Policy Cybernetics: A Systems Framework for Responding to and Learning from Complex Problems and Consequences in Public Affairs
Dissertation
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy in the Graduate School of The Ohio State University
By
Lisa A. Frazier, MPH
Graduate Program in Public Policy and Management
Ohio State University
2018
Dissertation Committee:
Anand Desai, Adviser
Joshua Hawley
William Hayes
Stephanie Moulton
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Copyright by
Lisa A. Frazier
2018
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Abstract
Every year, millions of Americans experience Medicaid enrollment states that do not align with their eligibility for the program. Status misalignment has economic, health, and social costs for individuals, State governments, insurers, and employers. As an entitlement program, Medicaid uses a means test to assess eligibility for the program; if an individual is determined to be eligible, they are entitled to the benefits of the program. The fact that misalignment occurs regularly and across all State programs, however, indicates that implementing the means test is not straightforward. Despite its importance to citizens, States, and industry, we still have a poor understanding of why enrollment misalignment is prevalent or how it occurs.
Existing research frames phenomena of enrollment misalignment (i.e., missed program take-up, churn, and fraud) as classification errors that represent unintended consequences of implementation or idiosyncratic individual behavior. The underlying Cartesian assumption that policy effects can be reduced to their constituent parts and traced back to prime causes fundamentally limits the ability of this treatment to provide insights about program enrollment.
Evidence from health services research and social policy indicate that demographic characteristics,
State program structures, and economic and social context each contribute to State variation in program take-up and churn. However, these studies fail to explicitly account for the interdependencies and interconnections among these factors that produce irreducible, complex phenomena. i This dissertation research uses the case of enrollment phenomena resulting from implementation of Medicaid’s means test to explore broader questions about the complex phenomena that characterize collective action systems. Drawing on insights from complex systems science and critical science studies, this research seeks to understand how complex policy systems work and where surprising patterns come from. Using both conceptual and computational systems models, this research explores how decisions of program design (e.g., eligibility criteria) and implementation (e.g., application and eligibility determination procedures) affect how beneficiaries and potential beneficiaries accumulate and move through the Medicaid system.
The primary contribution of this research is a conceptual framework for studying collective action programs as complex adaptive systems. Policy cybernetics integrates insights from systems science, complexity science, and implementation studies to guide inquiry concerning sources of policy resistance in dynamic systems such as Medicaid. The framework provides a logically consistent explanation for how complexity – interdependencies, feedback, emergence, and context
– can lead to policy resistance and hamper progress toward stated goals of a program.
Assumptions, concepts, and expectations are applicable to other public program contexts, including other social welfare programs, as well as law enforcement, criminal justice, and workforce development. In describing public programs as complex human systems, this research aims to explore and assess strategies to improve program performance across the many criteria against which we measure it (e.g., effectiveness, equity, efficiency).
As a proof of concept, policy cybernetics is applied to the problem of program enrollment in Medicaid. A set of models of Medicaid enrollment system illuminate the feedback structure among agents, rules, and environment that produces dynamic behaviors (e.g., churn) in implementation of the program’s means test. A simulation of Medicaid’s enrollment mechanism ii permits experimentation with several program interventions in a virtual world. These experiments examine the tradeoffs inherent in public program enrollment, and identify administrative strategies that increase the performance of the program enrollment mechanism (i.e., enrolling eligible individuals, not enrolling ineligible individuals) and minimize costly movement on and off the program (i.e., churn) among beneficiaries over relevant time horizons. These models illustrate that the program’s design, administrative structure, and individual attributes interact to produce program enrollment outcomes, both ‘intended’ (i.e., take-up) and ‘unintended’ (i.e., churn). Policy resistance – the intervention-dampening patterns that arise in response to the system itself) – is endemic to this complex system of collective action.
Policy cybernetics helps policy scholars and practitioners alike understand the multidimensional roots of policy resistance and the mechanisms by which it operates. It also provides a framework for exploring alternative strategies to deal with “unintended consequences”, and tradeoffs in policy objectives. Simulations allow decision makers to alter underlying assumptions and mechanisms to assess the consequences of their decisions across long time horizons in silica with zero social costs. Thus, a collectively developed simulation can be used to clarify values, make assumptions explicit, and make updated use of the evidence based in program planning, administrative decision-making, and performance evaluation.
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Acknowledgements
Thank you to:
My committee for their support and belief in my intention and ability to complete this endeavor, even in rather bleak times.
Dr. Anand Desai for his advice and mentorship, without which I would be a lesser teacher, scholar, and person.
Dr. Stephanie Moulton for her unfailing kindness and pragmatism.
Dr. Joshua Hawley for his sense of humor and confidence in me.
Dr. William Hayes for being a good boss and an even better human.
My friends and colleagues for their feedback, laughter, and pep talks.
Dr. Kristin Harlow for going through this process with me, every step of the way.
My family, the Doctors Frazier and spouses, for their support, inspiration, and standards of excellence.
My husband. For everything.
iv Vita
2002……………………………………………Olentangy High School
2006……………………………………………B.A. Anthropology & Political Ecology, Mount Holyoke College
2009……………………………………………M.P.H. Epidemiology, The Ohio State University
2009 to 2011…………………………………...Research Analyst, Health Policy Institute of Ohio
2011 to 2017…………………………………...Graduate Teaching Associate and Instructor, John Glenn College of Public Affairs, The Ohio State University
Publications
Frazier, Lisa A. (2016). More than the Affordable Care Act: Topics and Themes in Health Policy Research. Policy Studies Journal Yearbook 44 (S1): S70-S97.
Frazier, Lisa A. and Anand Desai. (2015). Hiking Across the Rugged Landscape of Human Health. In Health Informatics for the Curious: Why Study Health Informatics, eds. Vaidya & Soar. The Curious Academic Publishing.
Frazier, Lisa A. and Hyungjo Hur (2013). The Legacy of Chester Barnard in Contemporary Scholarship: Lessons for the Twenty-first Century Executive. In Mastering Public Administration: From Max Weber to Dwight Waldo, eds. Fry & Raadschelders. Washington, DC: CQ Press.
Fields of Study
Major Field: Public Policy and Management
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Table of Contents
Abstract…………………………………………………………………………………………… i
Acknowledgements……………………………………………………………………………… iv
Vita……………………………………………………………………………………………….. v
List of Tables…………………………………………………………………………………… vii
List of Figures………………………………………………………………………………….. viii
Chapter 1: Introduction…………………………………………………………………………... 1
Chapter 2: Current Perspectives on Medicaid Program Enrollment…………………………..... 11
Chapter 3: Bringing a Complex Systems Perspectives to Policy Phenomena………………….. 68
Chapter 4: Policy Cybernetics as a Framework for Inquiry and Intervention………………… 112
Chapter 5: Systems Models of Medicaid Mechanism………………………………………… 157
Chapter 6: A Systems Approach to Build Governing Intelligence……………………………. 221
References……………………………………………………………………………………... 231
Appendix A: Summary of Findings from Medicaid Literature……………………………….. 245
Appendix B: Administrative Burden in Bureaucratic Encounters…………………………….. 250
Appendix C: Philosophy of Policy Cybernetics………………………………………...…….. 255
Appendix D: Supplemental Medicaid Simulation Materials………………………………….. 262
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List of Tables
Table 1. Costs of Administrative Burden.………………………………………………...……. 55
Table 2. Bureaucratic Encounters…………………………...………………………………….. 62
Table 3. Roach & Bednar’s Logical Types and Levels…...……………………………………. 98
Table 4. Complexity Taxonomy…………………………...…………………………………...102
Table 5. Complexity Typology…………………………...…………………………………… 106
Table 6. Complexity in Purposeful Human Systems……...…………………………………... 107
Table 7. Summary of Medicaid Enrollment Literature……………………………………..…. 166
Table 8. Summary of Medicaid Complexity……………………………………………….….. 174
Table 9. Model Boundaries, Assumptions, and Initialization Chart……………………..……. 196
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List of Figures
Figure 1. Medicaid Program Logic ……………………………………………………………...14
Figure 2. U.S. Population Breakdown…………………………………………………………...16
Figure 3. Medicaid Eligibility Thresholds……………………………………………………….20
Figure 4. Federal Poverty Guidelines……………………………………………………………20
Figure 5. Medicaid Enrollment Gap.………………………………………………………..…...23
Figure 6. Economic Trends During Medicaid Era……..……………………………………..… 25
Figure 7. Enrollment Gap Over Time….……………………………………………………..… 31
Figure 8. Kaiser Estimates of Medicaid Spending and Enrollment…………………………..… 41
Figure 9. Feedback.………………………………………………………………………..……. 72
Figure 10. Bunge’s Equation………………………………………………………………..….. 77
Figure 11. The Complexity Gap in Policy Studies…………………………………………...… 82
Figure 12. Eligibility-Enrollment Contingency Table…..……………………………………… 92
Figure 13. Law of Requisite Variety………………………………………..…………………..123
Figure 14. DIKW Hierarchy……………………………………………………………………124
Figure 15. Signal Processing and Response in Policy…………………………………..…..… 126
Figure 16. Relationship of Social and Policy Systems………………………………………... 130
Figure 17. Stock and Flow Notation…………………………………………………………... 142
Figure 18. Map of Philosophy of Policy Cybernetics…………………………………………. 146
Figure 19. National Enrollment Gap…………………………………………………………... 161
Figure 20. Literature Outcomes……………………………………………………………….. 167
viii Figure 21. Enrollment in Context……………………………………………...……………… 169
Figure 22. Clarified Outcomes……………………………………………………………….... 170
Figure 23. Literature Subsystem Diagram……………………………………..……………… 175
Figure 24. Revised Subsystem Diagram………………………………………………………. 177
Figure 25. Literature Policy Structure Diagrams……………………………………………… 178
Figure 26. Revised Policy Structure Diagram………………………………………………… 181
Figure 27. Medicaid Enrollment Plumbing…………………………………………………… 184
Figure 28. Disaggregated Medicaid Loops…………………………………….……………… 186
Figure 29. Causal Loops of Medicaid’s Sorting Mechanism…………………………………. 187
Figure 30. Stock and Flow Structure of Medicaid Enrollment…………...…………………… 192
Figure 31. Baseline Scenario Projections……………………………………………...……… 199
Figure 32. Recession v. Baseline Caseloads…………………………...……………………… 201
Figure 33. Recession Scenario Projections……………………………………….…………… 202
Figure 34. Expansion Scenario Projections…………………………………………………… 205
Figure 35. Lower State Burden Scenario Projections……………………………………….… 207
Figure 36. Lower Citizen Burden Scenario Projections………………...…………………….. 209
Figure 37. Comparative Policy Enrollment Projections……………...……………………….. 210
Figure 38. Comparative Policy Movement Projections………………………..……………… 211
Figure 39. Medicaid Simulation Interface………………………………………….…………. 216
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Chapter 1: Introduction
The world is a complex, interconnected, finite, ecological-social-psychological-economic
system. We treat it as if it were not, as if it were divisible, simple, and finite. Our persistent,
intractable global problems arise directly from this mismatch. – Donella Meadows (1982,
p. 101)
Motivating Observations
Every year, Medicaid, the jointly administered State-federal health insurance plan for low- income and disabled individuals, covers one in five adults, and one in three children – about 74 million low-income Americans (Centers for Medicare & Medicaid Services [CMS], 2018).1 As an entitlement program, Medicaid employs a means test to define eligibility for the program; if an individual is determined to be eligible, they are entitled to enrollment in the program and receipt of its benefits. However, monthly enrollments fluctuate as people experience changes in their income, health status, and family composition, and as State programs work to keep up with review and processing of applications, claims, and renewals. In a program as large and complicated as
Medicaid, it is thus difficult for States to determine who is eligible and who is not. As a result, all
State programs report varying levels of coverage take-up (proportion of eligible residents who enroll in the program), churn of beneficiaries (movement of individuals on-and-off the program
1 In this research, State is capitalized when referring to the entities that make up the federal union. The distinction is made because I will also frequently refer to eligibility-enrollment states (not capitalized) and other conditions. 1 over long periods of time), and fraud (small number of residents enrolled in the program who are not eligible).2
These program classification errors result in financial and health care instability for families and individuals who experience them, and unpredictability and inefficiency in the State’s administrative tasks and budgets. As the nation’s (and most State’s) largest single health insurer, and a program that accounts for more than a quarter of total State spending (Rudowitz, 2016),
Medicaid’s operations are of great concern to public administration. Given the seemingly straightforward rules of the program – i.e., if you meet these criteria, you are entitled to benefits – and the problems associated with classification error, the phenomena of low take-up, churn, and fraud are often referred to as “unintended consequences”, “implementation failures”, or
“inefficiencies” in the program.
Seeking to find solutions to these problems, researchers and administrators of State
Medicaid programs have spent considerable time and resources asking question such as, How can
States eliminate fraud? What are the characteristics of the people most likely to churn, miss take- up, or commit fraud? Why do different States have different rates of churn, fraud, and take-up?
Despite an abundance of research focused on answering questions about program enrollment (in
Medicaid as well as other public benefit programs), our understanding about the causes and consequences of program classification error remains limited. In turn, this shortcoming limits our ability to identify policy levers that might be used to reduce error and (arguably) improve system performance.
2 Fraud has a more precise definition that this, but fraud is used colloquially and practically among many policymakers and the public. The more appropriate term for the phenomenon mentioned here – improper enrollment – is discussed in chapter 2, along with the proper definition of fraud. 2 Our limited understanding of implementation of Medicaid’s means test reflects a broader trend and problem in policy studies: a disconnect between the complexity of the world and our responses to that complexity, which I refer to as the complexity gap.3 The study of public policies and programs — their design, implementation, management, and effect on society – and the wicked problems they seek to address is a fundamental task in both the scholarship and practice of public administration. The predominant approach to studying both public policy and public management follows the tradition of Descartes, aiming to reduce the complexity of the world by breaking it down into smaller pieces such that we might determine relationships among the pieces across specific dimensions. The goal, in this approach, is to identify cause and effect.
This approach certainly has its place in scientific inquiry and has provided the basis for much of what we understand about many social phenomena, including Medicaid enrollment. It is possible that in the natural sciences, where randomized controlled experiments can be conducted, lineal4 causation exists and can be identified. However, social systems are not of nature; they are subject to the whims of humans: intelligent, purposeful, adaptive, curious, emotional, frustrating agents. The “causes” of the social world, particularly of public policy, are purposeful actions intended to have some effect. To speak of “unintended consequences” of a policy, and to acknowledge that we need to evaluate our programs to determine if they have had the desired effect, are both indications that this lineal conceptualization of causation is not sufficient to describe the underlying processes of our collective actions. To study policy phenomena as if this conceptualization were sufficient limits our ability to draw insights about what questions to ask,
3 Cook & Tonurist (2016) use this phrase, but I extend its meaning, which I discuss in detail in chapter 3. 4 Lineal is used to specify any direct line influence relationship over time. This relationship may have linear or non- linear functional forms (e.g., hyperbolic, exponential, periodic). Linear will be used only to denote the functional form defined by a straight line; lineal will be used to refer to forward temporal relationships. 3 what decisions to make, and what actions to take. This often results in a fundamental inability to address the problems most relevant to society, what Cook and Tonurist (2016) refer to as a
“governance crisis”.
Research Aims and Questions
In contrast, a recursive conceptualization of causation accounts for non-lineal — and non- correlative — relationships among decisions or actions.5 Recursive relationships also produce emergent properties. While non-lineal processes pose logical problems for Cartesian approaches to inquiry, a systems approach sees such dynamics as central characteristics of the way things work. A systems approach is valuable when we consider what sorts of phenomena we study in public administration: policymaking, including design and implementation of public programs; management of public institutions; and the effects of policy on society. In each of these subfields, we are ultimately interested in processes in which recursion is prevalent, interdependencies abound, and multiple objectives, motivations, and values are always in play. Thus, the outcome of interest is not just whether something gets done, but what gets done, how it gets done, and what tradeoffs are made in the process.
The purpose of this research is to suggest an alternative (i.e., systems) approach to dealing with the complexity inherent in policy phenomena in order to get traction on the complexity gap and make sense of unintended consequences. I do this by developing a conceptual framework to guide both inquiry and intervention in policy studies that confronts, harnesses, and respects complexity by conceptually and computationally distilling – but not reducing – recursive and
5 A may indeed cause B, but B may loop back around and affect A; this dynamic can be characterized as feedback or learning. 4 dynamic processes. A systems approach emphasizes holism – keeping the parts together and investigating the interdependencies, feedback, and dynamics that can produce surprising outcomes in collective action systems. This approach is a good fit for the questions ultimately of interest in policy studies: How does this problem (or unintended consequence) work? What can be done about it?
The fundamental gap in existing Medicaid research is that enrollment phenomena (i.e., churn, take-up, and fraud) are treated as distinct lineal outcomes, as functions of individual and institutional parts. In other words, scholars have taken a Cartesian approach. In breaking time into snapshots, feedback dynamics into linear cause and effect, people into characteristics, contexts into control variables, and compound patterns into discrete outcomes, existing studies make it difficult to capture the complexity and dynamics of implementing a benefit program. People move within the Medicaid system in different ways over time, through a series of actions and interactions with other actors and rules, within rich social, economic, and political environments.
Another aim of this research is to bring a systems perspective to bear on Medicaid enrollment in order to make sense of the enrollment gap and improve implementation. From a systems perspective, missed take-up, churn, and fraud all contribute to an enrollment gap – a mismatch between people eligible for coverage and those enrolled in coverage. Taking a systems approach, I explore the following questions: How does program enrollment – i.e., implementation of the means test – work? How do classification errors come about? What steps can States take to reduce misalignments like churn?
This research stands to make several contributions to policy and complexity sciences. First, it contributes to Medicaid research and administration by building understanding of how means
5 test implementation works. Second, contributes to policy studies more generally by seeking to identify the mechanisms by which policy implementation occurs, and using simulation to conduct prospective analysis. Third, contributes to complexity studies within the social sciences by operationalizing complexity in a way that is both conceptually and empirically useful and rigorous.
Lastly, contributes to the study and practice of policy phenomena and the education of policy professionals by developing a conceptual framework to make sense of complex phenomena and to guide decision making in complex contexts.
Thesis
The central claim of this dissertation is that unintended consequences are symptoms of using tools that are a poor fit for the job of dealing with the complexity that is inherent in the wicked problems of interest in policy studies. The predominant Cartesian approach has limited our understanding of how complex social phenomena and collective action decisions actually work. getting traction on the disconnect between problems and responses requires: 1) being clear and organized about the complexity of the phenomena; 2) changing our approach – not just our methods – for inquiry, including adopting conceptual frameworks that frame phenomena in terms of complex systems; and 3) taking a pluralistic approach to analysis, including leveraging computational simulation methods to explore ranges of possible outcomes under uncertain and interdependent (i.e., complex) conditions.
Organizing complexity. The first part of this dissertation unpacks the concept of complexity and its manifestation in policy studies. I argue that the problematic phenomena that we term “unintended consequences” of particular actions are the endogenous manifestations of complexity in our social systems – forms of what systems scientists term policy resistance. 6 Implementation of Medicaid enrollment provides a useful example of what makes policy phenomena complex: it involves diverse, purposeful, interdependent actors and rules interacting variably and dynamically within an integrated, irreducible whole, making it a purposeful human system. The feedback structure of those interactions drives the characteristic process of the system
– the mechanism by which Medicaid enrollment operates – which is a non-linear combination of interventions and endogenous responses. Furthermore, the system’s feedback structure produces various patterns, some of them surprising and problematic for society over time and in context.
These emergent properties – for example, the persistent churning of beneficiaries on and off
Medicaid – are manifestations of functional complexity, system behaviors that arise from endogenous actions of the system over time and in its broader context. Questions about how a system works are matters of mechanism; questions about what can be done about unintended consequences are matters of addressing functional complexity.
A conceptual framework for complex systems. Policy scholars have called for theories and frameworks that explicitly and systematically address complexity in policy studies. A conceptual framework that is properly fit for the complexity of the problems we face in policy studies is dilemma- and action-oriented in its purpose, frames complex phenomena within their systems context, and is reflexive about the role of human actors (including researchers and administrators) in changing the phenomena themselves. In chapter four, I present a conceptual framework for policy studies that meets these guidelines. Policy cybernetics borrows heavily from systems and complexity science and cybernetics. It posits that purposeful human systems operate via mechanisms and relate to each other via mechanism feedback – the dynamics by which adjoining systems send, filter, process, transmit, and respond to each other’s signals. Getting
7 traction on seemingly intractable problems thus requires abiding by the law of requisite adaptation
– the adaptive capabilities of the policy system’s responses must be greater than or equal to those of the social system itself in order to maintain a stable relationship. Policy design is mechanism design, and therefore inquiry and governance must be adaptive, leveraging policy learning as the means of building governing intelligence. Furthermore, I articulate agential systems realism as the nousology of policy cybernetics – a holistic, pluralistic, intersubjective pattern of reasoning that precedes both inquiry and intervention in collective action systems.
Simulating possibilities. In chapter five, I return to Medicaid enrollment, using policy cybernetics as a framework to explore the dynamics of this purposeful human system. I show that the so-called errors in Medicaid enrollment – missed take-up, fraud, and churn – are natural products of the system’s mechanismic operation over time and in context, and therefore cannot be
“solved” in any technical or analytical sense. The extant literature attributes persistent problems of missed take-up and churn to variation in population characteristics or program structure. But the simulation illustrates that these patterns are forms of policy resistance generated by the mechanism – the feedback structure of implementing the program’s means test. Eliminating policy resistance requires a change to the design of the system, which will inevitably produce policy resistance of its own. Because it allows the user to explore counterfactuals scenarios in complexly distilled (but not reduced) virtual worlds, simulation is useful to policymakers for prospective analysis, negotiation, and evaluation of potential interventions under different conditions.
This dissertation unpacks the systems science insight that there are no side effects, just effects (Sterman, 2006) – no unintended consequences, just consequences – in the context of policy studies. By integrating systems modeling and simulation into our reasoning – matching irreducible wholes with holism, variety with variety, complexity with complexity, adaptation with adaptation
8 – we can come to grips with that reality and make progress in both understanding complexity, explaining its consequences, and developing the capability to intervene in its presence.
Organization of Dissertation
This dissertation research uses the case of enrollment phenomena resulting from implementation of Medicaid’s means test to explore broader questions about modeling and acting upon the complex dynamics that characterize collective action systems. In chapter two, I begin by summarizing what we know about the size and scope of Medicaid’s enrollment gap. I synthesize the two primary literatures (health services research and social policy) that deal with the issue of
Medicaid enrollment to show that population characteristics and State program structures both contribute to current understandings of program take-up and churn. In chapter three, I draw on insights from complex systems science to highlight the gaps left by those literatures and re-frame the enrollment gap in terms of complexity. I contribute to both the Medicaid literature and to complexity science by explaining consequences, including problematic unintended consequences, as manifestations of functional complexity, and by distinguishing between types and taxa of complexity in purposeful human systems.
In chapter four, I use the foundation of complexity principles discussed in chapter three to introduce an alternative approach to responding to the complex phenomena that characterize policy studies. I walk through the assumptions, concepts, and expectations of policy cybernetics; lay out steps of how to use the framework in terms of articulating research questions and hypotheses, collecting and analyzing data, and drawing conclusions; and then circle back to reckon with some of policy cybernetics’ philosophical underpinnings. In chapter five, I show how policy cybernetics works in practice by returning to the question of how implementation of Medicaid’s means test
9 works. Through a set of systems models of Medicaid enrollment, I develop and present a simulation that sheds light on Medicaid’s sorting mechanism – the feedback structure among agents, rules, and environment that produces dynamic and sometimes surprising behaviors (such as churn) in the program – and experiment with several interventions currently under consideration by various States. Chapter six concludes with a discussion of the implications and contributions of this study’s findings for research, practice, and education, as well as its limitations and opportunities for future research.
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Chapter 2. Current Perspectives on Medicaid Program Enrollment
Medicaid accounts for 15% of national health care expenditures and is the single largest health care payer in every U.S. State (Rudowitz, 2016). Every year, Medicaid covers one in five adults, and one in three children – about 74 million low-income Americans (CMS, 2018). Having health insurance improves access to health services, alleviates the economic burden of health care costs, and is associated with improved health outcomes (Kaiser Family Foundation [KFF], 2017), making Medicaid an important safety net for the vulnerable populations covered by the program.
However, participation in the program in far from full – about 70% of eligible adults and 90% of eligible children take up coverage (Kenney, Haley, Pan, Lynch, & Buettgens, 2016a, 2016b).
Additionally, nearly 20% of all Medicaid enrollees experience a loss of their coverage each year
(Ku & Steinmetz, 2013). These phenomena contribute to an enrollment gap in Medicaid’s target population. Changes in health insurance, even if they do not include a period without any coverage, are associated with delayed care, loss of access to care and treatment, and discontinuities in disease management that are deleterious to health outcomes (Laverreda, Gatchell, Ponce, Brown, & Chia,
2008).
Passage of the Affordable Care Act (ACA) in 2010 created new pathways, expectations, and incentives for health insurance coverage, including expanded eligibility for Medicaid. The implementation of these changes has implications for individual, governmental, and social costs associated with health insurance and health care services, including the degree to which eligible populations take up and experience discontinuities in coverage. Congressional Republicans have not been successful in their bids to repeal the ACA, yet decisions by Trump administration officials 11 indicate that the face of Medicaid could change significantly in the coming years.6 Continued research on enrollment patterns in Medicaid thus remains relevant for health services and public administration research. Health services researchers have spent years in the lead-up to and since passage of the ACA estimating levels of uninsurance and Medicaid coverage, and scholars in public administration have studied the administrative side of program implementation. However, no one has provided a holistic review of these two streams of literature and how they relate to each other. Understanding their findings in context is important because Medicaid enrollment is both a matter of coverage and a matter of administrative rule-making and practice.
This chapter proceeds as follows: First, I provide an overview of the Medicaid program, and how its eligibility criteria have changed since its initial passage. Then, I define missed take- up and churn as part of a larger phenomenon of Medicaid implementation: the enrollment gap.
Reviewing the literature on take-up, churn, and improper enrollment, I provide an overview of
Medicaid enrollment patterns before and after passage of the Affordable Care Act. Next, I review and organize the literature on explanations for differential levels of take-up and churn among State programs. I discuss the difference between population and structural explanations for enrollment patterns. Finally, I discuss the degree to which enrollment classification errors may be considered unintended consequences of the implementation process and outline how future chapters address some of the gaps in the existing research.
Overview of Medicaid Eligibility and Enrollment
6 This dissertation will not cover any changes to the program proposed or made after February 2018. 12 Medicaid program logic. Medicaid is a jointly administered, means-tested entitlement program meant to serve as a safety net for those who are vulnerable to excessive medical costs.
The Social Security Amendments of 1965 established both the Medicaid and Medicare programs, which provide health insurance coverage via in-kind (rather than cash) benefits paid to health care providers, though for different populations and in different ways. While Medicare is a social insurance program for those 65 and older, eligibility for Medicaid is categorical – an individual must be a member of a category defined by statute in order to be entitled to the benefits of the program. The categories effectively make Medicaid an umbrella policy for a set of programs: One that provides health insurance coverage for low-income children and families, another that covers low-income blind or disabled individuals, another that insures against medical expenditures not covered by Medicare for the low-income elderly, and another that pays long-term care costs for many of the institutionalized elderly (Gruber, 2003).
Medicaid is also different from Medicare in its administration. While Medicare is run by the federal government, Medicaid is jointly run with the States, whose participation is voluntary.
Federal matching funds compelled all but two States to join the program by 1970; every State adopted Medicaid by 1982 (Kaiser Commission on Medicaid and the Uninsured [KCMU], 2012).
While States must comply with minimum guidelines in order to receive federal funding (which is based on State per capita income), the administration of benefits, implementation of enrollment procedures, and supplemental eligibility criteria are left to State discretion. As a result, though the structure of the program is broadly the same, there is significant variation in State Medicaid systems. In addition, in the more than 50 years since its passage, the federal policy has undergone a number of changes, many of which expanded the original categories for eligibility – i.e., children, elderly, and disabled with very low incomes. This includes establishing a link between Medicaid
13 and Supplemental Security Income for the aged and disabled (1972), expanding coverage to more low-income children through a series of budget reconciliation acts (1980s), and establishment of the State Children’s Health Insurance Program ([SCHIP] 1997) (Paradise, Lyons, & Rowland,
2015). Medicaid’s program objectives are generally assessed in terms of access to health care and cost-effectiveness of care for vulnerable populations (Figure 1).
Figure 1. Medicaid Program Logic
The Affordable Care Act. The ACA’s passage in March of 2010 brought the single biggest wave of changes to Medicaid in the program’s history. The ACA expands the program by creating a category for eligibility based purely on income at a threshold of 138% of the federal poverty level (FPL) (about $28,700 for a family of three), while requiring States to maintain any income eligibility thresholds above that level for existing categories, such as children or disabled workers. The legislation also specifies that Modified Adjusted Gross Income (MAGI) be used to
14 determine eligibility across categories, and eliminates asset tests as part of the determination process. To further simplify the enrollment process, the ACA also eliminates in-person determination interviews, reduces various verification requirements, requires States to operate online portals for application, and permits Medicaid providers to bill services for uninsured individuals to the program under presumptive eligibility. Although the U.S. Supreme Court ruled in 2012 that Medicaid expansion is optional, 32 States and Washington D.C. have taken up the expansion (as of February 2018). Nonetheless, changes to the program, as well as demographic and economic trends, have led to steady increases in Medicaid caseloads. Medicaid covered less than 7% of the population in 1970 (14 million); Medicaid and CHIP now cover nearly 23% of
Americans each month (74 million) (CMS, 2017; Figure 2). An additional 16.6 million people have gained coverage through Medicaid since the expansion took effect in 2014 (CMS, 2018).
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Figure 2. U.S. Population Breakdown
As the US and Medicaid populations have grown since 1965, the number of Americans living below the poverty line has remained relatively steady. The number of uninsured, after growing steadily, decreased significantly after passage of the ACA.
Means testing and classification error. While the ACA added another category for eligibility (i.e., low-income adults, regardless of dependents), none of the changes to Medicaid since its establishment have altered the fundamental logic of the program. Categorical eligibility is determined through a means test – an assessment by the State of an individual’s eligibility based on financial and health need. The means test is theoretically simple, employing two primary questions: 1) Is the individual in a covered demographic category? 2) Is the individual financially vulnerable to medical costs according to a particular income threshold? If a person is determined to be eligible (i.e., if they “pass” the means test), they are entitled to enroll in the program and receive benefits. Thus, all residents of a State are potentially eligible for Medicaid; the State cannot
16 deny coverage to any citizen or legal resident who meets the eligibility criteria.7 In practice, however, eligibility does not fully determine enrollment; there are classification errors in implementation of the means test.
Take-up refers to the percentage of all eligible people who are enrolled/participating in the
program. Its complement, missed take-up, is the percentage of all eligible people who are
not enrolled in the program.
Churn loosely refers to a number of categorical changes: moving on and off the program,
moving in and out of eligibility for Medicaid, moving from Medicaid into being uninsured,
or moving from Medicaid into another kind of insurance (employer-sponsored, individual).
Improper enrollment refers to people who are enrolled in the program, though they do not
meet the eligibility criteria; i.e., the percentage of people enrolled who are not eligible.
Improper enrollments are more common than fraud, which is “an intentional deception or
misrepresentation that an individual knows to be false or does not believe to be true and
makes knowing that the deception could result in some unauthorized benefit to him/herself
or some other person” (U.S. Department of Health and Human Services). As a simple
description of a misalignment between eligibility and enrollment state, improper
enrollment is a more appropriate term for this research.8
Classification errors highlight the effect of means testing on an entitlement program.
Consider the contrast between Medicaid and Medicare. Though both are redistributive policies
7 I use the term “citizen” in this research to refer to individuals who are potentially eligible for Medicaid enrollment. Truly, this population also includes non-citizen legal residents, and even non-legal residents in some States. But for the sake of simplicity and clarity in talking about individuals who might be enrolled in the program (as opposed to those who work on behalf of the program in some way), “citizen” will refer to this broader class of people. 8 As discussed later in this chapter, the vast majority of both improper payments and fraud are tied to providers and managed care plans, not beneficiaries. 17 aimed at alleviating disadvantage or vulnerability within some group (Lowi, 1972), levels of participation vary significantly. While 99% of eligible people are enrolled in Medicare Part A
(Remler & Glied, 2003) with its universal eligibility rule and automatic enrollment default, only
75% of eligible people (adults and children, combined) are enrolled in Medicaid (Kenney et al.,
2016).
Means testing presents variability, discretion, and noise in program implementation – the process of moving from “the formal declaration of what government is going to do” (Smith &
Larimer, 2009, p. 155), to what government does, to the effect on society of what government has done (Mazmanian & Sabatier, 1983; Lester & Goggin, 1998). Medicaid enrollments are reviewed and reported monthly, and periods of coverage and redeterminations occur in monthly increments.
States use this administrative timetable in part because their populations are dynamic, with frequent and irregular changes in the size and composition of the eligible population. Therefore, even if an initial assessment of categorical eligibility is correct, income, health, and household characteristics are likely to alter eligibility in time. Similarly, pathways to eligibility may change with time, even among those who are continuously eligible. The decisions of individual citizens and State agents introduce delays and discretion in enrollment application enrollment: Someone has to take the test, someone has to grade the test; the test is not compensatory; and people may not even know that there is a test to take.
In addition, while there are federal minimum requirements for the program, States have a great deal of discretion on the particular eligibility and implementation rules they set. States vary in the degree to which they cover different categories above the minimum threshold. Figure 3 shows median income thresholds for the primary eligibility categories for States that have expanded Medicaid versus those that have not. Note that States that have expanded their Medicaid 18 program under the ACA have higher eligibility thresholds across all categories. (Figure 4 shows the current dollar value of these thresholds.)
19
Figure 3. Medicaid Eligibility Thresholds
Figure 4. Federal Poverty Guidelines
20 The comparison of categories and thresholds presented in Figure 3 are simplifications. For example, in some States, former foster children who left foster care on or after their 18th birthday are eligible for Medicaid, regardless of income, until they are 26, and some States do not allow disabled workers to buy into the program at any level. Federally, thresholds vary for different medically needy groups (e.g., Neglected Children, Title X Family Planning, Breast and Cervical
Cancer Project), and Medicare beneficiaries qualify in different ways, at different thresholds, for different kinds of benefits (e.g., premium assistance, long-term care, prescription drug) (Kenney et al., 2016). Thresholds are further complicated by rules that allow individuals to “spend down” to Medicaid income thresholds by subtracting their medical expenditures from their gross incomes, and by the fact that an individual may be eligible for the program through multiple pathways (e.g., parent of enrolled child, pregnant woman). The particularities of the means test – who is eligible, through what category, for what period of time – create confusion for both individual citizens and
State agents, and an implementation environment ripe for error on both ends.
Dynamic Problem Definition: The Enrollment Gap in Medicaid Coverage
In this dissertation, the enrollment gap refers to the disparity between the population eligible for Medicaid and the population enrolled in the program at any given time, over a period of time. The gap between eligibility and enrollment is the result of classification error in implementation of the means test, because of a determination inconsistent with eligibility (by the
State), because of application inconsistent with eligibility (by a citizen), or both.
Missed take-up and churn contribute to the enrollment gap (Figure 5). Missed take-up represents the portion of the target population who are not covered by the program and are thus especially vulnerable to health care costs. Some churn represents continuously eligible individuals
21 who move on and off the program in error. Other forms of churn are also relevant to the enrollment gap because they represent individuals who, though they may experience temporary periods of ineligibility, are fundamentally vulnerable to health care costs because of economic or health status instability. While improper enrollment does not contribute directly to the enrollment gap (in fact, it could theoretically appear to the close the gap), it represents error in the distinction between eligible and ineligible individuals; it may also represent additional demand for coverage (i.e., financial vulnerability).
For individuals, falling in the enrollment gap, even for a short period of time, reduces access to health care (including many primary prevention services) and increases the financial burden associated with health care. Additionally, lack or instability of health care coverage through
Medicaid negatively affects both chronic disease management and labor force participation among vulnerable populations (Gruber, 2003; Kenney et al., 2016). For States, the persistence and unpredictability of the enrollment gap poses challenges for multi-year program planning and budgeting, and suggests inefficiencies, ineffectiveness, or inequities in the system. Missed take- up and churn, for example, reduce costs in the short-run by reducing monthly caseload, but may lead to higher costs down the line as individuals delay necessary care until they are acutely ill.
Continual movement of people on and off the program also increases administrative costs as States have to dedicate resources to application review and determination procedures. Unnecessary program costs are the primary reason that States may be concerned with fraud and improper enrollment, assuming that “undeserving” people who receive benefits are wasting taxpayer money
(Schneider & Ingram, 2005), and doing so intentionally.
22
Figure 5. Medicaid Enrollment Gap
Evidence of the Enrollment Gap
Historical context is important to understanding trends in enrollment and classification because demographic and economic trends drive Medicaid enrollment. Medicaid is an automatic stabilizer – in times of recession, falling income and loss of access to other insurance options drives increases in enrollments (because more people become eligible), thus maintaining aggregate demand for health care by replacing lost private consumption with increased government spending.9 More generally, when the relative proportion of children and other vulnerable groups grows, Medicaid enrollment grows, and vulnerable populations are particularly hard-hit by periods of economic downturn. The Great Recession (2007-2009) created one of these periods of increased demand for safety net services (Figure 6). The question of how to cover a larger (and growing) population of vulnerable people in light of rising health care costs and a volatile economic environment helped drive efforts for health reform at end of the first decade of the 2000s,
9 Medicaid also basically serves as a high-risk pool, protecting the private market from the sickest and poorest lives to cover. 23 particularly the expansion of Medicaid to include low-income individuals who had not previously been eligible.
Passage of the ACA, which occurred after prolonged debate about health care reform and at the end of one of the worst economic downturns since the Great Depression, was a watershed moment both in the scope of Medicaid coverage and in the study of it. Expansion of Medicaid meant that many more people were eligible for the program, creating more applications for States to review, more classifications to make.
The ACA’s provisions altered enrollment classification at the State level. The expansion of Medicaid created an additional category for eligibility (mainly low-income adults) and complicated classification and enrollment by a) creating marketplace Exchanges for subsidized coverage, and b) requiring individuals to hold health insurance coverage. States thus became responsible for classifying applications based on eligibility for Medicaid or subsidies; the means test got harder to grade. Because so many people live in income ranges that make them eligible for
Medicaid or federal subsidies – 59% of Americans lived under 400% FPL in 2016 (KFF, 2017) –
Medicaid enrollment and classification errors increasingly have become a concern for politicians and the public, not just for technocrats or academics.
The literature reflects the economic and legislative shift in this period. Most of what is known about Medicaid classification and enrollment comes from health services research (HSR), the interdisciplinary field that is concerned with health care access, costs, financing, and quality.
Health services research is interested in coverage as it is related to access to care for a target population, and also informs State and federal efforts to project future enrollment and costs, informing financing and other planning decisions. Since passage of the ACA, there have been
24 more studies on enrollment and classification in Medicaid, including a broadening of the construct of safety net coverage to include matters of continuity. Research increasingly concerns how people move on and off Medicaid and into other coverage states over time (churn), not just how many of the eligible and needy population is being covered by the program (take-up).
Figure 6. Economic Trends During Medicaid Era
25 Medicaid enrollment before the ACA. Health services research on Medicaid enrollment prior to passage of the ACA found missed take-up and churn to be common phenomena, particularly among adults. National estimates put adult participation in Medicaid in the years before expansion around two-thirds of the eligible population. Using Current Population Survey
(CPS) data, Harvard researchers estimate that average take-up between 2005 and 2010 was 63% among adults, with considerable variation among the States, ranging from 43% in Arkansas and
Louisiana to 83% in Massachusetts (Sommers, Tomasi, Swartz, & Epstein, 2012). The Urban
Institute estimates take-up at 67% among adults in 2009, based on American Community Survey
(ACS) data (Kenney, Lynch, Haley, & Huntress, 2012).10 Participation is generally higher among children; the Urban Institute estimates that nearly 83% of children take up Medicaid nationally, ranging from 63% in Nevada to 96% and 97% in Massachusetts and Washington D.C., respectively
(Kenney, Lynch, Haley, Huntress, Resnick, & Coyer, 2011). A small uptick in child participation from 2008 (82% nationally, ranging 57-95%) is consistent with the automatic stabilization expected during a recession (Kenney et al., 2011).
Using ACS data, the Urban Institute is able to provide a robust picture of individual attributes associated with higher levels of participation among adults. Demographically, women, citizens, those who speak English in the home, those with a functional limitation, the unmarried, and those receiving Supplemental Nutrition Assistance Program (SNAP) benefits participate at a higher level than their peer groups (Kenney et al., 2012). Additionally, those without access to a vehicle, and those without a phone in the home take up at a higher rate, perhaps because they are
10 These levels are lower than participation in other assistance programs: 72-83% take up unemployment benefits, 80- 86% take up the Earned Income Tax Credit, and 99% take up Medicare part A, though adult Medicaid participation is similar to 54-71% participation in the Supplemental Nutritional Assistance Program and 65% take-up of rental assistance (Remler & Glied, 2003). 26 among the most in need and may be better connected to social services. Urban Institute researchers also find that those without dependent children participate at a higher level than those with children
(70% versus 64%); the authors point out that this is likely picking up higher participation levels among aged, blind, and disabled (ABD) groups as compared to adults without functional limitations. Adults who identify as Hispanic11 or Native American participate at lower levels than other ethnic groups; young adults 19-24 take up less than other age groups; those with a bachelor’s degree or higher take up less than those with less education; full-time workers participate less than part-time workers; veterans who served after 2001 participate less than either civilians or vets who served prior to 2001; and those with the very lowest incomes – 0-132% FPL – take up at lower levels than those at higher income levels. Those living in a mixed metro region and those living in the South have the lowest regional participation rates (Kenney et al., 2012).
Health services research also finds that discontinuities in Medicaid coverage were more common among adults than children before the ACA. In an analysis of Medicaid Statistical
Information System (MSIS) data, researchers at George Washington University find that 21.5% of
Medicaid enrollees experienced a discontinuity in their coverage in 2006 (Ku, MacTaggart,
Pervez, & Rosenbaum, 2009). Children, who make up most of that population, had slightly better continuity overall (79.7%), while adults 19-64 had lower levels of continuous enrollment (68.3%)
(Ku et al., 2009). Similar to findings about take-up, those with functional limitations (i.e., blind and disabled) had higher levels of continuous enrollment – about 90% maintained their Medicaid coverage throughout the year (Ku et al., 2009).
11 While Latinx is commonly understood to be the more appropriate term for those of Latin American ethnic descent, “Hispanic” is the term used in Census (and other survey) data and in studies drawing from these datasets, so it is the term that I use here. 27 Benjamin Sommers uses data from the Medical Expenditure Panel Survey, Household
Component (MEPS-HC) from 2000-2004 to describe Medicaid disenrollment in more detail.
Among adults enrolled in Medicaid at the start of the survey wave, 21% were no longer enrolled
12 months later, and 14% of those enrolled at the start were uninsured 12 months later (Sommers,
2008). Among adults newly enrolled in Medicaid during the survey, 20% were disenrolled after six months, 43% at 12 months, and 55% after 23 months. Newly enrolled children had lower levels of disenrollment: 12% were disenrolled after six months, 26% at 12 months, and 36% at 23 months.
Sommers finds that adults who disenroll are demographically similar to those who are less likely to participate in the program in the first place (per Kenney et al., 2012). Men, younger adults,
Hispanics, those with more education, and those living in the West and South regions were more likely to disenroll than their peers, as were pregnant women (Sommers, 2008). Those enrolled in
Medicaid Managed Care plans, who participated in SSI or TANF, and individuals with disabilities were less likely to disenroll. Still, there were significant levels of disenrollment among those in particularly vulnerable categories; as a group, 31% of TANF recipients, SSI recipients, and pregnant women were disenrolled after 23 months.
Disenrollment is itself an interesting movement in Medicaid, but Sommers also identifies those whom he considers to be churning, i.e., disenrollees who return to Medicaid during the survey period. Among adult disenrollees, 17% reenrolled in Medicaid within six months, 34% acquired other insurance, and 49% were uninsured (Sommers, 2008). In this regard, children again fared better in terms of coverage; 28% of children who disenrolled re-enrolled within six months and 29% acquired another form of coverage, while 43% remained uninsured.
28 Enrollment after Medicaid expansion. With the passage of the ACA, millions more
Americans became eligible for assistance in securing health insurance coverage. Participation in, and movement among, different kinds of assistance categories became a broader and more complicated issue for States, as they worked with the federal government to implement the law.
States that have opted not to expand Medicaid must still comply with certain requirements, such as maintaining eligibility thresholds for existing categories and modernizing enrollment procedures.
For individuals neither eligible for Medicaid nor an employer-sponsored plan, the ACA provides for graduated financial assistance in securing private coverage. Individuals with incomes between 139% and 400% FPL in States that expand Medicaid receive federal subsidies to purchase insurance in the State’s health insurance Exchange (the subsidies cover those between 100-400% in non-expansion States).12 The ACA gives States the option to set up their own Exchange or to defer to the federal government to run an Exchange. The combination of Exchanges, Medicaid changes, and the individual mandate to hold coverage created broader health insurance landscapes
(and more consumers) in every State. Initial estimates of participation post-ACA indicate modest increases among both adults and children; from 67% in 2009 to 70.5% in 2014 among adults, and
85% in 2009 to 91% in 2014 among children (Kenney et al., 2016).
Take-up has faded into the background as States and scholars turned their attention to the sheer number of people who were expected to enroll in Medicaid. As more people are eligible, and the federal government picks up more of the costs for those individuals,13 the size of enrollments
12 These assistance categories cover a significant number of Americans. In 2016, 59% of Americans – about 190 million people – had incomes under 400% of the federal poverty line. While not a perfect proxy for Medicaid, 13% - - about 41 million people – had incomes under 100% (Kaiser 2017). 13 States initially receive a higher FMAP for expansion category enrollees than for those who enrolled in previously-established categories. 29 has budget and planning implications for State programs, even with steady levels of take-up. In addition, multiple types and pathways to assistance mean that there are more types and ways of moving in and out of assistance categories. During debate and passage, experts noted that while millions of uninsured Americans would gain coverage, millions more would be susceptible to these movements among assistance categories (i.e., churning), which could disrupt their coverage (e.g.,
Sommers & Rosenbaum, 2011; Graves, Curtis, & Gruber, 2011; Buettgens, Nichols, & Dorn
2012). Continuity ultimately matters for access; changing plans, even without a gap in coverage, is associated with a 65% increase in the likelihood of delaying care because of cost and a 37% decrease in the odds of having a usual source of care (Lavarreda et al., 2008). Health services research thus began to pay more attention not just to how many people Medicaid enrolled, but how many people were being covered under various assistance interventions, and how many were churning among those categories.
The fundamental logic of the Medicaid expansion is that by broadening eligibility categories and thresholds, more Americans are eligible for benefits, and thus more people will gain coverage through Medicaid. Using National Health Interview Survey (NHIS) and Massachusetts health reform data from 2006-2010, Long and Dahlen (2014) find that expanding eligibility for low-income adults in Massachusetts is indeed associated with increased in enrollment in the
Medicaid program compared to States without an expansion of coverage, especially among childless adults. In a descriptive study of State Medicaid enrollment data from 2013-2015,
Rosenbaum, Schmucker, Rothenberg, and Gansulas (2016) find that the majority of growth in enrollments occurred in States that expanded Medicaid in 2014. These findings are consistent with an earlier study of expanded eligibility for children in the 1990s; Shore-Sheppard (2008) found that while expanded eligibility in the 1990s did expand coverage and increase enrollments for 30 children, it had a relatively small effect on take-up rates (i.e., a 15-19% marginal increase).
