Hearts, Pocketbooks & kilowatt-hours: How Different Policy Approaches Work to Decrease Energy Use

If you want to get someone’s attention and change their behaviour, do you appeal to their heart and sense of greater good, or do you hit them in the pocketbook and offer dollars to sway their decision-making? In some ways, this is the policy-maker’s dilemma – given a public policy objective, what is the best policy tool to achieve it?

A recent study looks at this question from the perspective of electricity conservation, using a large real-world experiment to shed light on which policy tools work best. There is a role for public policy to support electricity conservation for a number of reasons: 1) it’s generally much more expensive to produce electricity than to conserve it, 2) electricity generation and distribution have environmental impacts (varying depending on the type of generation and distribution), and 3) electricity demand is growing in Canada, and most countries.

But how? And which ways are most effective? This new paper looked at two ways policy-makers have available to change behavior: the first appeals to the heart, by using moral suasion to ask people to reduce their electricity use during key times; the second appeals to the pocketbook, by charging more for electricity use during the same times.

The authors (Ito, Ida and Tanaka), along with an electricity generation company and government and industry collaborators in Keihanna area of Kyoto prefecture in Japan, ran a large experiment in which households were given a small financial incentive, an advanced electricity meter and a fancy in-home electricity display in exchange for participation in the study. The participating households were then randomly assigned to one of three different groups:

a control group, which received nothing further than the participation package;

a moral suasion group, which received education about the importance of energy conservation during critical peak times; and

an economic incentive group, which was charged higher prices for electricity use during the critical peak times.

Households were notified of critical peak times one day in advance and on the day of. For the economic incentive group, the change in price was large – from a regular rate of approximately 25c/kWh to 65c/kWh, 85c/kWh or $1.05/kWh. Different participants in this group were assigned different pricing on different days so that the authors could assess if households simply respond to the event of a price increase, or if the amount of price change matters. For more details on how the experiment was structured, see the paper.

The experiment ran for several months in different seasons, with almost 700 households participating. It revealed some fascinating results:

Both the moral suasion group and the economic incentive group reduced their electricity use during the critical peak times, by 8% and 18% respectively;

As the experiment was repeated (in cycles of 3 critical peak times), the moral suasion group’s response quickly declined – from 8% to nearly zero by the third cycle;

The economic incentive group’s response declined as well, but much less quickly or dramatically – by the fifth cycle it had declined from 18% to 12%; and

The greater the price change, the more households decreased their electricity use – meaning households responded not just to a higher price, but to how high the price.

There are some important implications for policy here – first, that both appeals to the heart and pocketbook can impact household behavior; second, that appeals to the pocketbook seem to have a larger and longer-lasting impact; and third, that the level of price change matters. (Note: the paper is not analyzing these two options as actual policy options and so does not consider the other elements of policy design that a real-world policy-maker would assess, such as burden, fairness, revenue-generation, etc.)

This study also provides evidence of the potential for real-world experiments of this size and scale to provide valuable insights for policy-makers. Properly conducted field experiments (also known as randomized controlled trials) are considered among the best ways of determining causality. The importance for Canada of using big data drawn from field tests to inform policy was highlighted by Matt Kahn(external link) at Sustainable Prosperity's Big Ideas conference in 2014.

Policy-makers are often given the tricky task of finding a way to meet an objective (e.g., reduce electricity demand) by proposing a policy tool (e.g., pricing or moral suasion). Knowing in advance the effectiveness of the policy options in changing behavior is always difficult. While forecasts and modeling can provide insight, and looking at ex-ante evaluations of policies can be helpful, having research like this that relies on real-world experiments provides a new level of understanding of policy options. Like any good policy researcher, I ♥ evidence.