High School Course Access and Postsecondary STEM Enrollment and Attainment

Rajeev Darolia, Cory Koedel, Joyce B. Main, J. Felix Ndashimye, Junpeng Yan

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14 Scopus citations


We study the effects of access to high school math and science courses on postsecondary science, technology, engineering, and mathematics (STEM) enrollment and degree attainment using administrative data from Missouri. Our data include more than 140,000 students from 14 cohorts entering the 4-year public university system. The effects of high school course access are identified by exploiting plausibly exogenous variation in course offerings within high schools over time. We find that differential access to high school courses does not affect postsecondary STEM enrollment or degree attainment. Our null results are estimated precisely enough to rule out moderate impacts.

Original languageEnglish
Pages (from-to)22-45
Number of pages24
JournalEducational Evaluation and Policy Analysis
Issue number1
StatePublished - Mar 1 2020

Bibliographical note

Funding Information:
Darolia Rajeev University of Kentucky Koedel Cory University of Missouri https://orcid.org/0000-0002-3984-533X Main Joyce B. Purdue University Ndashimye J. Felix Vanderbilt University https://orcid.org/0000-0002-4561-8803 Yan Junpeng University of Missouri 9 2019 0162373719876923 30 1 2019 31 5 2019 7 8 2019 9 8 2019 © 2019 AERA 2019 American Educational Research Association We study the effects of access to high school math and science courses on postsecondary science, technology, engineering, and mathematics (STEM) enrollment and degree attainment using administrative data from Missouri. Our data include more than 140,000 students from 14 cohorts entering the 4-year public university system. The effects of high school course access are identified by exploiting plausibly exogenous variation in course offerings within high schools over time. We find that differential access to high school courses does not affect postsecondary STEM enrollment or degree attainment. Our null results are estimated precisely enough to rule out moderate impacts. economics of education educational policy high schools postsecondary education econometric analysis longitudinal studies regression analyses National Science Foundation https://doi.org/10.13039/100000001 1532015/1745287 and 1531920 CALDER edited-state corrected-proof Declaration of Conflicting Interests The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: We gratefully acknowledge research support from the National Science Foundation (awards 1532015/1745287 and 1531920) and CALDER, which is funded by a consortium of foundations. ORCID iDs Joyce B. Main https://orcid.org/0000-0002-3984-533X Junpeng Yan https://orcid.org/0000-0002-4561-8803 1. Also see guidance from the President’s Council of Advisors on Science and Technology (2010) , which recommends expanding the availability of advanced science, technology, engineering, and mathematics (STEM) courses in high school. Two other recent examples are, among policy and advocacy groups, Randazzo (2017) , and in the media, Deruy (2016) , which is motivated by a report from the U.S. Department of Education’s Office of Civil Rights. 2. As a concrete example of a counterpoint to the “STEM crisis” narrative, there is clear evidence of an oversupply of workers in some STEM fields and at some levels of education, most notably in the market for individuals with advanced degrees in biological sciences ( Institute of Medicine, 2014 ; Offord, 2017 ). 3. Jackson (2010 , 2014 ) studies a program designed to promote advanced placement (AP) course-taking (not STEM focused) by providing monetary incentives to teachers and students. The incentives are substantial: up to a US$10,000 salary bonus for teachers plus additional performance incentives, along with exam fee and incentive payments to students (from US$100 to US$500) who pass the AP tests. Jackson (2010 , 2014 ) finds effects on students ranging from null to positive on a variety of short-term and long-term outcomes and is generally positive about the program’s efficacy. Although useful for understanding how, and how much, targeted and resourced interventions can affect student outcomes, the near-term applicability of the findings for efforts to meaningfully scale up STEM training is modest because most schools do not have the resources to implement these types of programs. 4. Our analysis conditions on university enrollment, which means that our data are well suited to examine shifts in major choice and attainment, conditional on entry into the 4-year public university system, but ill-suited to examine effects on the extensive margin of college (i.e., attendance). Below, we test for and find no evidence to support the hypothesis that the availability of high school math and science courses affects the composition of our sample of public 4-year college enrollees (see section “Tests for Effects on College Enrollment”). 5. As noted below, 17% of the variance in course access occurs within high schools over time. 6. Because variation in course access is such a weak predictor of course-taking, our study is ultimately uninformative about policies that require additional course-taking explicitly. Evidence on the effects of mandatory course-taking is mixed. Studies suggest short-term academic benefits but evidence on longer term outcomes is less promising because such initiatives can induce dropout (e.g., Allensworth, Lee, Montgomery, & Nomi, 2009 ; Cortes, Goodman, & Nomi, 2015 ; DeCicca & Lillard, 2001 ; Jacob, Dynarski, Frank, & Schneider, 2017 ; a related literature examines high school exit exams and similarly finds negative effects on graduation: for example, Jacob, 2001 ; Jenkins, Kulick, & Warren, 2006 ; Papay, Murnane, & Willett, 2010 ). The negative effects documented in some studies of course mandates make policies that expand course access without mandatory course-taking appealing. 