Additionally, Kenney et al. (2016) note that there is a lag between expansions in eligibility and increases in enrollments. Figure 7 shows how policy changes have generally expanded the sizes of both the eligible and enrolled populations, but the enrollment gap persists.
Figure 7. Enrollment Gap Over Time
31 Churn. With more people eligible and enrolled in the program, continuity of enrollment in
Medicaid continues to be an important topic of research in HSR. Churning from Medicaid to uninsured remains common after passage of the ACA, though experts expect disenrollment and bouts of uninsured among low-income individuals to become less common as States expand
Medicaid (e.g., Sommers, Arnston, Kenney, & Epstein, 2013; Long & Dahlen, 2014). In an update of their 2009 analysis of Medicaid Statistical Information System (MSIS) data, Ku & Steinmetz
(2013) find that 19% of Medicaid enrollees experienced a discontinuity in their coverage in 2010-
2011, a 3% reduction compared to 2006. Children and adults saw roughly the same gains in continuous enrollment, thus children continued to have better continuity of their Medicaid coverage (83% versus 72% among adults 19-64, and 86% among the aged). Continuity of coverage for the blind and disabled remained steady at 90%.
Evidence from case studies after the ACA suggest that Medicaid continuity continues to vary across States. In Maryland, 67% of the Medicaid population maintained their coverage throughout fiscal year 2011. Despite the State’s efforts to maintain coverage during income fluctuations, 12% of the Medicaid population became uninsured during the year (Milligan, 2015).
In Illinois, among the 25% of Medicaid beneficiaries who disenrolled in 2012, 36% returned to the Medicaid rolls within three months (Koetting, 2016). An evaluation of Alabama’s Medicaid program before and after expansion (2008-2012) finds that children who enrolled under the expansion were more likely to reenroll before the expiration of their eligibility period, suggesting that broader eligibility, not just longer periods, may itself be associated with improved continuity of coverage (Becker et al., 2015).
Consistent with Sommers’ 2008 findings, Daw, Hatfield, Swartz, & Sommers (2017) find that discontinuities in Medicaid coverage are common among pregnant women. Among 2,700 32 women who gave birth between 2005 and 2013,14 58% experienced a coverage change in the nine months leading up to delivery, and 62% of those women were uninsured for a least one month during pregnancy. Within six months postpartum, 55% of women who gained Medicaid or CHIP coverage for a delivery lost that coverage, driving the percent of uninsured back to pre-pregnancy levels (about 23% of women). In this nationally representative sample, the only significant predictor of an insurance lapse was household income.
Beyond churning off Medicaid into uninsurance, new pathways (and mandate) for health insurance coverage provided through the ACA have made churning among different sources of coverage (Medicaid, employer-sponsored insurance [ESI], or an Exchange plan) significant phenomena of interest. Medicaid is the single largest payer for health care in every State, and contracting with managed care organizations (MCOs) to cover beneficiaries is the preferred management tool in the majority of States. Medicaid MCOs also offer plans in the private market in 41 States. While more than half of non-elderly Americans get coverage through an employer- sponsored plan, ESI offerings have been steadily eroding as health care costs continue to rise and firms move to part-time and contract workers (Foutz, Damico, Squires, & Garfield, 2017). The churning of individuals among different sources of coverage is therefore of administrative importance to both State Departments of Medicaid and Departments of Insurance. Movement among various payers means private insurers will play an important role in population care and coverage management, and strategies to maximize public value (Ario & Bachrach, 2017).
In interviews with Medicaid plan executives, Rosenbaum (2015) finds that churn is important to the MCO business model because continuity of coverage and care among enrollees
14 Four pooled MEPS-HC panels. 33 keeps costs (both service and administrative) down in the long run. Officials report that the regulatory differences between offering in the marketplace and Medicaid are not of particular concern, but that maintaining enrollees as they shift between eligibility for Medicaid and subsidized exchange plans is difficult because individuals re-evaluate their coverage options when moving off of Medicaid. They report that low-income people choose rationally in the marketplace
(rather than defaulting to their Medicaid plan), opting for the best balance of high value benefits and low cost-sharing, even if it means changing plans. Thus, even if a plan has both Medicaid and exchange offerings, their revenue stream (i.e., lives covered) is subject to consumer preference fluctuations. This insight is consistent with the assumption that individuals make health care coverage decisions on the basis of a rational cost-benefit assessment (e.g., Moffitt, 1983; Remler
& Glied, 2003).
Since eligibility is correlated with coverage, one way of estimating churn among coverage sources is to estimate how many people churn among eligibility categories. Much of the research on churn since passage of the ACA uses this strategy, where income is used as a marker of coverage, assuming full take-up for the coverage option offered in that eligibility category.
Churning between Medicaid eligibility and eligibility for federal subsidies for an Exchange plan is common. Based on MAGI thresholds, researchers at the Urban Institute estimate that one-third of those eligible for some form of assistance – nearly 30 million non-elderly adults – shift between eligibility categories from year to year (Buettgens et al., 2012)15. This includes 7 million people moving from Medicaid to subsidy eligibility, 3 million moving from being subsidy eligible to ineligible, and nearly 20 million moving from Medicaid eligibility to ineligibility for any
15 Survey of Income and Program Participation, longitudinal, nationally-representative; 2001-2004 34 assistance, typically because of access to an ESI offering.16 A more recent study estimates that half of those eligible for Medicaid or subsidies experience an eligibility change within 12 months, though there is State variation (Sommers, Graves, Swartz, & Rosenbaum, 2014).17
In addition to expanding Medicaid eligibility for adults up to 138% FPL, States have the option to set up a Basic Health Plan (BHP) to cover adults up to 200% FPL, effectively changing the threshold for Medicaid eligibility up to that level. Sommers and Rosenbaum (2011) find that
35% of non-elderly adults with incomes under 200% FPL experience an income change that would shift them between a Medicaid/Basic Health Plan and subsidy eligibility within six months, and
50% shift within a year. In addition, 25% of the 39,000 adults in their Survey of Income and
Program Participation (SIPP) sample experience at least two eligibility shifts within a year18. They find that initial income is the best predictor of an eligibility transition, with those between 100 and
150% FPL being the most likely to shift.
Rather than using income eligibility as a proxy for coverage, Shore-Sheppard (2014) focuses purely on household income fluctuations as the outcome of interest. She finds that incomes for all Americans are highly variable within a year, especially at the lower end of the income distribution, suggesting that household economy (including income, assets, family composition, and health status) is fundamentally dynamic. Using SIPP data covering 1996-2010, Shore-
Sheppard defines four mutually exclusive and exhaustive income categories corresponding to thresholds of interest for Medicaid and Exchange implementation: below 138% FPL, 138-250%,
250-400%, and above 400% FPL. Because of the size of her sample, each income category has
16 Kenney et al., (2016) refer to people in these categories as falling in the “assistance gap”. 17 For example, a team at UC Berkeley estimates that 75% of California Medicaid enrollees remain in the same eligibility category at the end of the year, while 16.5% become eligible for Exchange subsidies (Dietz et al., 2014). 18 SIPP; 2004-2005, 2008-2009. 35 hundreds of thousands of person-months observations. She finds that 55% of the representative sample experiences at least one income category transition. Of particular interest is that 75% of adults spend at least one month below 400% FPL, meaning that three-quarters are eligible for some sort of assistance at some point during the year. Among the 45% who spend the entire year in one income category, about a quarter are under the Medicaid expansion threshold, and another quarter are between the Medicaid cutoff and the threshold for subsidy eligibility. Furthermore, Shore-
Sheppard finds that employment transitions (even without any period of unemployment) and family composition changes predict income changes that are sufficient to trigger an eligibility change. She concludes that “income volatility” is common and happens for a number of reasons, suggesting that assistance policies have no choice but to account for dynamic household economies.
Improper enrollment. Despite being the target of press coverage and political debate, the prevalence of beneficiary fraud is Medicaid is very low.19 Based on claims from four of the largest
Medicaid programs (Arizona, Michigan, Florida, New Jersey), the Government Accountability
Office (GAO) found that 8,800 out of 9.2 million enrollee files in fiscal year 2011 were either duplicates (i.e., on file in two States) or tied to a deceased individual (GAO, 2015). These files, which are likely consistent with HHS’s definition of fraud, represent 0.00096% of all enrollees.20
No direct measures exist of the more general problem of improper enrollments – the percent of enrollees who are not truly eligible but are not necessarily committing fraud. However,
CMS does track improper payments – “any payment that should not have been made or that was
19 The vast majority of both fraud and improper payments are attributable to providers and third-party insurers (i.e., managed care contractors). More on this in the second half of this chapter. 20 Fraud is the intentional deception or misrepresentation that an individual knows to be false or does not believe to be true and makes, knowing that the deception could result in some unauthorized benefit to himself/herself or some other person (U.S. Department of Health and Human Services). 36 made in an incorrect amount (including both overpayments and underpayments) under statutory, contractual, administrative, or other legally applicable requirements, or where documentation is missing or not available, and are not fraud” (U.S. Department of Health and Human Services).
Improper payments typically do not involve fraud, but rather are payments for which there is insufficient documentation, claims that are incorrectly coded, or for services that do not meet medical necessity criteria (CMS, 2015). Furthermore, the vast majority of the $51.5 billion in improper payments across Medicaid and CHIP are attributable to errors in provider and managed care documentation (CMS, 2015). CMS estimates the improper payment rate attributable to eligibility error to be 3% and 4% in Medicaid and CHIP, respectively.21
Overall, findings from health services research indicate that while passage of the ACA has changed the health insurance landscape in meaningful ways, the enrollment gap in Medicaid persists. The expansion of Medicaid eligibility (in most States) has led to coverage for millions of
Americans, but millions more of vulnerable individuals remain outside of the program. While the majority of enrollment research has focused on the individual characteristics and risk factors associated with program participation and churn, both researchers and administrators recognize that State administrative decisions and health services structure are important for successful implementation of the ACA, including coverage under Medicaid expansion and providing assistance for access to the Exchanges (e.g., Sommers et al., 2013; Rosenbaum, Schmucker,
Rothenberg, & Gunsalus, 2016). As household composition and economies continue to be
21 Total improper payments are based on a three-year rolling average from 2013-2015. The overall improper payment rates for Medicaid and CHIP are 9.8% and 6.8%, respectively. The estimated improper payment due to eligibility rate for Medicaid is 3.1%, but is 2.9% when adjusted for underpayments (which would be improper on the part of the State rather than the beneficiary). The estimated improper payment due to eligibility rate for CHIP is 4.2%, but is 4.0% when adjusted for underpayments. Federal guidelines stipulate that States should limit errors such that no more than 3% of State Medicaid spending is attributable to erroneous payments (U.S. Code subchapter XIX 1396a(a)(5)). 37 dynamic, new pathways to coverage and institutional arrangements ensure that churning among coverage and assistance categories will remain an issue. Addressing Medicaid’s enrollment gap requires a greater understanding of why people move on and off the program, not just who.
Explanations for Enrollment and Classification Error
Although implementation of the means test is far from simple in practice, the entitlement design of Medicaid seems relatively simple in its objective to provide health care coverage for vulnerable people. Why, then, are some people more or less likely to enroll in the benefit? Why do people move on and off the program? Why are some (very small) portion of ineligible people enrolled in the program? Classification errors may be thought of as examples of “unintended consequences” (Merton, 1936) of the program: outcomes not intended by purposeful action or foreseen by decision makers. However, even if the consequences are unintended, policy actors need to know (or at least have theories about) how such consequences came to be if they are to address them. Given the importance of coverage for both individuals and for program planning and assessment, it is important to understand why there is a persistent enrollment gap, and why the gap varies over time and across States.
There are two primary homes for research about the drivers of classification errors: health services research and public policy research (spanning public administration, political science, and sociology). Both fields are interdisciplinary and are generally concerned with questions of program implementation and evaluation. Both predominantly use variance studies to estimate the association between some set of independent variables and an enrollment outcome variable.
However, while there is some overlap in the methods and findings from these two literatures, there are also differences in purposes, assumptions, measures, and levels of analysis. 38 Health services research is generally concerned with how people get access to care, how much it costs, and what happens to them as a result. Those trained in public health and health economics dominate the field, which may explain HSR’s decidedly population-oriented approach to Medicaid enrollment outcomes. Enrollment status is typically expressed as a proportional sum of individual events (i.e., a population prevalence such as the percentage of the eligible population enrolled) and modeled as a function of average individual attributes and behaviors in a given month, with statistical controls for program and economic factors.
In contrast, policy studies focus on the actions of the State and other formal policy actors to achieve collective goals, with particular attention to features of the policy process and policy analysis. Thus, research from public administration (PA), political science, and sociology tends to explore possible structural explanations for enrollment patterns and variation across Medicaid programs. Enrollment outcomes are expressed as proportional sums of individual statuses (e.g., the percentage of enrollment among the eligible population), and modeled as a function of program features, controlling for population and environmental characteristics. Policy studies thus expand the boundaries of Medicaid enrollment studies by explicitly including the actions of the State in the population coverage equation. Furthermore, policy studies treat enrollment patterns as measures of program performance (i.e., how effective the program is at enrolling eligible individuals), and thus are generally interested in longer time horizons (e.g., over two-year budget periods, across multiple budget cycles). Taken together, the health services and policy literatures identify four categories of factors as drivers of public program enrollment patterns: economic forces, individual characteristics, program eligibility criteria (entitlement policy design), and program implementation features, including operational practices and institutional arrangements.
39 Economic factors. Scholars in both HSR and policy studies include State-level economic indicators in their explanations of Medicaid enrollment patterns. The relative health of the economy has implications for enrollment dynamics because of Medicaid’s role as an automatic stabilizer. When incomes are high, eligibility for public benefits falls, as do enrollments.
Conversely, when incomes fall, more people become eligible for benefits. In tax and transfer systems like Medicaid, increased eligibility and enrollments during economic downturns automatically stabilize aggregate demand for health insurance coverage (Elmendorf & Furman,
2008). Thus, economic conditions inevitably drive Medicaid caseloads to some degree. This also means that State and temporal variation in Medicaid enrollments are at least partially attributable to variation in local economic conditions. Indeed, the phenomenon of economic trends driving eligibility and enrollments is born out in the literature. Recession-sensitive enrollment dynamics
(and the related spending implications) are illustrated in Figure 8, an analysis of CMS data by the
Kaiser Commission on Medicaid and the Uninsured. Dietz, Graham-Squire, and Jacobs (2014) find that Modified Adjusted Gross Income (MAGI) (rather than health condition) is the primary pathway for eligibility among Medicaid enrollees in California.
40
Figure 8. Kaiser Estimates of Medicaid Spending and Enrollment
While economic trends deflect some responsibility for enrollment dynamics from specific policy actors or actions, they do not explain all observed variation in enrollment patterns. The economic context clearly does matter; enrollments and spending will respond to household income, and some people will churn as their incomes change. Individuals – be they citizens, administrators, or policymakers – can do little about economic pressures or exogenous shocks.
However, economic factors do not account for classification errors in Medicaid enrollments, particularly missed take-up. Neither the Federal Medical Assistance Percentage (FMAP) nor the
State’s per capita income (on which FMAP is based) appear to have an effect on State take-up rates (Sommers et al., 2012). State income also fails to explain coverage stability; churning is
41 highest in States with the lowest poverty rates (Sommers et al., 2014).22 Among individuals, the most impoverished (living under 132% FPL) have lower take-up than those at higher levels, suggesting that those who are the most financially vulnerable are more likely to fall in the enrollment gap (Kenney et al., 2012). Furthermore, income change is common, even among economically stable households, and even during years of stability or growth in the broader economy (Shore-Sheppard, 2014). Within-year “income volatility”, as Shore-Sheppard calls it, is a fact of life for most households, regardless of macroeconomic trends. Addressing the enrollment gap – the disconnect between eligibility and enrollment – requires turning to explanations beyond the economy.
Individual attributes as behaviors. Consistent with the logic of public health and health economics, enrollment studies in the HSR literature largely focus on the contribution of individual attributes to program enrollment patterns. Theoretically, demographic characteristics are either proxies for, or explain behaviors, meaning certain types of people are more or less likely to take up Medicaid coverage. As discussed earlier in this chapter, there is evidence that demographic characteristics do explain some of the variation in enrollment classification status. For example, children and adults with functional limitations have both higher levels of participation (Kenney et al., 2012) and better continuity of coverage through Medicaid compared to other groups (Ku et al.,
2009; Sommers, 2008).
One of the implications of modeling enrollment as a function of individual attributes is that individuals are themselves responsible for their enrollment status. The assumption is that eligible people will enroll in a program if the benefits of that program exceed the costs of participating
22 Additionally, Buchanan, Cappelleri, and Ohsfeldt (1991) find that Medicaid expenditures are highest in States with highest per capita income. 42 (Moffitt, 1983; Gruber, 2003; Remler & Glied, 2003). In other words, individuals make a proactive cost-benefit decision about whether or not to take up coverage.23 If this individual decision is the primary driver of enrollment status, then the fact that enrollment outcomes vary by demographic groups could be explained as differential revealed preferences for the benefit. As such, States would not necessarily bear responsibility for the disparities in the size of the enrollment gap across different groups.
However, evidence that people with certain attributes are more or less likely to enroll is not the same as evidence that characteristics are what explain decision-making regarding enrollment. If the decision is indeed a cost-benefit assessment, then it is necessary to gather information about the relative costs and benefits of the program to decision makers. The revealed preference for Medicaid benefits (i.e., enrollment) may be influenced by several non-monetary costs. For example, potential beneficiaries may not wish to enroll because of the social stigma of receiving public benefits, or a belief that they do not need health coverage (Remler & Glied, 2003).
It may also be that potential beneficiaries are not aware of the program or that they are eligible for it. Stuber and Bradley (2005) find that among the eligible, those least informed about the program perceive the highest barriers to entry, and are less likely to be enrolled in Medicaid. Across a number of public benefits, take-up is higher when information and clarity about eligibility thresholds and benefits is higher (Remler & Glied, 2003).
Finally, potential beneficiaries may be deterred because they find the process of enrollment
(e.g., application completion, financial verifications) to be more onerous than the benefit is worth.
23 This is also the logic behind an individual mandate to hold insurance. The assumption is that a mandate changes the calculation of the cost-benefit assessment for individuals deciding whether or not to buy health insurance by imposing an additional cost – a penalty – for not holding coverage. 43 Consider participation among recipients of Social Security Income (SSI), who have low incomes, limited assets, and an impaired ability to work at a substantial level due to advanced age or significant disability. This is a group for whom the cost-benefit calculus should clearly be in favor of enrollment, and for whom the social stigma of enrolling in Medicaid should be relatively low
(SSI has been a categorical pathway to Medicaid since 1972). Yet even some SSI beneficiaries are not enrolled in Medicaid, to say nothing of the aged, blind, or disabled who qualify for SSI but are not enrolled in that benefit (Gruber, 2003). One possible explanation is that the characteristics of the program, not just the individual, contribute to enrollment outcomes (Remler & Glied, 2003;
Blewett & Hempstead, 2014). Indeed, when controlling for State program features, Sommers et al. (2012) find that demographics do not explain State variation in levels of participation. This evidence suggests that policy decisions – the eligibility and enrollment rules that structure administration of the program – contribute the coverage status and the enrollment gap.
Eligibility criteria and entitlement design. Unlike economic or individual explanations, which focus on how non-State actors and forces contribute to Medicaid outcomes, structural explanations emphasize the role of programmatic features and policy decisions in driving enrollment patterns. These decisions include the design of the policy itself, in particular the eligibility criteria and income thresholds set to qualify for Medicaid (i.e., the means test). As discussed earlier in this chapter, expanded eligibility is associated with higher enrollments (Long
& Dahl, 2014; Rosenbaum et al., 2016; Shore-Sheppard, 2008) and fewer periods of being uninsured (Sommers et al., 2013; Long & Dahlen, 2014), as well as lower levels of program disenrollment (Ku et al., 2013; Becker et al., 2015). There is also evidence that improving the
44 value of benefits, by covering a broader range of services, for example, increases program participation (Remler & Glied, 2003; Sommers et al., 2012).24
Changing eligibility thresholds and increasing the value of benefits does not solve the problem of enrollment classification errors, however. Higher improper payment rates due to eligibility in CHIP compared to Medicaid may be partially attributable to higher income eligibility thresholds for children (CMS, 2015). Sommers et al. (2014) find that before passage of the ACA, churning on and off Medicaid was highest in States with higher eligibility thresholds. In addition to the Medicaid expansion, the ACA provided States with the option of implementing a Basic
Health Plan (BHP) to cover individuals up to 200% FPL.25 While such a plan would make more people eligible for Medicaid-style coverage, scholars disagree on the effect for continuity of coverage among low-income households. Graves, et al. (2011) estimate that while adopting and integrating a BHP into Medicaid would increase retention among those under 138% FPL, the increased churning among eligibility categories for those between 139-200% FPL would be sufficient to offset those gains. A stand-alone BHP would be even worse. However, others estimate that raising Medicaid eligibility to 200% through a BHP would reduce churn overall (Hwang,
Rosenbaum, & Sommers, 2012; Buettgens et al., 2012).
Expansions of income thresholds do not fundamentally change the design of the program.
Means-tested entitlements, such as Medicaid, provide benefits to specific groups on the basis of income, while universal entitlements provide benefits to an entire group or category of people,
24 There is no evidence that increasing benefits increases beneficiary fraud (CMS 2015). This is likely because Medicaid is an in-kind benefit, where payments are made directly to providers (or managed care plans), minimizing the incentive or ability of beneficiaries to defraud the program. 25 A BHP would cover people between 139-200% FPL in lieu of receiving federal subsidies to purchase insurance in the Exchange. Federal matching funds for Medicaid would therefore cover people up to 200% if the State adopts a BHP. 45 regardless of income. Programs with universal designs generally have higher participation levels than means-tested programs; for example, 99% of seniors take up Medicare Part A compared to
60-70% take-up in SNAP, unemployment, and rental assistance (Remler & Glied, 2003).
Compared to universal eligibility, the means-tested design includes more decisions in the enrollment process, both for citizens and for administrators. Individuals must consider if they qualify for the benefit, if they need the benefit, how they enroll, and whether the benefit is worth the process of getting enrolled. Administrators must consider if the applicant falls in a covered category and whether their income (as reported) qualifies them for benefits within that category.
Means testing is thus like any other classification or diagnostic test: information about a condition is gathered and then assessed according to some criteria, a two-step process that introduces error. The test (policy) designer can do things to increase sensitivity (true-positive) and specificity (true-negative) of the test, but errors (false positive, false negative) cannot be fully eliminated (Aschengrau & Seage, 2008; Koetting, 2016). Empirical evidence of an association between means testing and increased missed take-up (i.e., false-negative test results) in Medicaid is therefore consistent with testing design theory (Herd, DeLeire, Harvey, & Moynihan, 2013;
Moynihan, Herd, & Harvey, 2014; Herd, 2015). Making Medicaid a universal entitlement may indeed fix the enrollment gap, but this would represent a fundamental shift in the program and would likely involve tradeoffs on specificity.
While policy design sheds some light on enrollment outcomes, evidence suggests that changing eligibility thresholds and benefits will not eliminate classification error in Medicaid enrollment because such errors are inevitable given the means-tested design of the program.
Nevertheless, structural explanations for enrollment outcomes draw attention to the fact that
46 enrollment is not just a decision on the part of the individual citizen given the eligibility categories and thresholds.
Program implementation and administration. Beyond the means-tested entitlement design of Medicaid, the implementation of that design, through State-level decisions regarding the rules, processes, and procedures of means testing, has implications for individual citizen behaviors and enrollment outcomes. Implementation of a formalized policy involves all of the decisions, actions, and tasks of transforming policy specifications and initial conditions into program outputs and societal outcomes. (Birkland, 2011). In Medicaid, this includes features of program application, review, and determination for enrollment. Because Medicaid is jointly administered by the State and federal governments, and because States set up their own institutional arrangements for administration of the program, there are a lot of decisions, actions, tasks, and actors involved in implementation. Policy scholars, health services researchers, and administrators alike contend that the idiosyncrasy of those rules and arrangements makes variation in enrollment patterns (and classification errors) unavoidable.
Institutional arrangements. There is an old (and not very good) joke: “If you’ve seen one
State Medicaid program, you’ve seen one State Medicaid program.”26 Medicaid is a voluntary program (though every State participates), and States have substantial discretion in how they structure and administer the program. For example, 36 programs currently operate under at least one Section 1115 waiver of federal program provisions, and that number seems sure to grow under the Trump administration (Musumeci, Rudowitz, Hinton, Antonisse, & Hall, 2018).27
26 For example, see interview with Grace-Marie Turner, Federal Medicaid Commission in Lemov 2009. 27 Under Section 1115 of the Social Security Act, the Secretary of HHS can waive specific provisions of major health and welfare programs, including certain requirements of Medicaid and CHIP. In January 2018, Trump administration has indicated that they will grant new waivers for provisions not considered in the past; most notably, 47 Consequently, there are essentially 51 unique State Medicaid programs, with significant variation in participation and churn across the programs (e.g., Kenney et al., 2011, 2012, 2016; Sommers et al., 2012, 2014).
State decisions regarding administrative structure, including relationships with qualified health plans in Medicaid and the Exchange under the ACA, have implications for enrollment outcomes and spending, regardless of the particular provisions of the program (Blewett &
Hempstead, 2014; Ario & Bachrach, 2017). For example, States that administer their program locally have higher Medicaid expenditures than those that centralize administration at the State level (Buchanan, Cappelleri, & Ohsfeldt, 1991). Rosenbaum et al. (2016) find that States that allowed federal determination of Medicaid eligibility after passage of the ACA have higher
Exchange enrollments than States that use an “assess-and-refer” approach.28 They also find that
States with State-based Exchanges (rather than relying on the federal Exchange) have higher private plan enrollments. Many scholars have called for research that exploits State variation by conducting natural experiments of program design (e.g., Remler & Glied, 2003; Gruber, 2003;
Saunders & Alexander, 2008; Blewett & Hempstead, 2014; Rosenbaum, 2016).
Administrative burden. A key proposition in policy studies of Medicaid enrollment is that various features of implementation impose costs on those involved – both citizens and administrators. Currie (2004) points out that health economists have studied rules about eligibility, but have paid virtually no attention to how these rules are communicated to, or enforced upon, citizens. Moynihan and Herd (2010) point to a robust red tape literature in policy studies that
work requirements (CMS, Office of the Director 2018). In addition to 44 existing waivers across 36 States, there are an additional 23 pending across 22 States as of Feb 1, 2018 (Musumeci et al. 2018). 28 Under assess-and-refer, the state Medicaid office assesses eligibility and then refers to the federal level if the individual does not qualify for Medicaid under State rules. 48 focuses on the effects of implementation rules within an organization (i.e., bureaucratic costs), but wonder about the costs experienced by citizens as a result of administrative decision making (i.e., democratic costs).29 They posit that implementation of means-tested programs imposes costs that constitute administrative burden – “an individual’s experience of policy implementation as onerous” (Burden, Canon, Mayer, & Moynihan, 2012, p 742) – on individuals who seek to enroll, thus driving down their participation in such programs (Moynihan & Herd, 2010). Furthermore, they argue that administrative burden may be an intentional product of political preferences, as such forces shape how rules and arrangements are set (Moynihan, Herd, & Rigby, 2013).
In a review of program application forms and administrative features of all 50 States,
Moynihan et al. (2013) found that enrollment rules consistent with administrative burden
(including length of application, income and expense reporting, resident documentation, and in- person interviews) are associated with lower take-up in Medicaid programs. They also find that administrative burden for citizens is lower in States with a higher FMAP (i.e., States with lower per-capita income), and in States with unified Democratic control of political institutions, suggesting that the presence of burdensome rules is not independent of political or economic pressures.
Administrative burden on citizens is associated with similar patterns in other public programs. The proliferation of administrative rules and processes is associated with decreased welfare (i.e., Temporary Assistance to Needy Families, formerly Aid to Families with Dependent
Children) caseloads (Brodkin & Majmundar, 2010). Administrative burden also suppresses take- up of SNAP benefits among the elderly (Herd, 2015), and reduces the amount of time adolescents
29 Indeed, even Burden et al.’s 2012 paper that defines administrative burden focuses on the experience and perception of rules as onerous among administrators, not citizens. 49 spend on South Africa’s Child Support Grant program (Heinrich, 2015); both phenomena are associated with poorer health outcomes for their populations.
Application, review, and determination processes are administratively burdensome because they impose learning, compliance, and psychological costs on beneficiaries (Moynihan et al., 2014; Remler & Glied, 2003). The arrangements and procedures of implementation also have cost implications for the program itself. The proliferation of procedural rules, by making data management, verification, and reporting more complicated, reduce organizational efficiency and productivity (Lemak, Alexander, & Campbell, 2003), and may contribute to improper payments to providers and insurers (CMS, 2015).
Learning costs. To become beneficiaries of a public program, citizens must have – or take the time to gather – information about the program, its benefits, its eligibility criteria, and how to enroll and access services (Moynihan et al., 2014). When individuals have more information about eligibility thresholds and generosity of benefits, take-up is higher across a range of public programs, including unemployment, supportive housing, and Medicaid (Kahn, Katz, & Gutek,
1976; Remler & Glied, 2003). Certain people, often the most vulnerable and in need of services, are less informed and experience greater barriers to program information (i.e., their learning costs are higher). Not knowing or perceiving that they would not be eligible for Medicaid deters pregnant women from taking up Medicaid coverage (Currie, 2004). Among uninsured individuals interviewed at Community Health centers, those with health problems and African-Americans were more likely to be misinformed about eligibility rules. Those with mental health problems, individuals with less than 9th grade education, and women were more likely to perceive barriers to enrollment (Stuber & Bradley, 2005).
50 One way to reduce learning costs and increase participation is to increase public awareness about eligibility and benefits through outreach efforts. Wisconsin’s BadgerCare+ Medicaid branding campaign was associated with improved take-up (Herd et al., 2013), and vulnerable
Oregonians randomized to receive enhanced enrollment outreach (such as post card and email reminders about their eligibility with enrollment instructions) took up Medicaid at a higher level than those who received the State’s standard outreach protocols (Wright, Garcia-Alexander,
Weller, & Baicker, 2017). An Internal Revenue Service field experiment in California found that simple, concise messages about the Earned Income Tax Credit increased take-up among eligible individuals by 6% compared to the standard notifications (Bhargava & Manoli, 2015). Including an amount of the expected credit – whatever that amount was – increased take-up even more (31% versus 17% in the control group), suggesting that even marginal increases in program information contribute to individual decision-making.
Increased awareness about the program likely drives the “welcome mat effect” where people who were previously eligible sign up during an expansion period. This is one explanation for increased take-up among both adults and children after passage of the ACA (Sommers et al.,
2014).30 Other strategies that reduce learning costs are associated with increased participation, including implementation of online eligibility check (Herd et al., 2013) and open enrollment periods for both Exchange plans and Medicaid (Dietz et al., 2014).
Compliance costs. To become beneficiaries of a public program, citizens must complete program applications and reenrollment certifications, provide documentation and verification of their standing, and manage discretionary demands from administrators (Moynihan et al., 2014),
30 Participation among adults rose from 67% in 2009 to 70.5% in 2014 and 85% to 91% among children in that same period (Kenney et al. 2016). 51 which can be time- and energy-intensive. Application processes that impose higher compliance costs are associated with lower enrollments and levels or participation (Moynihan et al., 2013).
Compared to automatic or an opt-out enrollment model, the extra action of opt-in program enrollment reduces take-up. Medicare Part A, which enrolls individuals automatically following an application for Social Security retirement or disability benefits and does not require income information to determine eligibility, has a 99% take-up rate (Remler & Glied, 2003). Participation increased in Wisconsin’s Medicaid program after they instituted auto-enrollment based on State administrative data (Herd et al., 2013). Similarly, presumptive eligibility (enrollment by way of an episode of care from a Medicaid provider), enrollment linked to other programs (e.g., SNAP), and unified program applications are associated with higher take-up in Medicaid (Herd et al., 2013).31
Participation in both Medicaid and Exchange plans is higher when internal administrative processes are streamlined between the two systems, even in States that have not expanded their
Medicaid program (Rosenbaum et al., 2016).
States vary in the length of the continuous eligibility periods, the period of time enrollment lasts without reapplication by the beneficiary. Longer continuous eligibility periods (e.g., 12 months versus 6 months) are associated with reduced churning (Ku et al., 2013; Swartz, Short,
Graefe, & Uberoi, 2015). However, even with longer durations of enrollment, eligibility redetermination by the State is associated with improper disenrollments (i.e., disenrollment among continuously eligible individuals). In an Illinois case study, the most common reason for disenrollment upon redetermination was inadequate documentation, not loss of eligibility
(Koetting, 2016). Among those removed from the rolls, more than a third returned to Medicaid in
31 Medicare Part B, which enrolls individuals from Part A automatically unless the individual opts out, has also achieved a high take-up rate of 95.5% (Remler & Glied 2003). 52 less than three months (Koetting, 2016). This suggests that grace periods for documentation submission and review are important to continuity of coverage, and that renewal “lock out” rules
(like those included in Kentucky’s Section 1115 waiver) are likely to result in improper disenrollment and periods of uninsurance among vulnerable individuals (Sanger-Katz, 2018).
States require a range of status verifications for Medicaid enrollment. Reporting requirements (income, residence, employment) are associated with suppressed participation among eligible citizens (Sommers et al., 2012; Herd et al., 2013). Researchers and administrators alike expect that activity requirements (work, financial literacy course, physical exam, drug testing) will yield short-term program savings largely as a result of reduced take-up among the eligible, not exclusion of ineligible individuals (Williams, 2018; Sanger-Katz, 2018). In contrast, providing application assistance to potential beneficiaries increases participation significantly – more than 80% compared to an unassisted control group in an experiment in the SNAP program
(Schanzenbach, 2009). In a study of California Medicaid, Aizer (2003) finds that bilingual application assistance is beneficial for parents enrolling their children, particularly among
Hispanic and Asian children, whose parents are more likely to use English as a second language.
Psychological costs. In becoming beneficiaries of a public program, citizens may face stigma associated with participating in a public benefit, and may experience a loss of autonomy and increase in stress from dealing with program procedures, rules, and frontline administrators
(Moynihan et al., 2014). Psychological costs are imposed through rules, procedures, encounters, and attitudes that make it more mentally, emotionally, or socially taxing to enroll. Through encounters with policy – its eligibility and benefit parameters, its implementation, and its social acceptability – beneficiaries receive signals about their worth and standing in society (Schneider
& Ingram, 1997; Soss, Fording, & Schram, 2011). Experiences with public program 53 implementation also shape citizens’ attitudes and beliefs about the particular program and government more broadly (Soss, 1999), which may affect their behavior vis à vis program participation. For example, increasing outreach efforts and reducing application complexity are both associated with increased take-up (Wright et al., 2017; Bhargava & Manoli, 2015), perhaps because they reduce psychological costs to beneficiaries. Activity requirements and status verifications (e.g., work requirements, drug testing, income reporting) are also likely to impose psychological costs by sending signals about who is “deserving” of benefits (Schneider & Ingram,
1997).
Material costs. In addition to those costs pointed out by Moynihan et al. (2014), it is possible that beneficiaries will encounter monetary costs associated with participation in a public benefit. Some States have instituted cost-sharing in their Medicaid program, a practice that
Sommers et al. (2012) find is associated with decreased take-up. Monthly Medicaid premiums may have a similar effect, and are also likely to lead to more churning as beneficiaries are disenrolled for failure to pay premiums on time (Carroll, 2018). Table 1 summarizes the costs that contribute to administrative burden.
54 Table 1. Costs of Administrative Burden Type of Cost Application to Social Policy Examples from Medicaid Learning Citizens must learn about the Categorical eligibility and income program, whether they are eligible, thresholds the nature of the benefits, how to Community outreach and branding apply, and how to access services. Online application portals
Compliance Citizens must complete applications Length and detail of application and reenrollments, provide Activity and documentations documentation of their standing, and requirements and verifications avoid or respond to discretionary Review and renewal timeframes demands. Local Medicaid office service features
Psychological Citizens face stigma of participating Experience with frontline agents in an unpopular program, as well as Signals about “deservingness” the loss of autonomy and increase in Social acceptability of enrollment stress arising from program processes.
Material Citizens may face materials costs Cost-sharing when participating in the program. Premiums
Adapted by the author from Moynihan, Herd, & Harvey, 2014 (p. 46)
Implications of structural explanations. The decisions made by States regarding rules, processes, institutional arrangements, and procedures of means testing indicate that enrollment patterns are not purely a function of individual need or preference for benefits. The potential value of Medicaid coverage – both for individuals and the State – is modified by the costs of application, review, and determination processes. The primary implication of structural explanations for
Medicaid enrollment is that governments can make programmatic changes to reduce classification errors and close the enrollment gap; such patterns are not out of their hands (i.e., attributable to individual decisions or economic trends).
As a construct, administrative burden provides a useful way of thinking about the costs of program implementation. The work involved in policy implementation will always produce costs for some actor or another. However, from a governance perspective, the most efficient institutions
55 are those that place the burden on those actors who can accomplish it at the least relative cost
(Pindyck & Rubinfeld, 2013, chapter 13.3.6). To reduce classification error in Medicaid enrollment, policy scholars thus largely recommend “shifting the burden to the State” by streamlining enrollment from the perspective of the citizen (Herd et al., 2013; Moynihan et al.,
2014).32 Indeed, the federal government itself recommends that States build their data management and analysis capacity to reduce improper enrollments and fraud, rather than instituting additional reporting requirements on applicants and beneficiaries (CMS, 2015; GAO, 2015). Scholars argue that because implementation procedures receive much less attention than program eligibility design, reducing or shifting administrative burden away from citizens is a way for Medicaid programs to be more effective at covering eligible people without stoking additional policy debate
(Moynihan et al., 2014; Herd, 2015).
However, if governments can take steps to shift the costs of administrative burden away from citizens, they can also take steps to maintain or even increase those costs. Moynihan et al.
(2014) contend that administrative burden is a “hidden policy instrument” – an expression of political preferences to achieve some objective through administrative details that are seen as technical and inconsequential, and thus easier to institute (Moynihan et al., 2014). The promulgation of administrative rules and procedures that are burdensome to citizens thus constitute
“policymaking by other means” (Lineberry, 1977, p. 71). The application of discretion by frontline administrators may also take the form of administrative burden by providing street-level bureaucrats with the opportunity to exercise “bureaucratic disentitlement” of beneficiaries they
32 Practices that reduce learning, compliance, psychological, and material costs to citizens – including extending periods of continuous eligibility, improving outreach, providing application assistance, reducing reporting and verification requirements, and simplifying federal eligibility guidelines – are in the spirit of shifting the burden to the State. 56 deem undeserving of benefits (Lipsky, 1984), another form of policymaking (Burden et al., 2012).
State decisions regarding implementation of Medicaid’s means test are thus opportunities to render the entitlement no longer an entitlement (Herd et al., 2013).
Are Classification Errors an Unintended Consequence?
Merton (1936) defines consequences as those things that would not have happened if not for a purposive action (i.e., conduct with explicit motivation and conscious selection from a set of alternatives). Unintended consequences arise from gaps in information, errors in judgment, or faulty assumptions about the nature of the phenomenon (Merton, 1936). But as is evident from the research above, this is not necessarily the case with classification errors in Medicaid, specifically churning and missed take-up. Rules and practices that impose administrative burden on citizens and suppress their participation in public benefits may very well be intentional – if hidden – forms of policymaking.
If administrative burden and the enrollment gap associated with it are intentional, then classification errors are not errors at all, but rather intended consequences of actions taken to meet other goals or objectives. In addition to stated program objectives, State actors (including elected officials and administrators) are also motivated by political preferences, resource constraints, and their personal attitudes and beliefs about the program and beneficiaries (Downs, 1957; Moynihan et al., 2013).
No: Cost containment and predictability. Classification errors – or more specifically, maintaining a gap between the portion of a population eligible for Medicaid and the portion enrolled – may be a way for State actors to meet cost containment objectives. Because expenditures
57 rise with growth in Medicaid caseloads (Buchanan et al., 1991), keeping a cap on enrollments may be fiscally useful to States (at least in the short run), and could explain practices that reduce enrollments through missed take-up and churning off the program. State actors may also have a desire to limit expenditures of Medicaid (or of public programs more generally) as part of a political or ideological position.
Furthermore, unlike the federal government, most States have to balance their budgets.33
Predicting Medicaid costs and enrollments is thus important to State actors, particularly Medicaid administrators (Sommers et al., 2013). To aid in planning and management of the program rolls and spending, States have a budgetary incentive to limit enrollments both absolutely and relative to their predictions regarding take-up and churn. A desire to keep expenditures in line with budgetary projections may explain practices that maintain an enrollment gap, especially those that seek to eliminate any unnecessary spending.34
Elimination of false positives. The elimination of any improper payments is another goal that may motivate administrative behaviors and decisions, including the imposition of implementation costs on citizens to deter improper enrollment.35 Administratively, false positive enrollments (i.e., improper enrollments and fraud among ineligible people) represent means testing
33 Between 45 and 49 States, depending on what provisions are considered. http://www.ncsl.org/research/fiscal- policy/state-balanced-budget-requirements-provisions-and.aspx 34 In terms of predictability, churning is arguably a problematic phenomenon, but because of the short-term programmatic savings associated with people in the enrollment gap, it may be that the benefit of churning is seen as outweighing the costs. States also make their predictions with an assumption about an amount of churning that they deem to be acceptable, and perhaps among which groups of enrollees. For example, it may also be that better continuity of coverage among children and ABD adults is a priority because of a belief that they are more vulnerable or deserving than non-ABD adults. Testing the differential effects of implementation rules on churning across different eligibility categories is beyond the scope of this project, though it may be the subject of future simulation-based research. 35 As discussed earlier in this chapter, improper enrollment accounts for a small percentage of improper payments (about 3%). The vast majority of improper payments are attributable to providers and third-party insurers (i.e., manage care organizations), which require different administrative strategies, some of which are discussed below. 58 errors that contribute to programmatically unnecessary spending (i.e., waste). Politically, fraud is not only wasteful, but represents abuse of the entitlement by those who are “undeserving”.
Administrative rule-making presents an opportunity to challenge Medicaid’s entitlement form and legitimacy by implementing it through a deserving test, rather than a means test.
Political pressure to eliminate “waste, fraud, and abuse” incentivizes States to care more about eliminating improper enrollment (false positives) than minimizing missed take-up (false negatives) (Brodkin, 1987). The focus on keeping ineligible people off the program (maximizing specificity) often occurs at the expense of enrolling the eligible (maximizing sensitivity) (Koetting,
2016). Application and determination procedures such as interviews, frequent recertifications, and lengthy reporting requirements are used to ensure that beneficiaries are deserving and that spending is necessary (Moynihan et al., 2013). That these rules also suppress and disrupt enrollment among the eligible may just be a tradeoff that States are willing to make.
Yes: Equity considerations. Pursuit of other objectives has costs of its own, both in terms of policy outputs (i.e., performance measures like take-up, churn) and policy outcomes, such as equity, health, and social welfare. One consequence is across-group inequities in program enrollment. Classification errors are not uniformly distributed across sub-populations or groups; as discussed above, the extremely poor, people identifying as non-white, non-native English speakers, and poor pregnant women all have lower take-up and higher levels of churn. The learning, psychological, compliance, and material costs associated with implementation of means- tested programs are disproportionately high among members of marginalized groups who have tighter restraints on their resources (time, energy, social ties, etc.) and face more implicit bias in the system (Moynihan & Herd, 2010). Administrative burden, and bureaucratic disentitlement
59 particularly, end up being discriminatory against the most vulnerable individuals. Soss et al. (2011) characterize this as disciplinary action specifically aimed at punishing minorities.36 In addition to
Medicaid, there is evidence that administrative processes in voting (e.g., voter ID laws; Moynihan
& Herd, 2010) and welfare benefits (Brodkin & Majmundar, 2010) have similar discriminatory effects.
Health and health care outcomes. Coverage through Medicaid is associated with health care access and utilization. Fertig (2017) finds evidence that ACA’s Medicaid eligibility expansion revealed a pent-up demand for health care services, particularly primary care and preventive services. The increased utilization of primary care services is associated with a decrease in marginal program costs (Fertig, 2017). Similarly, Becker et al. (2015) find that while monthly expenditures and utilization are higher for children enrolled under expansion, they have lower emergency department use and are less costly on average than children enrolled under old eligibility rules. More generally, Aizer (2003) finds that increased take-up is associated with more efficient use of medical resources, e.g., fewer hospitalizations for preventable conditions.
Health insurance coverage and preventive health services are associated with positive health outcomes (KFF, 2017). While not Medicaid-specific, Bradley et al. (2016) find that States with a higher ratio of social and public health spending relative to private health spending have better population health outcomes. Because health insurance coverage improves access to health care services, which is associated with improved health outcomes, the threats to access to care and
36 Conversely, expansions and simplifications are associated with enrollments among people who were previously eligible but did not take up coverage, who are disproportionately from minority communities. The race/class undertones of enrollment practices show up in the phrase “woodwork effect” – people coming out of the woodwork, much like cockroaches, to sign up for benefits after an expansion of eligibility and/or public outreach campaign. 60 health outcomes posed by lack or loss of coverage (i.e., churning and missed take-up) may be unintended consequences of enrollment classification errors.
Social welfare. The exclusion of eligible people from Medicaid, regardless of the reason for it or intention behind it, also has consequences for society more broadly. Public health insurance is an income transfer (rather than a cash benefit) for low-income individuals under the logic that the utilization necessary health services has a clear social welfare benefit (Nyman, 2007).
Public benefit programs generally work this way; they have stated objectives regarding particular individuals, but they also contribute to overall social welfare (Bhargava & Manoli, 2015). For example, Gruber (2003) finds that Medicaid coverage is associated with increased participation the labor market due (at least in part) to improved health status.37 Heinrich (2015) finds that as adolescents spend longer periods of time on South Africa’s Child Support Grant program, gains in educational attainment and reductions in risky health and social behaviors grow. These findings suggest that focusing exclusively on enrollments and direct program spending – or even on classification errors – unduly ignores social outcomes that are ultimately consequences of interest to public programs (Wichowski & Moynihan, 2008).
Bureaucracy, program performance. Structural explanations for program enrollment indicate that the activities of means testing – the encounters between citizens and agents of the
State – themselves contribute to classification errors because those interactions are where classifications are made. Both citizens and frontline workers (bureaucrats, administrators) experience costs associated with the administrative rules and institutional arrangements that
37 This suggests that work requirements for Medicaid (newly permitted for States under the Trump Administration) will work on the wrong end. Medicaid increases the ability of beneficiaries to work; requiring work may keep people off of Medicaid (Carroll, 2018). 61 structure those interactions. Kahn et al. (1976) refer to these interactions as bureaucratic encounters – “the major intervening events in a sequence that include interactions between characteristics of client and agency that may determine outcomes of service delivery, or even whether a service delivery occurs at all” (p 185). Two important contributions of this work for implementation studies are that the authors distinguish between a) the initiating and directed (or receiving) actor in the encounter, and b) whether the actor is working on behalf of the program
(“in-role”) or independently (“not in-role”). Heinrich (2015) points out that, using this categorization, implementation costs experienced by citizens appear in cells 2 (as administrative burden) and 4 of Table 2, while costs to administrators appear in cell 3 (as administrative burden) and cell 1 (as red tape) (Heinrich, 2015).
