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
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.
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.
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.
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.
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.
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.
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.
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.
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.
We thank an anonymous reviewer for this astute observation.
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.
References
Adelman, C. (2006). The toolbox revisited: Paths to degree completion from high school through college. US Department of Education.
Alm, J., & Winters, J. V. (2009). Distance and intrastate college student migration. Economics of Education Review,28(6), 728–738.
Altonji, J. G., & Mansfield, R. (2018). Estimating group effects using averages of observables to control for sorting on unobservables: School and neighborhood effects. American Economic Review,108(10), 2902–2946.
Altonji, J. G., & Mansfield, R. (2011). The role of family, school, and community characteristics in inequality in education and labor-market outcomes. In G. Duncan & R. Murnane (Eds.), Whither opportunity? Rising inequality, schools, and children’s life chances (pp. 339–358). New York: Russell Sage.
Alvarado, S. E., & Turley, R. N. L. (2012). College-bound friends and college application choices: Heterogeneous effects for Latino and White students. Social Science Research,41(6), 1451–1468.
Bacher-Hicks, A., Billings, S. B., & Deming, D. J. (2019). The school to prison pipeline: Long-run impacts of school suspensions on adult crime (No. w26257). National Bureau of Economic Research.
Betts, J. R., & Mcfarland, L. L. (1995). Safe port in a storm: The impact of labor market conditions on community college enrollments. The Journal of Human Resources,30(4), 741–765.
Borman, G., & Dowling, M. (2010). Schools and inequality: A multilevel analysis of Coleman’s equality of educational opportunity data. Teachers College Record,112(5), 1201–1246.
Bozick, R. (2009). Job opportunities, economic resources, and the postsecondary destinations of American Youth. Demography,46(3), 493–512.
Bozick, R., & DeLuca, S. (2011). Not making the transition to college: School, work, and opportunities in the lives of American Youth. Social Science Research,40(4), 1249–1262.
Byun, S., Meece, J. L., & Agger, C. A. (2017). Predictors of college attendance patterns of rural youth. Research in Higher Education,58, 817–842.
Byun, S., Meece, J. L., Irvin, M. J., & Hutchins, B. C. (2012). Rural-nonrural disparities in postsecondary educational attainment revisited. American Educational Research Journal,49(3), 412–437.
Coleman, J. S., Campbell, E. Q., Hobson, C. J., McPartland, P., Mood, A. M., Weinfeld, F. D., et al. (1966). Equality of educational opportunity. Washington, DC: Government Printing Office.
Cox, B. E., McIntosh, K., Reason, R. D., & Terenzini, P. T. (2014). Working with missing data in higher education research: A primer and real-world example. The Review of Higher Education,37(3), 377–402.
Cullen, J. B., Jacob, B. A., & Levitt, S. (2006). The effect of school choice on participants: Evidence from randomized lotteries. Econometrica,74(5), 1191–1230.
Deming, D. J. (2011). Better schools, less crime? Quarterly Journal of Economics,126(4), 2063–2115.
Deming, D. J., Hastings, J. S., Kane, T. J., & Staiger, D. O. (2014). School choice, school quality, and postsecondary attainment. American Economic Review,104(3), 991–1013.
Downey, D. B., & Condron, D. J. (2016). Fifty years since the Coleman Report: Rethinking the relationship between schools and inequality. Sociology of Education,89(3), 207–220.
Duncan, B. (1965). Dropouts and the Unemployed. Journal of Political Economy,73(2), 121–134.
Dynarski, S. M., Hemelt, S. W., & Hyman, J. M. (2015). The missing manual: Using National Student Clearinghouse data to track postsecondary outcomes. Educational Evaluation and Policy Analysis,37, 53–79.
Engberg, M. E., & Gilbert, A. J. (2014). The counseling opportunity structure: Examining correlates of 4-year college-going rates. Research in Higher Education,55(3), 219–244.
Engberg, M. E., & Wolniak, G. C. (2010). Examining the effects of high school contexts on postsecondary enrollment. Research in Higher Education,51(2), 132–153.
Frenette, M. (2009). Do universities benefit local youth? Evidence from the creation of new universities. Economics of Education Review,28(3), 318–328.
Galster, G. C., & Killen, S. P. (1995). The geography of opportunity: A reconnaissance and conceptual framework. Housing Policy Debate,6(1), 7–43.
Galster, G., & Sharkey, P. (2017). Spatial foundations of inequality: A conceptual model and empirical overview. RSF: The Russell Sage Foundation Journal of the Social Sciences,3(2), 1–33.
Guzman, G. G. (2017). Household Income: 2016 American Community Survey Briefs. Washington, DC: United States Census Bureau.
Heckman, J. J., Stixrud, J., & Urzua, S. (2006). The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior. Journal of Labor Economics,24(3), 411–482.
Hill, L. D. (2008). School strategies and the "college-linking" process: reconsidering the effects of high schools on college enrollment. Sociology of Education,81(1), 53–76.
Hillman, N. W. (2016). Geography of college opportunity: The case of education deserts. American Educational Research Journal,53(4), 987–1021.
