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Global Status, Intra-Institutional Stratification and Organizational Segmentation: A Time-Dynamic Tobit Analysis of ARWU Position Among U.S. Universities

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Abstract

Ranking systems such as The Times Higher Education’s World University Rankings and Shanghai Jiao Tong University’s Academic Rankings of World Universities simultaneously mark global status and stimulate global academic competition. As international ranking systems have become more prominent, researchers have begun to examine whether global rankings are creating increased inequality within and between universities. Using a panel Tobit regression analysis, this study assesses the extent to which markers of inter-institutional stratification and organizational segmentation predict global status among US research universities as measured by position in ARWU. Findings indicate some support that both inter-institutional stratification and organizational segmentation predict global status.

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Notes

  1. Universities with global presence are sometimes called “world-class” universities. We chose to use Marginson’s “global research university” instead because this terminology is explicit about the emphasis given to research output and denotes a departure from the “multiversity,” which was the, largely nation bound, preeminent form of higher education organization in the 20th century.

  2. ARWU is sometimes described as the Shanghai ranking.

  3. There is no essential definition of GRU but there is some consensus in the literature of features of global or world-class status. We review these features below.

  4. Our study deals exclusively with ARWU. We selected ARWU as the evaluative technology for our analysis for several interrelated reasons. First, global rankings like ARWU are seen as integral to the development of global university competition and GRUs. Second, ARWU is a prominent ranking system that is both well known and influential. Third, there is precedent of using ARWU in academic research on university competition, stratification, and research output. Finally, ARWU are published annually, allowing time-series analysis, and are publicly available.

  5. The normative implications embedded in the term world class are not absolute. For example, Altbach and Balán’s book (2007) makes the world class research universities include an array of institutions in middle and low income countries that are central to state building by linking their countries with global science. In this way they do not limit the world class to a handful of North American and European universities that are most research productive.

  6. Of note, THE changed its methodology in 2010 in response to widespread criticism.

  7. Books from the mid-1990s into the mid-2000s constitute the bulk of this research. Higher education scholars doing research in English speaking countries, including Bok (2004), Clark (1998), Marginson and Considine (2000), Slaughter and Leslie (1997) and Slaughter and Rhoades (2004), broadly described increased competition and marketization among universities, albeit from varying perspectives. Studies into science and society, including Gibbons and colleagues (1994), and Etzkowitz and Leydesdorff (2000), also identified and theorized changing relations between the market, state, and knowledge producing organizations such as universities. Taken together, this work provides compelling empirical and theoretical evidence of a major shift towards market-like competition in higher education.

  8. Notably, 2008 is a recent exception where student aid grants exceeded R&D funding. This was part of the Obama Administration’s economic stimulus and is a temporary variation rather than an indicator of a new trend.

  9. Medical schools both train physicians and conduct an extensive amount of research in the life sciences. Indeed, the National Institutes of Health provide more funding for university-based research than does any other federal agency (Yamaner 2009). The majority of these funds go to medical schools. Thus, while we consider the presence of a medical school as an indicator of a university’s emphasis on the applied sciences, this variable could instead indicate the institution’s level of research focus. In such an interpretation, this variable would indicate inter-institutional stratification rather than within-organization segmentation.

  10. This is because a Tobit analysis assumes that a latent variable, commonly denoted Y it *, underlies our observed dependent variable, Y it . The latent dependent variable Y it * – in our case, ARWU score for all US research universities – is continuous, even though the observed dependent variable is censored due to the limited availability of data about this variable. We therefore interpret model coefficients as marginal effects like those in a linear regression because our project studies the underlying phenomenon denoted by Y i *, meaning university status as measured by ARWU score. For a detailed discussion of the latent variable motivation of Tobit regression, see Sigelman and Zeng (1999).

  11. All sampled universities are “ranked” in the general sense that all received an ARWU score. However, we use the word “ranked” specifically to describe the subset of sampled universities that rank in the top 100. Some of the “unranked” universities appear in the top 200 ARWU, but the raw ARWU scores for these schools are censored. In this sense, these universities are “unranked.”

  12. We interpret marginal effect by dividing the estimated coefficient by 100. This interpretation is common among “lin-log” models, in which the dependent variable appears in its raw, or linear, form and independent variables are logged (Gujarati 2003: 181–183).

  13. We do not comment on the statistical significance of the squared FTE term because this result, like all interaction terms, must be interpreted in the context of the main effect (i.e., the linear FTE term). Because the linear FTE term does not attain significance, we ignore the statistical significance of the squared FTE term.

  14. Although our conceptual model gives little explicit theoretical importance to geographical location, we suggest that the negative effect of a Southeastern location in comparison to a New England location may also represent a form of inter-institutional stratification since the Southeastern region of the country is substantially less wealthy than New England, likely reflecting lower levels of local input resources. The same is true for the negative significant effect of location in the Midwestern region in column two.

  15. We acknowledge that the size of the US system also plays a significant role in the frequency of its representation in world university ranking tables.

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Authors and Affiliations

  1. College of Education, Michigan State University, East Lansing, MI, 48824, USA

    Brendan Cantwell

  2. Department of Counseling and Higher Education, University of North Texas, Denton, TX, 76203-5017, USA

    Barrett J. Taylor

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  1. Brendan Cantwell
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Correspondence to Brendan Cantwell.

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Cantwell, B., Taylor, B.J. Global Status, Intra-Institutional Stratification and Organizational Segmentation: A Time-Dynamic Tobit Analysis of ARWU Position Among U.S. Universities. Minerva 51, 195–223 (2013). https://doi.org/10.1007/s11024-013-9228-8

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