Table 2. Bureaucratic Encounters Initiating Actor Intra-Organizational Extra-Organizational Directed Actor (program actors) (private actors) Intra-Organizational Organizational behavior Bureaucratic encounters (program actors) Within-program encounters Encounters among citizens and among state actors (e.g., leadership, state actors; beneficiary engagement management arrangement, and of program (e.g., service, client relationships) relations, application) “Red tape” “Administrative burden” 1 2 Extra-Organizational Bureaucratic encounters Social behavior (private actors) Encounters among state and Family relations, friendships, citizen actors; program management neighborhood relationships, social of beneficiary (enforcement, ties outreach, determination, and review) “Administrative burden” 3 4 Adapted by the author from Heinrich, 2015 (p. 404)
62 If implementation rules and procedures systematically (and discriminatorily) “disentitle” individuals, diminish health and social welfare, then simply shifting administrative burden to the
State (i.e., to cells 1 and 3) may not be enough. Additionally, burden may not involve a direct tradeoff between costs to administrators and citizens (as Moynihan, 2003 and Burden et al., 2012 suggest); means testing may produce administrative burden in the bureaucratic encounter from both perspectives. Expanding notions of program performance to include the degree and location of administrative burden (especially in social benefit programs) may be a way to understand the relationship between administrative actions and the effects of implementation on society
(Moynihan et al., 2014).
Costs experienced by administrators (that is, organizational “in-role” actors) have ramifications for program performance. When they perceive more burden in their own work, election administrators tend to shift responsibilities to others, oppose policy innovations, and believe policies to have less merit overall (Burden et al., 2012). Lemak et al. (2003) find that increased time spent on administrative and clerical tasks reduces organizational efficiency and productivity in substance abuse centers. Beyond bureaucratic encounters with citizens, these findings have implications for how States deal with the much bigger source of improper payments and fraud: providers. More stringent federal rules on reporting and verification of payments has led to increased classification of payments as improper (CMS, 2015; GAO, 2015), but it is unclear if this improved in-program accuracy would carry over to encounters with citizens.38 For example,
38 CMS and GAO recommendations and initiatives to reduce fraud among providers and managed care payers focuses on increased oversight by States. For example, participation in the Medicare and Medicaid National Correct Coding Initiative, more rigorous provider screening and review, and improved reporting and auditing of Disproportionate Share Hospitals.
63 Lemak et al. (2003) find some evidence that substance abuse centers shift resources away from providing treatment in order to meet administrative demands. Federalist delegation to the States can itself impose costs on in-role actors in the form of red tape (Sirpal, 2017). For example, States opting to impose work requirements for beneficiaries (per the Trump administration’s 2018 decision) will incur significant costs – anywhere between $75 and $200 million – associated with building capacity for enforcing those requirements through hiring and training additional staff and improving information technology and data infrastructure (Galewitz & Bartolone, 2018).
Ultimately, it is not possible to know for certain which consequences are intended and which are not, as that determination would need to be made on the basis of each program actor involved in each policy decision process. What is clear is that even if classification errors or the enrollment gap are not unintended, they have significant consequences for individuals, States, and society.
Summary
Medicaid provides health insurance coverage to millions of Americans each year. Since its establishment in 1965, however, there has been a persistent (though not necessarily consistent) gap between the proportion of the population eligible for coverage and the proportion enrolled in the program. Classification errors like missed take-up and churning on and off the program contribute to this gap. Research shows that classification errors and caseload levels vary by State, that certain population characteristics are associated with different enrollment patterns, and that economic conditions are correlated with changes in program caseload size.
64 Despite several changes to the program over the years, including expansions of eligibility and changes to administrative practice, the fundamental logic of Medicaid – a means-tested entitlement for those vulnerable to medical costs – remains largely the same. Implementation of that logic, which involves management of the benefit design (eligibility criteria), State and federal resources, population demand, and political signals and pressures, is left largely to each State’s program. The administrative rules and institutional arrangements that States use to implement the program may impose onerous costs on citizens known as administrative burden. Administrative burden may be used as a strategy to reduce caseloads or disentitle eligible individuals. Conversely, some scholars have suggested that shifting administrative burden to the State would allow administrators to be more effective at enrolling eligible people without adding new items to budgets or going through new policy debate (Herd, 2015; Moynihan et al., 2014). However, because means testing involves citizen-State interaction in the form of bureaucratic encounters, more attention needs to be paid to how interactions could be structured in such a way as to alleviate or exacerbate effects of administrative burden for both citizens and bureaucrats (Heinrich, 2015).
Because the administrative burden that shows up in implementation is policymaking by other means, it has consequences for programmatic efficiency and effectiveness, as well as social, democratic, and health outcomes.
Despite the robust research about the Medicaid eligible and enrolled populations, plenty of work remains to be done. One category of need involves addressing problems of measurement.
Gruber (2003), Currie (2004), and Kenney et al. (2016) (among others) have lamented the difficulty of estimating eligibility in the population because of the complexity of the eligibility rules. This calls into question estimates of take-up, since the denominator of that figure is the size of the eligible population. Graves (2012) and Planalp, Fried, and Sonier (2014) have pointed out 65 that estimates of churn are riddled with problems, including a lack of definition about what constitutes churning. Fraud and improper enrollments among Medicaid beneficiaries are difficult to measure because they are such rare events (GAO, 2015; Koetting, 2016).
Another shortfall of the literature is the degree of empirical support for recommendations made to address classification errors and the enrollment gap. Recommendations to improve take- up among the eligible and reduce churn in the HSR literature speak to the contribution of structural program features to the enrollment gap. Scholars suggest simplifying application and renewal rules and procedures (e.g., Ku, Steinmetz, & Bruen, 2013; Sommers et al., 2014; Dietz, 2014; Swartz et al., 2015) and aligning institutional arrangements toward program goals (e.g., Buettgens et al.,
2012; Sommers et al., 2014; Koetting, 2016; Rosenbaum et al., 2016; Ario & Bachrach, 2017).
While the logic of these recommendations is consistent with the administrative burden literature in policy studies, they do not necessarily follow from the study’s analysis. For example, Sommers et al. (2014) recommend that States extend continuous eligibility to 12 months, move to annual income redetermination, use administrative data to verify eligibility, and require MCOs to offer plans in the marketplace, but none of these features were included in their data or analysis.
Additionally, neither the HSR nor policy literatures make recommendations about benefit design
– the categories of eligibility or thresholds – perhaps because this is too political, would require federal action, or both.
Finally, while scholars have studied a range of factors that may contribute to particular enrollment outcomes, there is no systematic exploration of how these factors interact to perpetuate the enrollment gap overall. For example, there are no studies on the dynamics of churn among both eligibility categories and program enrollment, nor concerning how different means test classifications are related to each other: Does an increase in sensitivity (take-up) necessarily mean 66 an increase in false positives (improper enrollment)? Little is known about how much diversity there is among citizens and State actors in terms of their intentions, and how that might matter to enrollment outcomes. And while the administrative burden literature has made important contributions so far, we do not yet understand how much administrative burden is too much, nor where it has the biggest effect on means testing – on the State or the citizen? Overall, scholars and practitioners agree that context, individual behaviors, and program decisions matter, but not how they matter, nor how the enrollment gap persists.
This chapter has provided an overview of what is known about the classification and enrollment of individuals into the Medicaid program. It has discussed some of the evidence that enrollment phenomena, including program participation and churn, are tied to individual characteristics and State program features. While existing research has established that people take up coverage and churn at different levels and under different program conditions, little is known about how these patterns occur. Methodological limitations in the literature reflect and reify a conceptual gap in our approach to understanding means testing as a way to provide health insurance coverage for vulnerable individuals. By focusing only on whether or not someone is enrolled in the program, existing research ignores the meaningful process questions of what gets done, how it gets done, and what tradeoffs are made in the process. These are questions I explore in the chapters that follow.
67
Chapter 3: Bringing a Complex Systems Perspective to Policy Phenomena
In the last chapter I addressed the question of what we know about classification error and the enrollment gap in Medicaid by reviewing the two streams of literature that contribute to our understanding of the phenomena. I showed that while we have a good sense of the scope, magnitude, and trends of the phenomena and why they pose problems for government and individual citizens, we have a limited understanding of how these patterns happen or persist. In this chapter, I ask: given the scope of the enrollment gap and the problems associated with it, why have scholars and administrators had so little success getting traction on how implementation of
Medicaid’s means test works and what can be done about it? What is it that we do not yet understand about the enrollment gap?
One reason to ask more questions about Medicaid enrollment is to contribute to our knowledge of the issue itself, both intellectually and practically. Harold Lasswell argued that the primary objective of the policy sciences is to gain knowledge “of and in the decision processes of the public and civic order” (1971, p1; original emphasis). While existing research has produced knowledge of enrollment via Medicaid’s means test, it has not produced much knowledge in its processes – i.e., how people move on and off the Medicaid over time. The how is important because knowledge informs what actions governments take – how they intervene – to address public problems.
Medicaid enrollment phenomena and implementation of its means test are valuable for study because they share several characteristics with other public policy problems. For example,
Medicaid classification errors and the enrollment gap are examples of a larger category of interest
68 within policy studies39 – that is, those phenomena considered to be unintended consequences or side effects of a policy intervention. Additionally, as a core administrative task, the challenge of implementing Medicaid’s means test within a federal context is not unique, so insights may have implications for implementation of other public programs such as SNAP or TANF. Furthermore, the underlying problem at which Medicaid is aimed – affordable access to health care services for vulnerable populations – is like other wicked problems with which policy studies are concerned
(Rittel & Webber, 1973; Churchman, 1967; Ackoff, 1994). It is thus worth asking questions about both the inquiry and the actions around Medicaid enrollment because of the potential lessons for similar phenomena of interest in policy studies.
The central claim of this chapter is that we do not know how implementation of the means test works because Medicaid implementation is complex and our strategies for studying and intervening are not well-matched to that complexity. The data-generating processes of Medicaid enrollment – the ways in which people behave within the context of their environment and the rules of the program – are dynamic, interdependent, multidimensional, nonlinear, and purposeful, producing surprising patterns that are not traceable to a single source in the system. Complex patterns (including unintended consequences) are common characteristics across the domain of policy studies more generally, where we are concerned with the study and solution of wicked problems. Policy studies, including research concerning Medicaid enrollment, reduce that complexity under the assumption that breaking it into smaller parts will lead to insights into the system. I argue, however, that the reduction of complexity makes inaccessible the very features of
39 Policy studies is the term I use to refer to both the tasks of public administration (i.e., public policy and management) and the study of those tasks. For example, policy studies deal with the policy process (e.g., formulation, implementation), policy instruments, public institutions, and management tools. Policy studies includes scholarship from a range of disciplines, including public administration, political science, sociology, and public policy. 69 the system that are most necessary to understanding wicked problems. Gaining traction on intractable problems requires a shift in inquiry – not just a change in particular analytical methods, but a change in conceptual approach that explicitly accounts for complexity.
This chapter proceeds as follows: First, I provide an overview of complexity and complex systems. I discuss why these concepts are relevant to policy studies and how policy interventions occur within purposeful human systems. Next, I re-define the enrollment gap as a manifestation of a broader phenomenon in policy studies called the complexity gap. I explain what the complexity gap is, where it comes from, and why it is a characteristic problem of policy studies. Then, I show how the existing Medicaid enrollment literature reduces complexity and discuss how alternative analytic methods remain limited in their ability to provide insight into the complexity that defines policy systems. Finally, I bring clarity to complexity as a concept by specifying a typology and a taxonomy that organize complexity in logically consistent terms. I conclude with a call for a shift in conceptual approach in policy inquiry that confronts – rather than reduces – complexity.
Complexity in Policy Studies and Purposeful Human Systems
In this section, I provide an overview of complexity and discuss some of the ways in which
Medicaid enrollment and policy implementation more generally are complex. This is valuable because discussions of complexity in policy studies have largely come from the policy side – experts in a particular policy or organizational domain discussing why complexity matters in their space. These discussions largely fail to provide explicit, formal explanations or representations of complexity from complexity or systems science. I use the logic and language of complex systems to describe Medicaid enrollment in terms of its elements and processes. In so doing, it becomes
70 clear that the failure to capture the complexity inherent in and essential to policy systems fundamentally limits our ability to study or intervene in them effectively.
Complex processes. Complexity is commonly used to refer to objects, ideas, or institutions that have a variety of components or features. Formally, however, complexity is defined as a characteristic of certain kinds of processes and patterns, rather than objects. Complexity arises from processes of interaction, interconnection, and interdependency among diverse actors, rules, and conditions over time. While these interactions typically involve a variety of elements with diverse attributes, it is the context, with its variety of rules, behaviors, and interactions among those features that produces complexity, not the diversity of features or actors themselves (Simon,
1969; Page, 2008, 2010). For example, the rules of Medicaid’s means test are complicated, but the implementation of that intervention is complex.
Interdependency matters because it drives feedback (Figure 1) and variability (dynamic, non-linear change) over time. Feedback refers to recursive, rather than lineal40 causality, where A has an effect on B, but B in turn has an effect on A. Feedback relationships may be reinforcing (or positive, move in the same direction; e.g., increase in births increases population and increase in population increases births) or balancing (negative, move in opposite direction; e.g., increase in deaths decreases population and decrease in population decreases deaths). This is different than correlation or covariation among variables, or distributions of variables. For example, the feedback between the actions of citizens and State agents in a bureaucratic encounter is different than an association that may exist between gender or racial characteristics of citizens and agents.
40 Bateson (1979) distinguishes between linear and lineal as follows: Linear is a technical term in mathematics describing a relationship between variables such that when they are plotted against each other on orthogonal Cartesian coordinates, the result will be a straight line. Lineal describes a relation among a series of causes or arguments such that the sequence does not come back to the starting point. The opposite of linear is nonlinear. The opposite of lineal is recursive. 71
Figure 9. Feedback
Variety may be detectable or latent (Axelrod & Cohen, 1999). For example, individuals have latent attitudes and beliefs about public programs or government more generally that inform their behaviors, but those attitudes are not explicitly captured in surveys of health insurance coverage and income. Furthermore, categories used to define actors, rules, and conditions in policy studies change over time based on definitions, which are themselves variable. Lasswell (1951) refers to this as index instability. Variety and diversity in characteristics can contribute to complexity because they create more possible interactions and kinds of interactions (Page, 2010).
Processes that are complex are difficult to describe, explain, or predict (Page, 2010).
Elaborate feedback structure creates causal pathways that are difficult to determine and irreducible to any particular feature or actor. Because there are no “prime causes”, no one event or person is to blame (Desai, 2012b). However, complex processes are not chaotic or totally random (Mills,
2010). Rittel & Webber (1973) refer to unintended consequences as “ripple effects” that result from policy actions, which demonstrate some order and dependency on the events and processes that precede them.
Not all processes are complex. Sterman (2000) distinguishes between complex patterns and complicated ones. Whereas genuinely complex patterns arise from interactions and changes among 72 agents, rules, and context over time (what Sterman refers to as dynamic complexity), complicated patterns result from aggregations and combinations of variables (detail complexity), not their interdependencies. Nonetheless, complex processes and patterns are everywhere in the world of policy studies. Rescher calls complexity a “profoundly characteristic feature of the real,” particularly of social reality (1998, p. xiii).
Processes of implementation, Medicaid enrollment. Program implementation is a complex process because of the interactions, interdependencies, and changes among diverse and dynamic actors, rules, and contexts over time. The complexity of implementation is widely discussed among policy scholars, who point to high dimensionality (O’Toole, 1986); many layers of actors, stakeholders, and institutions, involving range of tasks and decisions, negotiated through different goals and norms (Goggin, Bowman, Lester, & O’Toole, 1990; Ostrom, 2007; Desai,
2012b; Moulton & Sandfort, 2015); non-hierarchical arrangements among actors and institutions
(O’Toole, 1997; Heclo & King, 1978; Mayer & Rowan, 1977; Sabatier & Jenkins-Smith, 1999); and the importance of context (Anderson, 1979; Stone, 2002; and Schneider & Ingram, 1997,
2005). Additionally, while implementation is considered a phase of the policy process (e.g.,
Lasswell, 1951; Jones, 1984), scholars acknowledge that it is more accurately described as an ongoing process (e.g., Easton, 1965; deLeon, 1999) that involves, “iteration, adaptation, and ex post factor error correction” through a series and variety of line decisions (Levin, 1993, p. 444).41
The joke about “one Medicaid program” in chapter two is partly a reflection of the complexity of means test implementation. Generalizability is difficult because of the variation, variability, idiosyncrasies, and interactions among citizens, bureaucrats, program rules, and
41 Line decisions include the “complex process of assemblage, coordination, bargaining among actors… When government acts, success is determined mostly by the quality of line decisions” (Levin, 1993, p. 443). 73 economic context, which affect how program rules are carried out. Enrollment outcomes are thus complex patterns because they result from feedback structure and cannot be traced to any prime cause. For example, it is not possible to ascribe fault for Statewide program churn to any one cause because it a product of feedback between actors, rules, and context.
Systems. The integrated whole within which actors, rules, and conditions interact over time
(i.e., where complexity arises) is a complex system (Forrester, 1961; Sterman, 2000). Like complexity, system commonly has a less formal meaning, referring to large structures, institutions, or ideas that have many components. In such a system (which may be said to be complicated), the elements are more or less independent from one another, related in simple or loose ways such that removing one would not fundamentally change the system’s behavior (Miller & Page, 2007). But when a system cannot be broken into its constituent parts, structure, or processes without losing the essence of the whole, when it is other than the sum of its parts (Koffka, 1935)42, it is an entity integrated by the interdependencies among its elements – it is a complex system. A complex system’s feedback structure generates its (largely nonlinear) dynamics (Sterman, 2000). Processes may be lineal (fit with a deterministic functional form) and stable (operating at a point equilibrium), but they are more likely to be recursive and dynamic.
The general effect and lesson for policy studies is that the future will not look like the past, even under the same policy intervention (Desai, 2012b). Because their behavior arises from the inter-activities of their elements (Forrester, 1961), complex systems demonstrate a number of irreducible properties that the separate parts cannot, and which make them difficult to control, manage, or predict (Senge, 1990; Axelrod & Cohen, 1999). These properties include:
42 Koffka’s quote is often mistranslated to “more than the sum”, but other is both correct and more consistent with Aristotle’s notion that “the totality is not, as it were, a mere heap… the whole is something besides the parts” (c. 300 BCE). 74 Adaptation Changes in rules and behavior within the system may occur due to environmental sensing (Mills, 2010; Desai, 2012b); random variation and selection
(Axelrod & Cohen, 1999); or experimentation, selection, mutation (Mills, 2010).
Emergence Systems may produce phenomena that are different than the phenomena of the elements or sum of those processes such that the source of the overall behavior is not directly identifiable or singular in origin (Mills, 2010; Page, 2011). Emergent properties evolve from simple rules and structure (Axelrod & Cohen, 1999), including dampening and amplification of effects that result from negative and positive feedback structures
(Sterman, 2000).
Robustness Systems are robust to change or perturbations; they are able to survive even when parts are damaged (Sterman, 2000; Mills, 2010).
Persistence & Resistance Endogenous interactions of agents and rules induce stable, long-term patterns (Epstein & Axtell, 1996). Endogenous responses make leverage points difficult to identify and act upon because behaviors are often counterintuitive and involve tradeoffs (Sterman, 2000).
Policy resistance The system’s endogenous response to the intervention itself,
including delays, dilutions, dampening, or defeat of interventions by unforeseen
(though not necessarily unforeseeable) reactions of people, institutions, or
environment (Meadows, 1982; Sterman, 2000). For example, a phenomenon such
as unemployment can arise from the structure of the market economy itself
(Lasswell, 1951); churn can arise from the structure of Medicaid itself.
Equifinality Systems may reach the same state from different initial conditions and in different ways (Von Bertalanffy, 1951).
75 Autopoiesis Systems that are capable of self-reproducing are said to be autopoietic
(Varela & Maturana, 1972). An autopoietic system produces itself from itself by auto-
organizing from bottom-up (agents) and top-down (institutions) interactions (Miller &
Page, 2007); co-evolution and -determination with their environment (Beer, 1972); and
auto-regulation and -maintenance of their processes (Ashby, 1956; Weiner, 1954; Mills,
2010).
Formal model of a complex system. Among scholars trained in traditional (i.e., Cartesian) methods, a common concern with systems models is that they are too broad and include too many factors to be useful, and that they create analytical problems by including multiple units of analysis.
However, systems models can be formalized in logically consistent ways that maintain fidelity of concept and analytic tractability. Bunge’s standard model of a concrete system (Bunge, 2004;
Figure 10) is an elegant formalization that can be applied in a range of systems contexts.43
Instead of a vector of variables that denote the attributes or decisions along one dimensional unit (e.g., a person, an organization), Bunge’s model defines a matrix of variables that are organized according to attributes of actor (component), context (environment), action rules
(structure), and feedback structure (mechanism). The elements of this matrix necessarily interact dynamically over time; they are not assumed to relate to each other through a pre-defined functional form, which may or may not include a time designation. Rather than a dependent variable, the “outcome” in Bunge’s representation is a compound manifestation of the interaction patterns produced by the elements of the system – i.e., system autopoiesis, which is driven by its mechanism. With the system itself as the unit of analysis, and distinction drawn among different
43 Concrete models include social and economic systems. 76 levels of action (rather than levels of actor), this formalization distills complexity instead of reducing it.
Bunge’s Basic Model of a Concrete System, µ(σ) (Bunge, 2004, p. 188)
µ(σ) = dynamic of{C(σ), E(σ), S(σ), M(σ)},
over time t, where the elements of the model are: µ(σ) = self-production and control of system And C = set of parts or components of the system
E = the collection of environmental items that act on and/or are acted upon by the system S = the structure or set of bonds or ties that hold the components of the system
together; organization, architecture M = the mechanisms or characteristic processes of the system
Figure 10. Bunge's Equation
Purposeful human systems. The complex systems of interest in policy studies have another essential feature that must be understood: they are purposeful. Unlike natural or mechanical systems, social (or “peopled”) systems (Ackoff, 1994) have at least one purpose, but the parts also have purposes and motivations of their own, which affects overall system behavior. Parts display both choice and influence in their purposes and functions, making social systems both self- organizing and re-organizing in practice, even if they are meant to be hierarchical or stable by design. This is exactly the situation that characterize social systems of collective action and governance. For example, the Medicaid program has a purpose (to provide coverage for vulnerable
77 individuals), but so do bureaucrats (to enforce rules and serve citizens) and citizens (to improve their own well-being and comply with rules).
However, human actors are not uniform, unified, or consistent in their purposes. People have diverse and dynamic motivations, intentions, and goals that are influenced both by instinct
(automatic decision making) and by deliberation (calibrated decision making). An established literature in cognitive psychology (also called behavioral economics) shows that humans use heuristics that may not be optimal (“rational”) but are deeply coded and conditioned (e.g. Tversky
& Kahneman, 1973, 1986). These heuristics may lead to certain biases or fallacies (e.g., base rate neglect, sunk cost fallacy), but they may also be adaptive (e.g., recognition heuristic) (Gigerenzer,
2007).44 Variation in use and effect of heuristics exists among individuals and within them, as humans negotiate different emotional, resource, and environmental conditions over time and social space.
Variation and variability in human decision making is fundamental to social systems; society itself arises out of complex human behaviors and interactions. A wide range of rules and reasons motivate personal and social behavior such that individual decisions may end up seeming contradictory in the aggregate. Information transfer in social systems is imperfect (Downs, 1957) and there is uncertainty and ambiguity in the information itself (Stacey, 2002). Rules and behavior are power-laden; behavioral, social, and institutional laws are both responses to and manifestations of interpersonal coercion (Stone, 2002). Furthermore, individual and social expectations and perceptions matter to individual actions and interactions (Desai, 2012b).
44 Miller & Page (2007) consider the overall result of use of heuristics in context to be that humans are thoughtful, but not brilliant; that they are flawed, but not idiots. 78 Given the diversity and variation of human behavior, social systems are motivationally complex. I refer to systems of this kind as purposeful human systems in order to emphasize the importance of human behavior and purpose – both instinctual and deliberative – in individual, structural, group, and social dynamics. Processes within purposeful human systems may be automatic, calculated, designed, or emergent. Both codified and behavioral rules and structures are motivated by dynamic combinations of rational (i.e., market economic), social, emotional, political, and ideological reasons of human actors.45 Mechanisms are a particularly useful construct in modeling purposeful human systems because the feedback between intervention (i.e., formal rule change meant to change the decision environment) and context is how learning and adaptation occur within social systems. Implementation is the mechanism or characteristic process of a program system; it is the doing or application of the intervention in practice, which is neither uniform nor straightforward.46 The feedback dynamics of implementation create dilemmas
(Easterbrook, 2014) – interconnected, dynamic, complex problems that are not amenable to analytic or technological solutions. Not all systems are complex, but all purposeful human systems are.
Medicaid system. Complexity in Medicaid enrollment arises from interdependencies among the variety of rules about who is deemed eligible (i.e., “deserving”) for benefits; how citizens, administrators, and providers go about negotiating enrollment procedures; and the values through which those rules and their interactions are mediated. The features of Medicaid enrollment
45 Structure, rules, institutions and organizations are logically the same construct in that they represent petrified processes (Desai, personal communication). 46 Axelrod and Cohen (1999) refer to mechanisms as being the (non-linear) combination of interventions (formal action) and complexity. However, I clarify that mechanisms are part of system complexity. Mechanisms are the how systems manage and respond to interventions; they are the policy system’s feedback structure. 79 are integrated into a whole that constitutes a purposeful human system. In terms of Bunge’s equation, Medicaid enrollment autopoiesis µ(σ) operates via the dynamics among:
Components C(σ): The attributes, behaviors, and variety of citizens, frontline bureaucrats,
administrative officials, elected officials, managed care bureaucrats, managed care
officials, providers, and their organizations.
Environment E(σ): The economic, social, and political conditions of the program’s State.
Structure S(σ): The program’s eligibility criteria, application procedures, and
(re)determination rules; i.e., the means test.47
Mechanism M(σ): Implementation of the program through means negotiation. The sorting
of citizens into program enrollment (or not) via a compound, dynamic set of processes
moving people in and out of the system.
Complexity is indeed a profound feature of reality. Wicked problems are complex, and implementation is a complex process creating complex patterns (e.g., policy resistance) within purposeful human systems. Because they are genuinely complex, we cannot understand the properties of such systems by reducing them to the properties of their elements, nor by reducing interactions and interdependencies among elements into dimensions of those elements, nor by reducing patterns resulting from feedback among elements to measures of correlation or covariation between them. Unfortunately, reductionist steps are precisely the moves we make in much of the policy sciences, including in our attempts to understand the dynamics of Medicaid enrollment. The mismatch between complexity of our dilemmas and our reductionist approaches leaves policy studies with a field-wide dilemma: the complexity gap.
47 Administrative burden – enrollment rules and procedures considered to be onerous to individuals – is a normative classification for certain features of program structure. 80
Problem Re-Definition: The Complexity Gap
Definition. The challenges of implementing Medicaid’s means test, including the resulting enrollment gap, are manifestations of a deeper problem facing policy studies: the complexity gap.
Cook & Tornurist (2016) define the complexity gap as the disconnect between the complexity of the world and the actions we take to address problems that arise within it. They argue that the governance arrangements and societal institutions we use in response to the challenges we face are a poor fit for the interconnected, interdependent, unpredictable reality of the problems, leaving our interventions in a persistent effectiveness shortfall, with responses that are incapable of fully addressing the problem. However, it is not just the institutional responses – the (technical) interventions that constitute policy and management – that are a poor fit for the complexity of the world. It is also our intellectual responses – the ways that we conceptualize, understand, and study the phenomena – that are not up to the task.
In trying to respond to complex phenomena, scholars and practitioners alike simplify complex problems, under the assumption that doing so will provide insight into the origins and operations of the problem. Simplification typically involves the Cartesian approach: breaking the phenomenon into its constituent parts and reducing what are fundamentally irreducible features of the system – interconnections, interdependencies, purposeful action, variation, and variability – into features of the parts. These simplifying assumptions are made explicitly and implicitly, in both formal modeling (e.g., policy analysis) and informal modeling (e.g., problem conceptualization).48 Responses to the problem (e.g., program logic and intervention design) are
48 Models are just another way of saying “simplification”. As Epstein (2008) points out, anyone, when faced with a problem to solve, has some model in their head, because a model is just an abstraction of the world/phenomenon/system of interest. 81 built on these models, and thus, built on our understanding (such as it is) of the problem. Therefore, the complexity gap is the disconnect between the complexity of the world and our responses to it, including both interventions and the understandings upon which they are based (Figure 11). This extends Cooks and Tornurist’s definition by incorporating the role of frames, meanings, and philosophical assumptions in organized responses to the social world.49
Figure 11. The Complexity Gap in Policy Studies
In Medicaid, the enrollment gap (the mismatch between the population eligible for coverage and the population enrolled) is a manifestation of the mismatch between: the interactions and purposeful actions of rules, actors, and context in program implementation; the assumptions
49 The role of frames, meanings, and philosophical assumptions in the policy sciences in taken up in detail in chapter 4. 82 scholars and practitioners make about relevant structures, context, and behaviors; and the eligibility, application, and determination rules and procedures that define the means test intervention. Another way of characterizing the complexity gap is as the difference between having knowledge of but not in the policy process (Lasswell, 1951). For example, in Medicaid, much is known about enrollment outcomes, but little is known about how implementation and the enrollment gap works; this disconnect is a manifestation of the complexity gap.
Policy studies and wicked problems. Generally, the complexity gap is a problem for policy studies because it limits our ability to get traction on “wicked problems” (Rittel & Webber,
1973). Wicked problems are problems in the sense that there is a discrepancy between the state of affairs as it is and as it ought to be; they are wicked in that sense that there is no definitive formulation of that discrepancy because there are myriad conditions and contingencies that produce the state as it is (Rittel & Webber, 1973), it is difficult to establish shared understandings
(Cook & Tonurist, 2016), and there is no analytical or objective solution to the problem
(Easterbrook, 2014). For example, climate change, hunger, poverty, nuclear weapon proliferation, and global non-state terrorism are characterized by patterns that cannot be traced back to a single origin because they arise from the interactions and interdependencies among diverse, purposeful agents and rules over time. These feedback structures make wicked problems difficult to describe, explain, or predict (Page, 2011). Moreover, even problems like the enrollment gap (which certainly seems less wicked than the others) are complex phenomena in that they involve elaborate interdependencies that make their operation difficult to trace. Policy studies are filled with these sorts of problems. As the world continues to get more elaborate and interconnected, complex problems (and their unintended consequences) are not likely to go away any time soon.
83 Decision making and action. The very nature of policy problems explains the complexity gap itself because complexity makes decision making – that is, responses to a problem or condition
– challenging. Stacey (2002) argues that complex phenomena create uncertainty and value conflict in the decision space and suggests that decision strategies should be matched to the levels of those dimensions. When information about a course of action is rich (low uncertainty) and consensus about goals and objectives exists (low value conflict), decisions are simple and rational decision making (optimization, technical fixes) is possible and advisable. As uncertainty or value conflict increase, even at low levels of the other, decisions become complicated. Such conditions require political decision making (negotiation, compromise, coalitions when there is more relative conflict) or judgmental decision making (logical incrementalism, mission-driven when there is more relative uncertainty). As uncertainty and value conflict increase, decision making becomes chaotic, behaving without rules or order, and disintegrating into anarchy.
Between ordered spaces and chaos, Stacey identifies a region where both conditions and decisions are complex. Value conflict and uncertainty about possible courses of action exist in moderate degrees, yielding a wide variety of choices for action. This variety of choices must be negotiated by decision makers under the continued pressure of various actors and goals and changing contexts, making the complex region a much more realistic representation of the decision making space inhabited by policy studies. This space demands decision making in the satisficing realm: brainstorming, searching for error, agenda-building and negotiation, trial-and-error, and strategic change.50
50 Stacey notes that these are models consistent with the garbage can model of decision making (Cohen, March, & Olsen, 1972) and muddling through (Lindblom, 1959). 84 Decision making is not an esoteric exercise. Both intervening in a problem and building understanding of it involve making decisions. Therefore, what is a challenge for decision making is a challenge for both policy action and policy study. Policies are organized, collective responses to societal problems in the form of interventions meant to change the decision environment
(Axelrod & Cohen, 1999). When problems are complex, interventions based on rational decision making, optimization, hierarchical structuration, and technological solutionism (Easterbrook,
2014) are not suitable. Cook and Tonurist (2016) argue that such responses have resulted in a
“governance crisis” wherein 19th century institutions prevent society from dealing with 21st century problems in useful, meaningful, or sustainable ways. Intervention – responding (or not) to problematic conditions or phenomena in the form of policy and management – is not only what social institutions do (Axelrod & Cohen, 1999), it is what humans do (Gigerenzer, 2007). The challenges posed by complexity for intervention decisions thus cannot be ignored.
Artifacts of Cartesian science in policy studies: policy economics. Intervention decisions are always based – whether consciously or unconsciously – on the way in which the decision maker understands the problem. Both mental and formal models involve selection and attention to particular aspects of a phenomenon. In Cartesian science, this decision process involves making a series of simplifying assumptions by breaking the problem into smaller parts and reducing features of its operation. Policy studies are filled with examples of reductionist artifacts – categories, models, and labels that are overly simple or technical (or antiquated) to study or represent complex phenomena. While these artifacts have their uses (for instructional purposes, for example), they limit the depth of our understanding if relied upon too heavily. For example, the distinctions between policy and management, and politics and administration; top-down versus bottom-up models of implementation; rational choice as utility maximization; and the stages model
85 of policymaking are each useful heuristics for thinking about various aspects of collective action, but none provide profound insight on their own.51
When problems are complex (as they are in policy studies), models and methods based on separation and reduction of the relevant phenomena result in “policy paradoxes” (Stone, 2002), seemingly contradictory or incompatible statements about collective action needs, preferences, and goals. Stone argues that these paradoxes are artifacts of the inappropriate application of the
“market model” of collective action – an approach that makes assumptions about policymaking consistent with neoliberal market economics – to problems that fundamentally cannot be understood (or acted upon) in such terms. The market model assumes that public policy works like a marketplace where (boundedly) rational actors satisfice based on maximization of their personal utility, where information and choices are complete and accessible, and where rules are well- understood. Under those assumptions, efficient allocation of resources is achieved, and average public preferences are represented in policy decisions through aggregations of individual preferences. There is also a hierarchical and institutional bias: elected officials put pressure on administrators and specify policy rules; administrators and rules set conditions for citizen actions; and people are compliant. Assumptions of the market model make policy analysis and evaluation simpler by smoothing out goal ambiguity and conflict, and by holding efficiency (utility maximization) as the primary evaluative criterion.
51 Kraft & Furlong (2015) and Smith & Larimer (2009) note that the earliest version is often credited to Lasswell (1951), but Jones (1984), Anderson (1979), and Ripley (1985) also made important contributions to the policy process model widely used today. The model is it is process-oriented, but also process-muddling and process-concealing because it implies lineal causation, distinct phases, and fails to account for feedback loops within or between phases (see Easton, 1965; deLeon, 1999; Schneider & Ingram, 1997; Stone, 2002 for examples of criticisms in this vein). 86 The market model intersects with Cartesian rationalism.52 The treatment of collective actions as simple aggregations of individual policy preferences is consistent with statistical inference from a sample to the population, assuming that attributes of a whole can be understood by breaking it into its constituent parts. Rational actors are defined according to the assumption that optimization is possible, desirable, and can be objectively measured, i.e., that utility functions can be standardized and mathematically formalized. The market model is also consistent with a world governed by laws – causation and time are lineal, deterministic, and immutable. I call this intersection of Cartesian science and neoliberal economics in policy studies policy economics.
The complexity gap describes the mismatch between policy problems and our responses; policy economics (and Cartesian, rational models more generally) reinforces that mismatch. As an approach to policy studies, policy economics is dominant in part because simplification of the complex world is necessary. Separation and reduction are often legitimate ways of simplifying the world because what is lost in accuracy is more than made up for by the efficiency of Cartesian models, categorizations, and heuristics. However, the tradeoff between accuracy and efficiency is too great when trying to understand something well enough to intervene in it. For example, the stages model is too blunt for policy scholars to understand how policy changes are codified, or how the Medicaid means test gets implemented. Downs (1957) points out that the “invisible hand” of the marketplace is not a proper explanation for how consumer preferences and firm behavior relate to each other. Using policy economics as a paradigm to understand how policy works is like using a hammer to fix a computer or a bench microscope to study bosons. It is simply the wrong
52 Appropriate, since Adam Smith was heavily influenced by Descartes. 87 tool for the job.53 The next section demonstrates how studies consistent with policy economics reduce Medicaid complexity, and in so doing, reinforce the complexity gap.
Assuming Away Complexity
Studies of Medicaid enrollment assume away complexity by reducing fundamentally irreducible features of purposeful human systems, including their recursive causal structure, the diversity of their actors, and the dynamics of their processes and autopoietic behaviors. Common strategies to improve scientific validity (e.g., more robust data collection) primarily focus on better capturing detail (rather than dynamic) complexity, which is consistent with a Cartesian approach to inquiry. Therefore, even with more data, better design, and more sophisticated methods, the policy economics approach to studying Medicaid reinforces the mismatch between the world and our interventions because, “complicated worlds are reducible, whereas complex ones are not”
(Miller & Page, 2007, p. 9).
Reducing irreducible features of Medicaid complexity. The designs and methods of existing literature separate and reduce complex features of the Medicaid enrollment system – its wholeness, interactions, and overall behavior – into features of its elements and the relationships among them. This includes reducing feedback relationships into deterministic ones, diverse patterns into average effects, dynamic processes into snapshots, and autopoietic patterns into discrete outcomes. Such reductions prevent scholars from understanding Medicaid’s dynamic complexity and how the system works.
53 But this does not mean that policy economics, the market model, or Cartesian methods are useless or have no place in policy studies. Such tools have been tremendously useful for training, developing shared understandings about particular phenomena, and answering specific research questions about relationships among various attributes and conditions. 88 Recursion. Existing studies reduce recursive causality to determinism (i.e., lineal causality) by treating enrollment as a function of the pressures that economic factors, eligibility criteria, program administration, and individual characteristics impose a particular decision.
Feedback, interrelationships, and interdependencies are reduced to measures of correlation and covariation among variables. For example, men and non-Hispanic whites are less likely to take up coverage than women and other ethnic groups (Kenney et al., 2012), and States with burdensome program features have lower take-up rates than those with less burdensome features (Sommers et al., 2012).
Regression-based models prevalent in the literature assume hierarchical relationships among systems elements. In health services studies, State-level control variables put program features in the background of an individual enrollment event (e.g., Stuber & Bradley, 2005), while population controls put individual characteristics in the background of program performance (e.g.,
Koetting, 2016). This implies a hierarchy where the State dictates the actions of bureaucrats through codified rules and bureaucrats enforce compliance (uniformly) among individual citizens
– i.e., enrollment is a means test. However, as policy scholars point out, implementation occurs through complex arrangements in which citizens, bureaucrats, and rules influence each other within bureaucratic encounters (Kahn et al., 1976; Heinrich, 2015), suggesting it is more appropriate to think about enrollment as the result of a means negotiation. While policy studies treat enrollment as an aggregate program measure, they have little to say about the individual response to administrative rules that may be burdensome and fail to capture the friction that results from the bureaucratic encounter. Furthermore, they make no distinction between administrative burden experienced by individual citizens and that experienced by individual bureaucrats or
89 frontline workers.54 Hierarchical assumptions effectively reduce the complexity of flat, networked interdependencies.
Diverse goals. Existing studies reduce diversity and variation of system actors and their behaviors to average characteristics and effects, which imply clear, aligned goals and consistent behaviors. For example, the language of enrollment – take-up, churn, fraud – assumes that individual program status is the result of an intentional action rather than the produce of a complex interaction. Health services studies assume that citizens make a cost-benefit assessment about the relative value of the program, but it is well-established that people rarely optimize because of various of social, cognitive, and resource conditions (e.g., Tversky & Kahneman, 1973, 1986;
Gigerenzer, 2007; Miller & Page, 2007) as well as institutional pressures and social values (Stone
2002). HSR studies also accept that Medicaid’s program goal is to cover medically needy populations, though policy scholars argue that administrative burden, as a form of policy-making by other means, reveals other objectives in play (Herd et al., 2013). Policy studies assume that program rules are applied uniformly and consistently even though organizational and personal preferences influence bureaucratic behavior (Downs, 1957) and may result in bureaucratic disentitlement (Lipsky, 1984). The policy literature has made the case that many of these simplifying assumptions are flawed but has not moved away from statistical models that smooth out variation and estimate average effects.
Processes. Existing studies reduce processes – phenomena that occur in and over time – to distributions at particular points in time. This reduction is most obvious is how Medicaid enrollment studies discuss enrollment outcomes, which are referred to as rates (e.g., take-up rate, churn rate). However, these are measures of population prevalence – percent of eligible population
54 See Appendix B for a table that proposes a scheme that distinguishes among these features. 90 enrolled (take-up), percent of eligible population not enrolled (missed take-up), percent of population moving on and off the program (churn).55 To understand the processes of program enrollment, it is necessary to know about the prevalence of people in each state, the incidence rate at which people move into any given state, and the duration of time people spend in each state, and these are different measures. Prevalence is a function of incidence (qualifying event or new entry into condition) multiplied by the duration of time spent in that condition (Aschengrau &
Seage, 2008), neither of which are represented in enrollment measures as they appear in the literature.56 Churning is a dynamic construct that implies movement over time – incidence – but is always represented as a population prevalence. In addition to being time-dependent, churn refers to several different phenomena (e.g., percent of ever enrolled disenrolled (Daw et al., 2017), percent of enrolled disenrolled, then reenrolled (Sommers, 2008), percent of not enrolled with income change across eligibility threshold (Ku et al., 2009)). A robust treatment of churn requires clear distinctions about what each of these phenomena are and how they happen (Gruber, 2003;
Graves, 2012; Planalp et al., 2014).
Autopoiesis. Existing studies reduce system autopoiesis, including emergent properties and mechanismic patterns, into discrete outcomes. Any given study focuses on one eligibility- enrollment status modeled as a dependent variable of interest. However, isolating each status as single dependent variables belies the dependence among the statuses – the fact that eligibility- enrollment status is a compound outcome of four possible conditions: eligible, enrolled; ineligible, enrolled; eligible, unenrolled; and ineligible, unenrolled (see Figure 12).57 In order to understand
55 Some of the confusion may be attributable to the use of “fractional rate” in reference to measures of proportion. 56 Nor are phases of the enrollment process that occur between being unenrolled and enrolled. These phases are discussed and included in the systems models presented in chapter 5. 57 Like any diagnostic gest, means testing has a set of outcomes based on the test and latent conditions; the distribution of the population across that set sums to 100% (Koetting, 2016). 91 the system, it is important to understand that these outcomes are mathematically and practically related to each other, and that movement among the states (i.e., churn) is fundamental to how the distributions change over time. Furthermore, researchers characterize classification misalignments
(e.g., missed take-up, spurious enrollment, churn) as errors. However, from a systems perspective, it is not clear that misalignments are always (or even often) the result of some actor making an error; they may instead be the product of endogenous responses to rules or interventions in the system (i.e., policy resistance).
Figure 12. Eligibility-Enrollment Contingency Table
Alternatives. In discussing limitations and extensions of their work, Medicaid scholars suggest that certain research strategies could provide more insight about program enrollment.
Generally, these suggestions fall in three categories: alternative data, research designs, or analytic methods.
Data. Gathering more observations would capture more dimensions of the phenomena of
Medicaid enrollment – that is, more of the problem’s detail complexity. One way to do this is to collect data at more time points (e.g., creating panel data for individuals and States), which would
92 help in establishing temporal precedence and looking at the process elements of enrollment. Detail complexity would be aided by collecting observations on additional measures, of program features and individual behavior (using both qualitative and quantitative instruments) to account for diversity and variation. For example, survey or interview items asking citizens if they have ever applied for benefits, why or why not, and what was their experience like; have States report on how long their determination process takes and how many people they have reviewing documentation (capacity). Researchers could determine if there is variation in the approval rate among frontline workers (i.e., do some bureaucrats approve a greater proportion of applications than others or make their determinations faster than others) by collecting operations data on State
Medicaid programs.
Design. Experimental, quasi-experimental, qualitative designs and prospective studies could each be useful in exploring counterfactual explanations for differential enrollment outcomes.
Most of the existing Medicaid enrollment literature is based on observational studies where data are collected retrospectively. One option is to “exploit the natural laboratory” of different States programs through quasi-experimental design, but as Gruber (2003) points out, it is unlikely that researchers could gather enough data to have statistical power to control for all the differences among programs. Experiments are the “gold standard” to eliminate alternative explanations, but they are not plausible (or ethical) in complex social settings (Gruber, 2003; Aschengrau & Seage,
2008). Furthermore, even policy experiments (like the Oregon Health Experiment) have assignment, attrition, and selection problems that challenge the validity of their findings (Nyman,
2007). Qualitative research could be helpful in understanding enrollment processes (e.g., what does the bureaucratic encounter look like and how does it work?) and diversity and variation of purpose among actors within the system (e.g., interviewing beneficiaries and eligible people about
93 their experience and reasons for (not) applying for benefits) but such studies are resource intensive, and face the normal challenges of qualitative research (e.g., limited generalizability to other settings).
Analytic method. There are a number of alternative analytic strategies that could further detail about system outcomes, diversity, process, and causality. Use of lagged variables could account for delays in system processes and distinguishing between confounding variables (Z is associated with X and Y, but not on the causal pathway), mediating variables (Z mediates how X causally effects Y), moderating variables (Z interacts with X in such a way as to moderate how much of an effect X has on Y by virtue of its level), and covariates (Z is related to Y, but not to X) through iterative model comparison could provide insight about causal relationships and potential feedback loops. Multilevel modeling (MLM), because it partitions covariance over different levels, could be used to identify differential group effects (e.g., differences in take-up across eligibility categories) and distinguish individual effects from policy effects (e.g., churn by health status and continuous eligibility period).58 MLM would also be useful for longitudinal analysis of Medicaid enrollment, where responses are correlated with each other, and analysis of high-dimensional problems, where there are many parameters to estimate (Snijders & Bosker, 2000). Multivariate analysis of enrollment would allow researchers to draw conclusions based on correlations between eligibility-enrollment states as dependent variables representing a compound system outcome.
Multivariate tests of specific effects are more powerful than in univariate analysis (i.e., smaller standard errors), especially if the dependent variables are strongly correlated (which they would be), and if the average number of measurements available per individual is less than a complete panel (which is a common issue in Medicaid administrative data) (Wickens, 2014).