Hillman, N. W., & Orians, E. L. (2013). Community Colleges And Labor Market Conditions: How does enrollment demand change relative to local unemployment rates? Research in Higher Education,54(7), 765–780.
Jackson, C. K. (2018). What do test scores miss? The importance of teacher effects on non-test score outcomes. Journal of Political Economy,126(5), 2072–2107.
Jencks, C., Smith, M., Acland, H., Bane, M. J., Cohen, D., Gintis, H., Heyns, B., & Michelson, S. (1972). Inequality: A reassessment of the effect of family and schooling in America. Basic Books.
Jennings, J. L., Deming, D., Jencks, C., Lopuch, M., & Schueler, B. E. (2015). Do Differences in school quality matter more than we thought? New evidence on educational opportunity in the twenty-first century. Sociology of Education,88(1), 56–82.
Kantor, H., & Lowe, R. (2013). Educationalizing the welfare state and privatizing education: The evolution of social policy since the new deal. In P. L. Carter & K. G. Welner (Eds.), Closing the opportunity gap: What America must do to give every child an even chance (pp. 25–39). Oxford: Oxford University Press.
Katz, M. B. (2010). Public education as welfare. Dissent,57(3), 52–56.
Klasik, D., Blagg, K., & Pekor, Z. (2018). Out of the education desert: How limited local college options are associated with inequity in postsecondary opportunities. Social Sciences,7(9), 165–190.
Klugman, J. (2012). How resource inequalities Among high schools reproduce class advantages in college destinations. Research in Higher Education,53(8), 803–830.
Klugman, J., & Lee, J. C. (2019). Social closure, school socioeconomic composition, and inequality in college enrollments. Social Science Research,80, 156–185.
Lapid, P. A. (2016). Expanding college access: The impact of new universities on local enrollment. Job Market Paper.
Leppel, K. (1993). Logit estimation of a gravity model of the college enrollment decision. Research in Higher Education,34(3), 387–398.
Manoli, D., & Turner, N. (2018). Cash-on-hand and college enrollment: Evidence from population tax data and the earned income tax credit. American Economic Journal: Economic Policy,10(2), 242–271.
McDonough, P. M. (1997). Choosing colleges: How social class and schools structure opportunity. Albany, NY: State University of New York Press.
Morgan, S. L., & Jung, S. B. (2016). Still No Effect of Resources, Even in the New Gilded Age?. RSF: The Russell Sage Foundation. Journal of the Social Sciences, 2(5), 83–116.
Michelmore, K., & Dynarski, S. (2017). The gap within the gap: Using longitudinal data to understand income differences in educational outcomes. AERA Open, 3(1), 2332858417692958.
Morsy, L., & Rothstein, R. (2015). Five social disadvantages that depress student performance: Why schools alone can't close achievement gaps. Report. Washington, DC: Economic Policy Institute.
National Center for Education Statistics. (2018). ‘Fast Facts: State-by-State Rankings.’ Retrieved December 4, 2018, from https://nces.ed.gov/fastfacts/display.asp?id=62.
Raudenbush, S. W., & Willms, J. (1995). The estimation of school effects. Journal of Educational and Behavioral Statistics,20(4), 307–335.
Rivkin, S. G. (1995). Black/white differences in schooling and employment. The Journal of Human Resources,30(4), 826–852.
Rouse, C. (1995). Democratization or diversion? The effects of community colleges on educational attainment. Journal of Business and Economic Statistics,13(2), 217–224.
Rumberger, R., & Palardy, G. (2005). Test scores, dropout rates, and transfer rates as alternative indicators of high school performance. American Educational Research Journal,42(1), 3–42.
Smeeding, T., & Thévenot, C. (2016). Addressing child poverty: How does the United States compare with other nations? Academic Pediatrics,16(3 Suppl), S67–S75.
Sutton, A. (2017). Preparing for local labor: Curricular stratification across local economies in the United States. Sociology of Education,90(2), 172–196.
Sutton, A., Bosky, A., & Muller, C. (2016). Manufacturing gender inequality in the new economy: High school training for work in blue-collar communities. American Sociological Review,81(4), 720–748.
Tinto, V. (1973). College proximity and rates of college attendance. American Educational Research Journal,10(4), 277–293.
Turley, R. N. L. (2009). College proximity: Mapping access to opportunity. Sociology of Education,82(2), 126–146.
Turley, R. N. L. (2006). When parents want children to stay home for college. Research in Higher Education,47(7), 823–846.
U.S. Department of Agriculture. (2015). Child nutrition programs—income eligibility guidelines. Federal Register, 80(190), 9–10.
Whitehurst, G. J. (2016). Family Support or School Readiness? Contrasting Models of Public Spending on Children's Early Care and Learning. Evidence Speaks Reports, Vol 1, #16. Center on Children and Families at Brookings.
Willms, J. D., & Raudenbush, S. W. (1989). A longitudinal hierarchical linear model for estimating school effects and their stability. Journal of Educational Measurement,26(3), 209–232.
Woods, C. S., & Domina, T. (2014). The school counselor caseload and the high school-to-college pipeline. Teachers College Record,116(10), 1–30.
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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DOI: https://doi.org/10.1007/s11162-020-09599-4