Well-Placed: The Geography of Opportunity and High School Effects on College Attendance

Abstract

Recent work has broadened the scope of school effectiveness research to consider not only academic achievement but also other outcomes, especially college attendance. This literature has argued that high schools are an important determinant of college attendance, with some contending that high schools matter more for college attendance than for academic achievement. A separate branch of research has illustrated how place-based opportunities facilitate college attendance. We merge these two literatures by asking if schools’ geographic context can explain apparent variation in effectiveness among Wisconsin high schools. We find that geographic context explains more than a quarter of the variance in traditional estimates of school effectiveness on college attendance, because factors like proximity to colleges are strongly associated with college attendance. Accounting for geography is therefore important in order not to overstate high schools’ role in higher education outcomes. Results are based on multilevel models applied to rich administrative data on every Wisconsin public high school entrant between 2006 and 2011.

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Notes

  1. 1.

    In this paper, we use 4-year college to denote any institution of postsecondary education that grants bachelor’s degrees, including those that also grant other types of degrees.

  2. 2.

    We use Willms and Raudenbush’s (1989) taxonomy of Type A (total) effects and Type B (school practice) effects but use a different name for each type, for conceptual clarity.

  3. 3.

    Both race and sex are very slightly time-variant in these data. We use each student’s modal race and sex over the observed years.

  4. 4.

    Among those  receiving FRPL, those who receive it for more years tend to have lower family incomes (Michelmore and Dynarski 2017). Michelmore and Dynarski also find a negative correlation between test scores and years of FRPL receipt. In our own data, we observe a large gradient in both test scores and college attendance across students’ years on FRPL. One cannot account for these gradients when measuring FRPL receipt at a single point in time.

  5. 5.

    We define blue-collar occupations in line with recent work (Sutton et al. 2016; Sutton 2017) as the Census-defined categories natural resources, construction, maintenance, production, transportation, and material moving occupations.

  6. 6.

    A considerable literature in sociology and economics has demonstrated the importance of the immediate neighborhood environment (e.g. within Census tracts) in producing educational attainment and achievement. We are limited to using measures of larger areas defined by school districts and counties because we do not have access to students’ addresses. However, this limitation is not a threat to our conclusions in that the omission of neighborhood-level factors necessarily leads to us underestimating the importance of geographic context in our analyses.

  7. 7.

    We use a linear probability model for our models of college attendance following similar recent work (Jennings et al. 2015). However, all results presented in this paper are substantively the same when using hierarchical logistic regression models (results available upon request). For ease of interpretation, we show results from linear models only.

  8. 8.

    The cubic specification reduces the Bayesian Information Criterion of our full model by 4434 relative to the quadratic specification and by 9122 relative to the linear specification. Therefore, including cubic transformations of test scores improves model fit to a dramatic extent that far outweighs the loss in parsimony.

  9. 9.

    This nonlinearity is theoretically compelling: we expect that a unit increase in distance to the nearest college will matter less the farther a student is from a college, since the student will probably have to move away from home whether she is, for example, 70 or 100 miles away from a college, but the same is not true in the case of 5 versus 35 miles. We also find that including the squared term significantly improves model fit: it reduces the Bayesian Information Criterion of the college attendance model by 38 compared to the model with a linear term alone. Most students are much closer to a 2-year college and so including a squared term for the distance to a 2-year college does not improve the model.

  10. 10.

    In Online Appendix B, we show that the variance in school practice effect estimates with respect to test scores drops by only 5% after accounting for geographic context. Thus, if high schools seem to matter more for college attendance than for academic achievement under traditional estimates (Jennings et al. 2015), schools’ apparent importance for the two outcomes converge upon adjusting for differences in geographic context. In Online Appendix B, we also show that geographic context coefficients all have low magnitude when academic achievement is the outcome, in contrast to many of the geographic context coefficients observed when 4-year college attendance is the outcome.

  11. 11.

    We thank an anonymous reviewer for this astute observation.

  12. 12.

    Results (available upon request) from an otherwise identical model that omits all other school-level characteristics estimates that students in rural districts have a 3-percentage point 4-year college attendance disadvantage relative to non-rural districts.

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Acknowledgements

We are grateful for comments on previous drafts of this work that we received from Nick Hillman, Eric Grodsky, Jordan Conwell, Monica Grant, Leafia Ye, and participants of the 2018 Midwest Sociology of Education Conference. We are also grateful for the expertise of Carl Frederick and others at the Wisconsin Department of Public Instruction who assisted us with Wisconsin’s Statewide Longitudinal Data System. The research reported here was supported by the Institute of Education Sciences, U.S. Department of Education, through Award #R305B150003 to the University of Wisconsin-Madison. The opinions expressed are those of the authors and do not represent views of the U.S. Department of Education. This work was also supported by a grant from the U.S. Department of Education, Institute for Education Sciences to the Wisconsin Department of Public Instruction (R372A 150031). Any views, opinions, findings, or conclusions expressed in this paper are those of the authors and do not necessarily reflect the views of the Institute for Education Sciences, the Department of Public Instruction, WCER, or cooperating institutions.

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Noah Hirsch and Christian Michael Smith contributed equally and are listed alphabetically.

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Hirschl, N., Smith, C.M. Well-Placed: The Geography of Opportunity and High School Effects on College Attendance. Res High Educ 61, 567–587 (2020). https://doi.org/10.1007/s11162-020-09599-4

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Keywords

  • College attendance
  • Geography of opportunity
  • School effects
  • College proximity
  • Local labor markets