58 Lynn, Heinrich, & Hill, 2000 provide examples of MLM in other policy domains. 94 Finally, while most studies use traditional statistical analysis with frequentist inference to analyze data, Bayesian regression methods could be used to leverage existing sources of data and extend current findings. Parameter estimation under Bayesian inference is a weighted combination of the prior and the likelihood and can therefore be thought of as a generalization of maximum likelihood estimation. Bayesian analytic methods have a number of attractive qualities for dynamic social phenomena like program enrollment. For example, parameter estimates are based on distributions rather than average effects and can be updated based on additional data from different data sources; all model assumptions are included and mathematically expressed in the regression equation by including prior information and expressions of uncertainty; and regression outputs have intuitive probabilistic interpretations (i.e., how likely the hypothesis is given the data, rather than how the data are given the null hypothesis) (Gelman, Carlin, Stern, Dunson, Vehtari, Rubin,
2013). Bayesian analysis is particularly useful for high-dimensional problems (i.e., those with a great deal of detail complexity) and phenomena with considerable uncertainty.59
Remaining Limitations. While each the strategies above confer some benefit related to detail complexity, they do not address the essential features of dynamic complexity; they do not elucidate feedback structure or permit emergence or autopoiesis. Many features that are treated as
“control variables” or are left out of a model all together are instrumental to understanding the phenomena of interest. For example, if actors and behaviors are interconnected and interdependent, then economic, political, and social conditions, administrative rules and practices, and sources of learning about those rules and conditions should all be endogenous elements of any model
59 Because all parameters include both prior distributions and likelihood, statistical power is not a concern. With relatively more data, the estimator will get weighted toward the likelihood, resulting in parameter estimates that converge with those MLE procedures. However, in the absence of much data, estimates will be weighted to the prior and Bayesian credible intervals will widen, making Bayes a powerful inference engine for limited-data applications. 95 (theoretical or empirical) of a system. Regression analysis – even with Bayesian inference – does not directly measure interrelationships, causality, or feedback dynamics (Roach & Bednar, 1997).
In other words, the alternatives discussed above are still Cartesian treatments of enrollment phenomena when holism is the appropriate approach to understanding the system. Cartesian models of Medicaid enrollment specify an outcome and a structure (functional form relating the variables), which prevents any insights about self-organizing patterns or latent relationships among the system elements. Thus, the fundamental mismatch between the world and our thinking about it remain.
Unfortunately, the problem is not only that current approaches to Medicaid enrollment (and policy economics more generally) attempt to reduce the irreducible; it is that complexity itself is a muddled concept. As outlined earlier in this chapter, the literature on complexity and complex systems describes myriad characteristics that ‘are complexity’ but those characteristics are poorly organized, leaving interested parties unclear about how to apply or operationalize the concept.
Advancing complexity science in policy studies requires addressing conceptual logic and consistency.
Organizing and Reconstructing Complexity for Policy Studies
Complexity as a concept is prolific in use, but poorly organized. Lists of complexity characteristics (e.g., Axelrod & Cohen, 1999; Sterman, 2000; Page, 2010; Miller & Page, 2007;
Desai, 2012b) not only make the concept overwhelming, but also signal a lack of the formal organization that would make it useful as a construct for empirical research. Using complexity as shorthand, as a catch-all label for a deep and critical concept, is problematic because it reduces the
96 meaning and relevance of the concept. It is necessary to be definitionally rigorous in order to pay the concept of complexity its due.
In this section, I contribute to complexity studies by synthesizing various features discussed in the literature and connecting them to a framework for logical consistency first developed by
Whitehead and Russell (1910-1913). Using this framework, I develop a taxonomy to distinguish the scale of complexity in a system, and a typology to define categories of complexity in a system.
Finally, I explicitly connect the typology and taxonomy to Bunge’s formalization of concrete system elements to show how different types and taxa of complexity manifest in purposeful human systems.
Complexity and logical consistency. One way to bring logical clarity and precision to the concept of complexity is to distinguish between the classes of features that make a system complex
(i.e., what complexity is) and the levels at which complex phenomena manifest (i.e., where complexity is).60 The distinction relevant and important for discussions of complexity because when we conflate different levels of phenomena (e.g., processes of systems with processes of elements) or different types of phenomena (e.g., processes with events), we are left with paradoxes, which lead to logical fallacies and errors in understanding and judgment. For example, attempting to discuss the emergent properties of the system and behaviors of individual actors at the same level results in a form of Russell’s paradox (Desai, 2012b).61
In Principia Mathematica, Whitehead and Russell (1910-1913) articulate a theory of logical types to address this paradox. Bateson (1979) and later Roach and Bednar (1997) built on
60 Note that Complexity is an adjective (or adverb), not a noun. Complexity is a characteristic of the world and its phenomena, not a thing unto itself. 61 Russell’s Paradox is the following contradiction that results from naïve set theory. Let R be the set of all sets that are not members of themselves. If R is not a member of itself, then it must contain itself. If R contains itself, then it contradicts its own definition as the set of all sets that are not members of themselves. 97 the theory of logical types; it is Roach and Bednar’s version that I reference here and present in
Table 1. Briefly, the theory of logical types holds that phenomena occur at different logical levels, i.e., hierarchical levels of activity. Simple actions are attributes or behaviors of an individual actor.
Lineal actions are caused behaviors of an individual actor, or a hypothesized cause-and-effect relationship. Contextual actions are interactions and feedback among actors. Metacontextual actions are interaction patterns or changes in interactions.62 Logical types refer to the classification scheme to describe the underlying processes (i.e., the “distance” from reality we’re interested in/characterize; kind of activity). This provides a framework to distinguish the scale of complexity in a system in a taxonomy from the features that are complex in a typology. Types of system complexity (e.g., purpose, timing, interdependency) are not directly tied to any particular level, but the taxa of system complexity is specifically organized by those levels (e.g., autopoiesis only occurs at the level of the system).
Table 3. Roach & Bednar's Logical Types and Levels
Construct Type Classification Description Actual Process Construct Level (perception) (real process) Metacontext Underlying Descriptions of Interaction patterns assumptions and interaction patterns Changes in interactions themes Context Categories of feedback Description of Interactions and interrelationships interactions Lineal Action Causal schema Description of caused Caused behavior behaviors Simple Categories Description of Simple characteristics Classification attributes Adapted by the author from Roach & Bednar, 1997 (p. 684)
62 Bateson’s analogy to levels of motion (1972) is useful: Position is a simple action, velocity is a lineal action, acceleration is a contextual action, and a change in acceleration is a metacontextual action. 98 Complexity taxonomy: levels of complex phenomena in a system. A taxonomy classifies objects or phenomena hierarchically, typically based on the origins of the class (Page,
2010).63 Complexity taxa thus specify the level of activity from which a system’s behavior – some aspect of its complexity – arises. Sterman (2000) distinguishes between detail (or combinatorial) complexity and dynamic complexity.64 I extend Sterman’s characterization by articulating another taxon of complex system behavior (i.e., functional) and by defining the taxa in terms of logical levels of activity – i.e., simple and caused behaviors of the elements, interactions among the elements, or interaction patterns of the system.
Detail complexity. Detail complexity refers to the level of system behaviors that arise from the simple and lineal actions of elements within a system. In Bungian terms, it is in the attributes and behaviors of a system’s components, structure, and environment that a system is combinatorially complex. For example, the combination of the number of cars traveling on a highway, the number of lanes, the speed limit, and the position and velocity of cars create detail complexity in a ground transit system. The combination of demographic characteristics, level of unemployment, the number of coordinating organizations involved in administering Medicaid, eligibility criteria, and application rules and procedures create detail complexity in a State’s
Medicaid program.
Dynamic complexity. Dynamic complexity refers to the level of system behaviors that arise from contextual actions among system elements over time. It is in the interactions among agents
(components), rules (structure), and proximal context (environment) that a system is dynamically complex. The mechanism (characteristic processes) of a system, because it is constituted of the
63 Logical levels are therefore taxa. 64 Dynamic complexity is introduced by Forrester (1971). 99 feedback structure among the other elements, demonstrates dynamic complexity. For example, traffic flow – the mechanism of a ground transit system – demonstrates dynamic complexity created by interactions among drivers in automobiles; the size, location, and number of lanes; traffic laws and norms; and weather conditions. Medicaid’s sorting mechanism demonstrates dynamic complexity created by feedback among citizens, bureaucrats, and program rules during the application and review process (e.g., interaction among citizen need, knowledge, and attitudes about benefits; bureaucratic understanding and attitudes about program rules and citizen eligibility).
Functional complexity. Sterman (2000) distinguishes between detail and dynamic complexity, noting that feedback structure is the only thing that can produce dynamic complexity.
However, he does not address the interaction patterns of feedback structure with the system’s broader environment, which is fundamental to understanding the self-regulatory and emergent properties of complex systems. Functional complexity refers to the level of system behaviors that arise from metacontextual actions of the system over time. It is in the interaction patterns and changes in interactions of a system’s mechanismic operation within its broader environment that a system is functionally complex. Manifestations of functional complexity – self-organization, adaptive learning, autopoiesis, policy resistance, and other emergent properties – cannot be demonstrated by the elements of the system, only by the system itself. For example, a traffic jam on an accident-free highway is an emergent property of the ground transit system created by the patterns and changes of traffic flow in context; it is not a property of cars or their interactions. In
Medicaid, patterns and changes in citizen sorting over time create caseload backlogs and systematic churning of people off and on the program – emergent properties of the Medicaid system itself, not of individual beneficiaries or bureaucratic encounters.
100 Drawing distinctions about the scale of complexity in purposeful human systems is important because the origins of complex phenomena inform the interventions individuals and societies make. Wicked problems are manifestations of functional complexity – they are emergent properties of systems in context – and therefore cannot be acted upon directly. Changing interaction patterns requires making changes to a system’s feedback structure, which itself involves making combinations of changes to rules and behaviors and monitoring how the interactions play out. For example, because traffic jams are emergent properties of freeway dynamics rather than the result of a specific behavior or relationship, strategies to alleviate traffic jams likely involve changes to public transit infrastructure, driver alert systems, speed limits, and lane and entry ramp configurations. Similarly, acting only on the origins of Medicaid’s detail complexity – eligibility criteria, individual characteristics and behaviors, application structure, or determination rules – will not solve the churn problem because the dynamics of program enrollment are functionally complex.
The combination of interventions, feedback and monitoring the interactions defines implementation as the mechanism of public policy. Yet rarely do researchers formally distinguish the simple actions and attributes of interventions (detail complexity) from the implementation of those interventions (dynamic complexity). In Medicaid, the eligibility criteria and determination rules of the means test are interventions that contribute to detail complexity; the feedback among citizens, bureaucrats, and rules through means negotiation is part of implementation’s dynamic complexity. When we conflate details with dynamics, we risk making errors in judgment about how to address functionally complex problems, thus perpetuating the complexity gap.
101 Table 4. Complexity Taxonomy Level of Complexity Level of Action In Policy Studies In Medicaid (Roach & Bednar) Functional Interaction patterns in Policy resistance Friction created in System self- the system itself (amplification, bureaucratic encounters organization and dampening, and change) Caseload dynamics and (re)production Program policy resistance institutionalization Policy gridlock Wicked problems Dynamic Interactions, delays, Behavioral changes Means negotiation Feedback structure and change among Actor interactions Bureaucratic encounter and variability among elements of the system Implementation Citizen sorting rules, factors, actors
Detail Lineal actions, Program purpose Means test Variety and diversity behaviors, and simple Target population Administrative rules of rules, factors, attributes of elements characteristics # of people in actors of the system Intervention rules enrollment category
Complexity typology: features of complex phenomena in a system. A typology classifies objects or phenomena non-hierarchically based on their attributes (Page, 2010).65 Types of complexity are classifications of information in a system; they describe features of systems and their elements that contribute to complex phenomena. As with all typologies, the boundaries among the types that follow are blurry (Page, 2010, p. 60); what is clearly a feature of dynamic temporality to one may be a manifestation of recursive causality to another. This is because types are socially (and politically and economically) constructed. Classification thus depends on the preferences, expertise, and biases of the person doing the classifying (Page, 2010, p. 61). However, logically consistent and exhaustive classifications are crucial for precise definition and
65 Logical levels are therefore taxa. 102 measurement of phenomena, and thus for scientific research. Typologies that efficiently categorize phenomena are particularly useful.
I define five types of phenomena in complex systems, each based on a characteristic of information transfer (activity) in the system. Purposeful human systems are characterized by high dimensionality, recursive causality, irreversibility of and change over time, diverse purposes among the system’s parts, and higher-order structures and behaviors (e.g., emergence).
High dimensionality. Dimensionality refers to the variety of sources, attributes, and transfer of information within a system. Dimensional complexity describes the class of system features related to that dimensionality. In Bungian terms, dimensionality is displayed by the system’s components, structure, and environment, and is commonly represented by random variables. System dimensionality is a type of complexity because variety, diversity, and volume
(perhaps, though not necessarily) of attributes or behaviors among system elements create many combinations of features by which elements may be categorized, and many possible behaviors with which those features may be correlated. For example, in Medicaid enrollment, diverse individuals (both citizens and bureaucratic actors), the variety of program rules for application and enrollment, and the variety of economic, social, and political conditions from State to State creates huge combination of possible characteristics and behaviors by which system elements may be categorized.
Diverse intentionality. Intentionality refers to the diversity and variability of reasons for the transfer of information within a system. Intentional complexity describes the class of system features related to that intentionality. Purposeful human systems and their elements display diverse intentionality, which may be represented by random variables (e.g., as characteristics of components), but is more often hidden in latent assumptions about the system or its elements (e.g.,
103 utility maximization assumed as motivation for individual and organizational behavior). System intentionality is a type of complexity because different motivations and strategies among different agents (including those in organizations or social groups) create conflict, goal misalignment, and opposing forces within a system. For example, missed take-up among the eligible may be a manifestation of the conflict between citizens seeking a benefit and frontline bureaucrats’ objective to comply with and enforce application and determination rules. Likewise, administrative burden may be a manifestation of administrative and elected officials having goals other than the stated program objective to cover all individuals in need (e.g., cost containment or ideological stance).
Purposeful human systems are also intentionally complex because actors have non-uniform purposes, motivations, and ways of thinking, some of which may be hidden, even from themselves
(Axelrod & Cohen, 1999; Miller & Page, 2007). Furthermore, their reasons for action are not necessarily consistent or coherent (Gigerenzer, 2007), creating additional variation and variability, and making the system more intentionally complex. For example, some bureaucratic actors may be motivated more by social equity considerations, while others are more concerned with minimizing the role of government.
Recursive causality. Causality refers to the direction of information transfer within a system. Casual complexity describes the class of system features related to non-lineal causal relationships among system elements. Purposeful human systems display recursive causality, which is represented by feedback loops.66 Recursive causality is a type of complexity because relationships and influence involve feedback and learning among interdependent and
66 Relationships may also be non-stationary: Non-stationary processes have no trending mean and must be piecewise defined. No single functional form, even with a transformation, will define it. The interactions or relationships among the variables change such that at least two covariance-correlation matrices are necessary to define the entire input space. 104 interconnected actors, rules, and conditions, creating non-lineal system dynamics and making it difficult to identify cause and effect. For example, in Medicaid enrollment, program rules, social and economic conditions, and the actions of bureaucrats influence the behavior of citizens with regards to application, which in turn influence the behavior of bureaucrats with regards to review and determination.
Dynamic temporality. Temporality refers to changes in sources, directions, and reasons for information transfer in time. Temporal complexity describes the class of system features related to dynamic temporality, where time is unique, i.e., no point in time is interchangeable with any other. Dynamic temporality is a type of complexity because systems and their elements are sensitive, but not totally tied, to initial conditions (path sensitivity); because time is irreversible
(i.e., once something is done, it cannot be undone (Merton, 1936; Rittel & Webber, 1973; Desai,
2012b)); and because feedback and delays create changes in system behavior over time, which must be presented in graphs of behavior over time (rather than distributions at particular points in time). For example, feedback and delays in the Medicaid enrollment process create changes in enrollment status among citizens, as do changes in household economy and individual behavior, which are sensitive to past states.
Higher-order behaviors. Higher-order structures and behaviors are information patterns and functionalities displayed only by the system itself (Axelrod & Cohen, 1999; Desai, 2012b).
They are transformations of energy that arise from the interaction of the system’s elements – its components, structure, environment, and mechanism – within the broader context. Higher-order behaviors are the class of complex features that include self-organization, self-learning, self- regulation, autopoiesis; and emergent patterns of interaction and purposes that separate elements do not demonstrate. In Medicaid, higher-order behaviors include the program’s caseload and
105 misalignment dynamics across periods of time and changes in context, and the continued expansion of Medicaid as program boundaries and assumptions change over time.
Table 5. Complexity Typology
Type of Complexity In Policy Systems In Medicaid High Dimensionality Many layers of actors, Multiple organizational actors Diversity and variety of institutions (State, managed care, providers) elements, combination of Economic, social, and political and citizens features, actors variables Diverse Intentionality Rational, behavioral, rule-based, Bureaucrats balancing rules and Diversity, variability, and and political decision-making political pressure with personal latency of motivations of among organizations and attitudes elements individual actors Diverse goals, intentions Recursive Causality Imperfect information flow Means test is not Interdependent, tightly-coupled Individuals learn and adapt to instantaneously applied; changes elements, recursive influence rules in individual characteristics Behaviors are context-dependent Dynamic Temporality Future will not look like the past Learning, psychological, Variability of elements and Delays in implementation compliance costs to individual; relationships, sensitivity to time, Population change feedback between citizens and irreversibility of time Economic change bureaucrats Higher-Order Behaviors Unintended consequences of Caseload dynamics Novel and robust patterns and interventions (policy resistance) Ongoing expansion of eligibility arrangements, emergent Tendency for policies to become properties, purposeful institutionalized organization
Mapping complexity to purposeful human systems. As discussed earlier in this chapter, scholars have developed an extensive list of characteristics associated with complexity. While thorough, the volume of such lists is too unwieldy and overwhelming for careful empirical work.
Roach and Bednar’s extension of Whitehead and Russell’s theory of logical types provides a useful
106 framework for organizing the overlapping insights within the complexity literature, including
Bunge’s formalization of a concrete system.
The taxonomy and typology presented above represent an important contribution: efficient classifications of the phenomena of complex systems by levels of action (taxa) and by kinds of action (types). Table 6 synthesizes the insights of the taxonomy and typology on the literature by showing how different features of complexity manifest at different levels of action (per Roach &
Bednar) and different aspects of systems and their elements (per Bunge). Detail complexity is the product of dimensionality and intentionality of a system’s components, structure and environment.
Dynamic complexity is the product of causality and temporality of a system’s mechanism, which are driven by dimensionality and intentionality of the other elements. Functional complexity is the product of higher-order behaviors of a system, which are driven by causality and temporality of its mechanism, which are driven by dimensionality and intentionality of components, structure, environment.
107 Table 6. Complexity in Purposeful Human Systems Taxa/level Type/kind Elements Explanation of Logical Level (Bunge) (Roach & Bednar) Functional Higher-order System Metacontextual actions: Changes, underlying complexity behaviors Mechanism assumptions, emergent properties and Causality Components dynamics of the system itself. Observed over Temporality Structure time. Dimensionality Environment Intentionality
Dynamic Causality Mechanism Contextual actions: Interrelationships and complexity Temporality Components feedback among elements of the system. Dimensionality Structure Observed over time. Intentionality Environment Detail Dimensionality Components Lineal actions: Hypothesized cause-effect complexity Intentionality Structure relationships; caused behaviors of Environment components. Observed at point(s) in time. Simple Classes: Behaviors and attributes of system elements, independent of others. Observed at point(s) in time.
Conclusion
In this chapter, I explored the question of why scholars and administrators have had so little success getting traction on Medicaid’s enrollment gap by bringing insights from complexity and complex systems science to bear on the issue. I argue that we do not know how implementation of the means test works because Medicaid implementation is complex but our strategies for studying and intervening are not well-matched to that complexity. By showing how complexity manifests and is dealt with in Medicaid research, I make a number of contributions to complexity in policy studies more generally.
First, I explained how policy phenomena, including Medicaid enrollment, occur within purposeful human systems. Purposeful human systems are integrated and irreducible entities made up of actors, rules, and conditions interacting dynamically over time. Their parts have diverse,
108 variable characteristics and motivations that often conflict or are orthogonal to the purpose of the overall system. Purposeful human systems work via mechanisms, the feedback structure among interventions and the rest of the system’s actors, context, rules, and boundaries. The interdependencies among a system’s elements (including its mechanism) makes purposeful human systems hard to describe, explain, or predict, and often creates dilemmas that are not amenable to technical solutions. The domain of policy studies is filled with primarily concerned with the kinds of phenomena characterizing purposeful human systems, including wicked problems, diverse and variable human and organizational behaviors, and unintended consequences of policy interventions.
Next, I re-defined Medicaid’s enrollment gap as a manifestation of the complexity gap in policy studies: the disconnect between the complexity of the world and our responses to it, including both interventions and the understandings upon which they are based. The complexity of wicked problems like the enrollment gap and the purposeful human systems from which they arise makes decision making challenging. Complexity makes it tempting to break systems and their phenomena into smaller parts, and to make simplifying assumptions about those parts in the models we use to understand and act on them. Policy economics describes the neoliberal, Cartesian approach commonly taken in policy studies, including the Medicaid enrollment literature.
Scholars reduce complexity in Medicaid enrollment through methods and assumptions consistent with the policy economics approach. By reducing feedback relationships into deterministic ones, diverse patterns into average effects, dynamic processes into snapshots, and autopoietic behaviors into discrete outcomes, existing studies conceal the dynamics by which people move within the Medicaid system over time. Regardless of their rigor, data, study design, and analytic strategies that fail to account for the feedback among agents and rules within rich
109 social, economic, and political contexts, are a poor fit for the complexity of enrollment phenomena, and thus reinforce the complexity gap in Medicaid studies.
Finally, I point out that beyond detail and dynamic complexity, purposeful human systems demonstrate functional complexity, behaviors that arise from interaction patterns and changes in interactions of a system’s mechanismic operation within its broader environment over time.
Functional complexity – including self-organization, policy resistance, and autopoiesis – can only be identified and understood as a property of the system itself. To be logically consistent and thus empirically and conceptual useful to systematic study, I distinguish between these different taxa
(or scales) of complexity and different types (or kinds) of complexity: high dimensionality, diverse intentionality, recursive causality, dynamic temporality, and higher-order structures and behaviors.
The phenomena of public administration as they have been described — policy design, implementation, management — ultimately fail to distinguish between the taxa and types of process. The labels of public administration are artifacts of theories of their time, just as geocentric maps of the heavens are artifacts of how we used to think about the universe. As with celestial maps from antiquity, we may continue to produce artifacts that look remarkably similar in function and meaning, or we may also produce new artifacts to describe, explain, and predict our world. If we accept that public administration issues operate within purposeful human systems, and that the constructs and theories we currently use do not necessarily capture the dynamic processes within those systems, then it is reasonable to revisit and revise our conceptualizations of collective action processes.
The complexity gap describes the mismatch between policy problems and our responses to them; policy economics as an approach reinforces that mismatch. Gaining traction on intractable problems thus requires a shift in inquiry – not just a change in particular analytical methods, but a
110 change in conceptual approach that “harnesses complexity” (Axelrod & Cohen, 1999). A number of policy scholars have called for a framework that explicitly and practically includes complexity in policy models and interventions. For example, Cook and Tonurist (2016), Easterbrook (2014), and Desai (2012b) argue for the value of systems science and simulation methods in policy studies; and Sandfort and Moulton (2015) and Stone (2002) present complexity-oriented alternatives to traditional market models. Even Downs (1957) and Lasswell (1951), avowed rationalists who contributed to the disciplinary reach of policy economics, argued that any useful theories of public policy must include the pressures and dynamics of political and social context. In the next chapter,
I introduce policy cybernetics as a framework that confronts and harnesses complexity in policy studies. In chapter five, I return to the enrollment gap, using policy cybernetics to describe, explore, and consider revisions to Medicaid enrollment implementation.
111
Chapter 4: Policy Cybernetics as a Framework for Inquiry and Intervention
In the last chapter, I addressed the question of how implementation of the Medicaid means test works by bringing complexity literature to bear on the issue. I showed that Medicaid enrollment and program implementation are complex processes; that such phenomena are characteristic of purposeful human systems, which display functional, as well as dynamic complexity; and that the dominant approach to policy studies is a poor fit for the complexity of policy processes, resulting in a complexity gap between the field and the phenomena of interest.
In this chapter, I address the question of what a framework for policy studies that is capable of dealing with complexity looks like. What are its assumptions, constructs, methods, insights, and applications? What are its implications for research design, data collection, and analytic methods?
Policy studies requires a framework for inquiry and intervention that is complexity-capable in order to get traction on complex problems. As discussed in the last chapter, complexity is a thick concept. The complexity gap is not the result of any particular data, design, or method, but rather a symptom of poorly-fit responses to the intricacies and dynamics of real life. Scholars have called for an approach to the policy sciences that matches the complexity of the problems (e.g., Lasswell,
1951; Axelrod & Cohen, 1999; Desai, 2012b; Sandfort & Moulton, 2015). The purpose of this chapter is to present a complexity-oriented conceptual framework to guide policy studies – both the study and steering of collective action systems – that has both positive and normative value to understanding systems of this kind.
The central claim of this chapter is that addressing the complexity gap in policy studies requires a shift in both thinking about collective action systems and intervening within them.
112 Policy cybernetics is a conceptual framework that rigorously, systematically, and coherently accounts for complexity in policy phenomena. It embraces complexity by framing public policy and governance phenomena as processes within purposeful human systems. It harnesses complexity by maintaining a dilemma orientation and prioritizing identification of and experimentation with a sufficient variety of strategies to address those dilemmas. It respects the reality of complexity by explicitly reflecting on the lenses through which policy scholars and practitioners understand policy phenomena and our role in them. It abides by and extends the law of requisite variety, treating the dynamics of policy studies as forms of signal processing and response within complex human systems that require requisite adaptation. The goal of policy cybernetics is to address the complexity gap in policy studies by treating the complexity that is inexorably part of the field seriously and rigorously, which has both conceptual and practical value.
This chapter proceeds as follows: First, I sketch present guidelines for a complexity- oriented framework for policy inquiry based on a synthesis of appeals from policy scholars. I then propose policy cybernetics as such a framework, walking through its primary assumptions, concepts, and expectations. I discuss the implications of policy cybernetics for both the study and practice of the policy sciences, noting the importance of abiding by the law of requisite adaptation, an extension of Ashby’s law of requisite variety. Next, I present a practical guide to doing policy cybernetics, articulating concrete steps to take to conduct policy studies within the policy cybernetics framework. Finally, I dive more deeply into the philosophical underpinnings of policy cybernetics, explaining how its mechanismic and agential approach to policy studies supports complexity-harnessing research and its applications to practice.
113 Guidelines for a Complexity Framework in Policy Studies
Many policy scholars have called for the field to bring complexity formally into both research and applied work (e.g., Lasswell, 1951; Desai, 2012b; Sandfort & Moulton, 2015). As discussed in the previous chapter, different data, designs, and methods are likely necessary, but not sufficient to address complexity in policy phenomena. Nor are novel theories sufficient if they fail to challenge deterministic assumptions or distinguish between the types of features that make processes complex and the scale at which complexity manifests. What is needed, then, is a conceptual framework that builds on the contributions of the policy economics paradigm in order to pose and explore questions about the complexity of policy processes, rather than questions that assume endogenous and emergent features away. While we must simplify, how we simplify matters.
Definition of a conceptual framework. A conceptual framework is an abstraction of some aspect of the world that serves as a coherent and rigorous frame of reference for decision-making regarding that domain, both in matters of inquiry and in matters of action. Conceptual frameworks create boundaries around a set of phenomena by specifying assumptions about particular concepts and expectations about their behavior under a variety of conditions. Those specifications are consistent with 1) an articulated purpose for understanding the conceptual relationships; 2) a particular frame about what part of the world is of interest; and 3) an underlying lens through which the thinker makes sense of the world.67 Conceptual frameworks are more general than theories, which make specific propositions about particular constructs under specific conditions.
Indeed, a conceptual framework draws from multiple literatures, theories, traditions, and sources
67 The lens refers to a deeper set of assumptions beyond the particular phenomena, concerned with matters of how people build knowledge, conduct inquiry, and apprehend the nature of reality. These are assumptions that I take up in section 5, below. 114 of evidence. Its logic and boundaries in turn shape problem definition, research questions and design, data collection and analytic methods, and interpretation of evidence, though its domain is broader than any of those particular features. A conceptual framework allows the thinker to distinguish what is relevant (inside the research question or practice objective) from what is not.
It can accommodate new theories, methods, and evidence, and may inform a range of decisions about inquiry and intervention, so long as they are consistent with its underlying frame, purpose, and lens.68
A complexity-oriented conceptual framework for policy studies is an abstraction of collective action phenomena that accounts for complexity rigorously, systematically, and coherently in the purpose of the abstraction, the framing of the phenomena, and the lens through which they are interpreted. Because complexity arises from the details, dynamics, and functions of policy and governance system processes within which scholars and practitioners are embedded, the concepts, assumptions, expectations, and methods of a complexity-oriented framework for policy studies need to be consistent with 1) a decision-making for action purpose, 2) a purposeful human systems frame, and 3) a reflexive, engaged lens.
Purpose: dilemma-oriented to harness complexity. The purpose of a conceptual framework (or any model) refers to the reason for making an abstraction of the world in the first place. Policy studies are concerned with decisions regarding phenomena that are considered to be socially problematic. These decisions include both choices of policy intervention (“point decisions”), and management of policy implementation and objectives (“line decisions”) (Levin,
1993). Because policy and management decisions regarding social problems have implications for
68 For more on conceptual frameworks, see Ravitch, Sharon M. & Riggan, Matthew (2016). Reason & Rigor: How conceptual frameworks guide research. Sage Publications. 115 democracy (e.g., Soss, 1999; Riccucci, 2009), policy models (including conceptual ones) have democratic implications and obligations (Dewey, 1927; deLeon, 1999). Lasswell argues that it is by maintaining a “problem orientation” that scholars and practitioners will nurture the “policy sciences of democracy” (1951, p. 88). Easterbrook (2014) clarifies that because policy studies are most often concerned with wicked problems, a dilemma orientation is generally more appropriate.
Mechanismic approach to explanation. A mechanismic explanation of a dilemma is one that explains its characteristics processes, or how the underlying system works (Bunge, 2004).
Mechanismic explanations are those that illuminate the core dynamics of the dilemma, which involve both the interventions (i.e., policy rules) and the complexity in which they operate (i.e., feedback structure and endogenous responses among the relevant elements) (Axelrod & Cohen,
1999). It is useful to clarify to that inventions themselves include both the policy choice and the management process – that is, both point and line decisions. To explain via mechanism is to explain in terms of interdependencies and change among rules, conditions, and actors over time.
The ability to identify potential leverage points within a dilemma and explore significant tradeoffs involved in acting upon those leverage points is what Axelrod and Cohen refer to as
“harnessing complexity” (1999). Mechanismic explanation facilitates harnessing complexity by focusing on structural explanations for system behavior, which can then be affected (though not necessarily controlled) through informed changes to policy interventions.
Pragmatic approach to modeling, inference, and intervention. A pragmatic approach to conceptualizing a dilemma is diverse in that it utilizes different methods, skills, ideas, and disciplines to explore and address the phenomenon of interest. Dilemmas should be investigated using a range of data and methods – including quantitative, qualitative, and computational – because human problems unfold in ways that require technical and experiential expertise, but also
116 intuition, speculation, and imagination (Lasswell, 1951). Simulation modeling provides a rigorous, systematic way of incorporating various forms of data into virtual lab experiments that can be run and re-run under different conditions and assumptions. This approach allows the modeler to explore many plausible explanations for the observed dilemma, as well as the consequences of many possible responses to it, with effectively zero social costs. This sort of prospective policy analysis is more attractive than putting a policy into place and letting it unfold, then trying – in vain – to put the genie back in the bottle (Desai, 2012b).
Simulation modeling also supports a dilemma orientation because it encourages participation among those interested in the phenomenon, be they scholars, practitioners, or citizens. A model is simply a representation of the parts and relationships relevant to some aspect of the world according to a set of assumptions (Rescher, 1998). Thus, people are always operating according to some (often implicit) model, but because their assumptions are explicit and can easily be revised according to the input of various audiences, simulations provide a way of facilitating the “creative interchange” (Lasswell, 1951, p. 102) between practitioners, scholars, and the public, which also encourages transparency and accountability (Desai, 2012b). Additionally, simulations are adept at aiding in real-time decision making, demonstrating tradeoffs, updating expectations based on changes to particular conditions, and disciplining policy debates about plausible ranges and possible outcomes (Epstein, 2006).
Frame: purposeful human systems-oriented to address complexity. A disciplinary frame is the basic structure defining the phenomena of interest to a particular field of study. Policy studies are concerned with legal, social, administrative, and organizational phenomena. Framing the policy domain in terms of the purposeful human systems in which these processes occur is one way to address the complexity inherent in the field.
117 Holistic approach to the phenomena of interest. Policy scholars have long acknowledged that policy phenomena, particularly dilemmas, are complex; for example, history demonstrates that policy interventions will look different in the future than they do in the past (Desai, 2012b;
Cook & Tonurist, 2016). One way of understanding the unpredictability of the social world is to consider that “the eye sees only what the mind is prepared to comprehend” (Henri Bergson, in
Sterman, 2000, p. 24); that is, that the framing of the phenomenon is instrumental in the understanding of it. Systems scholars posit that system behaviors result from the interdependencies among rules, actors, and environment over time and space (Forrester, 1961; Sterman, 2000).
Consequently, complex patterns are integrations – not summations – of the parts within a system
(Koffka, 1935; Ashby in Stogdill, 1970). Understanding policy dilemmas as integrated patterns requires putting them in the context of the purposeful human systems in which they occur, rather than isolating them from those systems (Lasswell, 1951; Easterbrook, 2014).
Pluralistic approach to modeling, inference, and intervention. It is necessary to take a pluralistic approach to modeling purposeful human systems because the notions of complexity and systems are more than just metaphors (Desai, 2012b); they present concrete challenges to describing, explaining, and acting upon policy phenomena. For example, it is necessary for policy scholars and practitioners to understand both bottom-up pressures (i.e., agent behavior), top-down pressures (i.e., institutional behavior) and the patterns that result from their interactions (Miller &
Page, 2007); and it is useful to know both about singularity (i.e., particular behavior of a few agents) and typicality (i.e., modal and average behavior of population of agents) (Lasswell, 1951;
Miller & Page, 2007). No one model or method permits the thinker to capture all of the forces of interest in their world (Box & Draper, 1987), but they are important tools for organizing and disciplining thinking (Desai, 2012b). By being “many model thinkers” (Esptein, 2008), policy
118 scholars and practitioners develop a large, diverse toolbox to have at their disposal as they study and address policy phenomena.
Lens: reflexive to respect complexity. As a scholarly field, policy studies are concerned with the rigorous, systematic, scientific study of policy phenomena. However, all scientific study is an “enactment of reality” (Roach & Bednar, 1997); the consciousness, training, and proclivities of the researcher affect, and are affected by, the object of study (e.g., Latour, 1987; Barad, 2007).
Science is therefore never value-free, but is always carried out through the lens of the researcher.
Policy scholars have pointed out that investigators are not without their own biases, positions, and motivations, and that we participate in the very phenomena we study (e.g., Downs, 1957; Lasswell,
1970; Desai, 2012b). This is true both of scholars and of practitioners and comes out in how we do our work. While this observer effect is not entirely avoidable, scholars and practitioners can reduce the potential harms wrought by the imposition of our values and assumptions on our systems of interest by being explicit about the lens through which we see the world and our role in it – what Haraway (1988) refers to as “situated knowledges”.
To further complicate matters, in policy studies, the phenomena themselves are constructed through many diverse and dynamic lenses of the agents who have their own values and expectations about what will and what should happen (Desai, 2012b). Stone (2002) argues that the ongoing negotiation of facts and values (which are in separable) makes policy a particularly poor venue for scholars or practitioners who fancy their approaches to be rational or objective in nature.
Indeed, the value-laden and contextual meanings of policy constructs – or “index instability”
(Lasswell, 1951, p. 100) – is an unavoidable and undeniable part of the field. Being reflexive in policy studies is therefore not only about situating the knowledge of the scholar or practitioner. It
119 also requires an acknowledgment that policy systems themselves are filled with purpose, tendencies, and bias – that the situation of the things we study is diffuse and constantly changing.
Policy Cybernetics as a Framework
Policy cybernetics is a conceptual framework (i.e., coherent abstraction) that is consistent with the guidelines set out above to rigorously, systematically, and coherently account for complexity in policy phenomena. It embraces complexity by framing public policy and governance phenomena as processes within purposeful human systems. It harnesses complexity by maintaining a dilemma orientation and prioritizing identification of and experimentation with a sufficient variety of strategies to alleviate/address those dilemmas. It respects (the reality of) complexity by explicitly reflecting on the lenses through which policy scholars and practitioners understand policy phenomena and our role in them. Drawing from systems and complexity science, cybernetics, policy studies, and feminist science studies, I specify assumptions and expectations about policy concepts that are consistent with these principles. The goal of policy cybernetics is to address the complexity gap in policy studies by embracing, harnessing, and living with the complexity that is inexorably part of the field – both its study and its practice.
Assumptions.
1. Purposeful human systems, their processes, and the dilemmas they create – irreducible and integrate – are the phenomena of interest. Recall that purposeful human systems are integrated wholes within which actors, rules, and conditions interact dynamically over time. They are necessarily open and complex systems that have at least one purpose, but whose parts have purposes and motivations of their own that affect system behavior. System elements display influence and choice in purpose and in function, making purposeful human systems self-organizing
120 and autopoietic. System behavior arises from the structure and transfer of energy among elements of the system, which is interdependent and recursive. Systems that coalesce around formal collective actions and their management have these characteristics, as do the societies in which they form.
Complex systems operate via mechanisms, the compound, recursive processes of dynamic complexity. Purposeful human systems are characterized by mechanisms that are combinations of legal, administrative, and organizational rules and the system’s endogenous responses to them.
The wicked problems of purposeful human systems are manifestations of functional complexity, which arises from a system’s mechanismic operation within its broader environment. Thus, neither policy processes, nor the dilemmas with which they are concerned, can be understood as isolated or discrete phenomena. They must be approached as irreducible patterns within integrated systems.
Systems science. This treatment of the phenomena of interest in policy studies in consistent with the methodological assumptions of systems science, which are covered in detail later in this chapter. Briefly, systems science is based on a holistic (rather than Cartesian) interpretation of the scientific method. While the design process for inquiry is largely the same (i.e., observe, formulate questions, hypothesize, experiment, analyze, revise), systems science posits that complex phenomena, because of the interdependencies, interactions, and non-lineal dynamics that characterize them, cannot be understood by breaking them into their constituent parts or isolating them in time. Rather, they must be investigated holistically, in terms of the feedback structure and changes over time that produce them. For example, systems science favors dynamic hypotheses, which are suppositions about the process by which a phenomenon arises, rather than covariations that are associated with it.
121 Cybernetics. Cybernetics – the science of communication and self-regulation in complex systems (Ashby, 1956) – falls within the domain of systems science.69 Wiener (1954) refers to it as the study of how systems record, preserve, transmit, and use information for self-regulation and self-control. While cybernetics originally referred to automatic control systems in machines, it has since been applied to living beings (Varela & Maturana, 1972) and organizations (Beer, 1972).
2. The study and regulation of purposeful human systems requires responses – scholarly and practical – that sufficiently match the complexity of signals they systems produce. This assumption speaks both to the existence of the complexity gap and to the dilemma orientation of policy studies. Because purposeful human systems are characterized by feedback between problematic phenomena and endogenous responses to them, policy studies fundamentally make contributions through responses to the complexity demonstrated by purposeful human systems.
Desai contends that, “the complexity of the objects of inquiry must be matched by the complexity of the approaches used to tackle them” (2012b, p. 7). However, Rescher points out that, “as an item’s complexity increases, so do the cognitive requisites for its adequate comprehension” (1998, p. 1). Responding to the complexity of purposeful human systems is no easy task.
Law of requisite variety. The law of requisite variety is a principle for dealing with responses in complex systems. It states that in order for a system to maintain stability or control, the variety of system’s responses must be greater than or equal to the variety of perturbations from the environment. Ashby (1956) articulated this principle on the basis that only variety “destroys” or absorbs variety. Von Bertalanffy (1951) viewed requisite variety in terms of information; he posited that the regulation of a system is proportional to the information (i.e., energy or decisions)
69 Cybernetics comes from the Greek kubernan: to pilot or steer.
122 available. This is such a powerful principle that it is referred to as the First Law of Cybernetics. In cybernetic terms, the complexity gap in policy studies can be thought of as an Ashby problem.
Figure 13. Law of Requisite Variety70
3. Researchers and practitioners are embedded within the purposeful human systems with which we are concerned. A reflexive framework acknowledges the role of researchers and practitioners as “participant-observers” within policy problems and processes.71 The notion of reflexivity is consistent with the cybernetic principle of signal processing and response: System self-regulation involves systematic filtering, processing, transmitting, and response to signals from
70 Norman, J. and Bar-Yam, Y., 2018, July. Special Operations Forces: A Global Immune System? In International Conference on Complex Systems (pp. 486-498). Springer, Cham. 71 Bronislaw Malinowski, 1922. 123 the phenomena and context of interest. This sense-and-respond feedback loop is itself a mechanism of autopoietic systems.
Signal processing and response. Ackoff (1988) conceptualizes human signal processing
(i.e., observation) in terms of a cognitive pyramid.72 Humans describe signals from the observed process by recoding data, which we then organize and transform into information. Humans then contextualize and interpret information to build knowledge, and then integrate judgments and meanings about knowledge to develop wisdom, or common understanding. Humans do this all the time, implicitly and automatically, but when we do so systematically, according to a transparent and replicable methodology, the process is scientific inquiry.73
Wisdom
Knowledge
Information
Data
Figure 14. DIKW Hierarchy74
72 This representation of the pyramid is largely attributed to Ackoff, but earlier explanations of cognition by Boulding in 1955, Henry in 1974, and Zeleny in 1987 used a similar hierarchy. 73 Science is systematic organization of knowledge but is not necessarily a common understanding. 74 The DIKW Pyramid has been attributed to a number of different scholars from different fields, including Nicholas Henry (1974. Knowledge Management: A New Concern for Public Administration. Public Administration Review 34(3): 189) and Russell Ackoff (1989. From Data to Wisdom. Journal of Applied Systems Analysis 16: 3-9). 124
Humans respond to the environment based on signal processing. Both humans and human organizations are characterized by their intervention – their participation – in the world (Axelrod
& Cohen, 1999). When we do this formally and collectively, it is policy intervention. Viewed in this way, there is no logical distinction between policy (point decisions) and management (line decisions) because both are forms of institutional response to the environment. In turn, the environment senses and responds to these responses, leading to phenomena such as policy resistance. Thus, there is a recursive (feedback) relationship between researchers and practitioners and the purposeful human systems with which they engage.
There is an interpretive and creative element to this sense-and-respond relationship.
Neither researchers, practitioners, nor the systems they study receive signals from the other intact.
For example, reality is “bound to be cognitively opaque” for humans because we have limited – and particular – capacity to “take inventory” of the features of reality (Rescher, 1998, p. 25).
Wiener (1954) characterized this transformation of information as immutable, and a form of feedback unto itself used to reduce entropy. In this regard, the seeming loss of information is actually part of system control; self-regulatory processes may involve the prevention (rather than preservation) of signal transmission for the sake of control. Cognitive boundaries thus serve as lenses through which information is altered, affecting the ways in which humans sense and respond to their environment. This is consistent with Aristotle’s notion of three human activities: signal processing is thinking activity (theoria) and responding is a doing activity (praxis), but there is a creative activity (poiesis) involved in that turnaround that makes reality work. Figure 15 illustrates this process in policy studies: the complex world generates data; policy studies gives the data form
125 (information), puts them in context (knowledge), and imposes values (wisdom), then uses these refined signals to act upon the complex policy world to bring about change.
Figure 15. Signal Processing and Response in Policy
Boundary behavior and agential realism. Barad (2007) takes up the role of boundaries in altering and (re)creating information in great detail. In short, she argues that science itself is a boundary project – a mechanism of engagement with the world that involves continual sensing and responding to signals as we perceive them. Furthermore, perception itself is a boundary, constructed by the ontological “cuts” we make to see reality and the epistemological lenses we use
126 to filter and process signals. The behavior of signals – or, as she conceptualizes them, waves – is altered through their encounters with the boundaries. When signals encounter a change in medium, this is refraction (bending); when they encounter an impermeable boundary, this is reflection
(bouncing); and when they encounter a perforation in the boundary, this is diffraction.
Furthermore, the perception of signals is refined by the particular training and worldview of the participant-observer; i.e., our epistemological lens. Positive (concave) lenses focus signals, while diffuse (convex) lenses spread them; compound lenses do both.
Barad argues that the human mind, which is both a physical and social structure, serves simultaneously as a change in medium, a lens, a boundary, and a perforation in that boundary. The particularity of boundaries and boundary behavior involved in engaging with reality is what drives
Barad to articulate agential realism – a reflective philosophical position that is at once a theory of knowing (epistemology) and a theory of being (ontology) that views the world as fundamentally probabilistic, not deterministic. By agential, Barad invokes a subjective epistemology, arguing that meanings are baked into our observations of the world. She argues that human actors participate in the constitution of the world by making “cuts” into reality that are culturally- embedded and value-laden. Nonetheless, as a quantum physicist, Barad argues strongly that, “there is a there there”, but that we only have knowledge of it through our meanings, not in any value- free way. Barad’s realism holds that while agents are constantly molding the universe in our attempts to know it, the universe “kicks back”, resulting in the surprises, inconsistencies, and incongruities we encounter in doing science. Humans value simplicity. The universe does not have to.
The upshot of agential realism is that as we learn more about the universe, we discover new elements of its complexity. The world is the same, but our perspective and understanding are
127 change, so the moves we make – both scientific and practical – change, too. Human agents are always sensing-and-responding in, to, and with the world. This position is reflexive, locating agents – researchers and practitioners – in the complexity of the world and phenomena themselves.
The phenomena of interest (purposeful human systems in the case of policy studies) are material- discursive subject-objects, the boundaries of which are continuously interrogated, created, and shaped by the agents within them.
Concepts.
Purposeful human systems of formal, collective action (policy systems). Policy systems refer to entities that integrate around collective action decisions and their administration (i.e., policy and management). As purposeful human systems, the elements within policy systems (e.g., citizens, bureaucrats, elected officials) also have purposes and motivations of their own, which contribute to the complexity of the system.
Policy systems coalesce around the task of governance – the direction, control, management of formal responses to some problem in society. Policy systems are thus embedded within broader social contexts, institutions, norms, behaviors, and phenomena, i.e., purposeful human systems of social action. It is within policy systems that both policy scholars and policy practitioners conduct their business (of inquiry and intervention, respectively).
Purposeful human systems of social action. The broadest system of interest in policy studies is society, a system of social action. A system of social action encompasses all sorts of actors, institutions, arrangements, and context, including friendship, kin, and community, economic, and national forces. It is this broad social system that is “the world” of interest to policy studies, as well as the system within which systems of formal, collective action are embedded.
128 Complexity gap. Recall that the complexity gap is the disconnect between the complexity of the world and our responses to it, including both formal interventions and the understandings upon which they are based. More specifically, the complexity gap is the mismatch or distance between social systems and the policy systems that attempt to govern them. An acute disconnect between the complexity of social systems and the policy system’s ability to respond to it results in a governance crisis (Cook & Tonurist, 2016).
Mechanism feedback. Mechanism feedback refers to the dynamics by which adjoining systems sense-and-respond to each other; i.e., how they send, filter, process, transmit, and respond to each other’s signals. Mechanisms are how systems operate; mechanism feedback is how systems communicate and relate to each other. Mechanism feedback is the adaptation of systems to other systems. Social and policy systems communicate and respond to each other via mechanism feedback.
