Jul 2017 – Briskee, Deliverables & reports
[D 2.4 Scientific working paper on determinants of time and risk preferences (Final 19 April 2014)]
Determinants of household preferences underlying the implicit discount rate: Findings from representative surveys in eight EU countries
Authors: Joachim Schleich, Xavier Gassmann, Corinne Faure, Thomas Meissner
Improving energy efficiency is commonly considered to be the cheapest short- to medium-term option for meeting energy and climate targets (e.g. IEA, 2016), and it is a prime policy goal in the European Union and in many countries. In particular, in 2014 the EU Council agreed to a 27% energy efficiency target by 2030 (relative to a baseline development). Following a review, the 2016 “Winter Package” increased this target to 30%1. In particular, the EU policy review involved an impact assessment relying on simulations with the energy-economic model PRIMES. In PRIMES and other similar models, so-called subjective or implicit discount rates (IDRs) govern household and company energy technology adoption behaviors. An IDR is estimated from observed choices among alternative technology options and net present value calculations as the discount rate that renders the actual technology choice reasonable (e.g. Dubin and McFadden, 1984)2. Since IDRs are derived from technology adoption behavior (i.e. IDRs are estimated to be higher when EET adoption is lower), there is a direct link between empirical results obtained on EET adoption and IDR estimates used in models. Noting that the factors behind IDR are typically blurred and fractional, BRISKEE deliverable D2.13 provides a comprehensive framework of the underlying factors of the IDR for household adoption of EET (see Figure 1).
The framework distinguishes three broad categories of factors: (i) preferences such as time preferences, risk preferences, reference-dependent preferences, and pro-environmental preferences; (ii) predictable (ir)rational behavior, i.e. bounded rationality, rational inattention, and behavioral biases, such as present bias or status quo bias; and (iii) external barriers to energy efficiency such as split incentives, lack of information, or lack of capital. Among others, this framework serves as a conceptual outline in three recent EU projects: BRISKEE, CHEETAH and PENNY. This deliverable (D2.4) empirically analyzes the relations among key factors of the IDR and socio-economic characteristics. The analyses rely on representative household surveys in eight EU countries carried out in BRISKEE. The findings will help modelers to relate the IDRs to available data on household characteristics such as income or age. In addition, policy makers may rely on the results to tailor policy interventions towards specific socio-economic groups. We organize the remainder of this paper as follows. Section 2 presents the methodology, i.e. the survey, the factors considered, and the co-variates. The literature review also refers to findings from BRISKEE deliverable D2.3, which presents the results of the multivariate analyses on factors related with technology adoption. Section 3 presents the findings of the econometric estimations. Section 4 concludes and proposes policy implications.