The Science of Higher Education

State Higher Education Policy and the Laws of Scale

Mario C. Martinez

• Publisher: Stylus Publishing
• Year: 2021
• ISBN:
  9781642670912
• Topic: Education
• Subtopic: Research in Education

Perennial conclusions from state-by-state funding-per-student analyses of underfunding and weak state commitment have become so common that they have diluted the potency of the argument to state policymakers for more higher education funding. In addition, there has been little in the way of testing or questioning the assumptions embedded in traditional funding per student analysis and its accompanying conclusions.

As state legislators balance the competing needs of education, health, trans­portation, and public safety budgets, they increasingly ask what return on investment (ROI) they get for the funding they provide, including from higher education. The ROI language, while potentially unsettling for its corporate-like and neoliberal connotation, will persist into the foreseeable future. We must ask questions both of adequacy (How much funding should the states provide?) and benefit (What benefits do states receive for the higher education funding they provide?). The focus on traditional funding per student analysis has remained static for over forty years, indicating the need for new ideas and methods to probe questions of adequacy and benefit.

The Science of Higher Education is an introduction to a new paradigm that explores state higher education funding, enrollment, completion, and supply (the number and type of institutions in a state) through the lens of what are commonly known as power laws. Power laws explain patterns in biological systems and characteristics of cities. Like cities, state higher educa­tion systems are complex adaptive systems, so it is little surprise that power laws also explain funding, enrollment, completion, and supply.

The scale relationships uncovered in the Science of Higher Education sug­gest the potential benefits state policymakers could derive by emphasizing enrollment, completion, or capacity policies, based on economies of scale, marginal benefits, and the return state's get on enrollment and completion for the funding they provide.

The various features of state higher education systems that conform to scale patterns do not alone provide definitive answers for appropriate funding levels, however. As this book addresses, policymakers need to take into account the macro forces, from demography to geography and the economy, that situate the system, as well the interactions between government and market actors that are at the core of every state higher education system and influence the outcomes it achieves.

PART ONE

FOUNDATIONS FOR A SCIENCE OF HIGHER EDUCATION

1

COMPLEX SYSTEMS AND STATE HIGHER EDUCATION

• Power laws explain features of complex adaptive systems, including biological systems, cities, and state higher education systems.

• New explanations of higher education systems give rise to new ways of thinking about higher education policy.

Organizations—from cities to corporations and universities—are complex adaptive systems; they are living and breathing, connected by dynamic interactions between and among people and groups. Dynamic interactions produce network effects (see Appendix A for a glossary of terms), many of which organize into predictable patterns between system features.

Scientists at the Santa Fe Institute (SFI) study complex systems, searching for order and patterns in natural systems as well as humanly constructed ones. Their work on cities opens new possibilities for how we think about human interaction, resource allocation, and public policy. The Massachusetts Institute of Technology’s Human Dynamics Laboratory studies patterns of information exchange in social networks that help explain the coordination of social groups (Pentland, 2014), an essential building block of any organization.

The mathematical function known as the power law explains various features of complex adaptive systems across a range of fields. The general power law that explains the relationship between mass and metabolism across species also explains the relationship between city population and various features of cities.

West (2017) and his colleagues at SFI showed that the power law explains differences between cities in terms of infrastructure needs, wages, spread of diseases, and even innovation, all relative to population. One city is a scaled version of another. New York City is a scaled-up version of Los Angeles for various infrastructure and socioeconomic features of the city. The power law accurately models scale behavior across cities for these features despite drastic city differences in social dynamics, geography, economic activity, and even government policies.

The central finding of this book is that power laws that apply to what West (2017) called a “science of cities” also apply to many features of state higher education systems (p. 7). We therefore have the foundation to examine how certain features of state higher education systems scale across states and influence policy.

Power laws accurately model state higher education funding, enrollment, completion, and supply (the number and type of institutions in a state), all relative to state population. Power laws also explain state differences between funding and public undergraduate enrollments and public undergraduate completions.

Additional mathematical regularities that describe various phenomena outside of higher education also apply to higher education. The 80/20 rule, made famous at the dawn of the Industrial Revolution by Italian thinker Vilfredo Pareto, describes a range of phenomena, from income distributions in different countries to the size variations of U.S. public companies.¹ In chapter 7, I refer to “Pareto-like imbalances” that describe diverse features of higher education, from enrollment variations across different sectors of higher education to the disproportionate representation of authors from just a few institutions in top academic journals.

