Net-to-Gross: a few Observations Vis-á-Vis the Long Term calmac san Francisco, California

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Net-to-Gross: A Few Observations Vis-á-Vis the Long Term


  • San Francisco, California

  • July 17, 2007

Love ‘em, hate ‘em, but…

  • Love ‘em, hate ‘em, but…

    • Free ridership and spillover/MT
      • Important concepts, reasonably well understood, important to program design, forecasting/procurement, policy making
      • But terms and measurements don’t always capture and convey what we want
  • Free ridership and spillover/MT

  • What matters is long term market change:

    • Totality of: Direct program participation, market effects, codes/standards, and other influences
  • Free ridership is limited as a short-term, participant-driven metric

Why Worry About Free Ridership?

  • Non-participant’s perspective

    • Don’t give my money to someone else to do something they were doing anyway
  • Program efficacy

  • Concerns can’t be cavalierly dismissed

    • and someone will always raise them!
  • But…

Program Effects are Often Acceleration (RER 2001)


  • But…there are problems, mountains of them

  • Adoption is non-linear and so is free ridership

  • Markets are dynamic and when interventions succeed change is accelerated

    • Line AD in previous slide is not what we observe
    • What is a true naturally occurring baseline 25 years after the first market interventions?
      • Today’s free riders are often yesterday’s market effects
  • Measurement techniques are limited

    • No pure control groups
      • Whatever happened to experimental design!
    • The vagaries of self reports
    • The pain of market tracking


  • Measurement challenges partially related to under and inconsistent investment

    • ~$1-2B/yr over past 15 years on programs
      • How much on evaluation and longitudinal baselines?
    • Evaluation efforts spotty and often half hearted
      • Real-learning the same inconclusive stuff over and over instead of conducting rigorous, consistent, and, yes, sometimes more costly, long-term research
    • No surprise we have very few reliable longitudinal data sets of market saturation, penetration, costs
  • Nationally, reported program data is weak!

    • Doesn’t support econometric analysis

Extreme cases

  • Extreme cases

  • Nationally, some fallout from the1990s?

    • Fear and stipulation
    • Let’s call the whole thing off
    • Maybe if we don’t measure it it will go away
  • But ex post NTG can provide vital feedback

    • Critical to improving program design
    • In some cases, partially reward/penalize depending on what administrators can realistically control
    • Not a substitute for multi-year market effects analyses

NTG Under Aggressive Program Funding

  • Over the long term

    • EE program $ essentially savings and investment fund
    • Customers are really using their own money
      • To enhance buying power
      • Stimulate new markets
      • Mitigate market barriers
      • Explicit in “Use it or loss it” approach for large C&I
    • Short-term free ridership becomes less of a concern
      • If long-term participation is widespread
      • Significant market effects are accomplished
      • Programs/policies adapt quickly to accomplishments/failures
    • Latter requires continuous measurement of both short-term program impacts and long-term market effects


  • Not measuring should not be an option

  • But traditional focus on “free riders” is suboptimal and can lead to wrong conclusions

  • “Free rider” term itself problematic/derogatory

    • Not consistent with traditional economic use of term
  • What matters are

    • Short-term “marginal program effectiveness (MPE)”
      • Let’s find a better term that gets at what we care about
    • Long-term market effects
      • And, yes, associated program attribution.


  • Attribution is obviously challenging

    • Precise attribution will always be difficult
    • “Chunky” attribution less so
  • Results should be used to

    • Optimize program and portfolio design
      • Know when to
        • declare victory, admit failure, redesign, or move on
    • Appropriately direct and motivate implementers, w/out
      • Penalizing for factors outside direct control
      • Creating perverse incentives
        • (e.g., maximizing short-term versus long-term impacts)
    • Improve forecasts and influence on procurement


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