school-choice and transit context.

Model-written summary. Check it against the paper before relying on it.

Integrity signals

Registry

Crossref · 10.15195/v6.a5

Cites

52 of 52 references verified0 citation errors · 0 not checkable

Statistics

Nothing to recompute.

Claims the paper makes

0 of 29 cited sources checked · 0 supported · 0 flagged

  • Findingown data

    Absenteeism is not associated with exposure to dangerous streets while riding on public transit.

    “We also show that absenteeism is not associated with exposure to dangerous streets while riding on public transit and exposure to property crime.”
    • Also stated in: § Abstract · plain
      “Exposure to dangerous streets while riding on public transit and exposure to high-property-crime streets are not associated with absenteeism.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Students are more sensitive to their surroundings while walking on the street or waiting for public transit than while sitting inside a bus or train.
    • Rests on an assumption: The most efficient route estimated using ArcGIS and GTFS represents the route students actually take to school.
    • Rests on an assumption: Officially reported crime incidents on street segments reflect the violent crime exposure that students experience or perceive.
    • Rests on an assumption: The home address of record is the student's actual residence.
  • Findingown data

    Students whose estimated routes require walking to and waiting at transit stops along streets with higher violent-crime rates have higher rates of absenteeism throughout the year.

    “We find that students whose estimated routes require walking along streets with higher violent-crime rates have higher rates of absenteeism throughout the year.”
    • Also stated in: § Abstract · plain
      “We find that students whose estimated routes require walking to and waiting at transit stops along streets with higher violent-crime rates have higher rates of absenteeism throughout the year.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The most efficient route estimated using ArcGIS and GTFS represents the route students actually take to school.
    • Rests on an assumption: Officially reported crime incidents on street segments reflect the violent crime exposure that students experience or perceive.
    • Rests on an assumption: The home address of record is the student's actual residence.
    • Rests on an assumption: Absenteeism results from exposure to danger on the way to school.
  • Findingown data

    Absenteeism is not associated with exposure to property crime.

    “We also show that absenteeism is not associated with exposure to dangerous streets while riding on public transit and exposure to property crime.”
    • Also stated in: § Abstract · plain
      “Exposure to dangerous streets while riding on public transit and exposure to high-property-crime streets are not associated with absenteeism.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Property crimes do not present as immediate a physical threat to victims or witnesses and cause less stress during students' commute than violent crimes.
    • Rests on an assumption: The most efficient route estimated using ArcGIS and GTFS represents the route students actually take to school.
    • Rests on an assumption: Officially reported crime incidents on street segments reflect the violent crime exposure that students experience or perceive.
    • Rests on an assumption: The home address of record is the student's actual residence.
  • Findingown data

    In the first model including all observed covariates, a 1 percent change in walking violence exposure predicts (on average) a 0.10 percent change in attendance.

    “Nevertheless, a 1 percent change in walking violence exposure predicts (on average) a 0.10 percent change in attendance.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The estimated public transportation routes to school are a reasonable proxy for students' actual routes and exposure.
  • Findingown data

    The predicted association between walking violence exposure and attendance is larger than the predicted association between neighborhood violence and attendance (β = 0.06).

    “This is larger than the predicted association between neighborhood violence and attendance (see Table B2 in the online supplement; β = 0.06).”
    Quote in the paperNot a substantial claim
  • May combine two claims. 25
  • Classified as probably not a substantial claim. 19

  • May combine two claims.

    Findingown data

    High-violent-crime transit stops are relatively equally distributed across the city, including a number of unsafe stops in whiter, wealthier, generally low-crime areas north of downtown.

    “Interestingly, the high-violent-crime transit stops are relatively equally distributed across the city, including a number of unsafe stops in the whiter, wealthier, and generally low-crime areas to the north of downtown.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Students excluded from the analysis tended to have slightly shorter commutes and were somewhat more likely to live within walking distance of their schools.

    “Students who were excluded from the analysis tended to have slightly shorter commutes and were somewhat more likely to live within walking distance of their schools.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingnone given

    Violent-crime exposure in all locations is highly variable and quite skewed, with the mean often being equal to the standard deviation.