Mathematical descriptions of patterns in complex adaptive systems generate fresh insights that can help solve system problems. Urban planners may compare existing infrastructure capacity against anticipated population changes and use the power law to forecast future infrastructure costs. In higher education, patterns of state higher education funding and outcomes can help policymakers decide whether to emphasize enrollment or completion policies, or perhaps both.

The application of complex adaptive systems to human organizations has its roots in the life sciences. Researchers took insights about living systems, laid by thinkers such as Galileo, and discovered that the scale principles applicable to mass and metabolism across species also explain the relationship between city population and various socioeconomic features and infrastructure needs. In turn, these insights apply to state higher education systems which, like cities, are complex adaptive systems.

The next section describes the enabling power of using the complex adaptive system concept as a framework for the study of state higher education systems. This sets the stage for an overview of the philosophical underpinnings of complex adaptive systems and links it to state higher education systems, policies, outcomes, and interactions between and among people and groups. Those who wish to begin with the domain-specific framework for higher education and skip the discussion of the philosophical basis for this book may wish to turn to chapter 2.

The Value of a Framework

• The complex adaptive systems framework describes state higher education systems and policy challenges from a new perspective.

Practitioners often lack the time (and patience) to review theories or conceptual frameworks. They want the application, the how. Researchers in a variety of fields, including in higher education, however, have long used frameworks to understand their subjects of interest and provide guidance to practitioners.

Luckett and Casey (2016) emphasized that frameworks help organize complex phenomena, which then translates into meaningful action. Luckett and Casey used a biological framework that outlines seven essential characteristics of life to describe the nature of social media. They initially relied on the framework and then suggested actionable policies and practices to turn social media into a constructive democratic platform.

Poverty researchers Daminger et al. (2015) stated that frameworks help us understand why something works, noting that “when practitioners understand why a particular strategy works, they will be in a better position to find new ways to apply it, as well as to effectively advocate for the resources they need” (p. 16). Daminger et al. utilized a behavioral science framework to explain that environment influences behavior, and they subsequently suggested nudge strategies to address chronic scarcity, based on their framework.

Frameworks increase understanding of why something works, not just how it works. Importantly, insights derived from frameworks in one field often find application in others. Findings from research on cities, specifically scale relationships between infrastructure and socioeconomic features, apply to state higher education higher systems. Cities and state higher education systems have much in common. Just as city officials provide funding for cities, state policymakers provide funding for higher education. Just as investment in cities produces public and private benefits, so too does investment in higher education.

Klinenberg (2018) described specific city and higher education investments that produce public benefits as social infrastructure. In cities, public libraries create a social space for a diverse range of people. Libraries facilitate the acquisition of social and human capital (technical skills) by spreading literacy and the development of social networks (human connections and network ties) through various community programs. Klinenberg identified universities as vehicles that facilitate the acquisition of social and human capital in a similar manner. Institutions of higher education are a form of social infrastructure.

The burgeoning work on cities offers frameworks useful from which to view persistent policy challenges in higher education, or what Churchman (1967) and social planners referred to as wicked problems. Wicked problems have no single, agreed-upon solution. Incomplete and changing dynamics influence how different people view the problem and frame ideas, all of which result in different solutions to the problem.

Many policy problems are wicked problems. Tandberg and Fowles (2018) charted the historical progression and application of various frameworks and theories to higher education policy, starting with wicked problems. According to the authors, there is no shortage of opinions on how to resolve difficult policy matters in higher education. Wicked problems arise because it is difficult to prioritize competing interests in an arena where there is no true-or-false or right-and-wrong dichotomy.

Wicked problems in higher education generate opposing policy solutions to perennial issues: Can we achieve both access and quality? How do we determine whether states underfund or adequately fund higher education? What does higher education achieve for the state funding it receives?

