Essay About Appropriate Use Of Economic Models And White Paper

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White Paper — Pew Center on Global Climate Change 1
White Paper
Insights Not Numbers:
The Appropriate Use of Economic Models
Janet Peace and John Weyant
April, 2008
White Paper — Pew Center on Global Climate Change 2
Insights Not Numbers:
The Appropriate Use of Economic Models1
Janet Peace and John Weyant
April, 2008
Executive Summary
Economic modeling has played a prominent role in the climate-change policy debate as
stakeholders have sought to understand the impacts and assess the costs of different strategies for
reducing greenhouse gas (GHG) emissions. Models are an invaluable tool for exploring
alternative policy choices and for generating insights about how the economy is might respond to
different types and forms of regulation. They cannot, however, predict future events, nor can
they produce precise projections of the consequences of specific policy.
Every model uses its own set of assumptions, definitions, structure and data — its results
ultimately depend on these attributes and choices. A proper understanding of economic models,
their uses and limitations, is therefore critical in furthering a constructive debate about options
for climate policy. As a starting point we highlight three general observations about the use of
economic models:
• While economic models have become increasingly sophisticated, forecasting the future
remains inherently uncertain. The longer the time horizon of the analysis, the larger the
uncertainties involved.
• Model results are strongly dependent on input assumptions and on the structure of the
model itself. Critical assumptions and structural biases are not always readily apparent to
the outside observer.
• What is left out of a model can be as important as what goes in. Whether a model
accounts for the benefits (or avoided costs) of climate mitigation, technological change
1 The maxim “insights, not numbers” has a long and illustrious history starting with Hamming (1962) who argued
that “insights not numbers” constitute the purpose of computing. The same maxim was subsequently applied by
Geffrion (1976) in the context of mathematical programming and by Huntington, et al. (1982) in the context of
mathematical modeling. We are also indebted to William Hogan who made the link to the Geoffrion piece and
Richard Richels for occasionally reminding us what our objectives in modeling ought to be. These ideas probably
all build on the work of W. Edwards Demming in the 1950s who, without ever explicitly using the phrase, surely
implied that insights, not numbers are the purpose of statistical quality control.
White Paper — Pew Center on Global Climate Change 3
spurred (or “induced”) by climate policy, or the “recycling” of revenues generated
through certain policies can have large effects on the results.
Many of the cost analyses published over the last decade rely on general equilibrium models that
use complex systems of mathematical equations and large amounts of data to simulate the
workings of the economy. Comparisons across multiple studies suggest that several categories
of assumptions are especially important in driving model results:
(1) specific features of the policy or policies being analyzed (including the degree of
flexibility allowed in meeting the emissions constraints);
(2) reference case (or baseline assumptions) about how the economy and environment will
perform in the absence of the policy;
(3) flexibility in the economy—that is, the ease with which consumers/producers can adapt
to emissions limits;
(4) pace and magnitude of technological change/innovation; and
(5) treatment of benefits (or avoided costs) from climate-change mitigation—what benefits
are included and how.
A detailed comparison of results from two modeling initiatives sponsored by the Pew Center
reveals that cost estimates can differ widely as a result of structural characteristics and
assumptions embedded in the model, even where other key parameters (such as the policy being
analyzed and base-case projections of future emissions) are the same. For example, the
responsiveness (or elasticity) of various components of the economy—including assumptions not
only about how readily low-carbon alternatives will be substituted for carbon-intensive goods
and services, but also about how readily individuals make trade-offs between consumption and
leisure are critical assumptions. A model which assumes a highly responsive