    “Violent-crime exposure in all locations is highly variable and quite skewed, with the mean often being equal to the standard deviation.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing). May combine two claims.

    Findingown data

    Many students were estimated to require a transfer, resulting in an average of 1.8 stops.

    “Many students were estimated to require a transfer, which results in an average of 1.8 stops.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    On average, students in the sample were estimated to take approximately 36 minutes to get to school.

    “On average, students in the sample were estimated to take approximately 36 minutes to get to school.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The estimate for walking violence exposure in the first model is very precise, with a standard error of less than 0.02.

    “The estimate is very precise, with a standard error (s.e.) of less than 0.02.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Students pass streets on public transit where there were 41 violent crimes during the school year, on average, and pass streets on foot where there were 27 violent crimes.

    “Students pass streets on public transit where there were 41 violent crimes during the school year, on average, and pass streets on foot where there were 27 violent crimes.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    The coefficient for walking violence exposure is larger for students who do not attend their first-choice schools (β = 0.09).

    “Although the coefficient for walking violence exposure is larger for students who do not attend their first-choice schools (β = 0.09)”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Although Harlem Park as a whole is quite violent, the violence is concentrated in a relatively small number of street segments.

    “The figure shows that although the neighborhood as a whole is quite violent, the violence is concentrated in a relatively small number of street segments.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    The coefficients for bus/train and school violent crime exposure are very small (-0.005 and 0.004, respectively) and very imprecise.

    “The coefficients are very small (-0.005 and 0.004, respectively) and very imprecise.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Findingown data

    Exposure to violent crime while riding the bus or train and violent crime around school are not associated with any change in attendance.

    “On the other hand, exposure to violent crime while riding the bus or train and violent crime around school are not associated with any change in attendance.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Findingown data

    Only around 8 percent of students were estimated to walk to school without taking public transit.

    “Only around 8 percent of students were estimated to walk to school without taking public transit.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingnone given

    Crime in lower-violence neighborhoods is less frequent but is also concentrated in a few city blocks.

    “Crime in lower-violence neighborhoods is less frequent but is also concentrated in a few city blocks.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Findingown data

    Including school-choice indicators and neighborhood fixed effects decreases the precision of the estimates and reduces the estimated walking violence exposure coefficient to 0.06.

    “Including these adjustments decreases the precision of the estimates and reduces the estimated walking violence exposure coefficient to 0.06.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The estimated public transportation routes to school are a reasonable proxy for students' actual routes and exposure.

  • May combine two claims.

    Findingreasoning

    The study compares students over the whole school year, and it is therefore impossible to test whether absenteeism increases in response to a specific event.

    “This study compares students over the whole school year, and it is therefore impossible to test whether absenteeism increases in response to a specific event.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Districts across the country are cutting back on traditional yellow-bus chartered transportation systems due to cost and instead relying on public transportation systems to get kids to school.

    “At the same time, districts across the country are cutting back on traditional”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Urban Institute 2017

      Not checked in the free run.

    • cites Vincent et al. 2014

      Not checked in the free run.

  • May combine two claims.

    Premisereasoning

    The mechanisms that generated the effects of neighborhood violence on academic outcomes are often conjectural and have not been evaluated with empirical evidence.

    “The mechanisms that generated these effects are often conjectural and have not been evaluated with empirical evidence.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationown data

    The lack of relationship between violent-crime exposure in ninth grade and attendance in eighth grade suggests that the timing of exposure matters and provides more evidence that the results are not driven solely by selection bias.

    “On the other hand, if there is no relationship, it suggests that the timing of exposure matters and, therefore, provides more evidence that the results are not driven solely by selection bias.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: Conditional on school preferences and neighborhood fixed effects, assignment to schools (and thus commutes) is not driven by unobserved student characteristics that also affect attendance.

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Using choice indicators allows comparison of students who list the same schools and presumably represent similar sets of geographic and academic preferences.