7. Students’ class ranks and ACT scores are determined during the treatment window (high school). A concern is that including these variables could dull the estimated coefficients of course access and course-taking. In recognition of this concern, we have estimated our models that exclude these control variables and confirmed that the results we show below are robust (results available upon request). We prefer the models that include the full suite of control variables for students because they improve precision with no indication that they substantively influence the parameters of interest. 8. C A s will be measured with error for mobile students during the late high school years because we cannot link individual students in the postsecondary and K–12 data systems, and thus cannot track individual mobility during high school. High school assignments are determined by the high school from which students graduated as coded in the higher education data system. This limitation is not unique to our study—it is also relevant for aforementioned prior studies that measure course access using peers’ course-taking. 9. Selectivity designations are based on the 2015 Carnegie Classifications of Higher Education (see http://carnegieclassifications.iu.edu ). 10. Some students will graduate after the 6-year window, but we follow convention in the literature of using 6 years for our primary analysis. Results are qualitatively similar when using graduation rates as measured over 7 or 8 years (omitted for brevity). 11. The state increased requirements to three math and science courses starting in 2010, after the time span of our data. 12. Other STEM includes technical subfields of education, military technologies, social sciences, health professions, and management sciences. 13. For a discussion of recent graduation requirements in Missouri, see https://dese.mo.gov/content/graduation-requirements-how-many-credits-does-student-need-graduate . 14. As an example of how a marginal course would affect our enrollment-adjusted course availability measure, consider a student who attends a school with 100 students and where 10 STEM courses are offered in each year during her 10th- to 12th-grade experience. We would calculate that this student has access to 10 available courses in high school each year, on average, yielding a courses available per year per 100 students of 10.0 (30/3). If one extra course is added in Grade 12, but the Grade 10 and Grade 11 offerings do not change, then the value of our measure would change to 10.33 (31/3). If course offerings at this 100-student high school shifted to a new “steady state curriculum” of 11 courses each year during Grades 10 to 12, the value of our measure would increase by one full unit, to 11.0. 15. Specifically, we coded courses as high school level, college preparatory level, or college level based on administrative course numbers, course grade level (a standardized reporting of the year in school in which students typically take the class), sequence number (identifies content of courses that are taught at more than one level), and delivery system. 16. A notable variable is high school enrollment, which we use as a covariate in our fully specified models and to adjust our preferred course-availability measures. Given that our course-availability measures cover courses offered in Grades 10 to 12, we use enrollment in Grades 10 to 12 for consistency. 17. The larger proportion of female students in our college-going sample is consistent with the well-documented female advantage in college entry (e.g., Bailey & Dynarski, 2011 ). 18. Although student transfers out of STEM are higher than transfers into STEM, the STEM enrollment and attainment shares end up being similar because initial STEM majors graduate at higher rates. 19. Based on the simple average of courses taken by public high school graduates in 1998, 2000, 2005, and 2009. See the Digest of Education Statistics, 2016 , table 225.10, published by the U.S. Department of Education. 20. For example, in math, this effectively includes high school courses above pre-algebra. These data come from students’ postsecondary records and, therefore, include courses taken outside of the Missouri public school system when applicable. 21. There are a small number of observations (about 0.2%) with zero recorded math and science courses; although odd, this is not impossible, and our results are insensitive to the exclusion of these observations from the analytic sample. 22. Note that the descriptive statistics are reported using the student-level data, and thus student weighted. 23. We decompose the variance in course availability per 100 students by regressing this variable on the vector of high school indicator variables. One minus the R 2 from the regression gives the share of the variance that occurs within high schools. 24. Average high school enrollment for White students is lower than that for Black, Hispanic, and Asian students. 25. The average class rank of university entrants in our sample is in the 70th percentile; among STEM entrants, the average class rank is in the 77th percentile. 