129
Figure 16. Relationship of Social and Policy Systems
Expectations. Given the assumptions outlined above, policy cybernetics has several expectations about how policy systems and the social systems in which they are embedded relate to each other..
The complexity gap will always exist. There will always be a disconnect between the complexity of social systems and the complexity of policy responses because policy systems coalesce around efforts to govern some aspect of the social system. Boundaries form around policy systems as a means of managing the complexity within social systems. Simplification is necessary because there are always tradeoffs between complexity and feasibility in sensing and responding to signals from the environment.
Furthermore, this gap will exist because the dilemmas around which policy systems organize are manifestations of functional complexity – i.e., higher-order properties and functions 130 that arise only at the level of the system itself. However, direct action (e.g., changes in design) must be taken within the system, which is a matter of trying to affect dynamic complexity. Because policy deals in dynamic complexity but dilemmas are functionally complex, the complexity gap will always exist. Rescher (1998) posits that problem complexity will always outpace solution complexity; the distinction between dynamic and functional complexity is why.
Mechanism feedback is the source of both wicked problems and their potential solutions.
Dynamic complexity is a product of a system’s feedback structure (i.e., mechanism). Functional complexity is a product of a system’s feedback structure interacting with the broader environment
(i.e., mechanism feedback). That interaction – the sensing and responding dynamics among social and policy systems – may produce patterns considered to be problematic (i.e., dilemmas).
However, the mechanism feedback that characterizes relationships and communication among systems is not inherently problematic, goal-dampening, or -defeating. It is the perspective of the agents – of society – that frames it as such. Mechanism feedback – interacting feedback structures
– can also have adaptive capabilities and be a source of strategies for addressing dilemmas.
As purposeful human systems, social and policy systems will always be changing.
Complex systems are characterized by their mechanismic operation. The sense-and-respond feedback structure of mechanisms makes them dynamic, capable of change through variation and interaction. In purposeful human systems, feedback structure is subject to change through the creative, purposeful actions of agents. Thus, selection also plays a role in mechanism dynamics.
Operations and changes within a policy system (dynamic complexity) also interact with the broader environment, including the operations and changes of social systems, creating dynamic patterns that apply additional adaptive pressure on the policy system (functional complexity).
Because of their complex properties, scale, and mechanismic operation, we must expect inter-
131 system dynamics, system behaviors, and internal behaviors not to be stable, at least, not in the long run.
Insights and implications of policy cybernetics. Given the expectations policy cybernetics has about mechanism feedback, policy systems, social systems, and the complexity gap between them, a number of recommendations about how to formulate policy responses
(inquiry and interventions) follow.
Maintain a dilemma orientation in both research and practice. Maintaining a focus on the purpose of policy inquiry and intervention – to address dilemmas – is necessary because of the complexity gap between policy and social systems. In conducting policy studies, we must simplify the complexity of the social world, but how we simplify matters. A dilemma orientation is a way of disciplining the way in which we simplify, an adaptive filter to guide our responses by informing what boundaries we draw, what assumptions we make, and what objectives we need to meet. It helps us distinguish among what features and scale of complexity are relevant to the problem, and those that are part of the broader system. In other words, a dilemma orientation is a creative decision process of filtering complexity for the sake of inquiry and practice.
Respect the law of requisite adaptation. The law of requisite variety states that if a system is to be stable, the variety of its responses (control capabilities) must be greater than or equal to the variety of the perturbations (problem signals). However, policy systems seek to control problematic elements in the broader social system for the sake of that system, not just for their own maintenance. This means that they seek to manage functionally complex signals within their environment, and complexity is not just variety, it is mechanismic change. Because they relate via mechanism feedback, management is thus about matching adaptations and adaptive capabilities, not just variety. It is the management of the relationship between these two systems that is relevant
132 to policy studies. Therefore, if the relationship between policy and social systems is to be stable
(i.e., for policy systems to get traction on social dilemmas), the adaptive capabilities of the policy system’s responses must be greater than or equal to those of the social system itself.75 This is the first law of policy cybernetics.
The law of requisite adaptation is aspirational; it is not a law in the sense of describing a feature of natural truth, but rather in the (appropriately) social sense of prescribing a kind of action.
In aspiring to the law of requisite adaptation, policy studies as a field takes up creative, adaptive learning as its guiding principle for activities regarding collective action. In this view, systems are steered (not controlled) through strategies (not solutions). The law of requisite adaptation is an extension of the law of requisite variety that addresses the challenges of managing purposeful human systems.
Govern via mechanisms. Purposeful human systems of social action and collective action operate via mechanisms and communicate via mechanism feedback. Complexity – detail, dynamic, and functional – is thus an inexorable part of intervening in these systems. As a result, policies are other than the sum of their parts: interventions – including the point decisions of policy choice, and the line decisions of management – combine and interact in non-lineal ways with endogenous feedback to the interventions in mechanisms of maintenance and adaptation.
Therefore, policy design is really mechanism design, even if we are not aware of it. Policy studies need to put energy and resources into thinking about the mechanism – i.e., how the intervention will actually play out, including the endogenous system responses that may dampen or change the effect. Getting traction on dilemmas requires strategies to change feedback structure
(mechanisms), not just simple structure (institutions, rules). If the “learning by doing” of policy
75 Adaptations are responses, but not all responses are adaptive. 133 management contributes to policy making (Levin, 1993), then learning from endogenous feedback to interventions – i.e., policy learning – can contribute to steering policy systems. In the cybernetic tradition, as artificial intelligence is based on machine learning algorithms, so may governing intelligence be based on policy learning processes.
Policy Cybernetics in Action
As a complexity-oriented conceptual framework for policy studies, policy cybernetics is meant to serve as a coherent frame of reference for decision-making in action. That is to say that its use is not simply to exist in the background, as a mental map for policy scholars as they go about their research. Rather, the abstraction of policy phenomena it lays out through its assumptions and expectations about purposeful human systems has implications both for how inquiry is conducted (scientific decisions) and what interventions are taken (practical decisions).
Policy cybernetics is concerned with the fundamental questions of policy studies: What is going on here? How does it work? and What do we do about it? It emphasizes broad boundaries, system processes and change over time, and the identification of leverage points to guide purposeful action but does not discard or disavow existing knowledge. It suggests that modeling and acting on the complexity of purposeful human systems requires a diverse methodological toolbox, including (but not limited to) computational modeling and simulation, and adaptive governance strategies more often than technological solutions. Drawing heavily from the systems science literature (especially Sterman, 2000), I provide some general guidelines on the implications of policy cybernetics in decision-making in both scientific and practical processes.
1. Setting boundaries: model a problem, not a system. While policy cybernetics emphasizes rigorous, direct treatment of systems and their complex processes, it is important to do
134 so in terms of an observation about a specific pattern or phenomenon that is considered problematic. A problem orientation keeps the boundaries of both research and practice from becoming unwieldy; attempts to represent an entire system will doom both inquiry and intervention to failure (Sterman, 2000).
The task of a dynamic problem definition is to identify the behavior of the phenomenon of interest over time, and to articulate its relevance to both communities of research and practice.
Specify the relevant actors (who may be individuals, groups, or organizations), key variables, time horizon(s), and (if applicable), the space of interest. Use existing data to create a Reference Mode
Diagram which illustrates the behavior of interest over time.76 The initial boundaries laid out in the dynamic problem definition are broad enough to reflect underlying complex system properties
(e.g., change over time, diverse actors, formal rules or interventions already in place), but clear and narrow enough to discipline the modeling process. Model boundaries and assumptions can, and likely will be relaxed later in the process (Desai, 2012b), but starting early with coherent boundary definition is crucial.
2. Specify the client and purpose of the model. Specifying an intended audience and purpose of the model helps the modeler maintain an action orientation and a more inclusive lens for the work. Modeling a problem with a particular reason and audience in mind focuses research questions and informs what aspects of the problem will be explored. Engaging directly with the client or audience – members of the bureaucracy, industry, the community, elected office, or the academy – may also make the modeling process more inclusive by explicitly including perspectives about the problem other than those of the designated modeler.
76 See Figure 19 in chapter 5. 135 Identify the client or audience for the model. Be clear about for whom the model and the modeling process are meant to be of use. This may be elected officials, administrative officials, or other organizational actors, or it may be for community members or advocacy groups. Identifying the client is fundamental because their goals and role inform what might constitute leverage points within the system. For example, a model of medication noncompliance for a hospital administration would look very different than a model for community health workers because of their different roles in decision making and support in that process. The audience for the model also informs model boundaries and the level at which you model the problem – e.g., in terms of individual patient decisions, community resources and characteristics, administrative decisions, or market pressures and incentives.77
Accounting for the needs and position of the audience, be clear about the insights you hope to generate from this work, and its potential contribution to policy practice or research. Setting expectations about the ways in which the model will be useful to the client will guide decisions in the modeling process. Orient research questions toward the stated purpose of the modeling process.
In policy studies, this generally means asking questions about how the problem works, what can be done about it, and what tradeoffs various actions may involve.
3. Contextualize and organize the problem within a concrete system. While Lasswell
(1951) calls for policy scholars to maintain a problem orientation, he also emphasizes the importance the understanding the problem in context. Formally organizing the problem within a system clarifies the ways in which the problem is dynamically and functionally complex. It also
77 Group Model Building is one way to solicit input and generate buy-in from a model audience. GMB is a participatory modeling approach that it is useful for engaging with a range of stakeholders regarding their perspectives on the underlying structure of complex problems. It is certainly part of the client and purpose phase, but for modelers using GMB, it is fundamentally part of the entire model building and refinement process. For a detailed explanation of the procedures and issues surround GMB, see Siokou, Morgan, & Shiell ,2014. 136 serves as a guide for data collection for the simulation by pointing out what parts and relationships are relevant to understanding how the problem works.
First, situate the problem in the relevant literature and recognize existing model boundaries.
Identify variables considered to be important to the problem, units and types of analysis, and key constructs and relationships, and discuss trends and inconsistencies in the findings. In addition to grounding the systems model in current knowledge, review of the extant literature suggests gaps in the knowledge base that would benefit from a systems approach.
Draw from the literature review to specify a Concrete System Model78 of the problem in terms of Bunge’s equation. Identify the components (actors), environmental conditions, structures
(rules, institutional bonds) that are relevant to the problem and the mechanism (characteristic process) by which it works. Explicitly and rigorously account for problem complexity by distinguishing between features of the problem that make it complex (i.e., types) and the scale of complexity demonstrated by its processes (i.e., levels).79 In particular, distinguish between the mechanism by which the system operates (dynamic complexity) and the overall system’s behavior that arises from the mechanismic operation (functional complexity).
Specifying this model is aided by working through exercises to broaden the boundaries of existing treatment(s) of the problem by suggesting alternative variables (parts) and structures
(relationships). Create Subsystem Diagrams80 to compare the overall architecture of existing models with one involving a different variety of agents and organizations (e.g., firm, market, consumers) and alternative couplings (i.e., how information and energy flows) among those agents. Compare existing theories on the causal structure of policy interventions with an alternative
78 See Chapter 5. 79 See Figure 21 in Chapter 5. 80 See Figures 23 & 24 in Chapter 5. 137 illustration of the information inputs and decision rules that agents use to govern rates of change within the system using Policy Structure Diagrams.81 Neither subsystem nor policy structure diagrams should include too much detail, but rather are meant to be used as simple tools to explore endogenous system characteristics. These diagrams are useful in working through iterations of the
Bunge equation.
4. Develop a dynamic hypothesis. A hypothesis is a proposed explanation for a relationship or phenomenon made on the basis of limited evidence, which serves as the foundation for further investigation. Dynamic hypotheses are suppositions about the mechanism by which a system behavior arises, providing a logical benchmark for analysis of endogenous system behavior.82 Because dynamic hypotheses explain the dynamics of a problem as an endogenous consequence of the system’s feedback structure over time, it is necessary to specify the feedback structure of the system (Forrester, 1961).83
Specify feedback structure. Feedback structure is the defining characteristic of a systems model because recursive causality – feedback loops – is the defining characteristic of a complex system. Reinforcing or “positive” feedback loops magnify the effect of changes outside the loop, often explaining how a system reinvests in itself to generate growth over time. Balancing or
“negative” feedback loops negate or dampen the effect of changes outside the loop, often explaining the way a system strives to reach a goal or how stocks of resources are depleted.84
81 See Figures 25 & 26 in Chapter 5. 82 Lasswell (1951) refers to these as “developmental constructs” or “world-encompassing hypotheses”. 83 Because they are suppositions about endogenous behavior, dynamic hypotheses are not formulated as “if X, then Y” statements, but rather compound statements about recursive and dynamic processes. 84 Positive and negative refer to the effect on growth, not the normative value of the pattern. Positive feedback loops may be “good” (producing virtuous cycles) or “bad” (producing vicious cycles) depending on the perspective of the observer. Negative feedback loops my likewise be good (goal supporting/achieving) or bad (goal defeating). 138 Start by drawing a Bathtub Diagram85 to show how units move in and out of the state(s) of interest and to capture lags in that process. This assists in thinking about the problem in terms of accumulations (stocks), rates of change (flows), and directions of movement (structure), which will suggest some initial dynamic hypotheses about how the phenomenon works. Next, capture feedback in the relationships identified in the bathtub diagram by creating Causal Loop Diagrams86 to include more of the most important variables and show the suspected causal links among them.
Causal loop diagrams do not distinguish among types of variables (i.e., stocks or flows), but they are powerful tools to systematically and concisely think through causal influence and feedback structure in a logically rigorous and consistent way.87 Consistent with Bunge’s concrete system model, neither bathtub nor causal loop diagrams imply any hierarchical structure among variables
(i.e., they are flat). These diagrams are useful in working through variations and iterations of dynamic hypotheses about endogenous system behavior.
5. Specify and calibrate a simulation model. Computational models (simulations) use algorithmic inference to generate estimates about possible states and behaviors based on both plausible (theoretical) and empirical (simulated and physical) values.88 They make use of both qualitative and quantitative data from various sources, allow for stochastic and non-stationary processes in addition to correlative and analytic solutions. Specification of the structure and
85 See Figure 27 in Chapter 5. 86 See Figure 29 in Chapter 5. 87 While they are simple to draw, understand, and revise, good modeling practice involves following standard guidelines for CLDs. See, for example, Sterman: https://thesystemsthinker.com/fine-tuning-your-causal-loop-diagrams-part-i/ https://thesystemsthinker.com/fine-tuning-your-causal-loop-diagrams-part-ii/ 88 Computational models have a Bayesian logic, meaning that they specify parameters for prior distributions, which are referred to as hyperparameters. A hyperparameter is a parameter whose value (usually in ratio or integer scale) is set before the inference process; i.e., it is assumed a priori rather than being derived or estimated through the data- fitting process. Hyperparameters must be set in order for the simulation algorithm to learn from the data and estimate parameters. The combination of hyperparameters and algorithms is what allows computational models to model emergence in a system. 139 assumptions of simulation models is necessary in order to conduct analysis of, and experiments with, interventions in simulated environments, and to build confidence in those procedures, findings, and the models themselves. Simulation modeling starts with choosing a general model, specifying model boundaries, parameters, and data sources, and testing the model’s fit to historical data and robustness to initial conditions.
Simulation methods. Choose a simulation method that based on what is appropriate to the purpose, audience, and problem of interest. There are three general categories of systems simulations for the social sciences: System dynamics models, agent-based models, and network analysis.
In system dynamics (SD) models, system behaviors and properties arise from structural relationships and interactions among accumulations (stocks) and rates of change (flows) of components within a system.89 Stock-and-flow diagrams represent macro-level system behavior in terms of aggregated characteristics and broad boundaries. SD simulations operate via differential equations.
In agent-based models (ABM), system behaviors and properties arise from the behavior and interactions among individual agents, which in turn behave as a function of individual attributes and behavioral thresholds within the constraints and conditions of their environment.90
Agent-space-rule diagrams represent micro-level system behavior in terms of individual characteristics and decision rules within a specific environment. ABM simulations operate via object-oriented conditional statements.
89 Beer Distribution Game: https://en.wikipedia.org/wiki/Beer_distribution_game 90 Schelling’s Segregation Model. http://ncase.me/polygons/ 140 In network analysis (NA), system behaviors and properties are characterized by the number, relative location of, and connections (ties) among actors (nodes) within a network. 91
Network diagrams represent system structure in terms of density and intensity of actors’ connective characteristics. NA simulations operate via measures of dependence and influence among nodes.
All simulation methods allow the modeler to leverage multiple types and sources of data.
The iterative and mixed-data design of systems simulation makes it important to follow a standard modeling protocol, use software appropriate to the method, get a working model running as soon as possible, and keep a detailed modeling log.92
Model parameterization. After choosing a structure that is appropriate for the problem and the purpose, specify simulation parameters and bring data to the model. Use a Model Boundary
Chart93 to summarize the scope and features of the model. Identify key variables and specify which are endogenous (i.e., within a feedback loop), which are exogenous (i.e., not in a feedback loop, but feeding variables that are endogenous to feedback structure), and which are excluded from the model (i.e., topically relevant, but not mathematically included within the boundaries of the model). Identify the source of data or prior value for each variable, as well as a brief note about the type of data (e.g., historical, qualitative interviews, survey statistics, regression estimates).94
91 Voting networks: https://en.wikipedia.org/wiki/Social_network_analysis#/media/File:Tripletsnew2012.png 92 For SD, see protocols from Sterman (2000) and the System Dynamics Society; for ABM, see Railsback & Grimm (2011) and Open ABM; for NA, see Carrington, Scott & Wasserman (2005). Popular software packages include Stella, VenSim, and AnyLogic (SD); NetLogo and AnyLogic (ABM); and Capsa and ArcGIS (NA). Rahmandad & Sterman (2012) outline simulation reporting standards that are useful to structure a modeling log and simulation reporting in manuscripts. 93 See Table 9 in Chapter 5. 94 The variables that are estimated from data are parameters; those that are designated with initial values are hyperparameters. 141 Using appropriate software and standard notation, create a simulation model. For example, to conduct a system dynamics analysis, convert the physical structure of accumulations, rates of change, and feedback identified in bathtub and causal loop diagrams into a Stock-and-Flow
Diagram (like the one below from Sterman, 2000, p. 193)95 that illustrates accumulations of people as they move through a system over time. Articulate a simulation logic that gets the model to run in equilibrium (i.e., steady state) to verify that model boundaries are clear and that all feedback loops are closed.
Figure 17. Stock and Flow Notation
Beta testing. Run the simulation according to prior assumptions and an algorithm
(differential equations, in the case of SD analysis) consistent with the values and relationships specified in the model boundary chart. First, calibrate the assumptions and algorithm to the reference mode to test if the model can reproduce the observed pattern. Next, conduct sensitivity analyses by changing start values (prior assumptions) to see how robust the model is to initial
95 See Figure 30 in Chapter 5. 142 conditions. Every simulation has limits at which it will no longer respond reasonably based on what is known about the behavior of the problem. It is important to identify how extreme initial conditions can be before the integrity of the simulation breaks down.
Tuning the simulation’s assumptions and algorithm in this way is integral to building confidence in the model. Before experimenting with policy interventions or making inferences about endogenous patterns or generalizability to other conditions, it is important to rigorously assess the parameterization of the model in terms of its ability to reproduce the problem of interest, its inclusion of features considered most important by the client or audience, and its ability to address research questions and meet practical aims.
6. Experiment in silica. Perhaps the greatest advantage of computational modeling is the ability to directly investigate counterfactual conditions, albeit in a virtual world. In addition to exploring how systems work in terms of their endogenous structures (i.e., their mechanisms), simulation provides a systematic, rigorous way to engage in thought experiments about complex systems. What if…? analysis is tremendously useful in the social sciences, where conditions of “all else equal” and “independent and identically distributed” are rarely realistic. In policy studies, simulation provides a venue to conduct prospective policy analysis without exposing any live people to intervention consequences, intended or otherwise. It also provides decision makers with the ability to compare and contrast different policy options and isolate particular effects.
Draw insights from the reference mode-reproducing model, policy structure and subsystems diagrams, the relevant literature, and stakeholders (including, but not limited to the client) to identify leverage points in the system where policy interventions could be tried.96 Return
96 There are likely a great many such point, but start with a few (no more than five) that you have reason to believe would have an effect on the problem and are feasible to adopt. Simulation permits the modeler to change anything in 143 to the model boundary chart and parameterize the policy interventions to be tested in the simulation. Run the simulation in the reference mode as the Baseline condition for analysis. Then run the simulation under each of the policy scenarios separately to explore the probable marginal effects of each intervention. The policy analysis can be extended as necessary or desired to include assessment of additional policies, to experiment with combinations of interventions, and to explore the effects of policies under different environmental conditions and population characteristics.
Present the Simulation Results97 that compare each of the policy scenarios to the baseline analysis in a way that will be interpretable to the client or audience. This may include a dashboard of trends over time; distributions of a particular variable across different simulation scenarios; differences in incidence, prevalence, and duration in various states of interest across different scenarios; or tables describing the estimated values and standard deviation (or Bayesian credible interval) of specific parameters of interest. Regardless of the manner in which results are presented, figures should emphasize that the simulation provides probable ranges of plausible outcomes under each of the conditions explored in the model.
7. Share, refine, repeat. Because they are experiments in a virtual world, simulation results are always conditional, contextual, and subject to change and adjustment. Furthermore, because both the world and our understanding of it are dynamic, this process is iterative and is meant to be continually refined through a process of feedback between modeler and audience.
However, to be useful to applied decision making processes, analysis must stop – or at least pause
– at some point. This is why confidence in the model, particularly on the part of the client or audience, is so important to policy cybernetics. When participants in a system, particularly those
the modeler and therefore experiment ad infinitum with effectively zero social costs, but the practical cost of taking time to run and interpret simulations does place constraints on how much experimentation is reasonable or desirable. 97 See Figures 31-38 in Chapter 5. 144 in a position to alter its structure, see important elements of reality represented in the model and draw insight from the patterns it produces, they are more likely to recognize and use policy simulations and their contributing elements as aids for decision making in practice.
After beta testing, bring the simulation back to the client, audience, and stakeholders.
Present the initial findings and discuss some of the insights about endogenous structure and system behaviors. Empower them to experiment and encourage their tendencies to challenge the assumptions of the model by creating a user-friendly Simulation Interface98 that includes parametric dials (for starting values) and policy switches (for intervention scenarios). Discuss the results of these experiments, including any “unintended consequences” of the policy interventions.
Work with stakeholders to identify any conditions or combinations of interventions under which those consequences are alleviated. Continue to explore the boundaries of the simulation by calibrating the model to additional samples, populations, cases, and conditions. Be wary of adding too much to the model; calls to include many more features may reflect a need for additional models to capture other problematic patterns. Recall that to be useful, a policy model should model a problem, not an entire system. Policy cybernetics is a framework that emphasizes iterative, adaptive learning and response in policy studies. Problem-centered and action-oriented models are an integral part of that process.
Philosophical Assumptions of Policy Cybernetics
As a purposeful, coherent, articulated abstraction, a conceptual framework sets boundaries around some part of the world through the assumptions and expectations it makes about particular concepts of interest. However, conceptual frameworks also depend upon and reflect deep,
98 See Figure 39 in Chapter 5. 145 philosophical assumptions; that is, assumptions about what fundamental problems drive curiosity.
Philosophical assumptions shape the purpose and frame of the abstraction, as well as the lens through which the thinker makes sense of the world. To be thoroughly reflexive about the role of the policy scholar or practitioner in the world of interest, therefore, requires careful explication and consideration of the ways in which we interact with it. Building off earlier efforts by Desai
(2012a), and informed by science studies (including Kuhn, Latour, Haraway, and Barad), I walk through the underlying assumptions of policy cybernetics below. My hope is that this will serve both the transparency of this framework and encourage other policy scholars to rigorously and systematically articulate (or at least consider) their own implicit assumptions about why we are curious.
Figure 18. Map of Philosophy of Policy Cybernetics 146
Nousology: how our minds grasp the world. Nousology refers to the study of the reasoning faculty, or the patterns by which our minds grasp and make sense of the world. It includes questions concerning the boundaries of the mind and the nature of intelligence. As such, it is more philosophically broad than theories of knowing (epistemology) or theories of being
(ontology), though it clearly informs and includes those philosophical studies.99 Nousology refers to the underlying assumptions and negotiations with the world that serve as predicates for all other studies. All assumptions – implicit, explicit, and unrealized – are functions and reflections of the thinker’s minding or grasp of the world.
The nousology of policy cybernetics is agential systems realism, an extension of Barad’s agential realism (2007) that emphasizes the importance of complexity and integrated wholes in how humans grasp the world they inhabit. From this perspective, our patterns of minding are the media, lenses, and openings through which we make sense of reality by studying and acting upon it. Signals are bent, refracted, and diffracted through the mind. Recognition, organization, interpretation, and motivation – both conscious and unconscious – occur throughout a co- constitutive, embedded process of “intra-acting” (Barad, 2007) with the world. We are not receiving the social world, but rather engaged in continual feedback between the world and our grasping of it, both as individuals and as societies. The reasoning faculty is a behavioral, cognitive, perceptual, cultural, evolutionary, and active sense-and-response process within an integrated,
99 The concept of nousology is a way of dealing with the issue at the core of Barad’s agential realism. She argues that agential realism is an onto-ethico-epistemology because it is simultaneously a theory of being and knowing, which is value-laden and has ethical (and axiological) implications and assumptions. Her deeper point is that these “ologies” are themselves of the human constructions – i.e., products of the way in which humans grasp the world by making categories and engaging in semantic exercises. I refer to this broader activity (or set of activities) as nousology to capture the interconnections and interdependencies among the various facets of the human negotiation of and participation in the complexity and depth of the world. 147 complex, and concrete world. As a nousology, agential systems realism favors pluralistic (rather than dualistic) conceptualizations of the world. For example, sense-making occurs through the intra-action of mind and matter rather than perception of matter by mind.
Philosophy of science: what science is and why we do it. Philosophy of science is concerned with the qualifications and purpose of science; that is, what counts as science and the reasons to engage in it. The philosophy of science includes the history and implications of science in society, and the qualities of theories, assumptions, and methods used to systematically organize knowledge.
The position of policy cybernetics is that the policy sciences are an applied activity, a combination of thinking, production, and action. Their purpose is thus to organize, explore, define, and redefine the boundaries of what is worth thinking about and what is worth doing. Rather than simply revealing boundaries, science is itself a boundary project, using various tools to engage and apply the lessons of fundamental curiosity to everyday human life. In other words, science is applied philosophy. Science is therefore not a separate domain from the rest of societal activities, but rather an integral part of it. As a social activity, there is and can be no single algorithm to science because it is both a reflection and application of messy human curiosity (Kuhn, 1962).
Ontology: the nature of the reality we study. Ontology is the study of being, existence, and reality. It includes the “is” statements that humans make about the world, its objects, and its phenomena. Consistent with Barad’s agential realism, the ontology of policy cybernetics is realist, but critical. Reality exists – it is – but our ability to make sense of it is inseparable from our intra- action with it. Reality is thus not just material, but rather material-discursive in that it – and we – exist within our co-constitution with it. Policy cybernetics furthermore assumes a probabilistic ontology in which reality is governed by irreducible, dynamic, and complementary mechanisms
148 rather than absolute laws. For example, ontological objects can have seemingly mutually exclusive properties (e.g., both wave and particle) depending on the experimental framework (Bohr, 1949).
Interconnections, interdependencies, and adaptations are generally the how of social reality, meaning that causation is conditional, recursive, and unstable.
Axiology: the appropriate values to apply to inquiry. Axiology is the study of values and valuation and includes what is worthy of regard in the process of inquiry. Axiomatic or “given” statements are expressions of assumed or accepted truths. Consistent with a nousology that favors pluralism over dualism, policy cybernetics rejects a stark distinction between facts and values, viewing both as forms of information. All signal processing – including scientific inquiry – is value-laden because humans make choices about what counts as relevant to what we observe, and what is worthy of observation. Those values become ossified and axiomatic through the processes of problem definition, investigation, and analysis (Rittel & Webber, 1973). Because information in policy systems is diverse and contested, policy cybernetics maintains a democratic and pluralistic axiology.
Epistemology: how, and based on what, we know. Epistemology is the study of knowledge and justified belief. It includes theories of knowing and what counts as evidence, and so concerns the construction of the “is” statements about the world.100 In the tradition of feminist scientists including Sandra Harding, Donna Haraway, and Karen Barad, the epistemology of policy cybernetics is “situated” (Haraway, 1988) and intersubjective. This position is tied to axiology: objective knowledge does not exist because there is no unbridgeable gap (as Hume contended) between facts and values. Rather, knowledge forms in zones with contested boundaries regarding
100 Epistemology is distinct from ideology, which refers to formed beliefs and the construction of “ought” statements about the world. 149 fact-value, knowable-unknowable, and subject-object. This epistemology also acknowledges the observer effect – that measures of certain systems cannot be made without affecting the systems themselves101 -- but does not acquiesce to social constructivism, which Haraway (1988) criticizes as being just another form of the “god trick”.
Justified belief is formed through concurrent, iterative, intersubjective, and transparent procedures of consideration (hypothesis generation), exploration, application, and adaptation. The synthesis of multiple points of view through inclusive, situated, and engaged discursive processes promotes reflection and critique. Transparency, intersubjectivity, and adaptability serve the democratic and pragmatic needs of policy studies as a field.
Methodology: how we design and conduct inquiry. Methodology is the study of methods applied to a field of study and refers to the design process for carrying out research. While it informs the physical procedures used to conduct inquiry (i.e., methods), a methodology is the logical procedure underpinning those particular moves, and thus serves as the bridge between theories of knowing and knowledge itself.102 The design process includes the devices to collect observations (instruments), what gets recorded as units of observation (data), the scheme of organizing and analyzing observations (methods), what features of observations that are used to explain them (evidence), and the inferential structure for drawing conclusions about the observations (logic). The Scientific Method, for example, is a logical procedure for conducting inquiry in order to generate science – a systematically organized and public (rather than personal)
101 The observer effect is distinct from Bohr’s complementarity principle, which is an ontological feature (i.e., a characteristic of reality rather than our observation of it). 102 In other words, epistemology can be thought of as theories of what counts as science, while methodology can be thought of as theories of how to do science. 150 body of knowledge based on conclusions drawn from reproducible and transparent procedures. It is therefore a methodology, not a method.
The social sciences have been dominated by an interpretation of the scientific method consistent with the rational empiricism of the Enlightenment.103 That is to say, a version of scientific methodology that is done according to assumptions consistent with objective epistemology and ontology, and an allegedly neutral (value-free) axiology. For example, this interpretation views scientific knowledge as a product and reflection of the underlying truth of reality based on direct, objective measurement of phenomena and identification of facts.
In contrast, the methodology of policy cybernetics is a different interpretation of the scientific method that is consistent with agential systems realism. The bridge between knowledge of purposeful human systems and ways of knowing them is a scientific design process oriented toward the complex dynamics of social phenomena. This interpretation of scientific methodology, because of the intersubjective epistemology, critical realist ontology, and reflexive axiology that shape it, has several implications for the physical procedures of inquiry.
Instruments Because devices of data collection are human-made, they are imbued with
values. Researchers and observers are themselves instruments, bringing their judgment and
biases to bear on problem definition and observation. Systematic, measurement, and
random error will thus always be part of data collection.
Data All recorded measures are interpretations. In addition to recording measures
of direct sensory experience, it is important to collect and record observations based on
theoretical and intuitive interpretations, which may better capture latent features or
phenomena. It is possible that seemingly contradictory observations can both be true.
103 For example, the atomistic approach of Descartes, Newton’s inductive reasoning, and Locke’s empiricism. 151 Methods In order to capture the integrated and complex features of purposeful human
systems, observations need to be organized and analyzed in ways that preserve
independencies, explore sources of uncertainty, and permit stochastic and non-stationary
processes. No one class or set of methods are likely to be effective at organizing and
analyzing the range of observations made about the phenomenon.
Evidence Because data represent a range of interpretations and observations, their
characteristics may be described in both statistical and conceptual terms. The primary
phenomenon of interest can be summarized in terms of such patterns as correlation,
covariation, stochastic variation, interaction, and emergence among features captured in
the data.
Logic Because purposeful human systems govern themselves through compound,
adaptive mechanisms, the structure for drawing conclusions is pluralistic and abductive.
Abductive inference begins with an observation or set of observations (as with inductive
inference) and seeks to find the best explanation. The premises do not guarantee the
conclusions (as they do in deductive reasoning), but rather serve as weights (or priors) on
the inference process. Abductive reasoning acknowledges that there are many possible
explanations for the phenomena we observe but favors the simplest explanation (even if it
is not that simple) as a way of maintaining the thinker’s orientation to the problem. This
means that both/and conclusions are more common than those of the either/or variety.
The logical procedure of inquiry is largely the same: Make observations about a phenomenon that is considered socially problematic. Ask open-ended, reverse causal questions
(Gelman & Imbens, 2013) about the observations, including why they are a problem, for whom, and in what context. Formulate dynamic hypotheses about how the system’s feedback structure
152 could be producing the phenomenon; i.e., consider endogenous explanations for the observed behavior. Test both dynamic hypotheses and models of the system’s feedback structure by experimenting with conditions, interventions, and assumptions in silica. Gather several kinds of data from multiple sources. Use a combination of computational, qualitative, and statistical methods to explore elements of the phenomenon in context, and use simulations to carry out experiments where different features are altered and controlled. Analyze and interpret the simulation results in terms of their ability to explain the observed behavior. Draw preliminary conclusions about the dynamic hypotheses and the model itself. Retest the hypotheses and model with different empirical and theoretical values. Revise the model; modify and generate new hypotheses.
This interpretation remains a methodology for generating a systematically organized and public body of knowledge based on conclusions drawn from reproducible and transparent procedures (i.e., it is scientific). However, policy cybernetics maintains that that body of knowledge is a cogitation subject to revision, expansion, oscillation, contestation, and displacement. Science is itself a production, not just a product. No procedure – neither physical nor logical – can escape that reality.
Rhetoric: how we communicate. Rhetoric refers to that which is spoken as a bridge between the self and the other; i.e., public speech or discourse. The rhetoric of policy cybernetics is meant to be inclusive and common to facilitate not just translation of inquiry into practice, but inclusion of practice into inquiry. Because policy studies are concerned with public problems and collective actions, policy cybernetics views public-centered communication – not just among communities of research and practice, but among social communities as well – as fundamental to matters of policy inquiry and intervention.
153 Praxis: what actions we take to achieve ends. Praxis refers to the enactment and application of knowledge, and includes actions taken to achieve particular goals or objectives.
Praxis is what governments do; it is what policy studies is about. The position of policy cybernetics is that both policy inquiry and policy intervention are applied activities; they are themselves forms of praxis. Knowledge of and in the policy process is meant to be used for a purpose: to address social problems. Policy practice is an active negotiation and redefinition of the boundaries around social phenomena through the application of information about and intervention toward fundamental problems. Policy cybernetics views praxis, like science, as applied philosophy. Policy action is therefore not distinct from inquiry, so there is no way for policy study (including analysis) to be agnostic about policy implementation and administration. As a conceptual framework, policy cybernetics aims to harness complexity precisely because of its orientation to praxis.
Discussion
In this chapter, I presented a complexity-oriented framework for policy studies by drawing on insights from complexity and systems science, cybernetics, feminist science studies, and policy studies itself. I argue that, in order to address the complexity gap, approaches to policy inquiry and intervention must be dilemma-oriented (to harness complexity), framed in terms of purposeful human systems (to embrace complexity), and reflexive about the role of scholars and practitioners in the phenomena of interest (to respect complexity). Through its conceptual insights, policy cybernetics makes a number of contributions to policy research and practice.
First, by maintaining a dilemma orientation and framing policy phenomena within purposeful human systems, policy cybernetics highlights the need for adaptive learning in policy studies. Because policy and social systems relate to and communicate with each other through
154 sense and respond processes, mechanism feedback is both the source of dilemmas and potential solutions. If policy actors abide by the law of requisite adaptation – if the adaptive capabilities of our policy system responses are greater or equal to those of the social systems itself – then we can develop governing intelligence and get traction on wicked problems. Policy studies can foster adaptive governance by treating policy design as a matter of mechanism design, not just interventions.
Secondly, because it is explicitly tied to systems methodology, policy cybernetics suggests specific moves regarding its use in empirical research. Systems and complexity need not be exclusively soft concepts. They can be organized, operationalized, and analyzed in concrete and empirical terms. Thoughtful and thorough articulation of problem, purpose, and boundaries paired with the tools of simulation modeling is a practical, systematic, and rigorous way to explore the often surprising and unpredictable dynamics of our purposeful human systems. The world is undeniably complex. Policy cybernetics provides guidance on how to illuminate order in a complex world.
Finally, by being explicit about its philosophical underpinnings and reflecting on role of researchers and administrators in policy and social phenomena, policy cybernetics marks a potential shift from a Cartesian, post-positivist, neoliberal paradigm in policy studies to a quantum, systems, pluralist approach. Agential systems realism is a nousology (a pattern of minding) that embraces interdependence, value conflict, uncertainty, adaptation, and negotiation as both socially constructed and very real features of the world and human engagement with it. Policy cybernetics is inclusive; it is a generalization. Agential systems realism does not preclude or prohibit using statistical inference, regression methods for parameter estimation, rational assumptions, or empirical observations. Rather, it emphasizes a pluralistic set of tools to make sense of the complex
155 systems and phenomena that characterize the domain of policy studies; a strategy that serves democratic and transparent objectives, not just empirical ones.
Paradigm shifts are not spontaneous. They are brought about by precipitating events, forces, or anomalies (Kuhn, 1962). Complexity in policy studies is no anomaly, and the need for models that harness it are significant. Nevertheless, there is a high cost to adopting new analytic methods, theories, or conceptual frameworks, and the burden of proof is on the novel model to demonstrate its value over the old. In the next chapter, I will return to the issue of classification error in the implementation of Medicaid’s means test. I will use policy cybernetics to describe, explore, and consider revisions to Medicaid enrollment implementation through prospective policy analysis and policy learning. In conducting reconnaissance on how policy cybernetics may be used to both study and steer the Medicaid system in ways that obey the law of requisite adaptation, I aim to provide a map of policy studies under a new paradigm.
156
Chapter 5: Systems Models of Medicaid Mechanism
Medicaid is public program meant to provide health insurance coverage to vulnerable populations. However, each year, millions of eligible Americans experience discontinuities in their
Medicaid coverage, or remain uninsured altogether. In addition, recent expansions in eligibility have been matched by more restrictions on eligibility (e.g., work requirements) and more onerous enrollment and renewal processes (e.g., shorter coverage periods) in many States.104 The gap between eligible and enrolled populations may reflect a disconnect in implementation of the program’s codified logic, or it may reflect efforts to restrict the program through administrative rulemaking; i.e., policymaking by other means.
As a process, policy implementation occurs within purposeful human systems, which are characterized by complexity. Complexity includes both “side effects” or “unintended consequences” of particular interventions and the diversity of characteristics, motivations, and objectives of the system’s actors. Given the complexity of the program, how does Medicaid enrollment work? Does focusing on the mechanism by which it operates help us make sense of the various changes to Medicaid (and other social programs) that have been in the news; e.g., imposition of premiums, work requirements, cost sharing, lock-out periods? What can be done about the enrollment gap – specifically, about churn and missed take-up – given the existing feedback structure of the system?
104 In order to avoid confusion in this chapter, I will use “State” when referring to governmental units and “state” when referring to conditions into which individuals accumulate during the enrollment process. 157 The purpose of this chapter is to use Policy Cybernetics as a framework to address these questions by providing conceptual and computational clarity to the issue of program enrollment classification, and by exploring the dynamics and complexity of the Medicaid system. This is an exploratory analysis of how the feedback structure of a program can drive policy resistance, a common policy problem. This preliminary work is a first step in a long-term effort to serve broader audiences, including Medicaid administrators, elected officials, and program advocates and partners. While each of these stakeholders are well-aware that Medicaid’s enrollment process is complex, no one has explicitly taken an endogenous view of the interdependencies, dynamics, and nonlinearities of the phenomena. The models that follow are an exercise in getting the wheels turning in that direction and providing proof of concept for the conceptual framework proposed in the previous chapter.
First, I provide a brief definition of the dynamic problem of Medicaid’s enrollment gap in terms of implementation of its means test (which is covered in great detail in chapter 2). Then I articulate the purpose of using systems thinking and modeling to explore Medicaid enrollment processes and discuss the intended audiences for conceptual and computational models of means test implementation. In the fourth section, I connect insights from the extant Medicaid enrollment literature (from chapter 2) to concepts from complex systems literature to highlight endogenous features of Medicaid enrollment. In the fifth section, I present a series of dynamic hypotheses about the mechanism by which program enrollment works by specifying a model of the system’s feedback structure. That model serves as the logical basis for estimating the parameters of interest through system dynamics simulation, the mathematical structure and data for which I present in section six. In section seven, I present results of the simulation model, which I discuss in terms of insights, implications, and limitations in section eight.
158
Dynamic Problem Definition: Enrollment Gap Over Time
Each month, Medicaid provides coverage to roughly 74 million Americans. However, nearly 25 million additional people who are eligible for Medicaid remain uninsured (CMS, 2018).
The disparity between the population eligible for Medicaid and the population enrolled represents an enrollment gap in the program. Being uninsured reduces access to health care (including primary prevention services and chronic disease management), is associated with poor health outcomes, makes individuals and families susceptible to financial hardships due to health care costs, and suppresses labor market participation (Laverreda et al., 2008; Gruber, 2003). Low- income households are particularly vulnerable to the poor economic and health effects of uninsurance; those who are eligible for Medicaid tend to be in poorer overall health and have less employment stability (KFF, 2017).
The enrollment gap and the classification errors that contribute to it also pose problems for
States: low-income individuals who have been uninsured tend to cost more in the long run (because they are sicker when they come into the system), and movement of individuals on and off of
Medicaid make program planning and budget management difficult. In addition to being salient to public service and administration, the enrollment gap is interesting to policy scholars as a measure of program performance relevant to a range of social programs (e.g., SNAP, EITC). Additionally, social program penetration has implications for democratic outcomes, such as political participation (Schneider & Ingram, 1997; Soss et al., 2011). Thus, while individual citizens are the units of analysis in enrollment studies, population-level measures of enrollment are relevant to assessing program performance.
159 The enrollment gap is a feature of the Medicaid program at both the national and State levels. Despite programmatic and population variation, no State covers all eligible members of its population. The Kaiser Commission on Medicaid and the Uninsured estimates that nationally, 99 million Americans were eligible for Medicaid in 2017, with about 74 million covered each month.
Based on estimates from the Urban Institute (Ku & Steinmetz, 2013) and CMS’s improper payment files (2015) respectively, about 20% of enrollees (14.8 million) experienced a discontinuity in their coverage during the course of 2017, and 3.5% of beneficiaries (2.6 million) were improperly enrolled.105 Figure 19 shows growth between 1965 and 2017 in the size of the
Medicaid eligible and enrolled populations, as well as the estimated size of the enrolled population experiencing discontinuities in coverage and the improperly enrolled population.
105 Recall from chapter 2 that estimates of improper enrollment are based on improper payments, which likely overestimate the number of beneficiaries who are enrolled despite being above the income threshold. Furthermore, improper enrollment is not the same as fraud; improper enrollment can occur because of administrative errors, whereas fraud is defined by intentional deception on the part of the beneficiary or provider. 160
Figure 19. National Enrollment Gap
Created through the Social Security Amendments of 1965, Medicaid provides for health insurance coverage to those deemed especially vulnerable to excessive medical costs. Medicaid is a particularly important source of health insurance coverage for children, pregnant women, those living in deep poverty, and those with special health care needs. In explaining classification errors, academic research and internal program reports emphasize the role of such demographic characteristics (e.g., Kenney et al., 2011; Buettgens et al., 2012; Sommers et al., 2012). While various individual attributes and behaviors are associated with program take-up and churn, scholars and practitioners alike have noted that program structure and context (e.g., federal policy guidelines; State application and determination rules; economic, political and social conditions) 161 contribute significantly to program-level outcomes. Indeed, Sommers et al. (2012) find that, when controlling for State program features, demographic characteristics do not explain State variation in participation levels. It is necessary to account for the implementation of Medicaid’s means- tested entitlement design – the processing of people through the definition of eligibility criteria
(i.e., categories and income thresholds), assessment of whether or not someone meets those criteria
(i.e., application and review), and the interactions among individuals and rules within the program environment – to understand how various enrollment patterns come about.106 In other words, we need to assess the Medicaid enrollment gap in terms of the complexity of implementation within a program system.
Purpose and Audience
The purpose of this modeling exercise is not just to describe or explain the enrollment gap in Medicaid, but also to demonstrate to scholarly readers that the complexities discussed in the literature can be explicitly and logically represented in systems models. Taking an endogenous view of Medicaid’s enrollment processes is useful because it allows interested parties to explore interdependencies and non-lineal relationships among elements within the system that can produce persistent or surprising overall patterns. For example, while churning of continuously eligible individuals off of the program may be explained their decision/preference not to stay enrolled, a systems perspective suggests that disenrollment may also be a consequence of the program’s structure. The purpose of taking an endogenous view of Medicaid enrollment is to provide a starting point for an iterative modeling process to improve Medicaid program implementation. The
106 Hasenfeld refers to programs like Medicaid as “people-processing” programs, in that they “achieve changes in their clients, not by altering personal attributes, but by conferring in them a public status and relocating them in a new set of social circumstances” (1972, p. 256) to determine eligibility for benefits. 162 models presented in this chapter serve as a proof of concept of policy cybernetics as a way to think thoroughly, inclusively, and explicitly about the complexity of a problem.
Simulation modeling refers to a class of methods that model complexity computationally, drawing from multiple data sources and including feedback from stakeholders. System dynamics
(SD) is a form of simulation modeling that is suitable for analyzing macro-level behaviors of complex human systems within particular problem contexts (Forrester, 1961; Sterman, 2000).
System dynamics models begin with building conceptual models of the key variables and relationships relevant to a problem based on insights from existing scholarship and experiences of diverse stakeholders. The modeler works to integrate and perspectives and insights into a conceptual model of the feedback structure of the system that is then transformed into mathematical relationships using differential equations. The modeler then brings stakeholder priors, prior findings, and available data to the model to simulate overall system behavior and generate estimates for parameters of interest. Because all assumptions are parameterized in a simulation, the model can be altered – i.e., changing start values, data inputs, relationships – based on feedback from stakeholders and additional/new sources of information. Simulations permit us to look at these patterns across different time horizons: monthly (because that’s how Medicaid enrollment and eligibility work), but also across 2-year budget cycles, and long-term scale (e.g.,
10 budget cycles, projecting into 50 years). This is helpful because some policy changes related to the enrollment gap could have effects relatively quickly (within 6 months), but changes can also reverberate years into the future. Simulations thus include multiple perspectives, sources of information, and development of shared meanings about complex phenomena, such as classification errors in program enrollment. They also serve as a tool for prospective policy
163 analysis regarding enrollment gap and classification errors to improve policy implementation and understand the costs associated with those errors and dynamics.
In addition to being a good fit for modeling the enrollment gap and the classification errors that contribute to it, system dynamics models provide an opportunity to address specific gaps in and limitations of the existing Medicaid enrollment literature. For example, using SD modeling, it is possible to account for compound (not just multivariate) enrollment outcomes; structural delays and dynamics in the enrollment process; rates of take-up and churn in the program (not just proportions); different kinds of churn within the system; and interactions among population characteristics, eligibility criteria, administrative rules, and program context. Modeling based on the feedback structure of the system permits exploration of the possible consequences of policy recommendations and options discussed in the literature; for instance, do administrative rules that
“shift the burden” (Herd et al., 2013) to the State reduce churn or otherwise effect how citizens flow across different states of eligibility and enrollment?