    “These choice indicators allow us to compare students who list the same schools and presumably represent similar sets of geographic and academic preferences.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    When youth are confined to a bus or train, they are not directly exposed as targets in the same way as if they were standing on or walking along a sidewalk.

    “They are also not directly exposed as targets in the same way they might be if they were standing on or walking along a sidewalk.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Study design and conductreasoning

    Adjusting for each school listed on high-school-choice applications reduces bias associated with different school preferences, though it does not account for all confounding between students.

    “Although this will not account for all confounding between students, it likely reduces the bias associated with different school preferences.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Crime counts in neighborhoods around schools and residences are higher than those along specific commuting routes because the number of crimes in each location is related to the size of the area in which it is measured.

    “Crime counts in neighborhoods around schools and residences are higher than those along specific commut-ing routes because the number of crimes in each location is related to the size of the area in which it is measured.”
    Quote in the paperNot a substantial claim

  • Probably not a substantial claim: may not pass S3 (load-bearing).

    Premisecitation

    The problem of chronic absenteeism is especially pronounced among high-school-aged and urban youth.

    “The problem of chronic absenteeism is especially pronounced among high-school-aged and urban youth (Balfanz and Byrnes 2012).”
    Quote in the paperNot a substantial claim0 of 1 sources checked
    • cites Balfanz and Byrnes 2012

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    The patterns of concentrated violence shown for Harlem Park are quite typical of other high-crime neighborhoods in the city.

    “The patterns shown here are quite typical of other high-crime neighborhoods in the city.”
    Quote in the paperNot a substantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Including an indicator for whether students attend their first-choice school adjusts for the influence of school desirability on willingness to attend every morning.

    “Because a school's desirability may influence students' willingness to be there every morning, we also include an indicator for whether students attend their first-choice school.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Study design and conductreasoning

    Limiting the analysis to first-time freshmen stably enrolled in a single high school avoids confounding between school mobility, grade retention, exposure to violence, and attendance.

    “This avoids confounding between school mobility, grade retention, exposure to violence, and attendance.”
    Quote in the paperSubstantial claim

  • Classified as probably not a substantial claim.

    Premisecitation

    The literature on adult commuting has established an association between increased transit difficulty and increased absenteeism from work.

    “Given that the literature on adult commuting has established an association between increased transit difficulty and increased absenteeism from work (Kluger and Reich 1998;Koslowsky et al. 1995;Wener et al. 2003)”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Kluger and Reich 1998 source

      Not checked in the free run.

    • cites Koslowsky et al. 1995 source

      Not checked in the free run.

    • cites Wener et al. 2003 source

      Not checked in the free run.

  • Probably not a substantial claim: may not pass S3 (load-bearing), S4 (based).

    Interpretationnone given

    When youth are confined to a bus or train, they are indirectly supervised by the driver and other adults on the bus or train.

    “Furthermore, when youth are confined to a bus or a train, they are being indirectly supervised by both the driver and the other adults on the bus or train.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Chronic absenteeism has been linked to lower achievement, course failure, student disengagement, and increased risk of dropping out of high school.

    “Chronic absenteeism has been linked to a number of negative outcomes for students, including lower achievement, course failure, and ultimately student disengagement and increased risk of dropping out of high school (Allensworth and Easton 2007;Allensworth et al. 2014; Balfanz and Byrnes 2012; Balfanz, Herzog, and Mac Iver 2007; Gottfried 2010; Mac Iver and Mac Iver 2010; Ready 2010).”
    Quote in the paperNot a substantial claim0 of 3 sources checked
    • cites Allensworth and Easton 2007

      Not checked in the free run.

    • cites Balfanz and Byrnes 2012

      Not checked in the free run.

    • cites Gottfried 2010 source

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    The lack of effect of property crimes in the same locations suggests that general crime or police presence is not the issue but that the risk of physical victimization associated with violent crime is.