26. The two most important compositional concerns are (a) changes in STEM course access in high school could induce some students to enroll in Missouri 4-year public universities who would not have enrolled otherwise and (b) changes in STEM course access could induce some students to switch from 4-year Missouri public universities to different universities. 27. Results are similar when using a logistic regression and are available upon request. The similarity is consistent with the discussion in Angrist and Pischke (2009) on the use of ordinary least squares (OLS) to recover average treatment effects with a binary dependent variable. 28. A small number of students during the time span of our study also take dual-credit courses delivered through an interactive television (ITV) system, which typically connects them to a course on a college campus. From a data perspective, ITV courses are similar to courses taken physically on college campuses, in that the Department of Elementary and Secondary Education (DESE) core course data files, upon which our main analysis is based, will not include these courses. 29. The first year that dual-credit courses were tracked by DESE is during the 1998–1999 school year. The 2008–2009 school is the final year of high school for the latest cohort in our analytic sample, but data on dual-credit courses in that year are missing (this is an idiosyncratic omission in the data files). Thus, we use the data period of 1999 to 2008 for our exploration of dual-credit courses (school years denoted by spring years). Also note that dual-credit enrollment has grown in recent years in Missouri and, as of 2018, accounted for roughly 3.0% and 1.8% of math and science high school course enrollment, respectively. 30. The match rates of 85% and 92% are high, but one could argue they should be even higher because dual-credit courses offered in high schools should be taught by high school instructors. We can only speculate as to why the match rates are not higher, but we suspect two reasons. First, districts may rely on outside personnel in some limited cases. Second, and more important, given that Missouri has many rural schools with limited course offerings, some students may take dual-credit courses in other schools or districts, in which case there would not be a “home school” match for the dual-credit course in the core data. Regardless of the cause, both of these are examples of data issues that result in an imperfect characterization of course availability at the school-by-year level in our core analysis. But together, they still result in just a small fraction of high school courses that are unobserved. 31. Finally, we also note that dual-credit course availability and enrollment steadily increased during the time span of our data panel, with enrollment in math and science starting at about 0.6% and 0.4%, respectively, when DESE first began tracking D.C. courses in 1999, and rising to 1.4% and 0.8% by 2008. The pattern of results over time in Table 6 gives no indication that our results change during later periods with more dual enrollment, which is further evidence that our null findings are not the result of bias due to the availability of unobserved dual-credit courses. 32. To elaborate briefly, at the upper bound with a course capacity of 20 students, if 20/100 students take each offered course and each course is accessible and not redundant, the simple expected increase in total courses taken during high school for a student who is exposed to one more course per year on average for 3 years (which is what is indicated by a 1.0 unit increase in our measure of course availability) is 0.60. A simple calculation of the lower bound is more difficult because pass-through can be affected by additional constraints, such as whether marginal courses fit into students’ course sequences, students are otherwise eligible for courses, and whether new courses are on new topics. That said, if we use our “topic availability” measure of course access, it is fairly easy to arrive at a lower bound of 0.20, and the online Supplemental Appendix shows that our results are similar (and even weaker) using that measure in the first stage (see the online Supplemental Table S5 ). 33. An explanation for the weak predictive power of our topical-availability instrument is that math and science courses on different topics may be viewed as substitutes by students attempting to satisfy various high school graduation and college requirements. In such a scenario, measures that privilege nonrepeat courses at the expense of fully measuring capacity will be less predictive of students’ course-taking behaviors. 34. Although the first stage based on our full sample of college attendees is weak, the evidence in the online Supplemental Table S7 suggests an area for future research. Namely, it may be that among a more targeted sample of high-performing students, and/or students with clear math and science aspirations, access to more advanced math courses may be particularly valuable. This possibility is not ruled out by the generally weak effect we estimate for our broad sample of college attendees. 35. This is consistent with findings from Conger, Long, and Iatarola (2009) .

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© 2019 AERA.


  • econometric analysis
  • economics of education
  • educational policy
  • high schools
  • longitudinal studies
  • postsecondary education
  • regression analyses

ASJC Scopus subject areas

  • Education


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