Explains agenda setting under conditions of ambiguity. Policy change occurs when three independent streams (problems, policies, politics) couple during brief policy windows, often pushed by policy entrepreneurs. Based on the Garbage Can Model (Cohen, March, Olsen 1972). Foundational work: Kingdon 1984.
Problem Stream: Indicators, focusing events, feedback about existing programs
Policy Stream: Solutions floating in a primeval soup; must meet criteria of technical feasibility and value acceptability
Political Stream: National mood, organized political forces, government changes
Policy Windows: Brief opportunities for coupling streams (open in problem OR political stream)
Policy Entrepreneurs: Individuals who invest resources to couple streams and push proposals
Why did issue X reach the governmental agenda at this particular time?
How did a crisis event open a policy window for reform?
Example: Blankenau (2001) used MSF to explain why national health insurance succeeded in Canada but failed in the US, focusing on how political streams and policy entrepreneurs differed.
Example: Zohlnhofer (2016) applied MSF to explain German labor market reforms using a modified approach.
NOT rational or linear. Problems, solutions, and politics develop largely independently. Major policy change requires alignment of all three streams during brief windows. Timing and entrepreneurship matter more than problem severity. Solutions may exist before problems (solutions looking for problems).
Strengths: Explains timing of agenda change; captures political messiness; intuitive; widely applicable across countries and policy areas.
Weaknesses: Hard to predict when windows open; mostly post-hoc explanation; vague concept definitions; difficult systematic measurement.
Assumes AMBIGUITY (unclear preferences, fluid participation) not just uncertainty
Windows are brief and unpredictable
Coupling requires entrepreneurial effort
311 peer-reviewed applications between 2000-2013 (Jones et al. 2016)
Explains long periods of policy stability (incrementalism) interrupted by sudden dramatic changes (punctuations). The same political system produces both stability and change due to bounded rationality and agenda dynamics. Foundational work: Baumgartner and Jones 1993.
Policy Monopoly: Dominant understanding/arrangement controlling a subsystem
Policy Image: How an issue is understood and framed
Venue: Institutional location where decisions are made
Negative Feedback: Dampens change, maintains stability
Positive Feedback: Amplifies change, produces punctuations
Bounded Rationality: Limited attention capacity of decision makers
Why did policy remain stable for decades then change dramatically?
How did issue reframing lead to major policy change?
Example: Jones and Baumgartner (2005) analyzed thousands of US federal budget changes, demonstrating leptokurtic distributions consistent with PET.
Example: Cross-national budget studies in UK, France, Germany, Denmark, Belgium, and Spain all found punctuated patterns (Jones et al. 2009), establishing punctuated equilibrium as a general empirical law of public budgets.
Most issues processed in parallel by specialized subsystems producing stability. When attention shifts to macropolitical arena (serial processing), dramatic change becomes possible. Policy images get reframed, venues change, monopolies are disrupted. Result: leptokurtic distribution (many small changes, few huge ones).
Strengths: Explains BOTH stability AND change; testable with budget data; strong cross-national empirical support.
Weaknesses: Hard to predict WHEN punctuations occur; better at explaining patterns than specific cases.
Uses Comparative Agendas Project data
NOT same as biological punctuated equilibrium
Serial vs. parallel processing is central mechanism
Attention is the scarce resource
Disproportionate information processing creates friction
Examines how existing policies shape future politics. Once enacted, policies restructure political processes by affecting citizens’ behavior, interest group power, government capacity, and problem definition. Core insight: ‘Policies create politics’ (Schattschneider 1935). Key theorist: Pierson 1993.
Resource Effects: Policies give citizens money, skills, or time enabling participation
Interpretive Effects: Policies send messages about citizenship and deservingness
Four Feedback Streams: Political agendas/problem definition; governance capacity/political learning; power of groups; meaning of citizenship
How did Social Security create an active senior citizen lobby?
Why do welfare recipients participate less in politics?
Example: Campbell (2002) found Social Security benefits provide incentives for political activity, with low/middle-income seniors most active in defending benefits.
Example: Mettler (2005) showed GI Bill veterans participated 50% more in civic organizations due to resource effects from education.
Example: Weaver and Lerman (2010) found contact with criminal justice system reduces political engagement and trust in government.
Policies are both outputs AND inputs to future politics. Generous, visible, universal programs create supportive constituencies. Punitive, hidden, means-tested programs demobilize recipients. Policies shape who participates, what groups form, how government learns, and what future policies are possible (path dependence).