    “Finally, the lack of effect of property crimes in the same locations suggests that general crime or police presence is not the issue but that the risk of physical victimization associated with violent crime is.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Premisecitation

    Because transit rarely brings students door to door, walking and waiting are inherently part of the experience, and even if the travel times are comparable, unreliable bus schedules require building in extra wait times to ensure on-time arrival.

    “Because transit rarely brings students door to door, walking and waiting are inherently part of the experience, and even if the travel times are comparable, unreliable bus schedules require building in extra wait times to ensure on-time arrival (Koslowsky et al. 1995).”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Koslowsky et al. 1995 source

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Premisecitation

    Accounting for the commute to school is increasingly important in the context of increasing school choice and open enrollment policies that require many students to travel long distances to get to school.

    “Accounting for the commute to school is increasingly important in the context of increasing school choice and open enrollment policies that require many students to travel long distances to get to school (Shedd 2015;Stein et al. 2017).”
    Quote in the paperSubstantial claim0 of 2 sources checked
    • cites Shedd 2015

      Not checked in the free run.

    • cites Stein et al. 2017

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    In cities like Baltimore, selective-enrollment, magnet, and charter schools are often located outside of highly disadvantaged neighborhoods, necessitating longer commutes for the disadvantaged students who are most likely to be taking public transportation.

    “Second, in cities like Baltimore, selective-enrollment, magnet, and charter schools are often located outside of highly disadvantaged neighborhoods, necessitating longer commutes for the disadvantaged students who are most likely to be taking public transportation (Burdick-Will, Keels, and Shuble 2013;Lubienski, Gulosino, and Weitzel 2009;Stein et al. 2017).”
    Quote in the paperSubstantial claim0 of 3 sources checked
    • cites Burdick-Will, Keels, and Shuble 2013 source

      Not checked in the free run.

    • cites Lubienski, Gulosino, and Weitzel 2009 source

      Not checked in the free run.

    • cites Stein et al. 2017

      Not checked in the free run.

  • May combine two claims.

    Premisecitation

    Racially and economically disadvantaged students are more likely to live in urban areas, and urban areas are more likely to have mass transit systems that students use to get to school.

    “First, racially and economically disadvantaged students are more likely to live in urban areas (Orfield and Lee 2005), and urban areas are more likely to have mass transit systems that students use to get to school.”
    Quote in the paperSubstantial claim0 of 1 sources checked
    • cites Orfield and Lee 2005

      Not checked in the free run.

  • Classified as probably not a substantial claim.

    Interpretationreasoning

    Students who show disengagement from school in other ways, such as grade repetition and mobility, are potentially more sensitive to influences that limit attendance.

    “Students who show disengagement from school in other ways, such as grade repetition and mobility, are potentially more sensitive to influences that limit attendance.”
    Quote in the paperSubstantial claim

  • Probably not a substantial claim: may not pass S2 (checkable).

    Interpretationreasoning

    Every day these students must weigh the incremental benefit of one more day at school against the possibility of real physical injury or death on the way to school.

    “Every day these students must weigh the incremental benefit of one more day at school against the possibility of real physical injury or death on the way to school.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The estimated public transportation routes to school are a reasonable proxy for students' actual routes and exposure.

  • Probably not a substantial claim: may not pass S2 (checkable), S3 (load-bearing).

    Interpretationreasoning

    Although the inclusion of covariates, such as last year's attendance, exposure to violence in other settings, and school preferences, limits that concern, it can never be entirely eliminated.

    “Although the inclusion of covariates, such as last year's attendance, exposure to violence in other settings, and school preferences, limits that concern, it can never be entirely eliminated.”
    Quote in the paperNot a substantial claim

  • May combine two claims.

    Premisecitation

    Baltimore City provides middle-and high-school students with public transit passes, but nearby suburban Baltimore County provides a charter bus service.

    “For example, Baltimore City provides middle-and high-school students with public transit passes, but nearby suburban Baltimore County provides a charter bus service (Baltimore City Public Schools 2018; Baltimore County Public Schools 2016).”
    Quote in the paperNot a substantial claim0 of 2 sources checked
    • cites Baltimore City Public Schools 2018

      Not checked in the free run.

