Systematic shifts in scaling behavior based on organizational strategy in universities

Abstract

To build better theories of cities, companies, and other social institutions such as universities, requires that we understand the tradeoffs and complementarities that exist between their core functions, and that we understand bounds to their growth. Scaling theory has been a powerful tool for addressing such questions in diverse physical, biological and urban systems, revealing systematic quantitative regularities between size and function. Here we apply scaling theory to the social sciences, taking a synoptic view of an entire class of institutions. The United States higher education system serves as an ideal case study, since it includes over 5,800 institutions with shared broad objectives, but ranges in strategy from vocational training to the production of novel research, contains public, nonprofit and for-profit models, and spans sizes from 10 to roughly 100,000 enrolled students. We show that, like organisms, ecosystems and cities, universities and colleges scale in a surprisingly systematic fashion following simple power-law behavior. Comparing seven commonly accepted sectors of higher education organizations, we find distinct regimes of scaling between a school’s total enrollment and its expenditures, revenues, graduation rates and economic added value. Our results quantify how each sector leverages specific economies of scale to address distinct priorities. Taken together, the scaling of features within a sector along with the shifts in scaling across sectors implies that there are generic mechanisms and constraints shared by all sectors, which lead to tradeoffs between their different societal functions and roles. We highlight the strong complementarity between public and private research universities, and community and state colleges, that all display superlinear returns to scale. In contrast to the scaling of biological systems, our results highlight that much of the observed scaling behavior is modulated by the particular strategies of organizations rather than an immutable set of constraints.

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“…The first group includes variables linked to the scale of universities, including the number of students (STUNUMBER), academic staff number (FANUMBER), and the number of highly qualified faculties (HQFNUMBER) [44][45][46]. The second group consists of variables related to vital resources of universities, namely land area per student (LANDPS), floor area of buildings per student (AREAPS), number of laboratories (LABNUMBER), research funding (RESEARCHFUND), and governmental subsidy amount (GOVTSUB) [44,45,47]. The last group includes dummy variables, which are accreditation strategy (QAMETHOD), ownership of universities (OWNERSHIP), and location (LOCATION) [48][49][50].…”

“…Table 6. Dependent and independent variables assigned in the regression model (based on [33,[44][45][46][47][48][49]). The results of the regression analysis are displayed in Table 7.…”