Strengths: Shows political consequences beyond intended effects; bridges policy analysis and political behavior research.
Weaknesses: Mostly US social welfare focus; mechanisms not always clearly specified; hard to test systematically due to selection bias.
Roots in historical institutionalism
Hidden vs. visible policies produce different effects (submerged state)
Path dependence: early choices constrain future options
Universal vs. means-tested programs have different political consequences
Explains policy change through competition between advocacy coalitions within policy subsystems. Coalitions are groups sharing beliefs and coordinating actions over time. Change results from external shocks, internal shocks, policy-oriented learning, or negotiated agreements. Developers: Sabatier and Jenkins-Smith (1988+).
Policy Subsystem: The arena where coalitions compete over a policy issue
Advocacy Coalitions: Groups sharing beliefs who coordinate to influence policy
Belief System (3 levels): Deep core (fundamental values, hardest to change); Policy core (basic positions on subsystem); Secondary aspects (instrumental decisions, easiest to change)
Policy-Oriented Learning: How coalitions update beliefs based on experience
External/Internal Shocks: Events that redistribute resources or challenge beliefs
What coalitions exist in this subsystem and what do they believe?
How did an external shock lead to policy change?
Example: Sabatier and Brasher (1993) tracked environmental policy at Lake Tahoe from 1964-1985, showing how coalitions formed and changed.
Example: Weible et al. (2016) compared coalitions and policy change regarding hydraulic fracturing (fracking) across 7 countries.
Example: Kubler (2001) applied ACF to Swiss drug policy, showing coalition opportunity structures affect policy change.
Actors join coalitions based on shared BELIEFS (not just interests). Coalitions compete using strategies to influence government. Major change requires: (1) external shock changing resources, (2) internal subsystem shock, (3) policy-oriented learning, or (4) negotiated agreement. Requires 10+ year timeframe.
Strengths: Explains long-term dynamics; beliefs matter not just interests; clear hypotheses; extensively tested worldwide (240+ applications).
Weaknesses: Requires long time periods; hard to study nascent subsystems; belief measurement inconsistent.
Devil Shift: Coalitions see opponents as more powerful and evil than reality
Policy brokers help find compromise between coalitions
Scientific information is filtered through beliefs
Best for HIGH-CONFLICT subsystems
Examines how policy narratives (stories) influence the policy process. Studies how actors strategically construct and use stories with characters, plots, and morals to shape public opinion, define problems, and achieve policy goals. Core insight: Narratives are the lifeblood of politics.
Policy Narrative Elements: Setting (context); Characters (heroes, villains, victims); Plot (events over time); Moral (policy solution)
Homo Narrans: Model of human as storytelling being
Three Levels: Micro (individual cognition); Meso (groups/coalitions); Macro (culture/institutions)
Narrative Strategies: Scope of conflict (expand/contain), causal mechanisms, cost/benefit distribution
How do competing coalitions use different villain/hero characters?
Does narrative congruence with beliefs increase persuasion?
Example: Shanahan et al. (2013) analyzed wind farm policy narratives in Massachusetts, finding winning coalitions focused on heroes over villains.
Example: Jones (2010, 2014) conducted experiments showing hero characters drive narrative persuasion on climate change.
Example: Gupta, Ripberger, and Collins (2014) examined nuclear power plant siting narratives in India, showing how narratives expand or contain conflict.
Example: Merry (2015) analyzed 9,918 tweets from Brady Campaign and NRA to study gun policy narratives on social media.
Policy debates are fought through competing narratives. Actors strategically construct stories to mobilize supporters, demonize opponents, simplify complex issues, and propose solutions. Narratives work through emotional engagement and cognitive shortcuts. Winning the narrative battle often means winning the policy battle.
Strengths: Captures real political communication; can be tested experimentally; measurable narrative elements.
Weaknesses: Relatively new framework; fewer applications than older theories; macro-level less developed.
Builds on ACF (coalitions tell stories)
Congruent narratives reinforce beliefs; breaching narratives can persuade
Heroes often more powerful than villains in persuasion
Evidence/science used strategically within narratives
Systematic approach to analyze how institutions (rules) shape human behavior and outcomes, especially in collective action situations. Explains how people create, follow, and change rules to solve shared problems. Developer: Elinor Ostrom (Nobel Prize 2009). Foundational work: Kiser and Ostrom 1982.