In taking a systems perspective to modeling Medicaid’s enrollment gap, we can explore questions about how implementation of the program works, as well as the potential consequences of making changes to its structure, including the tradeoffs that may be involved in various interventions. These questions include exploring the extent to which observed enrollment patterns are manifestations of the system’s mechanism (i.e., feedback structure) versus idiosyncratic agent behavior (e.g., benefit preference) or exogenous shocks (e.g., recession). While it is not possible
(or even necessarily useful) to know if consequences are intended or not, systems models that capture endogenous feedback and the structural elements of system behavior can elucidate what sorts of policy decisions affect undesirable outcomes like missed take-up and churn. Additionally,
164 they can aid in updating the mental models of stakeholders about the problem itself. In this way, simulations are tools for transparency and decision learning, not just decision making.
Medicaid Enrollment Complexity within a Concrete System
In this section, I summarize and extend insights from the extant literature to be explicit about problem complexity and begin to model the problem endogenously, i.e., within a system.
Using concepts and insights from chapter 3, this section begins to build understanding of the enrollment gap as a manifestation of complexity within a purposeful human system. I do this by integrating key insights from the extant literature about Medicaid enrollment and explicitly grounding them in complexity science. Systems representations serve as the foundation for the articulation of dynamic hypotheses and simulation modeling.
Insights from existing literature. The existing literature on Medicaid enrollment demonstrates that program implementation is multilevel and multidimensional.107 Average monthly caseloads, program participation, missed participation, and movement on and off the program, both in terms of individual beneficiaries and across State programs, are all outcomes of interest. Researchers find that economic and political factors, individual attributes, and program characteristics (such as eligibility thresholds and features of the application) contribute to observed enrollment patterns. In contrast to researchers in health services research, policy scholars focus on the program (rather than individual) level of these outcomes, articulating the importance of administrative burden, bureaucratic encounters, and application and determination delays in program enrollment. Overall, they find that individual characteristics do not explain much of the
107 I summarize the literature here; a full review of the Medicaid literature is covered in chapter 2. 165 State variation in enrollment outcomes after accounting for differences in program features, suggesting that more research needs to be done on program structure rather than population characteristics. Key constructs and relationships in the literature are summarized in Table 7.
Table 7. Summary of Medicaid Enrollment Literature Constructs Variables Enrollment Outcomes Avg. monthly caseloads Participation: % take-up, % missed take-up Churn: % disenrollment, % dis-and-re-enrollment, % continuity of coverage, % change in income eligibility category Individual Attributes Demographics: Age, Sex, Race/ethnicity, Education, Veteran status, Citizenship status, English proficiency, Census region, Urban/rural residency
Household economy: Income category, Household composition, Work status, Work type, Marital status, Parental status, Receipt of other benefits, Access to vehicle, Access to phone
Health status: Pregnancy, Functional limitation, Use of Rx benefit, Use of mental health services, Use of hospital benefits, Use of substance abuse treatments Entitlement Design Eligibility income thresholds (MAGI), # eligibility categories, (Eligibility Criteria) Generosity of benefits Program Administration Administrative burden: Application features (# questions, (Application, Review & language, reading level, submission methods), Application determination) assistance (linked/joint application, combined family application), Enrollment outreach, Application verifications (employment, income, residency, citizenship, substance abuse) Continuous eligibility/redetermination period, Presumptive eligibility, Initial enrollment period, Renewal grace period
Institutional arrangements: Level of program administration (county or State), Level of determination (federal or State), Exchange-MCO alignment, Basic Health Plan, Managed care penetration State Economic Factors Poverty rate, Per capita income, Unemployment rate, FMAP State Political Factors Institutional political control (legislative, gubernatorial), Presidential voting preferences
166 A review of the Medicaid enrollment literatures reveals several gaps in our understanding of the phenomena; specifically, that enrollment outcomes are related, that complexity is poorly articulated, but fundamental to understanding the outcomes, and that there is a need for definitional clarity, particularly regarding the idea of churn. Figure 20 is a summary representation of the relationships among eligibility and enrollment in the existing literature. It shows that churn is an ambiguous term that refers to several different kinds of movement; each interesting in terms of the enrollment gap, but distinct.
Figure 20. Literature Outcomes
Clarifying multidimensional outcomes. The behavior of the enrollment gap over time is a matter of multiple program outcomes: take-up, missed take-up, and improper enrollment are all relevant, as are various types of churning on and off the program. Systems perspectives encourage thinking in terms of compound or multidimensional outcomes. Contingency tables, which are used in statistics and very common in epidemiological studies, are a useful way of organizing Medicaid
167 enrollment outcomes because at any given time, all members of a State’s population are either eligible or not according to the eligibility criteria (i.e., categories and thresholds define exposure), and all are either enrolled or not (i.e., enrollment defines the outcome). Eligibility-enrollment status (whether aligned or misaligned) for the entire population can thus be captured in a two-by- two table with useful properties: At any time, the cells sum to 100% of population, and the cells illustrate the relative distribution of the population across the four possible eligibility-enrollment states.
Conceptually, it is possible to add value to the contingency table by representing not just the characteristics of the agents (i.e., their eligibility-enrollment status), but also the role of rules
(program features) and environment (political, social, institutional context) in enrollment outcomes, all of which shape the bureaucratic encounter – not just the individual preferences or discretion of citizens (for benefits) or bureaucrats (regarding rules). Figure 21 is a map of how the distribution of a population across Medicaid enrollment states is influenced by the various factors identified in the literature – individual characteristics, entitlement design, program administration, and economic and political context.
168
Figure 21. Enrollment in Context
Churn is not captured by a contingency table like the one above, but a series of contingency tables over time would. Furthermore, the contingency table representation of enrollment status illustrates the aforementioned issue of churn as a muddled construct. As used in the literature, churn can refer to movement among any of these states. This is where the behavior over time emphasis of systems modeling is again supported by an insight from epidemiology – the relationship between distributions and rates of change across various states over time. The percent of population in eligibility-enrollment states are measures of prevalence, the time someone spends in that state is duration, and the movement from one state to another over time is an incidence rate.
Take-up, missed take-up, and improper enrollment are measures of population prevalence in particular states; churn is a measure of a rate of change.
169 Figure 22 shows how prevalence of various enrollment states are related to various rates of churn. To clarify these constructs, I refer to number of enrollees per month moving from eligible and enrolled to eligible and unenrolled as administrative churn rate (what Sommers, 2008 called
“disenrollment”); moving from eligible and enrolled to ineligible and unenrolled as recovery churn rate (what Ku et al., 2009 call “discontinuity”); and the number per month moving from eligible and enrolled to ineligible and enrolled as spurious churn rate. This also means that the number per month moving from eligible and unenrolled to enrolled is the capture rate; from ineligible and unenrolled to enrolled is the spurious enrollment rate; the number per month moving from eligible and unenrolled to ineligible and unenrolled is the stabilizing rate; the number per month moving from ineligible and unenrolled to eligible and unenrolled is the destabilizing rate (these two phenomena collectively constitute what Shore-Sheppard, 2014 refers to as “income volatility”).
This terminology is revisited in the simulation model presented later in this chapter.
Figure 22. Clarified Outcomes 170
Bunge equation of Medicaid enrollment. A Bunge equation of a concrete system serves as a clean mathematical representation of problem complexity. It is a useful heuristic for organizing insights and gaps in the literature and considering the potential endogeneity of variables treated as exogenous in Cartesian models. It provides a familiar equation-based articulation of a system, which can be more manageable for organizing conceptual ideas than CLDs, stock-and- flow and other diagrams (discussed later in this chapter). It is simple and elegant, but it communicates complexity. For example, this equation captures the idea that enrollment is not instantaneous, but rather a dynamic bureaucratic encounter that involves interactions and interrelationships among individuals, rules, and context. Each of the elements is best thought of as a matrix of variables (rather than a single variable).
Recall that Bunge’s generic equation (2004, p. 188) for a concrete system (s) is:
µ(s) = d{C(s), E(s), S(s), M(s)}, over time
System components C(s): The set of actors (parts) relevant to Medicaid enrollment are
citizens (i.e., members of the possible program population), frontline program bureaucrats in
both public and managed care organizations, administrative officials in both public and
managed care organizations, elected officials, and frontline health care providers. System
components also include the organizations to which these individuals belong, and the attributes
and behaviors of these individual and organizations (e.g., demographic characteristics,
organizational type).
System environment E(s): The collection of environmental conditions that act on or acted
upon by other elements of the system include economic pressures (e.g., State unemployment
171 level), political context (e.g., legislative and gubernatorial party control), and social norms
(e.g., support for Medicaid expansion).
System structure S(s): The set of bonds that hold the components of the system together
include the relationships among the components (e.g., strength and direction of influence
between citizens and bureaucrats), and policy interventions and their organization (e.g.,
entitlement’s eligibility criteria, administration of application and determination rules).
System mechanism M(s): The characteristic process driving Medicaid enrollment’s
autopoiesis is the implementation of its means test, which is a non-lineal combination of the
interactions among the policy interventions (i.e., eligibility criteria and administration of
application and determination) and the relevant actors in context (per Axelrod & Cohen, 1999).
Implementation of Medicaid’s interventions is a means negotiation; the elements of the system
operate interdependently to reproduce the system through a mechanism of citizen sorting.
System autopoiesis µ(s): Medicaid program enrollment’s self-production is characterized by
the dynamics of its enrollment patterns in the population over time. This includes the degree
to which there is alignment between the eligibility and enrollment status of members of the
population. The relative distribution of the population across take-up, missed take-up,
improperly enrolled, and other states; the duration of their time in those states; and the rate at
which they move among those states may all be measures of autopoiesis. Program enrollment
autopoiesis is driven by the citizen sorting mechanism.
Medicaid complexity by level and type. Describing Medicaid complexity by level and by type is a way of rigorously and explicitly accounting for and categorizing the scale and features of enrollment that make it complex; it is a way of being logically clear and consistent about what complexity means in the context of Medicaid enrollment. Logical consistency is important to both 172 the construction and the interpretation of systems models. Logical distinctions also prevent the paradoxes that result from discussing properties of the system and properties of the elements at the same level; e.g., the logical problem that results from talking about an individual’s experience of churning on and off the program at the same level as the State’s problem of dealing with the persistent dynamics of churning among its population over time. Logical consistency is essential to distinguishing between what is a structural property from that which is an emergent property
(e.g., policy resistance is structural; caseload backlogs are an emergent property). Program enrollment is dynamically complex in its feedback structure (sorting mechanism); it is functionally complex in its caseload dynamics and self-regulation. Recall that levels of complexity are taxonomical or hierarchical. Functional complexity operates only at the level of the system, but this means that it includes system autopoiesis and all of the elements; i.e., higher-order structures/behaviors and all of the other features of complexity. Table 8 summarizes the scale and features of complexity in Medicaid enrollment.
173 Table 8. Summary of Medicaid Complexity Bunge System Feature of Complexity (type) Scale of Complexity (taxon) Element System Autopoiesis Higher-order structures and behaviors Functional complexity Medicaid As it plays out in the system’s broader Caseload and classification program’s social environment over time (metacontext), dynamics enrollment dynamics the program’s mechanismic operation Persistent (re)enrollment (feedback structure) creates emergent and backlogs, planning and robust patterns that are observed only at the budgeting shortfalls level of the program itself (i.e., Changes to program logic (e.g., unpredictable and untraceable to any expansions) element or linear combination of elements).
Mechanism Causality Dynamic complexity Implementation of The relationships between citizens, Citizen sorting program enrollment; bureaucrats, and program rules are Bureaucratic encounters, means negotiation characterized by feedback loops, creating experience of administrative endogenous responses to program burden interventions. Policy resistance
Temporality The ‘means test’ is not instantaneous; it involves delays and differential rates of processing. Changes in agents, rules, and context have irreversible effects. Components Dimensionality Detail complexity Program actors Enrollment is affected by a large number Individual attributes and of variables related to the diversity of behaviors Structure individual and organizational actors, Program eligibility criteria Program rules, behavioral rules (formal and informal), and (categories and thresholds), interventions program context. administrative rules and requirements Environment Intentionality State political and economic State In addition to codified policy objectives, conditions characteristics citizens, front-line bureaucrats, and officials have personal attitudes and preferences regarding the program.
Medicaid information and decision structures.
Information flow. Subsystem diagrams are used to compare the overall architecture and boundaries of different models of a problem. Deterministic models typically make simplifying
174 assumptions about the complexity of a phenomenon of interest by drawing narrow boundaries around what are considered to be the relevant sources (i.e., actors) and flow of information. For example, the existing Medicaid literature assumes a hierarchical and tightly coupled relationship between the program’s rules and citizens (Figure 23). Programs send signals about the features of
Medicaid benefits, and dictate the rules and conditions under which citizens may enroll. Citizens send signals about their need and preferences for benefits. Both the program and individual citizens receive signals from the environment about economic resources and political and social preferences (e.g., tax revenue, social stigma), but these signals are considered exogenous to the flow of information between citizens and the program itself.108
Figure 23. Literature Subsystem Diagram
108 State economic and political factors are “control” variables in most PA and HSR models of enrollment, reflecting their treatment either simply as general covariates (i.e., associated with enrollment outcomes; e.g., State political control) or confounders (i.e., associated with both enrollment outcomes and individual- or State-level characteristics; unevenly distributed across populations or States; e.g., economic downturn), though there is no clear distinction about whether or not a covariate is a confounder in any study covered in the literature review. Not on the causal pathway. 175 Alternatively, systems models draw broader boundaries around a problem to consider the ways in which information flows are endogenous, i.e., manifestations of the system’s feedback structure. Figure 24 shows a looser and flatter coupling between citizens and the program by distinguishing between program’s rules and the frontline bureaucrats who carry out those interventions; this is to capture the bureaucratic encounter discussed in the PA literature on program enrollment. Through entitlement design and administrative rules, programs send signals about the features of Medicaid benefits to citizens; and signals to bureaucrats about who and under what conditions citizens are qualified to receive benefits. Through application, citizens send signals about their need and preferences for benefits, but this information is always mediated through a bureaucratic encounter. Through determination, bureaucrats (including bureaucratic algorithms) inform both the program and citizens about the fit of citizen circumstance with rules of the program. Program officials, frontline bureaucrats, and citizens all exchange signals with the social and economic environment about resources and political and social preferences. Even this relatively small modification to the subsystem that includes the feedback of bureaucratic encounters involved in enrollment demonstrates how much looser the coupling is between citizens and the State.
176
Figure 24. Revised Subsystem Diagram
Decision points. Policy structure diagrams are used to model decision points and delays in a system. One of the ways that existing Medicaid enrollment literature simplifies system complexity is to focus on one decision-maker and one decision point in the enrollment process.
For example, the HSR literature treats enrollment outcomes as products of individual citizen decisions about whether or not to enroll in the program, while the social policy literature treats them as the result of program decisions about whether or not to enroll the individual (Figure 25).
For individuals, the decision is made based on relative benefits versus costs of enrollment, which is informed by the need for coverage, attitudes and preferences for Medicaid benefits, knowledge about the program’s benefits, eligibility criteria, and application process. The goal is to maximize personal utility (even if they do not expressly use that term). The program makes its decision based on its assessment of an individual’s eligibility and compliance with the requirements. Social policy scholars (e.g., Moynihan & Herd, 2013) point out that this assessment is informed by the attitudes 177 and preferences of the bureaucrats, and their interpretation of the signals they receive from administrative and elected officials about how to make these assessments. The goal may be accuracy (i.e., to be as sensitive and specific as possible in making assessments of eligibility for enrollment), but there may be other bureaucratic and political goals in play, including suppressing enrollments (i.e., “bureaucratic disentitlement”).
Figure 25. Literature Policy Structure Diagrams
An endogenous view on program enrollment considers how citizens, bureaucrats, and program rules affect movement of citizens through the program; i.e., how various decision-makers govern rates of flow within the system. Figure 26 shows an expanded (but still simple) policy structure diagram of program enrollment by showing that individuals, program bureaucrats, and program officials are involved in the decisions that lead to enrollment or not. These decisions, and the factors that contribute to them, mediate the relationships among the various outcomes of interest (i.e., eligibility-enrollment statuses), in part, because decisions create delays in status change. 178 Citizens have two decisions: Whether or not to try to apply, and whether or not to finish and submit an application. The first decision concerns the gross rate at which citizens apply and their retention in an unenrolled state. This decision is based on an initial cost-benefit assessment regarding personal need, knowledge of eligibility and benefits, and attitudes and preferences for
Medicaid benefits. The second decision concerns the net rate at which citizens apply and the rate at which they fatigue out of the application process. This decision is based on an updated assessment of the costs of application completion (i.e., administrative burden) relative to need, knowledge, and attitudes about program benefits. Program bureaucrats make the frontline decision for the program about whether or not to enroll an individual based on their application. Bureaucrats base the determination decision (which includes the rates of application approval and non- approval) on their assessment of individual eligibility and compliance with application rules, as well as their own experience of the review process as burdensome, and their personal attitudes regarding citizens and the program.
Program officials are responsible for institutional decisions. These decisions are based on federal mandates and guidelines, State budgetary considerations, and State political preferences regarding program issues such as spending, capacity, and efficiency. Under guidance (or pressure) from elected officials109, program officials decide how to treat applications under review; specifically, whether or not to grant access to benefits during that period. They also set the coverage period for enrollees (i.e., how often redetermination occurs), and decide whether or not set a grace period of reenrollment (i.e., whether or not enrollees are removed from the program immediately at the end of their eligibility period), which is related to the rate at which enrollees
109 Depending on the State, there may be statutes requiring legislative action on some of these changes, but by and large details of enrollment processes are left to administering agency. 179 churn off the program, either for administrative or income recovery reasons, and informs the rate at which eligibility renewal (i.e., redetermination) occurs.
In addition to these direct interventions, program officials make decisions that serve as indirect interventions, meant to affect the decisions of citizens and bureaucrats. For example, program rules may affect the bureaucratic determination decision by dictating the length of application review (within federal guidelines). They can also affect citizens’ submission decision by changing features of the application and engaging in public outreach. States also have discretion in deciding what categories of people are eligible for the program, and under what thresholds.
Changes to eligibility criteria require action from elected officials (the State legislature and governor, typically). Nonetheless, it represents a significant leverage point within the system by setting boundaries about the pool of eligible people (which has implications for the rates at which citizens apply and bureaucrats approve applications), and thus is an important opportunity for policy intervention.
The expanded policy structure diagram provides several lessons. First, it shows that there are multiple places in the system’s structure where interventions are or can be applied. However, not all leverage points are alike: some represent direct policy interventions (i.e., changes in the program rules), others are indirect interventions (i.e., changes to affect the decisions of citizens and bureaucrats. It also clarifies that not every variable identified in the extant Medicaid enrollment literature is an opportunity for intervention: for example, population characteristics are not policy actionable.
Policy structure diagrams that account for multiple decision-makers also show that generally, the way to govern flows of people through the Medicaid program is through changes to the rates at which people move and the duration of their stay in certain enrollment states.
180 Furthermore, the structure of decision making illustrates that changes in Medicaid administration are not so much about shifting the administrative burden to the State (Herd et al., 2013), but rather concern the degree to which program officials (through rules and procedures) distribute administrative burden among citizens and bureaucrats. That distribution is not necessarily a tradeoff between citizens and the State; for example, the work requirements approved by the
Trump administration in February 2018 increase administrative burden both for citizens (by increasing compliance and psychological costs) and for bureaucrats and the program overall
(Solomon, 2018).
Figure 26. Revised Policy Structure Diagram
181
Conceptual Model of Feedback Structure and Dynamic Hypotheses About Enrollment
As discussed in chapter 2, the extant Medicaid literature generally finds evidence that enrollment outcomes are associated with various individual attributes and program characteristics, including eligibility criteria and administrative rules. However, these studies are limited in their ability to provide insight into how these relationships operate because of their (largely) non- experimental study designs and statistical tests of correlations among variables. In contrast, systems models are explicitly concerned with dynamic hypotheses – suppositions about the causal structure by which system behaviors arise.110 Dynamic hypotheses are conditional, causal statements about what would happen in the system if some factor changed. SD models assume that behavior arises from structure, but dynamic hypotheses are about how behavior arises from structure; they are testable statements about the mechanismic behavior of the system.
Because dynamic hypotheses explain the dynamics of a problem as an endogenous consequence of the system’s feedback structure over time, systems methods use diagrams (rather than equations) to communicate both the hypotheses and the underlying feedback structure of the model. Bathtub diagrams articulate the plumbing of a system, i.e., how people are distributed and move within a system over time. They identify the primary parameters of interest as either stocks
(accumulations of people in particular states) or flows (rates of movement or change among those states); identify potential leverage points or valves that regulate how quickly people flow in or drain out; and clarify the directions of movement over time and potential delays or lags in the system. Causal loop diagrams further articulate the system’s feedback structure by including additional parameters that are relevant to the phenomenon of interest, specifying the directions of
110 Lasswell (1951) refers to these as “developmental constructs” or “world-encompassing hypotheses”. 182 causal influence among those parameters (polarity), and identifying feedback relationships as either positive (reinforcing) or negative (balancing). Because CLDs make the path and direction of influence explicit, they force the modeler to be clear about relationships among covariates – i.e., whether they have an independent effect on an outcome, or are confounders, mediators, or moderators of relationships among the parameters.
These diagrams can be thought of as computational equivalents of regression equations: they identify the variables and the general form used to estimate the parameters of interest.
However, their form and parameterization account for interdependencies, influence, and delays that result in recursive relationships (feedback) over time, so their estimations of parameters reflect causation rather than correlation. They provide logical benchmarks for analysis of endogenous system behavior by making assumptions about feedback structure explicit.111
Overall feedback structure and behavior. Figure 27 illustrates the basic logic of how people move in and out of Medicaid enrollment and an initial dynamic hypothesis: Enrollment dynamics in Medicaid arise from implementation of its entitlement design. Medicaid is designed to reduce the number of vulnerable people who are uninsured. Certain demographic and economic conditions increase the stock of eligible people, which is drained by the flow of people seeking coverage through the program. The flow of eligible people seeking coverage through the program increases the stock of enrolled people, which is drained by the flow of people disenrolling from the program. If the eligible population grows (because of increased births, immigration, or uninsurance, or decreased deaths, emigration, or private coverage), the stock of eligible people
111 They also allow the modeler to synthesize insights from extant literature and systems and complexity science (including subsystems and policy structure diagrams) to illustrate how various factors contribute to the outcomes of interest. Furthermore, they are useful in providing conceptual and computational clarity to the outcomes of interest; something that is an issue in the existing Medicaid literature (as discussed in chapters 2 and 3). 183 increases, which increases the flow of enrollments. Furthermore, if the economy weakens (as in a recession), the stock of eligible people increases, increasing the flow of enrollments, and slowing the flow of disenrollments due to loss of eligibility will likely slow. If the inflow of enrollments if faster than outflow of disenrollments, then caseloads will be higher than they otherwise would have been.112 The program is designed to respond to population and economic conditions by providing coverage to a segment of the population at a particular time.
Figure 27. Medicaid Enrollment Plumbing
112 Conversely, when size of eligible population decreases (because of demographic or economic changes), the flow of enrollments decreases, and the flow of disenrollments will slow, leading to smaller caseloads than there otherwise would have been (e.g., lower overall spending in budgets during economic boom). 184 Dynamic hypotheses about enrollment dynamics. The plumbing of the Medicaid program is more complicated than the bathtub diagram above suggests. A key insight from the policy structure diagram (Figure 26) is that in order to know about movement in and out of enrollment, we have to consider the intermediate states; i.e., how people accumulate during application, determination, and disenrollment. The formal structure of the program is not just a matter of entitlement (enrollment based on eligibility). Medicaid’s entitlement is also: 1) opt-in – individuals must submit an application to gain coverage; 2) means-tested – applications are reviewed by State bureaucrats to determine whether or not the individual meets the eligibility criteria on the basis of the information provided in the application; and 3) time-limited – once enrolled, coverage is only guaranteed for a particular period of time before beneficiaries must either renew their applications or are removed from the program rolls.113 These structural features create pools of interested people applying, people with submitted applications in review, and recently disenrolled people. These additional states and the movement of people among them create delays and backlogs that will result in “errors” – or rather misalignments – between eligibility and enrollment.
113 The time limitation of redeterminations periods (or continuous eligibility) can be contrasted with Medicare where death is basically the only exit from the program. 185
Figure 28. Disaggregated Medicaid Loops
Medicaid enrollment dynamics, including misalignments, arise from Medicaid’s time- limited, means-tested, opt-in entitlement structure. Misalignments and churn are manifestations of the Medicaid mechanism, the implementation of the program rules and criteria in practice. Figure
28 shows how different elements of the program’s design contribute to the program’s overall feedback structure. Combinations of balancing and reinforcing feedback loops in the various phases of the process indicate that the system will demonstrate S-shaped growth (i.e., goal-seeking with diminishing marginal returns), maintaining an enrollment gap and movements of individuals on and off the program. While the sizes of the populations applying, being reviewed, and enrolled generally balance out the size of the unenrolled population (negative feedback), growth in enrollments also increases disenrollments, which in turn increase the size of the unenrolled population (positive feedback). Figure 29 provides a full representation of the dynamic hypothesis 186 regarding the enrollment mechanism in a causal loop diagram. A diagram with additional detail, including all parameters included in the simulation, can be found in the appendix.
Figure 29. Causal Loops of Medicaid’s Sorting Mechanism
Coverage demand and opting in. The Demand Loops illustrate that gaining coverage through Medicaid involves a proactive decision on the part of individual citizens to apply.
Individuals may be aware of Medicaid and have some interest in coverage, compelling them to initiate an application (this may include doing an online eligibility check, doing research on the program, making a call, etc.). During this process, they may decide that the enrollment process is too costly (“more trouble than it is worth”), they may not follow through on all the requirements, they may decide they are not eligible, or they may simply forget to finish applying; they may return to the unenrolled stock through this application fatigue. 187 Opt-in hypothesis: The feedback structure of Medicaid’s opt-in requirement both balances and reinforces changes in program demand. If the population of unenrolled people increases, the demand for coverage increases, which increases the population of people who have initiated coverage applications, thus decreasing the size of the unenrolled stock. However, growth in the stock of initiated applications increases the flow of people fatiguing or “giving up” on applying, which then increases the population of unenrolled people.
Application, determination, and means testing. The Application and Determination Loops illustrate that gaining coverage through Medicaid involves means negotiation – a dynamic interaction between citizens and bureaucrats through the rules and procedures of application for coverage and determination of eligibility for it. People who have initiated an application may complete an application for coverage, resulting in them becoming people who have submitted an application. Some of those people are approved for enrollment (this includes both true and false positives), moving them into the enrolled population, and others are not approved (because their application is denied, further verification is necessary, or their application has timed out of the determination process; this includes both true and false negatives), moving them back to the unenrolled population.
Means test hypothesis: The feedback structure of Medicaid’s means test both balances and reinforces changes in application and determination. If the stock of people who have initiated applications increases, the flow of completed application increases, which increases the population of people who have submitted applications awaiting review, thus decreasing the stock who have only initiated applications. However, growth in the stock of submitted applications increases the flow of people not approved for coverage, which then increases the unenrolled populations.
188 Churn and time limitation. The Churn Loops illustrate that Medicaid coverage is time- limited; that the accumulation in the enrollment stock is dynamic, just like the rest of the stocks.
Beneficiaries must go through redetermination regularly if they are to stay enrolled in the program.
Beneficiaries who continue to demand coverage may be aware that they need to reapply, compelling them to complete a renewal. However, they may not be aware they have to reapply, they may not follow through on all requirements for renewal, their need or preferences for coverage may change, or they may forget to reapply, resulting in their disenrollment from the program, and returning them to the stock of unenrolled population – specifically, a subset of recently disenrolled people.114
Time limitation hypothesis: The feedback structure of Medicaid’s redetermination and renewal requirements both balances and reinforces changes in program enrollment. If the population of enrolled people increases, the flow of disenrollments increases, which increases the stock of recently disenrolled, thus decreasing the stock of people enrolled. However, as the stock of recently disenrolled people grows, so too do flows of people initiating (or re-initiating) applications for coverage.
Income volatility and entitlement. The Income Volatility Loop illustrates that having access to Medicaid coverage (i.e., being “entitled” to the benefit) is dependent on eligibility status, which is largely tied to income thresholds (simplified in this model to one categorical threshold).
People who are eligible for coverage may experience a change in circumstances (e.g., income, access to private coverage, health status, household composition) that results in becoming part of the ineligible population (i.e., they stabilize). However, people who are ineligible for coverage
114 Because we’re interested in how many and at what rate people “churn” on and off the program, I separate these people out. 189 may also experience a change in circumstances that results in them becoming part of the eligible population (i.e., they destabilize). As Shore-Sheppard (2014) demonstrates, a significant number of American households (55%) experience a change in income over the course of a year that would move them across the eligibility threshold.
Eligibility hypothesis: The feedback structure of Medicaid’s eligibility criteria both balances and reinforces changes in eligibility. If the population of eligible people among the unenrolled increases, there will be more people who may lose their eligibility (stabilize), which in turn will decrease the population of eligible people among the unenrolled and increase (decrease) the population of ineligible people among the unenrolled. However, as the population of ineligible people among the unenrolled increases, there will be more people who may gain eligibility
(destabilize), which in turn with decrease the population of ineligible people among the unenrolled and increase the population of eligible people among the unenrolled.
Overall, these hypotheses reflect the contention that enrollment outcomes, including misalignments and churning on and off the program, are consequences of enrollment’s feedback structure. Implementation of Medicaid’s eligibility, application, and enrollment structures constitutes a dynamically complex sorting mechanism. The patterns this mechanism produces over time are examples of functional complexity of the Medicaid enrollment system.
Simulation Model and Data
Simulation methods, including system dynamics modeling, provide ways to test these dynamic hypotheses. Computational models require explicit representations of all structural assumptions and consistent logic about the relationships among them. Simulations themselves thus test the hypothesized relationships, indicating whether or not the feedback structure as specified
190 can produce the dynamic behavior of interest. I use system dynamics simulation, which is based on integral and differential equations, to test hypotheses about the Medicaid mechanism.115
Stock-and-flow specification. Figure 30 (top panel) illustrates the simulation’s main stock and flow structure of how members of a State population move through a Medicaid enrollment system. The appendix includes the corresponding equations necessary to replicate the model.
Stocks, represented by boxes, track the number of people in various states of enrollment, rising and falling as individuals move into and exit out of different categories over time. Flows, represented by pipes with valves, are rates at which individuals move between categories. Stocks integrate inflows and outflows; inflows contribute to stocks and outflows reduce them. Flows are modified by transition probabilities (also called fractional rates, which are ratios rather than true rates of change) and delays. All else equal, transition probabilities increase flows and delays reduce flows. The bottom panel of Figure 30 shows how transition probabilities and delays influence rates of change in the model.116
115 System dynamics simulation is the analytic method used to estimate parameters in this model. I follow an SD protocol from Sterman’s Business Dynamics (2000). 116 Stocks and flows are said to be endogenous variables because they are part of feedback loops. Transition probabilities and delays are said to be exogenous variables because they are not part of feedback loops, but their values can be changed for any run of the simulation. 191
Figure 30. Stock and Flow Structure of Medicaid Enrollment
192 The model estimates the distribution and movement of a State’s population across the various states of enrollment per month over a ten-year period. Medicaid benefits are administered and reported monthly, but longer time horizons are of interest to states for program planning and budgeting purposes.117 Stocks represent any state where people can accumulate in the system. In the case of Medicaid enrollment, this includes: Eligible, Unenrolled; Ineligible, Unenrolled; people with Initiated Applications; people with Submitted Applications; Enrolled; Eligible, Recently
Disenrolled; and Ineligible, Recently Disenrolled.118 The stocks are mutually exclusive (no individual is in more than one state at any given time) and exhaustive (all members of a population are in one of the states in the model at any time), which facilitates calculation of population prevalence of each of the conditions of interest; for example, percent of population missing coverage, or percent recently disenrolled.
Flows represent the pathways and rates at which people move among states within the system. Individuals who are not enrolled may express interest in Medicaid coverage by Initiating application (e.g., through online eligibility check or engagement with a health navigator). Those who have initiated applications may transition to the submitted stock by Completing an application, or they may return to an unenrolled state by Not Completing an application for review.
Those who have submitted applications may become enrolled in the program by being Approved for coverage, or they may be Not Approved, returning them to an unenrolled state. Enrolled individuals may remain enrolled by Renewing their coverage before the coverage period lapses,
117 Ten years covers five budget cycles, which is valuable to State governments, but is also a long period of time in which economic and political conditions (among others) can change substantially. Thus, as with any projections, less confidence should be assessed to simulation results further into the future. 118 Because the prevalence of people who have recently disenrolled from the program is a relevant parameter in this model, these states are separated from their broader Unenrolled stocks, but the prevalence of unenrolled individuals includes both Unenrolled and Recently Disenrolled. 193 or they may transition to a recently disenrolled stock by Disenrolling. Recently disenrolled individuals all transition out by Refilling into the larger unenrolled population. Unenrolled individuals may also transition between eligibility and ineligibility by Stabilizing or Destabilizing.
The total population (sum of all stocks in the model) is dynamic; it may increase through
Population Growth or decrease through Population Loss.119 These rates of flow are affected by the relevant transition probabilities and delays (Figure 30 bottom panel).
Data sources and model calibration. Calibration of the model’s formal structure to empirical data is a second test of the dynamic hypotheses; given the specified structure and logic, can the model reproduce observed patterns? In this case, the number of individuals in each stock at the start of the simulation and the initial rates of movement across stocks were calibrated to reflect Ohio’s Medicaid program in 2016, and to mimic its behavior at that time. Using Kaiser estimates of CPS data, the model was seeded with Ohio population distributed between eligible and ineligible (both unenrolled). Adjustments were made to constants to calibrate the model to the observed level of caseloads in Ohio in 2016. This produced values across the other state variables
(which are measures not found in the literature or in CMS reporting documents). The distribution of the population across these states serve as the initial values for population parameters in the simulation.
Rates of change among the state variables depend on the level of the stock and a ratio (i.e., transition probability or fractional rate). At initialization, all flows are zero (because initialization represents a “snapshot” of the system). Constants come from the literature, federal guidelines, and
119 For the sake of simplicity, and because population dynamics are not specifically of interest in this model, all population change occurs through the Ineligible Not Enrolled population. This is not realistic, of course; nearly half of all births are covered by Medicaid, and many births and immigration occur among eligible populations. (https://khn.org/news/nearly-half-of-u-s-births-are-covered-by-medicaid-study-finds/) 194 state averages; some are assumptions by the author (when data or literature are not available).
Delays are the average duration of time spent in a specified state. The transition probabilities represent the percent of the population in a given stock moving to another state per month. They are thus ratios (or fractional rates) that weight the flow of people per month among various states.
Start values and assumptions based on the Ohio calibration are specified in Table 9.
After the initial start date for the model (2016), all data in the simulation are generated endogenously (i.e., simulated) based on the system structure, initial conditions of stocks and flows, and model constants, including transition probabilities and delay periods. Distribution of the population among the various states of eligibility and enrollment in Medicaid and the rates of flow among those states are the parameters of interest. The model simulates how people move on and off the Medicaid program and how changes in initial conditions and transition rules propagate through the system. Additional details on model calibration can be found in the appendix.
195 Table 9. Model Boundaries, Assumptions, and Initialization Chart Parameters Start Value Data source Population States (Stocks120) People (% population) Enrolled121 2,896,200 Calibrated to Kaiser estimates of CPS data (25.25%) for Ohio With Submitted Applications 339,818 Calibration (2.96%) With Initiated Applications 368,937 Calibration (3.22%) Eligible, Not Enrolled 160,133 Calibrated to Kaiser estimates of CPS data (1.4%) for Ohio Eligible, Recently Disenrolled 43,434 Calibration (0.38%) Ineligible, Not Enrolled 7,602,266 Calibrated to Kaiser estimates of CPS data (66.29%) for Ohio Ineligible, Recently Disenrolled 57,912 Calibration (0.51%) (Total population) 122 11,468,700 Kaiser Family Foundation estimates based on the Census Bureau's March Current Population Survey (CPS: Annual Social and Economic Supplements), 2017. Status Periods (Delays, months Constants)123 Completion period 2 Assumption (avg. time in Initiated Applications) Determination period 2.5 Federal guidelines124 (avg. time in Submitted Applications) Coverage period 10 Ohio average (avg. time in Enrolled) Lock-out period 1 Assumption (applies to both Eligible and (avg. time in Recently Disenrolled) Ineligible)
Rates of Change (Flows)125 per month Growth of Eligible Population (births and immigration), Loss of Eligible Population (deaths and emigration), Growth of Ineligible Population, Loss of Ineligible Population, Eligible Stabilizing, Ineligible Destabilizing, Eligible Initiating Application, Eligible Not Completing App, Ineligible Initiating Application, Ineligible Not Completing App, Completing Applications, Approved to Enroll, Not Approved to Enroll, Renewing Coverage, Disenrollment among Eligible, Disenrollment among Ineligible, Reintegration to Unenrolled (“refill”)
120 Stocks are prevalence variables, where the prevalence is equal to the number of people in the stock at a given time divided by the total population. Integrals of inflows, outflows. 121 Model is initialized at 0 people enrolled, but calibrated to Ohio 2016 caseloads. 122 Total population is the sum of the seven population states. These states are mutually exclusive and exhaustive. 123 Delays are duration variables, reflecting the average time spent in a particular state (i.e., stock), measured in months. 124 US code subchapter XIX 1396a(a)(8), (CMS, 2012) – 45 days for ABD, 90 for others 125 Flows are incidence rate variables, i.e., genuine rates of change measured in people/month. Derivatives; net rate of change depending on stock and fractional rate. Initial values for all flows is 0; at any given time, the rate of change is measured in people/month. 196
Transition Probabilities per month (Constants) 126 Eligibility Fractional population growth rate 0.5% Assumption based on US Census Bureau Fractional population loss rate 0.4% 2016 State files Fractional stabilization rate 2.5% Assumption based on Shore-Sheppard Fractional destabilization rate 3.25% 2014 Application Fractional demand rate, eligible 85% Assumption Fractional fatigue rate, eligible 15% Assumption Fractional demand rate, ineligible 1% Assumption Fractional fatigue rate, ineligible 30% Assumption Fractional net application rate 70% Assumption Enrollment Fractional approval rate 75% Assumption Fractional non approval error rate 5% Assumption Fractional specific non-approval rate 15% Assumption Fractional reapplication rate 57.5% Based on KFF, 2017 estimates Fractional administrative churn rate 15% Based on Ku & Steinmetz, 2013 Fractional recovery churn rate 20% Based on Shore-Sheppard, 2014 Fractional refill rate 100% Assumption, applies to both Eligible and Ineligible
Simulation Results: Experimenting with Enrollment Dynamics
The Baseline scenario projects the future if past conditions persist without any additional significant policy or contextual changes; i.e., it projects system autopoiesis arising from the program mechanism as is. In subsequent simulations, various conditions are changed to understand their likely effects on the system and how those effects develop. Various groups of changes are implemented concurrently to assess their combined effects on enrollment dynamics. Results are presented in behavior over time graphs of the primary parameters of interest.
Baseline: behavior arising from opt-in, means-tested, time-limited entitlement. The baseline scenario simulates the next ten years of Virtual Ohio’s Medicaid program under initial
126 Constants are fractional rate variables, i.e., ratios or transition probabilities measured in percent of people / month. 197 conditions presented in Table 9 (i.e., no policy or major contextual changes). This means that the program structure itself is stable, but the relevant population experiences a small level of net population growth (i.e., more births and immigration than deaths and emigration). Furthermore, the baseline scenario assumes a largely stagnant economy where more than half of people experience changes that move them across the eligibility threshold at some point over a year, with the probability of destabilizing into eligibility being slightly higher than the probability of stabilizing into ineligibility (consistent with Shore-Sheppard, 2014).
Under stable programmatic structure, the system demonstrates precisely the kind of behavior we would expect from an entitlement. Net population growth increases the number of unenrolled people in the system, which increases the flow of people into the system’s enrollment processes and accumulation into its enrollments states. For example, there are initially more ineligible and unenrolled, but this increases the flow of people destabilizing, which increases the population of eligible and unenrolled, which increases the flow of people interested in coverage, which in turn increases the stock of people with initiated and submitted applications. Thus, slow and steady net population growth produces slow and steady increases in the enrolled population.
However, the baseline scenario also demonstrates the opt-in, means-tested, and time-limited features of the program’s structure; the flows and accumulations of people out of the program also increase. For example, the flow of people fatiguing during the application process and the accumulations of disenrolled people both increase as the total population grows.
After 10 years, Virtual Ohio’s total population is projected to be 12.93 million people with
3.18 million enrolled in Medicaid, 796,770 in process (i.e., either in the stock of people with initiated applications or stock of people with submitted applications) and 227,146 eligible people missed (i.e., eligible but not enrolled or recently disenrolled, not in process) (Figure 31, top panel).
198 The enrollment gap – the mismatch between the eligible population and the enrolled population – persists as movement of people among program states grows (Figure 31, bottom panel), and eligible and enrolled populations grow proportionally.
Figure 31. Baseline Scenario Projections
199 Recession: behavior as an automatic stabilizer. Because Medicaid eligibility is tied to income, it is said to function as an automatic stabilizer during economic downturns, covering a larger percent of the population to off-set declines in health services consumption through private insurance coverage. The recession scenario simulates the next ten years of Virtual Ohio’s Medicaid program under assumptions consistent with a sustained economic downturn.127 The program structure is stable, but the probability of destabilizing increases to 4%, the probability of stabilizing decreases to 2% to reflect increased income instability. Demand among both eligible and ineligible non-enrollees increases (to 90% and 2%, respectively) because levels of uninsurance go up during recessions, even among those over the Medicaid eligibility threshold. Both administrative and recovery churn decrease; recovery churn (to 15%) because economic downturns suppress the chance of experiencing a change in income that would move an enrollee into ineligibility, and administrative churn (10%) because recessions may increase the chance of enrollees renewing coverage before their coverage period is up. Finally, the size of the population is assumed to be net stable (i.e., in dynamic equilibrium where population growth is equal to loss) because poor economic conditions suppress immigration and birth rates.
Under these economic and population conditions, the system demonstrates the characteristic behavior of an automatic stabilizer. Increased economic destabilization increases the number of eligible unenrolled people in the system, which increases the flow of people into the program’s processes and accumulation into its enrollment states. Furthermore, increased demand for coverage increases the flow of people (both eligible and ineligible) into the application and determination processes, and both increased demand and decreased churn off the program drive
127 Admittedly, 10 years would be a very long (and unlikely) recessionary period, but the purpose is to illustrate differences in system dynamics versus the baseline scenario. 200 up caseloads. However, the recession scenario also demonstrates that balancing off-sets in the program (i.e., flows and accumulations of people out of the program) continue to operate, even under economic downturn. For example, the flow of people fatiguing out of the application process and accumulations of disenrolled people both increase during recessionary periods. These off-sets by no means fully balance out the effect of economic downturn on caseloads (Figure 32), but they do maintain an enrollment gap as more people move on and off the program (Figure 33). After 10 recessionary years, Virtual Ohio’s total population is projected to be 11.47 million people with
5.56 million enrolled in Medicaid, 970,700 in application process and 243,000 eligible people missing from the program.
Figure 32. Recession v. Baseline Caseloads
201
Figure 33. Recession Scenario Projections
Policy change: how direct and indirect interventions play out through the system. The ability to mimic general trends – growth with population and automatic stabilizing – are both good tests of the dynamic hypothesis (i.e., that this is the structure that produces system behavior). But it is also useful to test interventions in specific parts of the system. In Medicaid, States have several options about where they can intervene. They can change eligibility, administrative rules from the citizen’s perspective, or administrative rules from the State’s perspective. I explore each of these
202 scenarios and their projected effects on enrollment gap and movement of people on and off the system below.
Expansion of eligibility. In the years since its initial passage, categorical and income thresholds for Medicaid have been extended several times to expand eligibility to more of the population. The expansion scenario simulates the next ten years of Virtual Ohio’s Medicaid program under assumptions consistent with an expansion of eligibility thresholds similar to the expansion enacted under the Affordable Care Act in 2014. In the expansion scenario, the initial distribution of the population is changed; 5% of the ineligible unenrolled population is shifted to the eligible unenrolled population. The model does not capture the particular rules of that expansion (e.g., new categories or higher income thresholds), but it does reflect a one-time change in the criteria by which individuals are deemed to be eligible. Because the change in this decision rule pertains to the unenrolled population, the distribution of people among the other states (e.g., submitted applications, disenrolled) do not change at the outset, though they will be affected over time. The expansion scenario is otherwise the same as the baseline scenario in its assumptions and start values.
Under the change to eligibility expansion, the system demonstrates an initial bump that resolves to growth consistent with the pre-expansion dynamics, though at levels adjusted to a more inclusive program. The instantaneous initial increase in the size of the eligible unenrolled population (and decrease in the ineligible unenrolled population) creates a “bullwhip effect”
(Forrester, 1971) in the flows of people starting and completing applications and being approved, which creates a similar bump in levels of initiated and submitted applications and caseloads in the first 18 months (see Figure 34, top panel). Likewise, the flow of people out of the system through disenrollments, fatigue, and non-approvals also experiences an initial bump (Figure 34, bottom
203 panel). However, the bullwhip resolves within about 18 months, returning the growth of the system’s stocks and flows to baseline behaviors, though adjusted to the higher levels created through expansion. While the expansion leads to more enrollments, it also increases the size of the eligible population and movements of people among program states, and thus the enrollment gap persists. After 10 years of expanded eligibility, Virtual Ohio’s total population is projected to be
12.93 million people with 3.19 million enrolled in Medicaid, 796,700 in application process, and
215,000 eligible people missing from the program. (Comparison to Baseline is discussed in section
8 below).
204
Figure 34. Expansion Scenario Projections
Reducing administrative burden on the State. While existing studies on Medicaid enrollment in social policy largely focus on the degree to which rules associated with means testing are associated with administrative burden on citizens, administrative rules, particularly those related to the determination phase of the means test, may also impose burden on the bureaucrats responsible for carrying them out.128 The lower State burden scenario simulates the next ten years of Virtual Ohio’s Medicaid program under assumptions consistent with changes to the program
128 For example, as in Figure 26. 205 that reduce administrative burden on bureaucrats by increasing determination capacity and reducing redetermination frequency. In this scenario, the average coverage period is extended to
12 months, which represents a decrease in the burden of redeterminations for bureaucrats. To reflect an increased administrative capacity for reviewing applications, the average review period is reduced to 1.5 months, and the means test is more accurate – non-approvals of eligible applications decreases to 3% and approvals increase to 80%. Other assumptions and initial conditions are the same as the baseline scenario.
When changes are made that reduce administrative burden on bureaucrats, the system demonstrates small gains in reducing misses in the eligible population. The changes to the probabilities associated with transitioning out of the submitted application stock and the change in duration of time enrolled create an initial bump in the enrolled population and suppression in the flow of non-completions and non-approvals in the first year. However, there is also a small initial bump in the flow of disenrollments, and a drop-off of the flow of completions and approvals because there are fewer unenrolled people. The initial bump wears off quickly, returning the growth of the system’s stocks and flows to baseline behaviors, though adjusted to the higher levels created by the initial bump. Nevertheless, the changes associated with less administrative burden on the State lead to more enrollments and a small reduction in the enrollment gap (Figure 35).
