Public Policy Analysis and Development Training Course

Define a bounded policy problem with causes, affected groups and decision needs.

• Problem statements separating symptoms, causes, consequences and desired conditions
• Issue trees used to identify root causes and causal pathways
• Systems mapping for feedback loops, interdependencies and unintended consequences
• Define population segments affected differently by the stated public problem
• Baseline measures selected before intervention objectives are drafted
• Decision questions framed around mandate, scope, timing and available authority

Map stakeholders, incentives and influence to inform policy development choices.

• Stakeholder mapping using interest, influence, position and salience criteria
• Power-interest grids identifying manage-closely, keep-informed and monitor groups
• Political economy analysis of winners, losers, incentives and veto points
• Map institutional mandates, accountabilities and dependencies across delivery actors
• Coalition building strategies for supporters, neutral actors and sceptical groups
• Conflict-sensitive approaches for policies affecting rights, resources or identities

Assess and synthesise evidence suitable for a specific policy question.

• Evidence questions structured through population, intervention, comparison and outcome criteria
• Source hierarchy covering systematic reviews, evaluations, administrative data and expert evidence
• Assess validity, reliability, relevance, timeliness and transferability of evidence
• Triangulation across quantitative findings, qualitative research and practitioner knowledge
• Administrative data limitations including missingness, definitions, coverage and reporting incentives
• Evidence briefs separating findings, assumptions, uncertainty and recommendation implications

Build a theory of change that makes intervention logic and assumptions explicit.

• Causal pathways connecting activities, outputs, intermediate outcomes and public outcomes
• Theory of Change diagrams showing assumptions, external factors and feedback loops
• Identify necessary and sufficient conditions for intended policy outcomes
• Assumption testing through evidence review, stakeholder challenge and sensitivity analysis
• Causal inference concepts: correlation, confounding, selection bias and counterfactuals
• Draft measurable outcome statements with target populations and timeframes

Set policy objectives and appraisal criteria that guide defensible option choices.

• SMART objectives adapted for public value, equity and statutory obligations
• Outcome hierarchies linking strategic goals, policy objectives and delivery measures
• Define decision criteria for effectiveness, equity, cost, feasibility and legality
• Weighting criteria where trade-offs require transparent senior-level judgement
• Threshold criteria used to exclude unlawful, unsafe or impracticable options
• Performance indicators distinguished from targets, milestones, outputs and outcomes

Develop a credible long list and short list of policy options.

• Do-nothing and business-as-usual baselines established before option generation
• Policy instrument typology: regulation, taxation, spending, information and service provision
• Nudge, boost and choice-architecture approaches assessed against behavioural evidence
• Generate options at different intervention intensities and implementation timescales
• Bundle complementary instruments where single measures cannot address causal pathways
• Shortlist options using mandate, evidence, feasibility and stakeholder acceptability

Apply proportionate economic appraisal to compare policy options and assumptions.

• Cost-benefit analysis identifies monetised costs, benefits and net present value
• Discount rates applied to future costs and benefits consistently
• Cost-effectiveness analysis compares unit costs against non-monetised outcome measures
• Opportunity cost considered when public funds displace alternative interventions
• Sensitivity analysis tests uncertain assumptions, price changes and demand scenarios
• Distributional weights and incidence analysis reveal who bears costs and gains

Use multi-criteria decision analysis to compare options with mixed evidence.

• Multi-criteria decision analysis structures choices where outcomes resist monetisation
• Scoring scales defined with evidence anchors and consistent rating rules
• Weight criteria through decision-maker workshops and documented rationale
• Calculate weighted scores while retaining qualitative explanations for important trade-offs
• Sensitivity tests identify rankings that change under alternative criterion weights
• Decision matrices communicate comparative strengths, weaknesses and residual uncertainty

Prepare proportionate regulatory impact analysis for proposed rules and requirements.

• Regulatory impact assessment defines the problem, objectives and affected parties
• Compliance costs estimated for businesses, citizens, agencies and service providers
• Administrative burden measured through information obligations, frequency and transaction time
• Enforcement models compare inspection, licensing, self-regulation and responsive regulation
• Proportionality tests match assessment depth to anticipated impact and risk
• Draft regulatory options including exemptions, transitional arrangements and review clauses

Assess how policy options affect groups differently and address avoidable harm.

• Equality impact assessment identifies differential access, outcomes and unintended exclusion
• Disaggregate evidence by protected characteristics, geography, income and service need
• Human rights principles applied to participation, accountability, non-discrimination and remedy
• Gender analysis examines roles, power, time burdens and resource control
• Distributional incidence maps benefits, costs and risks across population groups
• Mitigation measures designed for groups facing disproportionate policy burdens

Design consultation and participation processes that produce usable policy evidence.

• Consultation objectives distinguish information gathering, testing, deliberation and consent
• Participation spectrum selects inform, consult, involve, collaborate or delegate approaches
• Sampling plans include affected groups, seldom-heard communities and delivery practitioners
• Consultation questions test problem framing, options, impacts and implementation barriers
• Accessible engagement methods address language, disability, digital access and safeguarding
• Response analysis records themes, evidence quality, dissent and resulting policy changes

Assess whether preferred options can be delivered under real operating constraints.

• Implementation readiness assesses authority, funding, capability, systems and supplier capacity
• Risk registers capture causes, events, consequences, controls and accountable owners
• Delivery chain mapping identifies handovers, dependencies and frontline decision points
• RACI matrices clarify responsible, accountable, consulted and informed roles
• Scenario planning tests disruption from political change, shocks and delivery failure
• Phased implementation plans sequence pilots, scale-up, communications and contingency actions

Design monitoring and evaluation arrangements that support accountability and learning.

• Results frameworks align inputs, activities, outputs, outcomes and impacts
• Indicator definitions specify numerator, denominator, source, frequency and accountable owner
• Evaluation questions distinguish process, outcome, impact, economic and equity studies
• Comparison groups and counterfactuals selected for credible impact evaluation designs
• Data collection plans address ethics, consent, confidentiality and data quality
• Learning loops translate monitoring findings into policy adjustment and review decisions

Write concise policy advice that enables accountable senior-level decisions.

• Decision briefs lead with the decision required and recommended course
• Executive summaries state problem, evidence, options, costs, risks and next steps
• Options tables compare criteria, evidence strength, implementation requirements and trade-offs
• Recommendation narratives distinguish factual findings from professional judgement
• Visual evidence displays use charts, logic models, maps and decision matrices
• Quality assurance checks test traceability, clarity, accuracy and internal consistency

Build, review and present a complete policy analysis pack for a defined public problem.

• Define a decision-ready policy problem, baseline and target population
• Present stakeholder, political economy and evidence analyses supporting the proposal
• Construct a theory of change with assumptions and measurable outcomes
• Compare shortlisted options using appraisal criteria and documented trade-offs
• Specify implementation, consultation, risk, monitoring and evaluation arrangements
• Review peer feedback and present a justified recommendation to decision-makers