Overview

The report, presented at TPRC 2023, introduces a multi-centric, agile framework for monitoring, evaluating, and guiding broadband and digital opportunity policies. It addresses the challenges posed by rapid technological transformation and emphasizes iterative learning cycles for policy improvement.

Core Concepts

  • Digital Opportunities Compass
    A structured tool for assessing broadband and digital opportunity initiatives across six dimensions:
    Contexts, Governance, Connectivity, Skills, Applications, Outcomes.
    This compass enables stakeholders to identify gaps and prioritize interventions effectively.

  • Policy Learning Approach
    The framework advocates for continuous feedback loops, integrating empirical evidence and stakeholder input to refine strategies. This concept aligns with the idea of a “policy learning machine,” where iterative evaluation drives adaptive policymaking.

Policy Learning Approach: Expanded Details

The Policy Learning Approach emphasizes continuous, evidence-based adaptation of broadband and digital opportunity policies through structured learning cycles. It treats policymaking as an iterative process rather than a static one.

Core Principles

  1. Feedback Loops
    • Policies are monitored and evaluated against measurable outcomes.
    • Insights from implementation feed back into design, creating a cycle of improvement.
  2. Empirical Evidence Integration
    • Uses quantitative and qualitative data (e.g., adoption rates, affordability metrics, community capacity indicators) to inform decisions.
    • Encourages experimentation and pilot programs to test interventions before scaling.
  3. Stakeholder Engagement
    • Involves local communities, nonprofits, and research institutions in co-creating solutions.
    • Promotes transparency and shared accountability.
  4. Adaptive Governance
    • Recognizes that broadband ecosystems evolve rapidly.
    • Advocates for flexible frameworks that can pivot based on new technologies or socio-economic shifts.

Why It’s Like a “Policy Learning Machine”

  • Functions as a systematic engine for learning, where:
    • Inputs: Data from programs, community feedback, and external research.
    • Processing: Analysis through frameworks like the Digital Opportunities Compass.
    • Outputs: Adjusted policies, refined strategies, and updated metrics.

Practical Applications

  • Digital Opportunity Programs: Adjusting subsidy models based on real-world affordability data.
  • Infrastructure Planning: Using adoption trends to prioritize underserved areas.
  • Skills Development: Iteratively improving training programs based on learner outcomes.

Visual: Policy Learning Cycle

Policy Learning Cycle Policy Design Policy Implementation Policy Monitoring & Evaluation Inputs
Continuous cycle: Design → Implementation → Monitoring & Evaluation → Inputs → back to Design.

Key Contributions

  • Agility in Policy Design
    Encourages flexible, data-driven decision-making to respond to evolving broadband needs.
  • Opportunity-Centered Metrics
    Provides indicators for measuring digital inclusion beyond mere access, incorporating affordability, skills, and community capacity.

  • Collaborative Governance
    Highlights the importance of multi-stakeholder engagement, including local governments, nonprofits, and research institutions.Learned from Broadband Champions Webinar

  • Skills Development: Iteratively improving training programs based on learner outcomes.

Connection to Digital Opportunities Intelligence Network (DOIN)

This report serves as a foundational pillar for Project Compass (Merit Network) and the Digital Opportunities Intelligence Network (DOIN).

  • The “Compass”: The Digital Opportunities Compass described in this report provides the structured dimensions (Contexts, Governance, Connectivity, Skills, Applications, Outcomes) that DOIN uses to measure and categorize digital opportunity.
  • The “Intelligence”: DOIN extends this framework by applying Bayesian networks and GraphRAG to operationalize the “Policy Learning Approach.” It incorporates algorithms such as Gini coefficients to quantify digital inequality and Hajek estimators to derive accurate population estimates from survey data. These rigorous metrics feed into the Bayesian models, turning theoretical feedback loops into active, predictive intelligence systems.
  • The “Network”: By mapping these dimensions into a Knowledge Graph, DOIN creates the “multi-centric” system envisioned by the authors, connecting isolated data points into a cohesive policy guidance engine.

In essence, while this report provides the map (the Compass), DOIN builds the navigation system (the Intelligence Network) to guide policymakers through it.

This framework is critical for ensuring that broadband expansion and digital opportunity programs are effective, inclusive, and sustainable, particularly in rural and underserved communities.

Citation

Bauer, Johannes M. and Dagg, Pierrette Renée and Rhinesmith, Colin and Byrum, Greta and Schill, Aaron, A Comprehensive Framework to Monitor, Evaluate, and Guide Broadband and Digital Equity Policy (August 30, 2023). Available at SSRN: https://ssrn.com/abstract=4557340 or http://dx.doi.org/10.2139/ssrn.4557340