    • cites Baltimore County Public Schools 2016

      Not checked in the free run.

  • May combine two claims.

    Interpretationown data

    Based on the final model, when walking violence exposure doubles, we predict an approximately 6 percent increase in absenteeism, which translates to an additional day of absence for the average attender.

    “Based on our final model, when walking violence exposure doubles, we predict an approximately 6 percent increase in absenteeism, which translates to an additional day of absence for the average attender.”
    Quote in the paperSubstantial claim
    • Rests on an assumption: The estimated public transportation routes to school are a reasonable proxy for students' actual routes and exposure.

  • May combine two claims.

    Interpretationreasoning

    Students who do not take their estimated routes are combined with those who do, and this likely weakens the measure of actual exposure.

    “Students who do not take their estimated routes are therefore combined with those who do, and this likely weakens the measure of actual exposure.”
    Quote in the paperSubstantial claim

  • May combine two claims.

    Interpretationreasoning

    Avoiding danger on the way to school may mean taking a longer-than-necessary route to school, waiting for an unreliable ride to a safer bus stop, or avoiding the trip to school altogether.

    “Avoiding danger on the way to school may mean taking a longer-than-necessary route to school, waiting for an unreliable ride to a safer bus stop, or avoiding the trip to school altogether.”
    Quote in the paperNot a substantial claim
    • Rests on an assumption: The estimated public transportation routes to school are a reasonable proxy for students' actual routes and exposure.
41 of 41

  • The paper relies on this without support.

    Little is known about exactly what it is about disadvantaged neighborhoods that increases absenteeism.

    “but little is known about exactly what it is about disadvantaged neighborhoods that increases absenteeism.”

  • The paper relies on this without support.

    Students are more sensitive to their surroundings while walking on the street or waiting for public transit than while sitting inside a bus or train.

    “We expect that students will be more sensitive to their surroundings while walking on the street or waiting for public transit than while they are sitting inside the bus or train.”

  • The paper relies on this without support.

    Property crimes do not present as immediate a physical threat to victims or witnesses and cause less stress during students' commute than violent crimes.

    “These crimes do not present as immediate a physical threat to victims or witnesses, and therefore, we would expect them to cause less stress during students' commute.”

  • The paper relies on this without support.

    The most efficient route estimated using ArcGIS and GTFS represents the route students actually take to school.

    “to estimate the most efficient specific, street-by-street route to school for every student.”

  • The paper relies on this without support.

    Officially reported crime incidents on street segments reflect the violent crime exposure that students experience or perceive.

    “We capture safety along the estimated route to school using officially reported incidents of violent crime.”

  • The paper relies on this without support.

    The home address of record is the student's actual residence.

    “Using the GTFS data, we calculated routes from each residential address of record to the enrolled school location with an arrival time before the start of school.”

  • The paper relies on this without support.

    Absenteeism results from exposure to danger on the way to school.

    “we propose and test a mechanism for the effect of neighborhood of residence on school outcomes: absenteeism that results from exposure to danger on the way to school.”

  • The paper relies on this without support.

    The estimated public transportation routes to school are a reasonable proxy for students' actual routes and exposure.

    “Violentcrime exposure is measured along the students' estimated public transportation routes to school, not their actual behavior.”

  • The paper relies on this without support.

    Conditional on school preferences and neighborhood fixed effects, assignment to schools (and thus commutes) is not driven by unobserved student characteristics that also affect attendance.

    “This means that the models compare two students who live in the same neighborhood and wanted to go the same set of schools but were assigned different schools and therefore commutes by the school-choice system.”
9 of 9

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

Each contradiction between two passages listed here was confirmed by an independent check.

  • Interaction sign conflicts with the claimed first-choice-school comparison

    Minor

    The interaction is defined using an indicator for attending a first-choice school. If the coefficient is 0.09 for students who do not attend their first-choice school, the reported positive interaction of 0.06 implies a larger coefficient, 0.15, for those who do-not the reverse.

