Understanding households’ investment behaviour

A large survey of 1,500 to 2,000 households each in 8 EU member states (FR, DE, IT, PL, RO, ES, SE, UK – a total of 15,000 households – was carried out in July/August 2016. This representative survey provided deeper insights into households’ investment behaviour and attitudes concerning energy efficiency investments in their homes.

The aim of the study was to understand the role of the factors behind Implicit Discount Rates (IDRs) for energy efficient technology adoption (see the page Why discount rates?). Realising that the factors behind IDRs are typically blurred and fractional, BRISKEE also provides a comprehensive framework for understanding the underlying factors of the IDR for household adoption of energy efficient technologies. This understanding can be used to design better policies that lower the implicit discount rates, and it was used for more accurate energy-demand and macro-economic modelling.

At the micro level, the BRISKEE project has shown which of the factors conceptually underlying the  implicit discount rates are empirically related with the adoption of various energy efficiency technologies (EET).

The survey also provides information on how the factors underlying the implicit discount rate such as risk and time preferences (including also present bias and loss aversion), environmental preferences, social norms or external barriers to energy efficiency (e.g. lack of capital) are related with individual and household characteristics. The survey is unique in many ways: BRISKEE allows for a much larger sample than previous studies, and findings are demographically representative. It is the first multi-country study to jointly explore the effects of time and risk preferences on energy technology adoption, thereby also considering present bias and loss aversion. It also developed a comprehensive conceptual framework, which relates the implicit discount rates to its underlying factors. This framework also helps clarifying the role of policy interventions.

For the end-use technologies in (see figure), participants were asked to rate the nine decision criteria regarding their importance in their most recent purchase decision on a five-point scale ranging from “played no role” (numerical value 1) to “very important” (numerical value 5). 

For all technologies (appliances, lighting and building technologies), there is a set of criteria that are clearly less important: recommendations by professionals, financial support measures as well as recommendations by friends and family.

Policy implications from the micro-level analysis

For household technology choices, insights from the psychology and behavioural economics literatures suggest that both time and risk preferences (including also present bias and loss aversion) may help explain the energy efficiency paradox according to which households may fail to invest in energy-efficient technologies even though these appear to pay off under prevailing market conditions.The below table suggests possible policy interventions based on the survey findings and analysis.

The detailed results from the (micro level) survey themselves can help policy makers design more effective programmes and tools for implementing energy efficiency in households.

Table: Summary of key findings and policy implications

FACTOR

IMPLICATIONS FOR POLICY

Preferences and behavioural biases

 

Less patient participants and those with higher present bias were found to be less likely to have adopted energy efficient technologies

Alter the timing of the cost/revenue streams, less up-front outlays; offer rebates (rather than tax breaks), delay payments; low-interest loans;

More risk-averse and more loss-averse participants were found to be less likely to have adopted retrofit measures

Lower perceived financial/technological risk of adopters, e.g. via warranties (technical risk), energy performance contracting (financial risk); information: highlight “asset character” of investment in energy efficiency (less vulnerable to changes in energy prices); in information and communication programs could frame failure to invest in EET as a loss;

Participants with strong pro-environmental preferences were found to be more likely to have adopted energy efficient technologies

Provide information on environmental effects (e.g. via labelling);

Participants exposed to strong pro energy efficiency social norms were found to be more likely to have adopted energy efficient technologies

Use social comparisons in information campaigns; try to shape social norms, e.g. via information campaigns;

External barriers

 

Households renting their dwelling (rather than owning it) were found to be less likely to have adopted energy efficient technologies

Retrofit: labelling/building certificates; facilitate pass through of additional retrofit investment costs;

Households who do not have their own electricity meter were found to be less likely to have adopted energy efficient technologies

Promote metering of individual dwellings (e.g. via regulation);

Households with limited access to capital were found to be less likely to have adopted energy efficiency measures

Low-interest loans, rebates; energy performance contracting;

Key Messages from the micro-level analysis

The detailed results from the (micro level) survey themselves can help policy makers design more effective programmes and tools for implementing energy efficiency in households.

Key Messages from the micro-level analysis

SHOULD REBATES TARGET ONLY LOW-INCOME HOUSEHOLDS?

Find the answer on this question among 5 key policy take-aways from the CHEETAH analysis of an 18 000-household in-depth survey. >> More