Tandberg and Fowles (2018) described how researchers have framed higher education policy to address wicked problems over the last 40 years. They started with Cohen et al. (1972), who referred to colleges and universities as organized anarchies where problem and solution choices are akin to garbage cans. Organizational garbage cans are a collection of choices looking for problems, solutions looking for answers, and decision-makers looking for work. Competing stakeholder preferences, changing technologies, dynamic group interactions, and fluid organizational participation characterize organized anarchies. The problems that organized anarchies attempt to solve are invariably complex and ambiguous, often wicked.

Wicked problems arise in what Hogarth (2001) called wicked learning environments, which are distinct from kind learning environments. A kind learning environment is an arena where accurate feedback connects decisions to outcomes. Wicked learning environments feature more ambiguity and complexity. The link between decisions and outcomes is murky, ambiguous, or even nonexistent. Wicked learning environments describe the complex environment in which so many higher education policy problems arise and why leaders have difficulty advancing definitive solutions.

Kingdon’s (1995) multiple streams theory builds on the garbage can metaphor and wicked environments and addresses how different stakeholders advance a policy solution and look for windows of opportunity to do so during the policy process. Higher education researchers have applied Kingdon’s theory to different aspects of state higher education policy, such as the policy process for addressing governance reform (McLendon, 2003).

Tandberg and Fowles (2018) also showed how theories ranging from principal agent theory to boundary spanning inform higher education policy, and they encouraged further consideration of different perspectives. The common theme throughout these studies describes state higher education systems as complex organizations working on difficult problems in unpredictable and nonlinear policy worlds.

The complex adaptive system framework and power law methodologies applied to state higher education systems reveal relationships between population and funding, population and supply (the number and different types of institutions), and funding and enrollment and completion. These relationships offer a new way to compare states across common system features. Policymakers can also assess their individual states. For example, the power law relationship between funding and enrollment produces predicted enrollments for each state, which can be compared against actual enrollments.

Complex Adaptive Systems and Intersubjective Worlds

• State higher education systems are social systems, a specific type of complex adaptive system.

• Dynamic and complex human interactions lie at the heart of social systems.

The study of complex adaptive systems is varied and multidisciplinary. Complex adaptive systems as livable and changeable systems have been used to describe biological systems, traffic flows, and governments. Multiple interacting subjects and forces comprise any complex adaptive system, producing dynamic effects that escape simple cause-and-effect characterization.

Smil (2019) offered what is perhaps the most comprehensive description of growth and scale in complex adaptive systems to date. His volume covers historical and contemporary findings for topics ranging from the growth of microscopic organisms to scale in human populations, megacities, and entire civilizations. Complex adaptive systems commonly exhibit scale in the form of exponential or finite growth patterns.

The Science of Higher Education describes state higher education systems as complex adaptive systems that conform to growth patterns like those Smil (2019) documented. State higher education systems are also what Smil called a social system, a specific type of complex adaptive system (I use social systems and complex adaptive systems interchangeably to describe higher education). Though biological and social systems are both complex adaptive systems that exhibit scale relationships among various features that characterize them, physical laws drive biological systems, whereas network effects that emanate from dynamic human interactions drive social systems.

Social Systems and Intersubjectivity

Social systems live and breathe through this network of dynamic human interactions, including state higher education systems. Interactions may be between two people or among many; formal or informal; planned or unplanned; direct (linear) or indirect (nonlinear); persona...

Table of contents

• Cover
• About This Book
• Half-title Page
• Title Page
• Copyright
• Dedication
• Contents
• Foreword
• Preface
• Acknowledgments
• Part One: Foundations for a Science of Higher Education
• Part Two: Scale Patterns
• Part Three: Outcomes and Return on Investment
• Appendix A: Glossary of Terms
• Appendix B: 2015 All State Data (Original Units)
• Appendix C: Logarithmic Regression Results (Predictions)
• Appendix D: Population:Funding Scale Results
• Appendix E: Population:Enrollment Scale Results
• Appendix F: Population:Completion Scale Results
• Appendix G: Population:Institutions (Supply) Scale Results
• Appendix H: Funding:Enrollment Scale Results
• Appendix I: Funding:Completion Scale Results
• Appendix J: Statistical Results: All Scale Analysis
• Appendix K: Linear and Scale Ranking Comparisons
• Appendix L: Exponents, Logarithms, and Scale Review
• References
• About the Author
• Index
• Also available from Stylus
• Backcover