1 of 1

Cites

Flagged first, then by citations

Research works printed but not linked: 10

  1. Allensworth, Elaine M., and John Q. Easton. 2007. What Matters for Staying On-Track and Graduating in Chicago Public High Schools: A Close Look at Course Grades, Failures, and Attendance in the Freshman Year. Research Report. Chicago, IL: University of Chicago Consortium on Chicago School Research.
  2. Allensworth, Elaine M., Julia A. Gwynne, Paul Moore, and Marisa de la Torre. 2014. Looking Forward to High School and College: Middle Grade Indicators of Readiness in Chicago Public Schools. Chicago, IL: University of Chicago Consortium on Chicago School Research.
  3. Harris, Douglas N., and Matthew Larsen. 2014. What Schools Do Families Want (and Why)? School Demand and Information before and after the New Orleans post-Katrina School Reforms.†New Orleans, LA: Education Research Alliance for New Orleans.
  4. Kozol, Jonathan. 1991. Savage Inequalities: Children in Americaís Schools. New York, NY: Broadway Books.
  5. Mac Iver, Martha Abele, and Douglas J. Mac Iver. 2010. Keeping on Track in Ninth Grade and Beyond: Baltimoreís Ninth Graders in 2007-08. Baltimore, MD: Baltimore Education Research Consortium.
  6. Schwartz, Amy Ellen, Agustina Laurito, Johanna Lacoe, Patrick Sharkey, and Ingrid Gould Ellen. 2016.†The Academic Effects of Chronic Exposure to Neighborhood Violence†(No. 195). Syracuse, NY: Center for Policy Research and The Maxwell School of Syracuse University.
  7. Shedd, Carla. 2015.†Unequal City: Race, Schools, and Perceptions of Injustice. New York, NY: Russell Sage Foundation.
  8. St. Jean, Peter K. 2008.†Pockets of Crime: Broken Windows, Collective Efficacy, and the Criminal Point of View. Chicago, IL: University of Chicago Press.
  9. Stein, Marc L., Jeffrey Grigg, Curt Cronister, Celeste Chavis, and Faith Connolly. 2017. Getting to High School in Baltimore: Student Commuting and Public Transportation. Baltimore, MD: Baltimore Education Research Consortium.
  10. Vincent, Jeffrey, Carrie Makarewicz, Ruth Miller, Julia Ehrman, and Deborah McKoy. 2014. Beyond the Yellow Bus: Promising Practices for Maximizing Access to Opportunity through Innovations in Student Transportation. Berkeley, CA: Center for Cities and Schools.

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Related

Authors
Burdick-Will, J., Stein, M., & Grigg, J.
Affiliations
Johns Hopkins University (US)
ORCID
Venue
Sociological Science
Publisher
Society for Sociological Science
Published
2019
Volume
6
Pages
118-142
ISSN
2330-6696 (ISSN-L)
DOI
10.15195/v6.a5
OpenAlex
W2912294567
Semantic Scholar
d4197021b588709405f12699001fd250ab11b36d
Cited by
81
References
52
Citations by year
2026: 9 · 2025: 9 · 2024: 17 · 2023: 6 · 2022: 17 · 2021: 10 · 2020: 9 · 2019: 4
FWCI
20.14
Type
journal article
Language
en
Open access
Licence
cc-by
Locations
  • Sociological Science (publishedVersion, cc-by)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
Funders
  • Spencer Foundation
  • Johns Hopkins University
  • Hopkins Population Center, Johns Hopkins University
Topics
  • Social Sciences › Sociology and Political Science › Crime Patterns and Interventions
  • Social Sciences › Education › School Choice and Performance
  • Social Sciences › Sociology and Political Science › Urban, Neighborhood, and Segregation Studies
Keywords
school absenteeism, public transport, neighborhood effects, property crime, student attendance, high school students, neighborhood selection, school choice

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Claims read by deepseek-v4.1-flash under definition version 3 (extract-v3) on 2026-10-09.

Classified by jev-1.13-20260917 (gold-guideline-v0.2) on 2026-10-09.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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