After 10 years of lower burden for State actors, Virtual Ohio’s total population is projected to be
12.93 million people with 3.7 million enrolled in Medicaid, 671,600 in application process, and
200,000 eligible people missing from the program. (More on the comparison to baseline and other policy changes is discussed below. To see marginal effect of each policy change, see Appendix
D.)
206
Figure 35. Lower State Burden Scenario Projections
Reducing administrative burden on the citizens. As studies on Medicaid in social policy point out, administrative rules in the enrollment process may be burdensome to individual citizens.
The lower citizen burden scenario simulates the next ten years of Virtual Ohio’s Medicaid program under assumptions consistent with changes to the program that reduce administrative burden on citizens by reducing psychological, information, and compliance costs associated with becoming
207 and staying enrolled. In this scenario, the average time to complete an application is reduced to 1 month, the probability of completing an application is increased to 85%, fatigue is reduced to 5% and 20% for eligible and ineligible people, respectively, and administrative churn is reduced to
10%. In addition, demand is increased to 90% and 2% for eligible and ineligible people respectively to reflect increased awareness about the program (due to outreach, for example). Other assumptions and initial conditions are the same as the baseline scenario.
When changes are made that reduce administrative burden on citizens, the system demonstrates gains in reducing misses in the eligible population. The changes to the probabilities associated with transitioning out of the initiated applications and enrolled stocks and the change in duration of time to complete an application create sustained growth in the enrolled population, likely at the expense of the specificity (fractional true-negative rate) of the means test. The initial bump in the flow of approvals and drop off in non-completions are likely attributable in part to higher demand and less fatigue among the ineligible unenrolled population. The increased flow of disenrollments, particularly among the ineligible, suggest that this may be the case. Nevertheless, the changes associated with less administrative burden on citizens lead to more enrollments and a small reduction in the enrollment gap (Figure 36). After 10 years of lower burden for citizens,
Virtual Ohio’s total population is projected to be 12.93 million people with 4.81 million enrolled in Medicaid, 751,700 in application process, and 188,000 eligible people missing from the program. (More on the comparison to baseline and other policy changes is discussed in section 8 below. To see marginal effect of each policy change, see the appendix.)
208
Figure 36. Lower Citizen Burden Scenario Projections
Discussion of Preliminary Findings
The model predicts that caseloads will grow slowly and steadily under current trends, but the enrollment gap and churning will persist. Initial gains through expansion of eligibility would wear off within a few years (about 36 months). Reducing administrative burden in the system – both for citizens and bureaucrats – would increase caseloads and reduce the size of the enrollment gap (i.e., fewer missed eligible). Reducing administrative burden on the State would reduce disenrollment among the eligible, while other interventions would have little effect compared to
209 baseline. Figure 37 shows that reducing administrative burden to citizens and the State is projected to increase caseloads and growth in caseloads while reducing the prevalence of missing among the eligible. Figure 38 shows that reducing administrative burden to the State is expected to decrease disenrollment among the eligible and application completion, but not growth in those movements.
Figure 37. Comparative Policy Enrollment Projections
210
Figure 38. Comparative Policy Movement Projections
Given leverage points. Given Medicaid’s feedback structure, there are several ways that policymakers (elected and administrative officials) can affect outcomes in enrollment.
Specifically, to reduce churning off the program and missed enrollment among eligible people:
Reduce application and review delays and extend the coverage period (redetermination or
disenrollment delay). When it takes longer for individual citizens to complete an
application, and when it takes longer for bureaucrats to review applications, people
211 accumulate within unenrolled in-process states (i.e., initiated and submitted applications),
contributing to the enrollment gap. When beneficiaries are covered on the program for a
shorter period of time (i.e., when the redetermination or “continuous eligibility” period is
shorter), the stock of enrolled people drains back into the unenrolled population, increasing
the flow of people churning off the program.
Increase transition probabilities associated with becoming enrolled and decrease
probability of disenrollment. This seems obvious but is important. Without changing the
people in the system or the structure of the system itself, the State can leverage the ability
of the existing structure to cover eligible people by taking steps to increase the probability
of transitioning out of unenrolled stocks and decreasing the probability of transitioning out
of the enrolled stock. This includes increasing the accuracy (sum of true positive and true
negative / total population) of State actors in “grading” the means test and reducing the
fractional rate of administrative churn.129 It also includes making changes to the application
process to increase the probability of completing an application and decreasing the
probability of fatiguing out of the process.
The key lesson for those interested in affecting enrollment outcomes is to change the flow of people by changing rules, procedures, incentives, not the people themselves. Demographic characteristics and economic conditions are not policy actionable, but how people move through the system (e.g., application, determination) is.
129 Specifically, to increase enrollments among the eligible, the State can work to increase the sensitivity (true positive/all eligible) of approvals while maintaining or increasing specificity (true negative/all ineligible) to reduce the prevalence of improper enrollments. 212 Feedback structure and behavior. In addition to highlighting leverage points in the system, the simulation model provides general insights about Medicaid enrollment implementation processes and outcomes.
An enrollment gap and churn are endemic. Making changes to system delays and transition probabilities would not eliminate the problems of error and churn. These phenomena are part of the feedback structure of the system; they occur by virtue of the definition of the system.
For example, when stocks of enrolled people grow, flow of disenrollments do, too, unless those pathways are in some way changed or blocked. Misalignments between eligibility and enrollment and thus manifestations of the mechanism by which the system operates, not idiosyncratic behaviors that can be traced to a faulty actor or set of actors. In order to address these forms of policy resistance, the State would have to change the feedback structure of the system itself by changing the accumulation states and/or the way in which people flow in and out of them. For example, States could immediately eliminate churn off the program by making Medicaid a
“forever” or “until death” benefit, effectively sealing the drain on the Enrolled tub (which would lead to the system having other endogenous responses, other forms of policy resistance).
Administrative burden is indeed policymaking by other means. Recall that administrative burden is an individual’s experience of policy implementation as onerous (Burden et al., 2012).
Transition probabilities between various program enrollment states in part reflect administrative burden (they also reflect preferences and fit with the objectives of the program). The leverage points discussed above indicate that, without changing the causal structure of the program, States can take actions that will alter the flow and accumulation of people within the system. Such rules, practices, and procedures propagate in the system and become part of how a collective action gets applied and enforced in practice – i.e., policy in action.
213 Implementation is complex, even when simplified considerably. This simulation simplifies the enrollment processes significantly130, and yet it is still a useful tool for thinking through several scenarios and potential interventions. For example, social policy scholars have called for shifting administrative burden to the State to increase take-up and reduce churn.
However, the simulation illustrates that this may not be an effective strategy. Shifting the burden to the State may reduce the transition probability of moving from Submitted to Enrolled and increase the probability of moving from Enrolled to Recently Disenrolled or from Submitted to
Unenrolled. A structural explanation encourages us to consider that some of what is burdensome for citizens may end up being burdensome for the State, and vice versa. However, there may be several actions that the State can take that reduce administrative burden for both the State and the citizen; e.g., longer coverage period, work requirement & verification.
A simple simulation also provides a way to explore the possible effects of various waiver options currently being discussed by the States. For example, creating “lock-out” periods for people disenrolled from the program or not approved for enrollment represent additional delays that would increase accumulations of eligible people unenrolled in the program. Defunding health navigators, which would likely lower the probabilities of initiating and completing an application, could increase the prevalence of eligible people missed by the program. Imposing work requirements on beneficiaries, which would likely reduce the probabilities of submitting an application and having that application approved, could also increase the prevalence of missed individuals. One important question for States is potential gains to specificity are worth it, given that the prevalence of improper enrollments are already very low.
130 For example, by assuming that eligibility rules are clear, by not including non-State actors involved in implementation (e.g., managed care bureaucrats, health care providers), and by excluding population characteristics other than eligibility. 214 Finally, this simulation is also useful for exploring the effects of potential changes to the context of the program, that is, to scenarios brought about by exogenous forces. For example, if proposals to expand Medicare to some portion of non-elderly adults move forward, the distribution of people between eligible and ineligible states would change significantly, as would the stabilization and destabilization rates. If in the next 20-30 years, inland States like Ohio see increased immigration from coastal States due to climate change, their Medicaid caseloads may grow not only because of the population growth, but because inter-State refugees may be particularly vulnerable, both in terms of income and health status.
Not only are the interdependencies and variation within the Medicaid system more profound this captured in this model, but all of the policy actions discussed above are influenced by political and budgetary considerations. For example, a political push to limit Medicaid growth involves turning it into a block grant program. Understanding the effects of this fundamental change in the program would require significant changes to the model, but simulating the possible effects would undoubtedly be useful to States, providers, and advocates. Despite the limitations of this particular iteration of the model, simulation is a useful tool to play out and explore the downstream effects of various changes in the system. Even the preliminary findings from this version shed some light on “surprising” but common patterns. Figure 39 provides a snapshot of what a simulation interface looks like for this model. Additional views can be found in Appendix
D.
215
Figure 39. Medicaid Simulation Interface
Limitations. Like all models, this simulation is both useful because of its simplicity and limited by it. This model is only calibrated to one State at one time, which is why these findings are preliminary. Calibration to additional States and years is required to build confidence in the findings discussed here, and is fundamental to drawing conclusions about what is likely to happen or what actions could be taken in any particular case. A second limitation is the largely independent process of the modeler in building this simulation and specifying assumptions from published literature and personal knowledge. Taking full advantage of simulation as a tool for systems exploration and analysis involves generating more inclusive mental maps; for example, through a group model building process.
This model assumes certain relationships between the transition probabilities regulating rates of flow and different policy interventions. For example, increased probabilities of initiating and completing an application are interpreted as forms of reduced administrative burden on
216 citizens by making the application process easier. This assumption is taken from the literature, but the relationship is not directly represented within the boundaries of the model. The model’s boundaries also exclude cost considerations (e.g., per member per month payments, administrative costs), which are certainly relevant to States as they consider the consequences of changes in context and administration.
Finally, it is possible – even likely – that rates of movement among different enrollment states vary among different populations because the transition probabilities and delays are different. For example, the probability of disenrolling from Medicaid is likely lower for ABD
(aged, blind, or disabled) individuals than for the broader Medicaid population because they are both less likely to experience a change in income that would make them ineligible and more likely to have knowledge about renewal requirements. The probability of demanding coverage (initiating an application) among individuals with relatively higher incomes over long periods of time (e.g., those in two-income, middle-class households) is likely lower than for chronically low-income individuals because periods of eligibility may be shorter and be off-set in part by access to wealth.
Strategies for addressing these limitations are discussed as part of future work in the next chapter.
Summary
In this chapter, I used policy cybernetics as a framework to confront and explore the complexity of Medicaid enrollment, and to understand how the enrollment mechanism works.
Using a series of systems models, I find that eligibility-enrollment outcomes, including those considered to be erroneous, are mechanismic – that is, that they arise from the feedback structure of the system itself. The system dynamics simulation provides empirical evidence that in order to
217 change program-level outcomes, it is necessary to change the structure or plumbing of the system, not the characteristics of the individuals.
The primary lesson from this chapter is that program implementation always involves more than the interventions themselves; it involves the dynamic complexity of the system and its endogenous responses to the interventions. Policymakers and scholars can use simulation to identify leverage points and experiment with changes in policy interventions in the virtual world to consider how changes in interventions may play out in the real world. Overall, the systems models presented in this chapter demonstrate how policy resistance is endemic to Medicaid implementation, which has implications for learning about and intervening in other public programs. I discuss contributions to public administration research, teaching, and practice in the next chapter.
Technical Notes: Explanation of How to Read Systems Diagrams
Reading bathtub diagrams.
Use the “snapshot test”. At any particular point in time, all people are in a state/stock/tub, and those states are mutually exclusive. Flows are genuine rates, meaning they refer to units per time period. Therefore there are never people “in the pipes”; the rate is just how many move from one state to another per month. This is equivalent to the difference between population prevalence
(people in a state) and incidence rate (people/time). Delays (how long people are in a stock) are equivalent to the duration of time spent in a state.
Reading causal loop Diagrams. Variables are nouns or noun phrases; arrows signify verbs or actions; hashes on arrows represent delays.
218 The polarity of an arrow is not about what does happen (observed correlations), but rather about what would happen if there were a change in that variable assuming all else equal (in the simulation, there is not ceteris paribus assumption; all the variables are permitted to move and interact). A positive (+) polarity indicates that all else equal, if X increases (decreases), then Y increases (decreases) above (below) what it otherwise would have been. A negative (-) polarity indicates that all else equal, if X increases (decreases), then Y decreases (increases) below (above) what it otherwise would have been.
A feedback loop that reinforces (R) or amplifies the original change is positive (changes in the same direction, which can be increase or decrease). If it balances (B), opposes, or dampens the original change, it is negative (changes in opposite direction).
Reading stock-and-flow diagrams. The basic structure of system dynamics simulations is based on stocks and flows. From a set of integral or differential equations it is possible to construct a stock and flow diagram; from a stock and flow diagram, it is possible to generate the integral or differential equation system.
Stocks are accumulations, represented by boxes, and track the number of people in states, rising and falling as individuals move in and out over time.
• Stocks cannot constrain flows in or out; only valves/drains can do that; stocks can only
change through their rates
• Stocks = integrals; state variables; levels; prevalence; stocks integrate their flows; the net
flow into the stock is the rate of change of the stock
• �����(�) = ∫ [������(�) − �������(�)]�� + �����(� )
219 o Inflow(s) is the value of the inflow at any time s between initial time t0 and the
current time t.
• Delays are durations of time a unit spends in a stock. If outflow is slower than inflow, then
the stock will accumulate/increase. Stocks create delays by accumulating the difference
between the flows; they accumulate past events. When the input to a delay changes, the
output lags behind and continues at the old rate for some time.
Flows, represented by pipes and valves, are the rates at which people move between states per time period.
• Flows = derivatives; rates; incidence; the net rate of change of any stock is the inflow less
the outflow, defining the differential equation. In general, flows are the functions of stock
and other state variables and parameters.
• ( ) = ������(�) − ������� (�) (differential equation)
• Valves control flows, and represent leverage points in a system (opportunities to intervene
and make changes)
Infinite source clouds are source or destination sinks outside the model boundary (e.g., population); a stock with infinite capacity (unlike stocks in the real world) from which flow originating outside the boundary of the model arises or into which flow drains. The feedback structure related to that variable is not of interest in this model.
Auxiliary variables are other exogenous variables (outside feedback loop) that contribute to flows (i.e., constants).
This is the mathematical information captured in bathtub diagrams and stock-and-flow diagrams.
220
Chapter 6: A Systems Approach to Build Governing Intelligence
In order to make sense of wicked public problems and the consequences – intended and unintended – of collective actions, policy studies requires a paradigm in scholarship, practice, and pedagogy that matches the complexity of the problems with which we deal. The aim of this research was to address the complexity gap by developing a conceptual framework to guide both inquiry and intervention in policy studies that confronts, harnesses, and respects complexity by conceptually and computationally embracing complex processes within systems of collective action and governance. By taking a systems approach to answering questions about implementation of Medicaid’s means test and the mismatch between eligible and enrolled populations, I contribute to broader needs to understand how policy systems work, the nature of consequences, and what can be done about them. As a framework, policy cybernetics advances the field of policy studies in research, practice, and training by formally bringing complexity science to bear on policy systems.
Key Findings and Contributions
In this work, I sought to address limitations in our understanding of how consequences of policy interventions come about and what can be done about them by bringing systems thinking and complexity science to bear, modeling policy phenomena – conceptually and computationally
– in terms of their system dynamics. Medicaid is a useful as a case study because an enrollment gap remains despite continual expansions to the program’s covered population. Furthermore, its implementation involves interdependencies among formal and informal rules and individual actors
221 across different conditions – i.e., it is complex. Any public assistance program that combines categorical eligibility requirements with administrative enrollment procedures (e.g., SNAP, SSI, housing assistance) stands to gain insights from the Medicaid case. The operationalization of complexity in this research may spur new research questions and methods in scholarship relating to these programs and may influence decisions regarding program (re)design and implementation.
Policy resistance. The main policy insight of this dissertation research is that policy resistance (for example, in the form of unintended consequences) is unavoidable because it arises naturally from the design and operation of the policy system itself. I find that missed take-up and churn, phenomena typically viewed as implementation failures, are in fact endemic – part of the mechanism by which the program operates. In policy systems, mechanisms are non-lineal combinations of particular interventions (e.g., rules, norms, and institutional arrangements) and the system’s endogenous responses to those interventions.
My synthesis of the findings from health services research and social policy shows how several classification outcomes of interest – missed take-up, churn, and improper enrollment – are related phenomena that contribute to enrollment gaps in Medicaid programs across the country.
Using constructs from complexity and systems science demonstrates that program enrollment occurs through a means negotiation among citizens, bureaucrats, and eligibility, application, and determination rules over time and under political, economic, and social pressures. Implementation of the program occurs through its sorting mechanism – a characteristic process that involves feedback and delays in eligibility, application, and determination, which inevitably produce errors or misalignments between eligibility and coverage status. The system dynamics simulation model of the Medicaid enrollment system provides managers a systematic approach to clarifying their assumptions and understanding the consequences of operating on the basis of those assumptions.
222 The insight that policy resistance is unavoidable is valuable to policy studies because it highlights the need for scholars and practitioners alike to explore sets of potential interventions in context, and in silica, using methods that allow us to account for and explore sources of uncertainty. If we look at enrollment only in terms of discrete or deterministic relationships, we miss the complex picture, and the observed outcomes – the misalignments between eligibility and enrollment that contribute to the enrollment gap – seem paradoxical or surprising. This work illustrates how simulation is an invaluable tool for prospective policy analysis, permitting policymakers to experiment with potential actions and possible conditions to explore plausible outcomes.
Complexity. The main conceptual insight from this research is that complexity, which both defines and disrupts policy system operations, can be operationalized for policy studies. By critically assessing the analytic moves made in the Medicaid enrollment literature, I show that the reduction of complexity in a Cartesian tradition perpetuates the complexity gap, the mismatch between problem complexity and our responses to it. Alternative data, research designs, and analytical methods considered to be more scientifically rigorous will not rectify the disconnect because they still operate under Cartesian logic, which assumes that the way to understand a complex system is to break it apart and study its component pieces independently.
I invoke Roach and Bednar’s (1997) take on Whitehead and Russell’s (1910-13) theory of logical types to extend Forrester (1971) and Sterman’s (2000) distinctions between detail and dynamic complexity, arguing that there is another level to the complexity taxonomy. Functional complexity refers to phenomena that emerge from the operation of systems in context, which can only be understood at the level of the system. Levels of complexity are distinct from kinds or types of complexity, which are non-hierarchical categories of system features that contribute to
223 complexity at any level. Purposeful human systems are characterized by higher-order behaviors, as well as high dimensionality, diverse intentionality, recursive causality, and dynamic temporality among their elements.
Organization and operationalization are important for policy studies because logically understood, complexity helps makes sense of unintended consequences: they are manifestations of policy resistance, which is a form of a system’s functional complexity. By treating the concept of complexity seriously and rigorously, policy scholars can directly investigate the matters that make our problems wicked and our consequences surprising.
Policy cybernetics. Conceptual and practical insights converge in this research with the presentation of policy cybernetics, a conceptual framework that puts complexity to work for policy studies through systems modeling and simulation. Through this conceptual framework, I argue that a) a complexity gap is unavoidable because humans are limited in our ability to understand our world (i.e., problem complexity will always outpace response complexity); b) the purposeful human systems with which we deal and in which we are embedded are dynamic (i.e., they are always changing); and c) purposeful human systems operate via mechanisms and relate to each other via mechanism feedback – the dynamics by which adjoining systems send, filter, process, transmit, and respond to each other’s signals. Thus, getting traction on seemingly intractable problems requires abiding by the law of requisite adaptation – the adaptive capabilities of the policy system’s responses must be greater than or equal to those of the social system itself in order to maintain a stable relationship. The practical implication of this framework’s assumptions and insights is that policy design is mechanism design – it involves sensing and responding to dilemmas, including endogenous system responses to interventions. Collective action systems
224 must leverage policy learning as the means of building governing intelligence.131 Simulation, in the absence of actual experience, is the ultimate classroom and laboratory for policy learning.
A final lesson for policy studies is that harnessing complexity and abiding by the law of requisite adaptation requires more than changes in data, methods, or design. By walking through the philosophical assumptions of policy cybernetics (which is just one framework for taking a systems approach to policy studies) I argue that confronting complexity requires a paradigm shift, a different pattern of reasoning. A different nousology has implications for teaching, practice, and research. Agential systems realism – an extension of Barad’s agential realism (2007) – is scientific and realist in its reasoning, but it is also holistic, pluralistic, and critical, acknowledging the social construction of all our “-isms”. As a consequence, it is a pattern of reasoning imbued with a sense of responsibility to engage and challenge the boundaries of how we inquire, intervene, and influence within our field.
Extensions and Limitations
This research does have limitations, and addressing those limitations provides opportunities for future work to build a robust research agenda.
Updating the Medicaid simulation. It is important to note that the simulation is to show the feasibility of taking a systems approach to addressing complexity in policy analysis. If spatial and temporal generalizability is an objective, then there are empirical limitations to the simulation.
The findings are limited because I calibrated the model to one State at one point in time – it is a prototype rather than a final product. While all States face similar challenges in implementing the means test, there is considerable variation in the particularities of the structure (as mentioned
131 An adaptive, sense-and-respond rather than command-and control approach. 225 earlier in chapter 2). For the findings to be relevant to a specific context, it is necessary to calibrate the model to data from that context, which is an important line of future work for this research.
Because States do vary in the eligibility, application, and determination rules of their programs, as well as in the characteristics of their populations, calibration also involves changing the values of the parameters (e.g., period of continuous eligibility). Good simulation practice dictates conducting extensive sensitivity analyses to these various conditions to check how robust the model is to extremes – for example, under an exceptionally high level of population growth that might be associated with the immigration of climate refugees from coastal to inland States.
Another limitation of the simulation is that many of the model parameter values are drawn from the Medicaid literature and, when other sources were not readily available, my own knowledge of the subject. It stands to reason that more data, analyses, and calibrations are necessary to update the veracity of the assumptions regarding the policy context. For example, feedback from State Medicaid experts and primary data collection from Medicaid offices would provide valuable updates to parameters dealing with the determination phase of enrollment.
Additionally, Bayesian analyses of State Medicaid enrollment data could be valuable sources of fractional true-positive and false-positive rates in the simulation because they provide distributional (rather than point) estimates that reflect uncertainty, which is particularly useful for rare events such as improper enrollment.
Because system dynamics simulations focus on the feedback structure of a system, people and organizations are homogenized, an assumption that deviates significantly from reality.
However, there are several ways to address this limitation. One option is to create simulations based on different categories of subpopulations with their own levels of “risk” for Medicaid eligibility: a Safe population (low risk of need), a Trapped population (high risk of need), and an
226 At Risk population (high risk of movement of need). While the structure of the program is essentially the same for each of these populations, the mechanism is likely different because people with different levels of risk and movement are likely to interact with the structure in different ways and have different outcomes. Rule changes are likely to have different effects on these populations; for example, certain features of administrative burden (e.g., work requirements, income verification) are likely to be more burdensome to those At Risk (i.e., those who churn) than to those who are Trapped because their employment and income circumstances change frequently.
In addition, recessionary effects tend to have larger effects on poorer populations, so take-up during economic downturn is likely to increase more for those At Risk than those who are Safe.
In a different vein, agent-based models of citizens interacting with bureaucrats could be used to investigate more of the variation and dynamics among these actors within the sorting process. A network analysis would be useful for exploring the extent to which the strength and density of relationships among beneficiaries, providers, managed care payers, and advocates matter to enrollment patterns.
Finally, there are several changes to the model I could make to improve the value of the simulation for State actors. Setting up policy switches that automatically change initial conditions to reflect contextual changes would be helpful for quickly exploring policy changes under common contextual conditions. For example, a Recession switch would increase the Destabilization rate, but lower the Eligible Population Growth rate, and increase both Eligible and Ineligible Demand rates. For the sake of policy scoring and budgetary implications, I could tie caseloads to costs
(through per member, per month expenditures to managed care organizations, for example), and applications to costs (through marginal administrative review cost) to explore implications for
State spending. For example, by increasing administrative burden, caseloads may be suppressed
227 somewhat, but the cost to the State associated with increased churn may not justify the policy changes. Lastly, because many other social service programs have the same basic logic – i.e., opt- in, means-tested, time-limited entitlement – the simulation could be adapted to estimate the dynamics of enrollment in those programs, as well.
Exploring the boundaries of policy cybernetics. Policy cybernetics is limited in the same way all conceptual frameworks are – it is a model, and all models are wrong. This framework is not a “solution” to complexity in policy studies; indeed, a main theme of this dissertation has been the unavoidable truth that there will always be a gap between the complexity of the world and our understanding and responses to it. There is no panacea for dealing with complexity, but as a coherent, rigorous frame of reference for decision-making in the policy domain, policy cybernetics advances the way we think of complex problems. I present this framework as a useful way to make sense of complex phenomena in policy and governance, but the framework is limited by only being applied to the context of implementation of Medicaid’s means test. This dissertation does not explore the extent to which policy cybernetics is useful for other policy problems.
There are several ways to advance the framework. One potentially fruitful way of exploring its relevance and applicability would be to use policy cybernetics to bring clarity to the construct of administrative burden. Theoretically, the construct of administrative burden holds promise but needs furthers clarification. For example, existing literature does not sufficiently address the difference between burden experienced by individuals who are State actors versus those who are non-State actors (an issue I have begun to address in Appendix B). In addition to making sense of the complexities of bureaucratic encounters, policy cybernetics may be helpful in understanding the complexities of other policy and governance issues, for example:
228 o Implementation: The dynamics of contracting and partnership arrangements with
non-profit organizations to deliver social services
o Policy process: How problem definition and intervention formulation develop
among formal and informal policy actors
o Public organization management: The dynamics of public service motivation and
managing organizations for public value
o Voting patterns: How the interdependencies and dynamics of party identification,
voting motivation, social pressures, media environment, individual preferences
affect vote choice, and how vote choice interacts with electoral institutions
Another extension of policy cybernetics is to explore its potential as a frame of reference for teaching about complexity in policy studies – how to understand, manage, and make decisions about complex problems in the public sector. Policy cybernetics, because of its methodological emphasis on both systems thinking and simulation modeling, is a conceptual framework that could be paired with simulation-based learning about decision making in public affairs education. A teaching simulation game that captures key issues in policy systems (a public sector equivalent to
Forrester’s Beer Distribution Game) could provide a standard playground for learning about feedback, emergence, and other non-linear dynamics (including policy resistance) in a policy context, thus incorporating systems education into policy programs. Creating something as part of a standard curriculum of contextual learning in public affairs and policy studies is important because simulation learning promotes long-term thinking and emphasis on public values, mimics the sequential nature of decision-making (Desai, 2012b), and illustrates how models can be used to develop shared understandings and mutually agreed-upon strategies among diverse stakeholders
(McFarland et al., 2016). 229
Closing
Policy studies deal in complexity – with complex problems that know no discipline. Ackoff
(1994) calls the problems that arise from this complexity “messes”. Churchman (1967) and Rittel and Webber (1973) referred to them as “wicked problems.” Regardless of what we call them, they are the lifeblood and purpose of our field. We need new tools and a shift in our thinking that match the complexity of the problems with which we deal. If public administration is, as Adlai Stevenson called it, “the stark reality of responsibility,” then it is our responsibility as scholars, practitioners, and educators to give our problems the respect they deserve by treating them as complex phenomena within purposeful human systems that are other than the sum of their parts. Models built within the policy cybernetics framework will not yield definitive answers about what will happen in our complex world, but they at least have the potential to illuminate core dynamics explained via mechanisms, the foundation of scientific contribution. Policy cybernetics is about adaptive organizational learning to build our governing intelligence, piloting of systems of collective action through our efforts to achieve our greatest societal goals.
230
References
Ackoff, R. L. (1988). A Theory of Practice in the Social Systems Sciences. Systems Research, 5(3), 241-246. Ackoff, R. L. (1994). Systems thinking and thinking systems. System Dynamics Review, 10(2-3), 175-188. Aizer, A. (2003). Low Take-Up in Medicaid: Does Outreach Matter and for Whom? American Economic Review, 93(2), 238-241. Anderson, C. W. (1979). The Place of Principles in Policy Analysis. American Political Science Review, 73(3), 711-723. Ario, J., & Bachrach, D. (2017). Building the Coverage Continuum: The Role of State Medicaid Directors and Insurance Commissioners. The Commonwealth Fund. New York: The Commonwealth Fund. Aristotle. (c. 350BCE). Metaphysics, Book VIII. Arkes, H. R., Faust, D., Guilmette, T., & Hart, K. (1988). Eliminating the Hindsight Bias. Journal of Applied Psychology, 73(2), 305-307. Aschengrau, A., & Seage III, G. R. (2008). Essentials of Epidemiology in Public Health. Sudburry, MA: Jones and Bartlett. Ashby, R., & Stogdill, R. M. (1970). The Process of Model-Building in the Behavioral Sciences. Columbus: Ohio State University Press. Ashby, W. R. (1956). An introduction to cybernetics. London: Chapman & Hall. Axelrod, R., & Cohen, M. D. (1999). Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York: The Free Press. Barad, K. (2007). Meeting the Universe Halfway: Quantum Physics and the Entanglement of Matter and Meaning. Durham: Duke University Press. Bardach, E. (1977). The Implementation Game: What Happens After a Bill Becomes Law. Cambridge, MA: MIT Press. Bar-Yam, Y. (2004). Multiscale Variety in Complex Systems. Complexity, 9(4), 37-45. Bateson, G. (1979). Mind and Nature: A Necessary Unity. Hampton Press. Becker, D. J., Blackburn, J., Morrisey, M. A., Sen, B., Kilgore, M. L., Caldwell, C., . . . Menachemi, N. (2015). Enrollment, Expenditures, and Utilization After CHIP Expansion: Evidence from Alabama. Academic Pediatrics, 15(3), 258-266.
231 Beer, S. (1972). Brain of the Firm: A development in Management Cybernetics. New York: Herder & Herder. Bhargava, S., & Manoli, D. (2015). Psychological Frictions and the Incomplete Take-Up of Social Benefits: Evidence from and IRS Field Experiment. American Economic Review, 105(11), 3489-3529. Birkland, T. (2011). An Introduction ot the Policy Process: Theories, Concepts, and Models of Public Policy Making (3rd Edition. ed.). Armonk, NY: ME Sharpe. Blewett, L. A., & Hempstead, K. (2014). The Need for State Health Services and Policy Research. Health Services Research, 49(S2), 2035-3040. Bohr, N. (1927). Discussions with Einstein on Epistemological Problems in Atomic Physics. Open Court. Brussels: Fifth Solvay congress. Boisot, M., & McKelvey, B. (2011). Connectivity, Extremes, and Adaptation: A Power-Law Perspective of Organizational Effectiveness. Journal of Management Inquiry, 105. Boulding, K. (1956). General Systems Theory: The Skeleton of Science. Management Science, 2, 197-208. Box, G., & Draper, N. (1987). Empirical Model Building and Response Surfaces. New York: John Wiley & Sons. Bradley, E., Canavan, M., Rogan, E., Talbert-Slage, K., Ndumele, C., Taylor, L., & Curry, L. (2016). Variation in Health Outcomes: The Role of Spending on Social Services, Public Health, and Health Care, 2000-2009. Health Affairs, 35(5), 760-768. Brodkin, E. (1987). Policy Politics: If We Can't Govern, Can We Manage? Political Science Quarterly, 102, 571-587. Brodkin, E., & Majmundar, M. (2010). Administrative exclusions: Organizations and the Hidden Costs of Welfare Claiming. Journal of Public Administration Research and Theory, 20, 827-848. Buchanan, R. J., Cappelleri, J., & Ohsfeldt, R. (1991). The Social Environment and Medicaid Expenditures: Factors Influencing the Level of State Medicaid Spending. Public Administration Review, 51(1), 67-73. Buettgens, M., Nichols, A., & Dorn, S. (2012). Churning Under the ACA and State Policy Options for Mitigation. Robert Wood Johnson Foundation, Timely Analysis of Immediate Health Policy Issues. Washington, D.C.: Urban Institute. Bunge, M. (2004). How Does It Work? The Search for Explanatory Mechanisms. Philosophy of the Social Sciences, 34(2), 182-210.
232 Burden, B., Canon, D., Mayer, K., & Moynihan, D. (2012). The Effect of Administrative Burden on Bureaucratic Perception of Policies: Evidence from Election Administration. Public Administration Review, 72, 741-751. Carrington, P. J., Scott, J., & Wasserman, S. (2005). Models and Methods in Social Network Analysis . Cambridge: Cambridge University Press. Carroll, A. E. (2018, January 11). The Problem with Work Requirements for Medicaid. Indianapolis, IN, USA. Centers for Medicare & Medicaid Services. (2015). Annual Report to Congress on the Medicare and Medicaid Integrity Programs for Fiscal Year 2015. Department of Health & Human Services. Baltimore: Department of Health & Human Services. Centers for Medicare & Medicaid Services. (2015). Medicaid and CHIP Improper Payments Report. CMS. Baltimore, MD: Department of Health & Human Services. Centers for Medicare & Medicaid Services, O. o. (2016). 2016 Actuarial Report on the Financial Outlook for Medicaid. Centers for Medicare & Medicaid Services. Woodlawn, MD: US Department of Health & Human Services. Centers for Medicare & Medicaid Services, O. o. (2018, January 11). Opportunities to Promote Work and Community Engagement Among Medicaid Beneficiaries. Centers for Medicare & Medicaid Services. Baltimore, MD, USA: Department of Health & Human Services. Chapman, J. (2004). System Failure: Why Governments Must Learn to Think Differently (2nd Edition ed.). London: Demos Open Access. Checkland, P. (1981). Systems Thinking, Systems Practice. Chichester: Wiley. Churchman, C. (1967). Wicked Problems. Management Science, 14(4), 141-146. Cohen, M. D., & Axelrod, R. (1999). Harnessing Complexity: Organizational Implications of a Scientific Frontier. New York: The Free Press. Cohen, M. D., March, J. G., & Olsen, J. P. (1972). A Garbage Can Model of Organizational Choice. Administrative Science Quarterly, 1-25. Colby, M., & Teare, C. (2016, May 4). Lessons from California's Public Reporting on Pathways to Health Insurance Coverage. Retrieved from Health Affairs Blog: http://healthaffairs.org/blog/2016/05/04/lessons-from-californias-public-reporting-on- pathways-to-health-insurance-coverage/ Cook, J. W., & Tonurist, P. (2016). From Transactional to Strategic: Systems approaches to public service challenges. OECD, Observatory of Public Sector Innovation. Paris: OECD.
233 Currie, J. (2004). Child Research Comes of Age. The Canadian Journal of Economics, 37(3), 509-527. Davis, M. S. (1971). That's Interesting! Towards a Phenomenology of Sociology and a Sociology of Phenomenology. Philosophy of the Social Sciences, 1(2), 309-344. Daw, J. R., Hatfield, L. A., Swartz, K., & Sommers, B. D. (2017). Women in the United States Experience High Rates of Coverage 'Churn' in Months Before and After Childbirth. Health Affairs, 36(4), 598-606. DeLeon, P. (1997). Democracy and the Policy Sciences. Albany , NY: SUNY Press. DeLeon, P. (1999). The Missing Link Revisited: Contemporary Implementation Research. Policy Studies Rview, 16, 311-339. Denhardt, R., & Denhardt, J. (2000). The New Public Service: Serving Rather Than Steering. Public Administration Review, 549-559. Desai, A. (2012). Logic of Inquiry, course notes. John Glenn School of Public Affairs. Desai, A. (2012). Public Policy Inquiry and Simulations . In A. Desai, & A. Desai (Ed.), Simulation for Policy Inquiry (pp. 1-13). New York: Springer. Dewey, J. (1927). The Public and Its Problems. New York: Holt. Dietz, M., Graham-Squire, D., & Jacobs, K. (2014). The Ongoing Importance of Enrollment: Churn in Covered California and Medi-Cal. Berkeley: UC Berkeley Labor Center. Downs, A. (1957). An Economic Theory of Political Action in a Democracy. Journal of Political Economy, 65(2), 135-150. Easterbrook, S. (2014). From computational thinking to systems thinking: A conceptual toolkit for sustainability computing. Proceedings of the 2nd International Conference on Information and Communication Technologies for Sustainability (pp. 1-10). Stockholm: Creative Commons. Easton, D. (1965). A Systems Analysis of Political Life. New York: Wiley. Elmendorf, D. W., & Furman, J. (2008). If, When, How: A Primer on Fiscal Stimulus. The Brookings Institution. Washington, D.C.: The Hamilton Project. Emery, F., & Trist, E. (1965). The Causal Texture of Organizational Environments. Human Relations, 18(1), 21-32. Epstein, J. M. (2006). Generative Social Science: Studies in Agent-Based Computational Modeling. Princeton, NJ: Princeton University Press. Epstein, J. M. (2008). Why model. Journal of Artificial Societies and Social Simulations, 11(4), 12.
234 Epstein, J. M., & Axtell, R. (1996). Growing Artificial Societies: Social Science from the Bottom UP. Washington, D.C.: Brookings Institution Press. Espejo, R. (2006). What is Systemic Thinking? System Dynamics Review, 10(2-3), 199-212. Fertig, A. R., Carlin, C. S., Ode, S., & Long, S. K. (2017). Evidence of Pent-Up Demand for Care After Medicaid Expansion. Medical Care Research and Review, 1-9. Forrester, J. W. (1961). Industrial Dynamics. Forrester, J. W. (1971). Counterintuitive behavior of social systems. Technology Review, 73(3), 52-68. Forrester, J. W. (1994). System Dynamics, Systems Thinking, and Soft OR. System Dynamics Review, 10(2-3), 199-212. Foutz, J., Damico, A., Squires, E., & Garfield, R. (2017). The Uninsured: A Primer - Key Facts about Health Insurance and the Uninsured Under the Affordable Care Act. Menlo Park: Kaiswer Family Foundation. Retrieved from https://www.kff.org/uninsured/report/the- uninsured-a-primer-key-facts-about-health-insurance-and-the-uninsured-under-the- affordable-care-act/ Galewitz, P. (2018, January 11). Trump Administration Clears Way to Force Some Medicaid Enrollees to Work. Kaiser Health News. Menlo Park, CA, USA. Galewitz, P., & Bartolone, P. (2018, January 12). Trump's Work-for-Medicaid Rule Puts Work on States' Shoulders. Kaiser Health News. Menlo Park, CA, USA. Gelman, A., & Carlin, J. (2014). Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors. Perspectives on Psychological Science, 9(6), 641-651. Gelman, A., & Hill, J. (2006). Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge, UK: Cambridge University Press. Gelman, A., & Imbens, G. (2013). Why Ask Why? Forward Causal Inference and Reverse Causal Questions. Cambridge: NBER Working Paper . Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., & Rubin, D. S. (2013). Bayesian Data Analysis, 3rd Edition. Boca Raton: Chapman & Hall/CRC. Gigerenzer, G. (2007). Gut Feelings: The Intelligence of the Unconscious. New York: Penguin. Goggin, M., Bowman, A., Lester, J., & O'Toole, L. (1990). Implementation Theory and Practice: Towards a Third Generation. Glenview, IL: Scott, Foresman / Little, Brown. Government Accountability Office. (2015). Medicaid: Additional Actions Needed To Help Improve Provider and Beneficiary Fraud Controls. United States Government Accountability Office. Washington, D.C.: Government Accountability Office.
235 Graves, J. (2012). Better Methods Will Be Needed to Project Incomes to Estimate Eligibility for Subsidies in Health Insurance Exchanges. Health Affairs, 31(7), 1613-1622. Graves, J., Curtis, R., & Gruber, J. (2011). Balancing Coverage Affordability and Continuity Under a Basic Health Program Option. New England Journal of Medicine, 365(24), e44. Gruber, J. (2003). Medicaid. In N. B. Research, & R. A. Moffitt (Ed.), Means-Tested Transfer Programs in the United States (pp. 15-77). Chicago: University of Chicago Press. Haeckel, S. (1999). Adaptive Enterprise: Creating and Leading Sense-and-Respond Organizations. Cambridge, MA: Harvard Business School Press. Haraway, D. (1988). Situated Knowledges: The Science Question in Feminism and the Privilege of Partial Perspective. Feminist Studies, 14(3), 575-599. Heclo, H., & King, A. (1978). Issue Networks and the Executive Establishment. Public Administration Concepts Cases, 413, 46-57. Heinrich, C. J. (2015). The Bite of Administrative Burden: A Theoretical and Empirical Investigation. Journal of Public Administration Research and Theory, 26(3), 403-420. Herd, P. (2015). How Administrative Burdens Are Preventing Access to Critical Income Supports for Older Adults: The Case of the Supplemental Nutrition Assistance Program. Public Policy & Aging Report, 25, 52-55. Herd, P., DeLeire, T., Harvey, H., & Moynihan, D. (2013). Shifting Administrative Burden to the State: The Case of Medicaid Take-UP. Public Administration Review, 73(s1), S60- S81. Hwang, A., Rosenbaum, S., & Sommers, B. D. (2012). Creation of State Basic Health Programs Would Lead to 4 Percent Fewer People Churning Between Medicaid and Exchanges. Health Affairs , 31(6), 1314-1320. Jones, C. (1984). An Introduction to the Study of Public Policy (3rd Edition ed.). Monterey, CA: Brooks, Cole. Kahn, R. L., Katz, D., & Gutek, B. (1976). Bureaucratic Encounters - An Evaluation of government Services. The Journal of Applied Behavioral Science, 12, 178-198. Kaiser Commission on Medicaid and the Uninsured. (2012). A Historical Review of How States Have Responded to the Availability of Federal Funds for Health Coverage. Washington, D.C.: Kaiser Family Foundation. Kaiser Commission on Medicaid and the Uninsured. (2014). Medicaid Enrollment June 2013 Data Snapshot. Menlo Park: Kaiser Family Foundation. Kaiser Family Foundation. (2017). Estimates based on Census Bureau's March Current Population Survey (CPS: Annual Social and Economic Supplements). Menlo Park: Kaiser
236 Family Foundation. Retrieved from https://www.kff.org/other/state-indicator/distribution- by- fpl/?currentTimeframe=0&sortModel=%7B%22colId%22:%22Location%22,%22sort%2 2:%22asc%22%7D Kenney, G. M., Haley, J., Pan, C., Lynch, V., & Buettgens, M. (2016). A Look at Early ACA Implementation: State and National Medicaid Patterns for Adults in 2014. Washington, D.C.: The Urban Institute. Kenney, G. M., Haley, J., Pan, C., Lynch, V., & Buettgens, M. (2016). Children's Coverage Climb Continues: Uninsurance and Medicaid/CHIP Eligibility and Participation Under the ACA. Washintong, D.C.: The Urban Institute. Kenney, G., Lynch, V., Haley, J., & Huntress, M. (2012). Variation in Medicaid Eligibility and Participation Among Adults: Implications for the Affordable Care Act. Inquiry, 49(3), 231-253. Kenney, G., Lynch, V., Haley, J., Huntress, M., Resnick, D., & Coyer, C. (2011). Gains for Children: Increased Participation in Medicaid and CHIP in 2009. Washinton, D.C.: Urban Institute, Robert Wood Johnson Foundation. Koetting, M. (2016). Medicaid Contradictions: Adding, Subtractig, and Redeterminations in Illinois. Journal of Health Politics, Policy and Law, 41(2), 225-237. Koffka, K. (1935). Principles of Gestalt Psychology. London, UK: Lund Humphries. Kraft, M., & Furlong, S. (2015). Public Policy: Politics, Analysis, and Alternatives (5th Edition ed.). Thousand Oaks, CA: CQ Press. Ku, L., & Steinmetz, E. (2013). The Continuity of Medicaid Coverage: An Update. Washington, D.C.: Association for Community Affiliated Plans. Ku, L., MacTaggart, P., Pervez, F., & Rosenbaum, S. (2009). Improving Medicaid's Continuity and Quality of Care. Washington, D.C.: Association for Community Affiliated Plans. Ku, L., Steinmetz, E., & Bruen, B. (2013). Continuous-Eligibility Policies Stabilize Medicaid Coverage for Children and Could be Extended to Adults with Similar Results. Health Affairs, 32(9), 1576-1582. Kuhn, T. (1962). The Structure of Scientific Revolutions. Chicago: University of Chicago Press. Lasswell, H. D. (1951). The Policy Orientation. In D. Lerner, & H. Lasswell, The Policy Sciences (p. ?). Stanford: Stanford University Press. Lasswell, H. D. (1971). A Pre-View of the Policy Sciences. New York: Elsevier. Latour, B. (1987). Science in Action: How to Follow Scientists and Engineers Through Society. Cambridge: Harvard Univesity Press.
237 Lavarreda, S., Gatchell, M., Ponce, N., Brown, E., & Chia, Y. (2008). Switching Health Insurance and Its Effects on Access to Physician Services. Medical Care, 46(10), 1055- 1063. Lemak, C. H., Alexander, J. A., & Campbell, C. (2003). Administrative Burden and Its Implications for Outpatient Substance Abuse Treatment Organizations. Psychiatric Services, 54(5), 705-711. Lemov, P. (2009, August 19). Opening up Medicaid . Governing. Washington, D.C., USA. Lester, J., & Goggin, M. (1998, September). Back to the Future: The Rediscovery of Implementation Studies. Policy Currents, 1-7. Levin, M. A. (1993). The Day After an AIDS Vaccine is Discovered: Management Matters. Journal of Policy Analysis and Management, 12(3), 438-455. Lindblom, C. E. (1959). The Science of "Muddling Through". Public Administration Review, 19(2), 79-88. Lineberry, R. L. (1977). American Public Policy: What Government Does and What Difference It Makes. New York, NY: Harper & Row. Lipsky, M. (1980). Street-Level Bureaucracy. New York, NY: Russell Sage Foundation. Lipsky, M. (1984). Bureaucratic Disentitlement in Social Welfare Programs. Social Science Review, 58, 3-27. Long, S. K., & Dahlen, H. (2014). Expanding Coverage to Low-Income Childless Adults in Massachusetts: Implications for National Health Reform. Health Services Research, 49(S2), 2129-2146. Lowi, T. J. (1972). Four Systems of Policy, Politics, and Choice. Public Administration Review, 32(4), 298-310. Lynn, L., Heinrich, C., & Hill, C. (2000). Studying Governance and Public Management: Challenges and Prospects. Journal of Public Administration Research and Theory, 10(2), 233-262. Matland, R. (1995). Synthesizing the Implementation Literature: The Ambiguity-Conflict Model of Policy Implementation. Journal of Public Administration Research and Theory, 5, 145-174. Mayer, J. W., & Rowan, B. (1977). Institutionalized Organizations: Formal Structure as Myth and Ceremony. American Journal of Sociology, 83(2), 340-363. Mazmanian, D., & Sabatier, P. (1983). Implementation and Public Policy. Glendale, IL: Scott, Foresman.