Action Situation: Where actors interact, make decisions, and take actions
Actors: Participants with preferences, resources, and information
Rules-in-Use: Actual rules people follow (vs. rules-in-form on paper)
Physical/Material Conditions: Nature of the resource or good
Community Attributes: Shared norms, trust, social capital
Three Levels: Operational (day-to-day), Collective Choice (rule-making), Constitutional
Why do some communities successfully manage common pool resources?
What institutional designs lead to sustainable outcomes?
Example: Tang (1992) compared farmer-managed vs. government-managed irrigation systems, finding farmer-managed showed better rule congruence.
Example: Ostrom (1990) identified 8 design principles from long-enduring common pool resource governance systems.
Example: Studies of fisheries in multiple countries found institutions key to sustainability (Basurto 2005; Cinner et al. 2012).
Focus on how RULES structure situations. People are interdependent; outcomes depend on others’ actions. Institutional arrangements can align individual and collective interests. Not just state solutions; communities can self-govern. Rules exist at multiple nested levels.
Strengths: Rigorous framework; applicable to diverse settings from fisheries to software; extensive empirical testing; Nobel Prize recognition.
Weaknesses: Complex; steep learning curve; less focused on traditional government policy processes.
8 Design Principles for successful commons governance
Challenges tragedy of the commons narrative
Neither privatization nor government control always best
SES (Social-Ecological Systems) framework is the extension for environmental contexts
Polycentricity: multiple governing authorities at different scales
Explain why and how governments adopt new policies. Innovation = government adopts policy new to itself. Diffusion = policy spreads across jurisdictions. Unified model combines internal determinants (jurisdiction characteristics) with external diffusion effects (influence from other governments). Developers: Berry and Berry 1990.
Internal Determinants: Motivation (problem severity, public opinion, electoral pressure); Resources/obstacles (wealth, legislative capacity, interest groups)
Diffusion Mechanisms: Learning (observing/emulating successful policies); Competition (keeping up with neighbors); Coercion (pressure from higher-level governments); Normative pressure (adopting because expected)
S-Curve: Typical adoption pattern with slow start, rapid middle, tapering end
Why do some states adopt policies earlier than others?
Do states copy neighbors or ideologically similar states?
Example: Berry and Berry (1990) tested why US states adopted lotteries, finding both internal factors and neighbor adoptions mattered.
Example: Berry and Baybeck (2005) used GIS to determine lottery diffusion was due to interstate competition (fear of losing revenue), not learning.
Example: Shipan and Volden (2008) studied antismoking policy adoption by US cities, testing learning, competition, imitation, and coercion mechanisms.
Example: Mintrom (1997) found policy entrepreneurs significantly influence school choice policy adoption.
Policy adoption influenced by BOTH internal factors (resources, problems, politics) AND external factors (what other governments are doing). Governments learn from, compete with, and are pressured by other governments. Event History Analysis (EHA) tests which factors predict adoption. Different policies spread through different mechanisms.
Strengths: Clear methodology (Event History Analysis); highly testable hypotheses; useful for explaining policy spread across US states.
Weaknesses: Less developed theory of WHY diffusion happens; binary adoption variable oversimplifies policy variation.
Event History Analysis (EHA) is the key method
Regional diffusion vs. national interaction models
Leader-laggard pattern common
Policies can be complements, contingent, or substitutes
Not just copying; policies adapted during transfer
| Theory | Key Question | Core Concept | Key Phrase | |||||
| MSF | Why did this reach the agenda now? | Three streams coupling during policy windows | Solutions looking for problems | |||||
| PET | Why stability then sudden change? | Serial vs. parallel processing; bounded attention | Attention scarcity drives punctuations | |||||
| PFT | How do policies reshape politics? | Resource and interpretive effects on citizens | Policies create politics | |||||
| ACF | How do coalitions compete for policy? | Belief systems; coalitions; shocks; learning | Devil shift; beliefs drive coalitions | |||||
| NPF | How do narratives influence policy? | Characters, plots, strategies; homo narrans | Stories shape policy battles | |||||
| IAD | How do rules shape collective action? | Action situations; rules-in-use; design principles | Self-governance is possible | |||||
| IDM | Why do policies spread across governments? | Internal determinants + diffusion mechanisms | Learning, competition, coercion | |||||