From Theory to Econometrics to Energy Policy: Cautionary Tales for Policymaking Using Aggregate Production Functions

• DOI: 10.3390/en10020203
• Publication Date: 2017-02-15T15:09:07Z
• Authors: Matthew Heun; João Santos; Paul Brockway; Randall Pruim; Tiago Domingos; Marco Sakai
• Keywords: 2101; Technology; 2102; 2105; T; 1700; energy policy; econometrics; CES; Solow residual; cost share principle; 02 engineering and technology; CES; 7. Clean energy; econometrics; Solow residual; cost share principle; 0202 electrical engineering, electronic engineering, information engineering; energy policy

Development of energy policy is often informed by economic considerations via aggregate production functions (APFs). We identify a theory-to-policy process involving APFs comprised of six steps: (1) selecting a theoretical energy-economy framework; (2) formulating modeling approaches; (3) econometrically fitting an APF to historical economic and energy data; (4) comparing and evaluating modeling approaches; (5) interpreting the economy; and (6) formulating energy and economic policy. We find that choices made in Steps 1–4 can lead to very different interpretations of the economy (Step 5) and policies (Step 6). To investigate these effects, we use empirical data (Portugal and UK) and the Constant Elasticity of Substitution (CES) APF to evaluate four modeling choices: (a) rejecting (or not) the cost-share principle; (b) including (or not) energy; (c) quality-adjusting (or not) factors of production; and (d) CES nesting structure. Thereafter, we discuss two revealing examples for which different upstream modeling choices lead to very different policies. In the first example, the (kl)e nesting structure implies significant investment in energy, while other nesting structures suggest otherwise. In the second example, unadjusted factors of production suggest balanced investment in labor and energy, while quality-adjusting suggests significant investment in labor over energy. Divergent outcomes provide cautionary tales for policymakers: greater understanding of upstream modeling choices and their downstream implications is needed.