238 McFarland, L., Milstein, B., Hirsch, G., Homer, J., Ansersen, D., Irving, R., . . . R, M. (2016). NASPAA Student Simulation Competition: Reforming the U.S. Health Care System within a Simulated Environment. Journal of Public Affairs Education, 22(3), 363-380. Meadows, D. H. (1982). Whole Earth Models and Systems. CoEvolution Quarterly, 98-108. Meadows, D. H. (2008). Thinking in Systems: A Primer. White River Junction, VT: Sustainability Institute. Merton, R. K. (1936). The Unanticipated Consequences of Purposive Social Action. American Sociological Review, 1(6), 894-904. Miller, J. &. (2007). Complex Adaptive Systems: An introduction to computational models of social life. Princeton, NJ: Princeton University Press. Milligan, C. (2015). From Coverage to Care: Addressing the Issue of Churn. Journal of Health Politics, Policy and Law, 40(1), 227-232. Mills, A. (2010). Complexity Science: An Introduction (and Invitation) for Actuaries. Society of Actuaries. Moffitt, R. (1983). An Economic Model of Welfare Stigma. The American Economic Review, 73(5), 1023-1035. Moulton, S., & Sandfort, J. R. (2015, February). Analyzing the Implementation of Policy and Programs Through Strategic Action Fields. Policy Studies Journal, ??-?? Moynihan, D. P. (2003). Normative and Instrumental Perspectives on Public Participation: Citizen Summits in Washington, D.C. The American Review of Public Administration , 33(2), 164-188. Moynihan, D., & Herd, P. (2010). Red Tape and Democracy: How Rules Affect Citizenship Rights. The American Review of Public Administration, ??? Moynihan, D., Herd, P., & Harvey, H. (2014). Administrative Burden: Learning, Psychological, and Compliance Costs in Citizen-State Interactions. Journal of Public Administration Research and Theory, 25(1), 43-69. Moynihan, D., Herd, P., & Rigby, E. (2013). Policymaking by Other Means: Do States Use Administrative Barriers to Limit Access to Medicaid? Administration & Society, online, 1-28. Musumeci, M., Rudowitz, R., Hinton, E., Antonisse, L., & Hall, C. (2018). Section 1115 Medicaid Demonstration Waivers: The Current Landscape of Approved and Pending Waivers. Menlo Park: Kaiser Family Foundation. Norman, D. (1993). Things Make Us Smart: Defending Human Attributes in the Age of the Machine. Reading, MA: Addison-Wesley.
239 Nyman, J. A. (2007). American Health Policy: Cracks in the Foundation. Journal of Health Policy, Politics and Law, 32(5), 759-783. O'Toole, L. (1997). Treating Networks Seriously: Practical and Research-Based Agendas in Public Administration. Public Administration Review, 45-52. O'Toole, L. J. (1986). Policy Recommendations for Multi-Actor Implementation: An Assessment of the Field. Journal of Public Policy, 6(2), 181-210. O'Toole, L. J. (1995). Rational Choice and Policy Implementation: Implications for Interorganizational Network Management. American Review of Public Administration, 25, 43-57. O'Toole, L. J. (2000). Research on Policy Implementation: Assessment and Prospects. Journal of Public Administration Research and Theory, 10(2), 263-288. Page, S. E. (2008). The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies. Princeton, NJ: Princeton University Press. Page, S. E. (2010). Diversity and Complexity. Princeton, New Jersey: Princeton University Press. Paradise, J., Lyons, B., & Rowland, D. (2015). Medicaid at 50. Kaiser Commission on Medicaid and the Uninsured. Pickel, A. (2007). Rethinking Systems Theory: A Programmatic Introduction. Philosophy of the Social Sciences, 391-407. Pindyck, R., & Rubinfeld, D. (2013). Microeconomics (8th edition ed.). FIND IT: Pearson. Planalp, C., Fried, B., & Sonier, J. (2014). ACA Coverage Expansions: Measuring and Monitoring Churn at the State Level. Minneapolis: SHADAC. Pressman, J., & Wildavsky, A. (1973). Implementation: How Great Expectations in Washington Are Dashed in Oakland. Berkeley: University of California Press. Quinn, M. ( 2018, February 19). Implementing States' Medicaid Wishes Won't Be Cheap. Washington , D.C., USA. Rahmandad, H., & Sterman, J. D. (2012). Reporting Guidelines for Simulation-Based Research in Social Sciences. System Dynamics Review, 28(4), 396-411. Railsback, S. F., & Grimm, V. (2011). Agent-Based and Individual-Based Modeling: A Practical Introduction. Princeton: Princeton University Press. Remler, D., & Glied, S. A. (2003). What Other Programs Can Teach Us: Increasing Participation in Health Insurance Programs. American Journal of Public Health, 93(1), 67-74. Repenning, N. P. (2003). Selling System Dynamics to (other) Social Scientists. System Dynamics Review, 19(4), 303-327.
240 Rescher, N. (1998). Complexity: A Philosophical Overview. London, UK: Transaction Publishers. Resnicow, K., & Page, S. E. (2008). Embracing Chaos and Complexity: A Quantum Change for Public Health. American Journal of Public Health, 98(8), 1382-1389. Riccucci, N. M. (2009). The Pursuit of Social Equity in the Federal Government: A Road Less Traveled? Public Administration Review, 69(3), 373-382. Richardson, K. (2007). Systems Theory and Complexity: Part 4 of the Evolution of Systems Thinking. Emergence: Complexity & Organization, 9(1-2), 166. Richmond, B. (1994). Systems Thinking/System Dynamics: Let's Just Get On with It. System Dynamics Review, 10(2-3), 135-157. Ripley, R. B. (1985). Policy Analysis in Political Science. Belmont, CA: Wadsworth Publishing Company. Rittel, H., & Webber, M. (1973). Dilemmas in a General Theory of Planning. Policy Sciences, 4(2), 155-169. Roach, D., & Bednar, D. (1997). The Theory of Logical Types: A Tool for Understanding Levels and Types of Change in Organizations. Human Relations, 50(6), 671-699. Rosenbaum, S. (2015). Addressing Medicaid/Marketplace Churn through Multimarket Plans: Assessing the Current State of Play. Journal of Health Politics, Policy and Law, 40(1), 233-242. Rosenbaum, S., Schmucker, S., Rothenberg, S., & Gunsalus, R. (2016). Streamlining Medicaid Enrollment: The Role of the Health Insurance Marketplaces and the Impact of State Policies. New York: The Commonwealth Fund. Rudowitz, R. (2016, December). The Kaiser Commission on Medicaid and the Uninsured. Retrieved March 2017, from Kaiser Family Foundation: http://files.kff.org/attachment/Issue-Brief-Medicaid-Financing-The-Basics Sabatier, P. A., & Jenkins-Smith, H. C. (1999). Theories of the Policy Process. Boulder, Colorado: Westview Press. Salmon, W. (1967). The Foundations of Scientific Inference. Pittsburgh, PA: University of Pittsburgh Press. Sandfort, J., & Moulton, S. (2015). Effective Implementation in Practice: Integrating Public Policy and Management. San Francisco: Jossey-Bass. Sanger-Katz, M. (2018, January 18). Hate Paperwork? Medicaid Recipients Will Be Drowning in It. New York Times. New York, NY, USA.
241 Saunders, M., & Alexander, G. (2008). Turning and Churning: Loss of Health Insruance Among Adults with Medicaid. Journal of General Internal Medicine, 24(1), 133-134. Schanzenbach, D. (2009). Experimental Barriers of the Barriers to Food Stamp Enrollment. Madison: IRP, Wisconsin. Schneider, A. L., & Ingram, H. M. (2005). Deserving and Entitled: Social Constructions and Public Policy. Albany: SUNY Press. Schneider, A., & Ingram, H. (1997). Policy Design for Democracy. Lawrence, KS: University of Kansas Press. Schwaninger, M. (2004). Methodologies in Conflict: Achieving Synergies Between System Dynamics and Organizational Cybernetics. System Dynamics Review, 21(4), 1-21. Schwaninger, M. (2006). System Dynamics and the Evolution of the Systems Movement. Systems Research and Behavioral Science, 23(5), 583-594. Scott, W., & Davis, G. (2007). Organizations and Organizing: Rational, Natural, and Open System Perspectives. Upper Saddle River, NJ: Prentice Hall. Senge, P. M. (1990). The Fifth Discipline: The Art and Practice of the Learning Organization. New York: Doubleday. Services, C. f. (2018). Medicaid and CHIP Application, Eligibility Determinations, and Enrollment Report. Baltimore: CMS. Shore-Sheppard, L. (2008). Stemming the Tide? The Effect of Expanding Medicaid Eligibility on Health Insurance. The BE Journal of Economic Analysis & Policy, 8(2), 1935. Shore-Sheppard, L. D. (2014). Income Dynamics and the Affordable Care Act. Health Services Research, 49(S2), 2041-2061. Simmons, J., Nelson, L., & Simonsohn, U. (2011). False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant. Psychological Science, 22(11), 1359-1366. Simon, H. A. (1969). The Sciences of the Artifical. Cambridge, MA: MIT Press. Simon, H. A. (1980). The Behavioral and Social Sciences. Science, 209(4452), 72-78. Sirpal, S. (2014). The Affordable Care Act and Incentivized Health Wellness Programs - A Tale of Federalism and Shifting Administrative Burden. Journal of Health and Human Services Administration, 37(3), 327-349. Smith, K. B., & Larimer, C. (2009). The Public Policy Theory Primer. Boulder, CO: Westview Press. Snijders, T. A., & Bosker, R. J. (1999). Multilevel Analysis: An Introduction to Basic and Advanced Multilevel Modeling. London: Sage. 242 Solomon, J. (2018, June 25). More Evidence that Complex Medicaid Waivers Will Likely Cause Harm. Off the Charts. Washington, D.C., U.S.A.: Center on Budget and Policy Priorities. Sommers, B. D. (2008). Loss of Health Insurance Among Non-Elderly Adults in Medicaid. Journal of General Internal Medicine, online. Sommers, B. D., & Rosenbaum, S. (2011). Issues in Health Reform: How Changes in eligibility May Move Millions Back and Forth Between Medicaid and Insurance Exchanges. Health Affairs, 30(2), 228-236. Sommers, B. D., Arntson, E., Kenney, G. M., & Epstein, A. M. (2013). Lessons from Early Medicaid Expansions Under Health Reform: Interviews with Medicaid Officials. Medicare & Medicaid Research Review, 3(4), E1-E19. Sommers, B. D., Graves, J., Swartz, K., & Rosenbaum, S. (2014). Medicaid and Marketplace Eligibility Changes Will Occur Often in All States; Policy Options Can Ease Impact. Health Affairs, 33(4), 700-707. Sommers, B. D., Tomasi, M., Swartz, K., & Epstein, A. (2012). Reasons for the Wide Variation in Medicaid Participation Rates Among States Hold Lessons for Coverage Expansion in 2014. Health Affairs, 31(5), 909-919. Soss, J. (1999). Lessons of Welfare: Policy Design, Political Learning, and Political Action. American Political Sceince Review, 93, 363-380. Soss, J. (2005). Making Clients and Citizens: Welfare Policy as a Source of Status, Belief, and Action. In A. Schneider, & H. Ingram, Deserving and Entitle: Social Constructions and Target Populations. Albany, NY: SUNY Press. Soss, J., Fording, R., & Schram, S. (2011). Disciplining the Poor: Neoliberal Paternalism and the Persistent Power of Race. Chicago, IL: University of Chicago Press. Spalter-Roth, R. M., Hartmann, H. I., & Andrews, L. (1992). Combining Work and Welfare: An Alternative Anti-Poverty Strategy. Washington, D.C.: Institute for Women's Policy Research. Spalter-Roth, R., & Hartmann, H. (1996). Small Happinesses: The Feminist Struggle to Integrate Social Research with Social Acitivism. In H. Gottfried, Feminism and Social Change (pp. 206-224). Urbana: University of Illinois Press. Stacey, R. D. (2002). Strategic management and organisational dynamics: the challenge of complexity (3rd ed.). Harlow: Prentice Hall. Sterman, J. D. (1994). Learning In and About Complex Systems. System Dynamics Reviews, 10(2-3), 505-514. Sterman, J. D. (2000). Business Dynamics: Systems thinking and modeling for a complex world. Boston, MA: Irwin/McGraw-Hill. 243 Sterman, J. D. (2006). Learning from Evidence in a Complex World. American Journal of Public Health, 96(3), 505-514. Stone, D. (2002). Policy Paradox: The Art of Political Decision Making. New York: WV Norton. Stuber, J., & Bradley, E. (2005). Barriers to Medicaid Enrollment: Who Is at Risk? American Journal of Public Health, 95(2), 292-298. Swartz, K., Short, P. F., Graefe, D. R., & Uberoi, N. (2015). Reducing Medicaid Churning: Extending Eligibility for Twelve Months or to End of Calendar Year is Most Effective. Health Affairs, 34(7), 1180-1187. Terreberry, S. (1968). The Evolution of Organizational Environments. Administrative Science Quarterly, 12(4), 590-613. Thornton, D., Mueller, R. M., Schoutsen, P., & von Hillergersberg, J. (2013). Predicting Healthcare Fraud in Medicaid: A Multidimensional Data Model and Analysis Techniques for Fraud Detection. Procedia Technology, 9, 1252-1264. Tufte, E. (1974). Data Analysis for Politics and Policy. Englewood Cliffs, NJ: Prentice Hall. Tversky, A., & Kahneman, D. (1973). Availability: A Heuristic for Judging Frequency and Probability. Cognitive Psychology, 5(2), 207-232. Tversky, A., & Kahneman, D. (1986). Rational Choice and the Framing of Decisions. Journal of Business, S251-S278. Varela, F., & Maturana, H. (1972). Mechanism and Biological Explanation. Philosophy of Science, 39(3), 378-382. Von Bertalanffy, L. (1951). Problems of General System Theory. Human Biology, 23(4), 302. Weiner, N. (1954). The human use of human beings: Cybernetics and society. New York, New York: Avon. Weiss, C. H. (1998). Evaluation (2nd ed.). Upper Saddle River, NJ: Prentice-Hall. Weiss, M., Bloom, H., & Brock, T. (2014). A Conceptual Framework for Studying the Sources of Variation in Program Effects. Journal of Policy Analysis and Management, 33(3), 778- 808. Whitehead, A., & Russell, B. (1910). Principia Mathematica. Cambridge, UK: Cambridge University Press. Wichowsky, A., & Moynihan, D. P. (2008). Measuring How Administration Shapes Citizenship: A Policy Feedback Perspective on Performance Management. Public Administration Review, 68, 908-920. Wickens, T. D. (2014). The Geometry of Multivariate Statistics. New York: Psychology Press. 244 Williams, M. (2018, February 26). Medicaid Changes Require Tens of Millions in Upfront Costs. Roll Call. Washington , D.C., USA. Wright, B. J., Garcia-Alexander, G., Weller, M. A., & Baicker, K. (2017). Low-Cost Behavioral Nudges Increase medicaid Take-Up Among Eligible Residents of Oregon. Health Affairs, 5, 838-845.
245
Appendix A: Summary of Findings from Medicaid Literature
Summary of Findings on Prevalence and Distribution of Enrollment Patterns Study Authors, Analytic Dependent Independent Key Finding YR Method Variables Variables (control) Take-up Remler & Glied Review Participation Individual attributes Take-up is higher when information about benefits, 2003 among eligible eligibility, and enrollment is clearer and more widely available. Stuber & Bradley Regression Knowledge of Individual attributes Knowledge of program is lower and perceived 2005 eligibility rules (Program barriers are higher among the most vulnerable Perception of simplification index) populations (e.g., those with health problems, less barriers than 9th grade education) Kenney et al. 2011 Linear Participation Individual attributes Medicaid take-up among children varies widely by probability among eligible state, but generally improved from 2008 to 2009. children Kenney et al. 2012 Linear Participation Individual attributes Medicaid take-up among adults is lower than among probability among eligible (Program outreach) children. It is lower among Hispanics, young adults, adults recent veterans, and the very poor. Sommers et al. Regression Participation Individual attributes Medicaid take-up varies widely by state. Better 2012 among eligible Program features benefits and broader eligibility is associated with adults higher take-up. Demographic characteristics are not associated with variation in take-up when controlling for state program features. Caseloads (Number of Enrollees) Long & Dahlen Regression Caseloads of State eligibility level Massachusetts’s expansion of eligibility for childless 2014 childless adults for childless adults adults is associated with increased enrollments compared to other states. Rosenbaum et al. Descriptive Caseloads State eligibility level Across all states, expansion of eligibility is associated 2016 Statistics with increased enrollments. Shore-Sheppard Regression Caseloads Individual attributes State eligibility expansion for children is associated 2008 Participation State eligibility level with increased enrollments and a small increase in among eligible take-up. children Churn Sommers 2008 Regression Disenrollment Individual attributes Disenrollment is higher among adults than children; Reenrollment reenrollment is lower among adults than children. Disenrollment is higher among men, pregnant women, young adults, Hispanics, the more educated, and those living in the West or South. Disenrollment is not primarily driven by change in eligibility. Koetting 2016 Case study Disenrollment Program One-quarter of Medicaid beneficiaries disenrolled in (Illinois) Reenrollment characteristics 2012; one-third of those reenrolled within 3 months. Service utilization Most common reason for disenrollment is failure to Individual attributes provide all necessary redetermination information. Becker et al. 2015 Regression Reenrollment Individual attributes Children who enrolled under expansion category Eligibility category more likely to reenroll before end of eligibility period than children enrolled through other categories. Daw et al. 2017 Regression Continuity of Individual attributes, Pregnancy is a particularly volatile time for income coverage pregnancy and insurance coverage for women. In 9 months prior to giving birth, 58% of pregnant women experienced coverage change; 62% of those were uninsured for at least one month. Change in family income is associated with loss of coverage. Ku et al. 2009 Regression Continuity of Eligibility category Continuity of Medicaid coverage is better among coverage children than adults, but better still among those with functional limitations. Ku & Steinmetz Regression Continuity of Program eligibility Continuity of Medicaid coverage is better among 2013 coverage period, children than adults, though both have improved 246 simplifications, coverage continuity since 2009 study. Those with eligibility category functional limitations continue to have better (% in poverty) continuity. Milligan 2015 Case study Continuity of Income level 67% of Maryland’s Medicaid population maintained (Maryland) coverage Service utilization coverage throughout 2011.
Buettgens et al. Regression Change in Individual attributes Among adults eligible for Medicaid or subsidies, one- 2012 Forecast coverage third will shift between eligibility categories within a eligibility year. category Sommers et al. Regression Change in State poverty rate Assuming Medicaid expansion to 138% FPL, 50% of 2014 Forecast coverage Generosity of benefits adults eligible for Medicaid or federal subsidies will eligibility (State demographics) shift between eligibility categories each year. category Dietz et al. 2014 Regression Change in Individual attributes Among California Medicaid enrollees, 25% will shift Forecast coverage to a different eligibility category within a year. eligibility Income is the primary pathway for eligibility. category Sommers & Regression Change in Individual attributes Assuming Medicaid expansion to 138% FPL, 50% of Rosenbaum 2011 Forecast coverage adults under 200% FPL with experience a shift in eligibility eligibility within a year. The transition is most likely category among those 100-150% FPL. Shore-Sheppard Regression Change in Individual attributes 55% of households experience at least one income 2014 income category Household transition per year that would trigger change in composition eligibility. The lowest-income households experience (State unemployment the most income changes. rate)
Summary of Findings on Drivers of Enrollment Patterns Study Authors, Analytic Dependent Variables Independent Variables Key Finding YR Method (controls) Economy Sommers et al. Regression Change in coverage State poverty rate Assuming expansion to 138% FPL, the 2014 Forecast eligibility category Generosity of benefits percent of the population experiencing a (State demographics) change in eligibility category will be higher in states with lower poverty rates compared to states with higher poverty rates.
Buchanan et al. Regression State Medicaid State economy Higher expenditures are associated with 1991 expenditures State politics higher expenditures in the previous year, Level of program higher per capita income, and larger number administration of enrollees. Shore-Sheppard Regression Change in income Individual attributes Income changes are very common across the 2014 category Household composition population, and within various economic (State unemployment conditions. Income volatility will continue to rate) pose challenges for the management income- based programs. Individuals Sommers et al. Regression Participation among Individual attributes Demographic characteristics are not 2012 eligible adults Program features significantly associated with take-up at the state or national level. Remler & Glied Review Participation among Individual attributes Individual characteristics, including sense of 2003 eligible Program features social stigma, do not explain take-up levels in Benefit generosity public programs. Dietz et al. 2014 Regression Change in coverage Individual attributes Health status does not explain changes in Forecast eligibility category eligibility category; household income is the primary pathway for eligibility.
247 Entitlement/ Eligibility Design Remler & Glied Review Participation among Individual attributes Means testing (categorical eligibility) reduces 2003 eligible Program features take-up. Take-up is higher in programs with Benefit generosity more valuable benefits. Sommers et al. Regression Change in coverage State poverty rate Assuming expansion to 138% FPL, the 2014 Forecast eligibility category Generosity of benefits percent of the population experiencing a (State demographics) change in eligibility category will be higher in states that had more generous pre-ACA Medicaid eligibility thresholds. Long & Dahlen Regression Caseloads of childless State eligibility level for Expansion of eligibility is associated with 2014 adults childless adults increased enrollments (in Massachusetts Individual attributes compared to non-expansion states). Becker et al. 2015 Regression Disenrollment Individual attributes Expansion of eligibility is associated with a Reenrollment Eligibility category reduction in disenrollment and improved continuity of coverage. Rosenbaum et al. Descriptive Caseloads State eligibility level Expansion of eligibility is associated with 2016 Statistics increased enrollments. Graves et al. 2011 Regression Change in coverage Basic Health Plan option Any Basic Health Plan option will increase Forecast eligibility category the proportion of people changing eligibility category compared to standard Medicaid expansion. A BHP that is completely separate from Medicaid will increase churn more than one that is integrated with Medicaid. Buettgens et al. Regression Change in coverage Individual attributes Basic Health Plan will decrease churning 2012 Forecast eligibility category Basic Health Plan option between Medicaid and Exchange by 16% to 200% FPL compared to standard Medicaid expansion with no BHP. Hwang et al. 2012 Regression Change in coverage Basic Health Plan option Basic Health Plan will decrease proportion of Forecast eligibility category to 200% FPL people changing eligibility category by 4% compared to standard Medicaid expansion with no BHP. Administrative Rules, Enrollment Function Administrative burden Brodkin & Regression TANF disenrollment TANF caseloads Administrative practices, both formal and Majmundar 2010 (State characteristics) informal, have systematic effects on welfare (Individual attributes) caseloads and procedural exit from the program. Moynihan et al. Regression Compliance burden in State political control States vary with regards to administrative 2013 Medicaid State economy burden. States with unified Democratic party control of their political institutions have lower levels of administrative burden. Moynihan et al. Qualitative Administrative burden Learning costs Costs imposed on individual citizens by the 2014 Descriptive Psychological costs enrollment processes in Wisconsin’s Compliance costs Medicaid program suggest administrative burden is a political instrument. Herd 2015 Descriptive SNAP participation Administrative burden SNAP enrollment processes impose a number among elderly of costs on the elderly, posing barriers to participation. Heinrich 2015 Regression Educational attainment Administrative burden Administrative burden in South Africa’s Child Risky behavior Length of time in Support Grant reduces time in program program among adolescents. More time in the program predicts better social outcomes among teens. Learning Costs Stuber & Bradley Regression Knowledge of Individual attributes Knowledge of eligibility is higher among 2005 eligibility rules (Program simplification those in states with higher degree of program Perception of barriers index) simplification. Perception of barriers to program enrollment is lower in these states. Bhargava & Regression EITC claims among Outreach messaging Take-up of benefits is higher among those Manoli 2015 eligible (Individual attributes) receiving concise messages compared to standard messages. Take-up is even higher among those receiving concise messages with an expected credit amount. Wright et al. 2017 Regression Participation among Enrollment outreach Take-up is higher among Oregonians eligible efforts receiving enhanced enrollment outreach Enrollment assistance through postcards and email reminders 248 (Individual attributes) compared to those receiving standard outreach. Take-up is higher among those receiving personalized outreach and enrollment assistance. Compliance Costs Aizer 2003 Regression New Medicaid Application assistance Application assistance is most effective for participation among Individual attributes Hispanic and Asian children, especially those eligible children (State demographics) for whom English is a second language. (State welfare caseloads) (Business cycle) Schanzenbach Regression SNAP participation Application assistance Those receiving application assistance are 2009 among eligible (Individual attributes) more likely to apply and enroll in SNAP program. Remler & Glied Review Participation among Individual attributes Take-up is higher in programs with opt-out 2003 eligible (rather than opt-in) enrollment, presumptive eligibility, and enrollment that is linked to another program. Sommers et al. Regression Participation among Individual attributes Income verification associated with lower 2012 eligible adults Program features take-up. Lower cost-sharing requirements and a broader scope of services are associated with higher take-up. Ku et al. 2013 Regression Continuity of coverage Program eligibility Gains in continuity of coverage were greater period among children in states with a 12-month Program enrollment continuous eligibility policy compared to simplifications children in other states. Eligibility category (% in poverty) (Individual attributes) Herd et al. 2013 Regression Participation among Program enrollment Employment, income, and citizenship eligible features, administrative verification are associated with lower take-up. burden Unified program applications, presumptive eligibility, online eligibility check, enrollment outreach, application assistance, and branded enrollment campaigns are associated with increased take-up. Swartz et al. 2015 Regression Change in coverage Program continuity Assuming expansion to 138% FPL, extending Forecast eligibility category options coverage to the end of the calendar year will (Monthly participation be most effective at reducing churn, followed level) by extending continuous eligibility to 12 (Monthly disenrollment months. A three-month grace period on level) eligibility will have no effect on churn, and basing eligibility on an estimate of annual income will increase churn. Koetting 2016 Case study Disenrollment Program characteristics Largest disruption of coverage among (Illinois) Reenrollment Service utilization Medicaid enrollees is the federal requirement Individual attributes for annual redetermination. State case workers (rather than managed care agents) are better at making appropriate redeterminations. Institutional Arrangements (Medicaid-Market Management) Buchanan et al. Regression State Medicaid State economy Higher expenditures are associated with local 1991 expenditures State politics (rather than state) program administration and Level of program a higher ratio of physicians per 1,000 people. administration Graves et al. 2011 Regression Change in coverage Basic Health Plan option A Basic Health Plan, even if it is integrated eligibility category with Medicaid, will produce more churning among coverage sources than a standard Medicaid expansion and subsidy structure alone. Rosenbaum et al. Descriptive Caseloads State eligibility level States that allowed the federal marketplace to 2016 Statistics Exchange management determine eligibility for Medicaid (rather than asses-and-refer to state program arrangement) have higher Medicaid enrollments. States with state-based marketplaces have higher enrollments.
249
Appendix B: Administrative Burden in Bureaucratic Encounters
Program Friction.1 Features of implementation; administrative rules, procedures, processes, strategies involved in implementation of stated policy objectives; neutral in principle (if not in practice) Program Personal Democratic Bureaucratic Institutional Behaviors/attitudes2 (private) (citizen as seeker) (administrator as (state) (single actions) gatekeeper)
Implementation Citizen à Administrator à Citizen à Administrator à Encounters3 Citizen Citizen Administrator Administrator (interactions) (social, (bureaucratic) (democratic) (institutional) Initiator à Directed interpersonal) Administrative Blue tape Bureaucratic tax Democratic tax Red tape burden4 Individual experience Social costs of Bureaucratic costs Democratic costs Institutional of program friction program features of program of program costs of program as onerous/ costly; to citizens in features to citizens features to rules to hinders decision- their personal in their administrators in administrators in making and action5 behavior; the bureaucratic their democratic their state Learning, degree to which encounters; the encounters; the encounters; the psychological, and friction degree to which degree to which degree to which compliance costs constrains and friction constrains friction friction complicates and complicates constrains and constrains and social attitudes democratic complicates complicates and conduct interactions with bureaucratic institutional related to the program interactions with decision-making program bureaucracy citizenry and action (ex: “bureaucratic disentitlement”) Administrative Social benefits Bureaucratic Democratic Institutional value6 of program benefits benefits of benefits of Individual experience features to of program program features program rules to of program friction citizens in their features to citizens to administrators administrators in as facile/ beneficial; interpersonal in their in their their state facilitates decision- encounters; the bureaucratic democratic encounters; the making and action degree to which encounters; the encounters; the degree to which Resource and friction loosens degree to which degree to which friction loosens psychological and simplifies friction loosens friction loosens and simplifies benefits personal and simplifies and simplifies institutional attitudes and democratic bureaucratic decision-making conduct related interactions with interactions with and action to the program program citizenry bureaucracy (ex: bureaucratic easing)
250
Program friction is meant to be descriptive of the administrative (formal) components of implementing a program, not the costs or benefits of those rules, procedures, processes
• This is “policymaking by other means” (Lineberry, 1977); policymaking is both the
“design” or state objectives and the implementation of them; both “hidden” (Moynihan et
al., 2014) and exposed policy instruments
• Some amount of friction is necessary in policy implementation because interventions
through rules and structure of some kind is what public programs are. Friction is not a bad
term, it’s a neutral one – descriptive of the rules, requirements, procedures, framing, etc.
involved in implementation of any intervention.
• Additionally, some amount of delay is unavoidable, though the degree and location of delay
can be altered. Delay may be considered a cost (i.e., part of administrative burden), but it
is also a benefit; delays may be both sources of persistence and resistance to changes or
disruptions in the system, so I consider them to be neutral (unlike Bozeman, 1993 who
considers delays as part of the feature of red tape).
Programs – their friction and their stated objectives, their costs and benefits – have effects on individuals’ behaviors, attitudes, and decisions
• Thus, the costs and benefits associated with friction (administrative burden and value)
apply to individuals both “inside” and “outside” the formal structure of program
administration, and both official state and contracted actors (i.e., managed care
administrators). As Downs (1957) points out, bureaucrats (i.e., administrators, frontline
workers) and elected officials have their own ”rationality” and utility to serve, so there’s
251 no perfect alignment between the stated goals of a policy and the implementation of its
rules (either from the democratic or bureaucratic side).
“Bureaucratic encounters” (KKG, 1976) are only one kind of implementation encounter that is relevant to how implementation of something like the means test works. KKG identify four types of interactions (intra– and extra-org), but calls the whole set “bureaucratic encounters”. I refine this terminology to distinguish between citizen-only, administrator-only, and citizen-administrator encounters.
• Like any interaction, there are feedback relationships between and among citizens and
administrators.
• THUS, implementation encounters are the types of interactions among administrators and
citizens relevant to a specific policy
The cost of program friction to individuals involved in implementation
• Because friction is necessary (it is what interventions are), some amount of administrative
burden (experienced costs) is inevitable.
• Given the types of implementation encounters that occur, there are four types of
administrative burden that result from various degrees and kinds of program friction
present in policy implementation.
• “Red tape” is already an established construct; the others are meant to mirror and imitate
that imagery.
• This is a modified version of the definition put forth by Burden et al. 2012; their original
definition refers to experience of policy implementation as onerous, which is fine, but I’m
252 being more specific about what that really means. Implementation is a process; friction
refers to sticky features of that process (i.e., friction)
• Per Moynihan, Herd & Harvey 2014, administrative burden consists of psychological,
learning, and compliance costs, but I emphasize that all individuals (not just citizens)
within the implementation process may experience these costs of program friction.
Important: these types of administrative burden are not the rules/procedures/processes
themselves, but rather the costs of the rules as experienced by the bureaucrat or the citizen.
• Transaction costs = search & information, policing & enforcement, bargaining & decision
costs
Citizen-state engagement is not transactional – no private ownership of the benefit, market failure
(public good)
• While friction has costs (administrative burden), friction also has benefits – administrative
value, which makes policy implementation beneficial to the actor’s goals (i.e., max
personal utility for citizens, max program efficiency for administrators)
• Administrative value consists of resource/material and psychological (personal and
professional) benefits to both citizen and administrators within the implementation process
that result from program friction
• This is distinct from public value (Moore, 1995), which is something that Soss and
Moynihan and Brodkin talk about. While administrative value may increase public value
in the program, that assumes that maximization of public value is the goal of administrators
and maximization of personal utility is the goal of citizens. Because of social and political
preferences, that may not be true.
253 • Thus, contrary to Moynihan et al.’s assertions, administrative burden need not be an
intentional play by the state to suppress or discourage enrollment. Like any intervention,
friction may impose burden/costs both by design and by accident, but the experience of the
burden doesn’t vary according to that intention. It may be “policymaking by other means”
(Lindberr,y 1977), but it may not always be fair to saddle the effects of the rules with
specific intention in that way. Intentionally increasing administrative burden is
“bureaucratic disentitlement” (Lipsky, 1984), but that’s hard to know.
• Program friction that (in practice) “shifts administrative burden to the state” (Herd et al.,
2013) has relatively higher levels of red tape and democratic tax.
• Program friction that (in practice) “shifts burden” to the citizen has relatively higher levels
of blue tape and bureaucratic tax.
• But because these effects are always both designed and incidental, implementation involves
the fine tuning of the amount and type of friction to maintain what Michie (2008) calls
“fidelity” of intervention, but sufficient adaptation to context.
• In Axelrod and Cohen’s terms, administrative burden and value end up being a mechanisms
of policymaking by other means because they are a (nonlinear) combination of program
friction (intervention) + complexity (the adaptions and responses of individuals within the
system)
• Implementation involves consideration of a) the levels of burden and value likely to result
from various types of program friction, b) the tradeoffs between burden and value for each
encounter, and c) the effects of burden and value on population and program outcomes.
Questions that follow from this treatment of implementation encounters and administrative burden:
254 • What are the relationships between these different kinds of burden? (i.e., what happens to
blue tape when red tape goes down? Is there a causal relationship or simply as correlation?)
• Is there a correlation between taxes and friction?
• What levels of administrative burden explain program “performance” (however we define
that)?
• What are the relationships between different kinds of value?
• What are the relationships between burden and value?
• What are the relationships among burden, value, public value and program performance?
255
Appendix C: Philosophy of Policy Cybernetics
Philosophical Explanation of Policy Cybernetics Based on Desai, 2012 Paradigm (side by side) Policy Economics Policy Cybernetics - A typical pattern or model; - Market structure - Mixed self-regulating community worldview underlying - Quasi-optimal collective actions structure theories - Dichotomous world, Newtonian - Conflicted, negotiated collective - What is the laws actions conventional/accepted - Claim rationality and logical - Conditional/joint world, Quantum model of inquiry? empiricism. Separation and & cybernetic laws categorization reflect reality. - Respect the law of requisite (Descartes, Smith, Wilson, Downs, variety. Elements of inquiry-action Simon, Thompson) are not separable or discrete. (Weiner, Ackoff, Lasswell, Stone, Dewey, Shields) (Barad, Haraway) - Policy systems are both self- regulating (autopoietic) and steered (participatory) = cybernetics Nousology (intelligence, Rational Empiricism Agential Systems Realism (Barad mind, grasp) - Scientific (i.e., systematic, 2007) - Reasoning faculty, sense- debatable/Socratic, common) - Scientific (i.e., systematic, making, patterns of minding; - Rational, reductive debatable/Socratic, common) medium, boundaries of the - Agential (constitutive), Systems mind, patterns of thinking; - “Received view” (i.e., passive) (pluralist) predicate of all studies model of mind (Hacking 1981) - How do our minds grasp - Dichotomy of reason/ignorance, - Active, co-constitutive model of the world? inquiry/action mind (the mind cuts both ways); - Dualist states of mind/matter there is organization and - Grasp occurs thru perception & interpretation at every ‘step’ of both cognition (bounded, but can be study and action unbiased) - Spectrum (inseparability) of - Whole is grasped thru sum of its reasoning, inquiry-action parts - Pluralist intra-action of mind- - The mind serves as a positive matter (i.e., focusing, rational) lens - Grasp occurs thru enactment - Signal processing thru refraction (situated & fallible perception, & reflection (medium + lens) cognition, creation) - Symbols: Telescope, microscope, - Whole is other than the sum of its visible light/energy/ waves parts - Minds as many lenses (i.e., positive, negative, mixed, distorted, dirty), and a medium - Signal processing thru refraction & diffraction (medium + lens + barrier)
256 - Symbols: Prism, spectragraph, electromagnetic light/energy/waves Frame - Social science: Knowledge and - Social sciences: Knowledge and Basic structure of the lens, laws are reducible to Newtonian laws are not reducible to physics; a i.e., underlying concepts of physics as a guidepost on sciences; wider toolbox is necessary and field those are the correct tools (models) appropriate for the complexity of PA: Inferences about legal, Comte’s “queen science” social, administrative, and - Requires a generalized frame, organizational problems methodology Philosophy of Science - Way of systematically revealing - No algorithm to systematic - Theories, assumptions, and organizing knowledge of the organization of knowledge (i.e., methods, history, world science) (Kuhn 1970) implications, purpose of - Science is a product made - Science is experiential, thus it is a science (poiesis) through rational thought compound activity of thinking - What is science and why do (theoria) (theoria)-production (poiesis)- we do it? - Science reveals boundaries action (praxis) - Theoretical elements describe - Science is a boundary project scheme by which science is - Theoretical elements are heuristics produced for scientific experience (science is applied/enacted philosophy) Philosophy (love of - Uncovering natural laws of - Understanding mechanisms of wisdom) phenomena phenomena Fundamental problems - social/moral laws are special - No hierarchy among natural, that drive curiosity cases and thus can be reduced to social, and metaphysical Why are we curious? natural form mechanisms - Metaphysical laws are not the - Metaphysical claims are requisite domain of science for science - Concerned with fundamental, not particular, problems (Lasswell 1951) Purpose (intention) Truth (Knowledge of Reality) Understanding (Wisdom) Why do we inquire? - To describe, explain, represent - To understand how phenomena phenomena (positive) and their work, respond, and unfold characteristics - To develop strategies and - To solve the underlying (stable, predictions for practical purposes natural) equation (“technological (pragmatism) solutionism”) Ontology (being) - Positivist: objective reality exists, - Co-constitutive: reality exists, but - Nature of being, existence, is tangible/material; options and cannot be separated from our intra- reality; “is” statements about information are material; action with it; reality, options and data-generating process, preferences are revealed information are material- signals - Atomist: objects of study are discursive; preferences are latent - What is the nature of the particular, having narrowly - Systems-Mechanismic: reality is reality we’re studying? defined characteristics and wave-particular (interconnected), responses on different conditions; behaviors of interest are other than entities reduce to entities; social the sum of their parts; systems are driven by individual Complementarity (Bohr); actions (markets) Uncertainty principle (Heisenberg)
257 - Determinist: causation is absolute - Probabilistic: causation is (even if not linear), stable, & recursive, dynamic & non- stationary; absolute laws (of stationary; laws are conditional, society, of institutions) govern diverse, interconnected & dynamic; behavior; effects are traceable to no prime causes or causers prime causes - Strategic/Adaptive: People make - Rationalist: People make decisions according to heuristics, decisions/choose largely rules, and calculations, both independently, and by seeking to personal and social. Information and maximize personal utility and choice sets may be limited and making optimal tradeoffs. biased. Sufficiently full relevant - Governed by irreducible natural, information and choice set. social, and metaphysical laws, - Governed by social laws that are including democratic laws, law of ultimately reducible to natural, requisite variety Newtonian laws (local, medium- - Dilemmas and strategies are sized) interwoven - Problem and solution are identifiable (as are actors responsible for them) Phenomenology (to - Phenomena exist separate from - Phenomena, experience, & appear, show) consciousness; consciousness are inseparable Experience of - Experience of consciousness is (reality is material-discursive) consciousness not relevant to (empirical) science - Science must be What is experience? phenomenological Axiology (worthy, accepted Values not relevant to Inquiry No Inquiry without Values truth) - Value-free b/c it is true that… - Value-laden b/c it is true that… - Axiomatic “given” - Science is about facts, and facts - Values, facts are adjoined as statements; assumed values are distinct from values information built into RQs and data (neutrality) - What is (true), what is not (false) collection - Facts distinguish b/w what is are adjoined - What are the appropriate (true) and what is not (false) - Information is variable, biased, values to apply to inquiry? - Facts are unbiased, dynamic, random, communal consistent/stable, essential, - Adaptation/strategy negotiates efficient, independent information - Rationality maximizes/satisfices - Information may be use of facts unobservable, unmeasurable, latent; - Facts are observable, not possible to formalize measurable, evident/manifest; able mathematically to be formalized mathematically Therefore, it is the case that… Therefore, it is clear that… - Axioms are value statements and - Scientific axioms and researcher/field-embedded conclusions are not associated with - Present is connected to both past the consciousness/values of the and future, but is not the same as researcher past or future; value and temporal - The present is and therefore is contexts matter what matters; value and temporal - Prefactual, counterfactual, contexts are unimportant/irrelevant semifactual, profactual, predictive,
258 - Counterfactual-oriented lab and retrodictive-oriented field, lab, experiments (other possible and virtual experiments useful in explanations for empirical gleaning information observations) are the gold standard - Estimators of information to identify facts (inference) should include bias, - Estimators of fact (inference) variance, and randomness as should minimize “error” - parameters (stochastic, non- unbiased, consistent, non-random stationary) to be useful - Rational empiricists have a - No field or tradition has a monopoly on social? Science monopoly on science b/c science isn’t a market good - Mai-ntain a value orientation (Lasswell) - conceptual systems are pluralistic and contested (Desai 2012) Epistemology (knowledge) Reflected/Revelatory Situated/Mediated - Features of knowing, - Empirical belief is justified (a - Pluralist beliefs are justified (a evidence, justified belief; posteriori, sense data) priori and a posteriori, sense, construction of “is” - DGP Signals are measurable, experiential, intuitive data) and statements about DGP discrete; refined thru analysis (into updated - Based on what do we parts) - DGP Signals may be latent, know? How do we know? - Analysis is a discrete procedure linked; interpreted thru synthesis What is fact or justified of distinction (hypothesis testing), - Synthesis is a concurrent, iterative belief? refutation, replication, procedure of consideration falsification, verification (hypothesis generation), exploration - Dualist/Objective: there is an application, adaptation unbridgeable gap b/w is (fact) & - Holist: no essential reduction of ought (value) statements (Hume’s know-known, fact-value (Kant), Law), b/w knower/known, subject-object; the observer effect subject/object - Objectivity is not possible (no - Absolute: knowledge as Truth “god trick” – Haraway) - Fact = observation + model (Churchman) - Situated (Haraway 1987): knowledge as cogitation, use - Intersubjectivity (Desai 2012) (Ideology) (formed belief) - Ideological belief that markets Normative, “ought” are the preferred institutional form, statements even in policy. Not based on What do I believe? evidence.
259 Methodology (research Reduced Scientific Method Generalized Systemsist Scientific design) - Discrete steps Method - Features of the design - Investigate the counterfactual - Iterative and concurrent process of research; the (experiments are gold standard) - Investigate counterfactual, general procedure for - Reduction of entities into smaller prefactual, & other possibilities organizing, transforming, entities (wholes into parts; DGP (thought experiments) and interpreting data to into relationships; phenomena into - Aggregation of entities (systems as information to knowledge input/outcome) other than the sum of their parts; - How do we design inquiry? - Reduction of complexity DGP as interdependencies; - Hierarchies of data (& data phenomena as dynamics) sources), evidence, methods, and - Confrontation of complexity models - Flat/pluralistic treatment of data, - Explain via mathematical sources, evidence, methods, models functions – optimization, - Explain via mechanisms – delineation, distinction (what, adaptation, stochastic variation, & why) quasi-optimization (how) - Allow the “data to speak for - Observer effect cannot be escaped themselves” through methods, instruments, or logic Instruments (device of Lenses to focus the DGP Lenses & barriers that affect DGP measurement) - Measurement error - Researchers are instruments Scheme of filtering, - Random error / uncertainty - Instruments are human-made and measuring signals - Can be refined to more therefore include values How do we collect accurately reflect reality - Systematic, measurement, random observations? error will always be part, but bias is also a reality of how we make sense of the world
Data (thing given; storable Empirical Empirical, Experiential, Intuitive and transmittable) - Measures of the “thing itself” - Measures are interpretations of Recorded, stored signals; thru sensory observations observations units of observation - Unmeasurable does not count - Unmeasurable may count What are our (considered “unobserved) - Seemingly competing observations observations? - Dualistic constructs, variables can both be true (“observational defined by exclusivity paradox”) - Methods (mode, journey) Statistical Inference Computational (systems) Inference Scheme of organizing, - Reductionism - Holism, irreducible transforming signals - Closed-form (analytical) - Open-form (simulated) How do we organize - Unit/level(actor)- particular - Multi-scale (actions) observations? - Identification thru best fit - Free parameterization thru (regression) stationary functions possible (simulation) non-stationary (summative combination) of dynamics (mechanisms) of distributional characteristics stochastic characteristics (variance & central tendency) (probability densities) - Outliers & endogeneity relegated - Components of error term (bias, to error term random, endogeneity) of parametric - Quantitative models of DGP interest
260 - Quantitative and Qualitative models of DGP Evidence (obvious to eye Statistical characteristics Computational characteristics or mind) - Data described by average - That is, both statistical & Organized, transformed features and variation conceptual characteristics signals; units of analysis; (distribution) - Data described by distributional & What is information? - Data features explained in terms constructs characteristics of correlation, covariation - Data features explained in terms of corr, cov, stochastic variation, complexity (interactions, structure) Logic (the word, reason) Dualistic (Either/Or) Pluralistic (Both/And) Scheme of clarifying/ - “Correct” inference - Useful inference explaining/ explicating - Induction, deduction (if…then…) - Abduction, induction, deduction signals; mode of valid - Mathematical - Mechanismic inference What is the structure for drawing conclusions? Science (to know) A Product Productive process Systematically organized - A re-creation of the world in - Situated within a particular body of knowledge (i.e., digestible form framework conclusions) - Conclusions, though subject to - Not only subject to revision, What do we know (not revision and expansion expansion, but also to oscillation, have faith in)? displacement; a cogitation Rhetoric (public speech, Exclusive, Technical Inclusive, Common discourse) - Analytic, synthetic statements - Translation (“carried across”) - Bridging from the self to - Quantitative - Qualitative, quantitative the other - Esoteric, technically specific - Democratic, straightforward, - How do we communicate? Praxis (doing) Agnostic Pragmatic - Enactment or application - No position on implementation - Knowledge is to be used for a of knowledge - Action is distinct from inquiry purpose; to address social problems - What actions are taken to - Action is not distinct from inquiry; achieve ends? they are- linked activities Pedagogy Socratic Orientation Critical Orientation Teaching strategies, - Assimilating (analytical)/ - Accommodating (what if), actions, judgments, Converging (technical) Diverging (why) decisions - Logical/mathematical, naturalist - Linguistic, existentialist How do we teach? - Read/write - Visual, kinesthetic Curriculum - Quantitative methods - Qualitative & quantitative methods Subjects of a course of - Statistical modeling - Computational modeling study - Rational models of individual - Mixed models of individual choice What do we teach? choice (behavioral, political, rule-based) - Market models of public action - Community models of public action
261
Appendix D: Supplemental Medicaid Simulation Materials
Detailed Causal Loop Diagram
262
Calibration to Ohio Data
263
Baseline Scenario Results
264
Recession Scenario Results
Eligibility Expansion Results
265
Changes to Delays
Coverage extension to 12 months.
266 Determination period to 1.5 months.
Application completion period to 1 month.
267 Delay period comparisons.
Changes to Transition Probabilities
False-negatives to 3%.
268
True negatives to 15%.
Approvals to 80%.
269 Lower State burden comparisons.
Applications to 85%.
More Applications Scenario
270
Fatigue to 5% (L+), 20% (L-).
More Applications Scenario
Demand to 90% (L+), 2% (L-).
271 Lower citizen burden comparisons.
272 Administrative churn to 10%.
273 Burden Scenarios
274 Interface
275