By Sebastian Berger – University of Bern
Ian Schneider and Cass Sunstein discuss behavioral considerations for effective time-varying electricity prices. They suggest increasing the use of opt-out tariffs in which residential customers, by default, receive a version of a time-varying electricity tariff and have the right to opt out in favor of a flat (i.e., standard) rate. Time-varying electricity pricing promises to be a suitable tool in the management of energy grids as consumer responses to varying prices may deflate critical peaks in energy use and ensure an overall better management of the energy grids. The legitimization of an opt-out default rests on the idea that the potential benefits of time-varying tariffs are strongest if a large share of customers participates.
And in fact, default effects have proven highly effective in real-world decision contexts, among them enrollment into retirement plans (e.g., Choi et al., 2004), organ donation (Johnson and Goldstein, 2003), and selection of “green” energy tariffs (e.g., Ebeling and Lotz, 2015). Therefore, it is not surprising that this effective strategy is suggested when trying to augment the use of time-varying electricity rates to reap the associated individual and social benefits. This comment, rather than discussing how effective such a default setting strategy may be in present context, aims to enrich the debate about additional behavioral considerations that may crucially affect the efficacy and acceptability of Schneider and Sunstein’s proposal. Generally, there are at least four important considerations that I think deserve attention: price-elasticity, salience of consumption, privacy concerns, and perceived (subjective) fairness.
Previous research has repeatedly suggested to use price increases as environmental levers in energy use (e.g., Jessoe and Rapson, 2014, Wolak, 2011). However, vast research on resource conservation suggests an actual low sensitivity to prices (Azevedo et al., 2011; Jessoe and Rapson, 2014; Levitt and List, 2009). It seems that conclusive and coherent evidence that varying prices actually steer demand sufficiently – a necessary precondition for flexible rates to work in the intended way – is still debated. Related research even suggests that moral incentives may sometimes outperform financial incentives when motivating “green” behaviors (e.g., Bolderdijk et al., 2012). Thus, empirical research has yet to back the conclusion that consumers actually respond sufficiently to frequent price changes under the real-world assumption that it is costly or painful to gather price information and given consumers’ potential inertia about their energy bill.
A second important consideration for the efficacy of time-of-use pricing is the salience of prices provided in real-time. Salience as a psychological tool for resource conservation has only recently been tested in a RCT (Tiefenbeck et al., 2016). Using real-time feedback about water consumption while showering, the researchers provide evidence that receiving real-time feedback drastically decreases resource use and that associated energy savings can accumulate to the equivalent of several thousands of hours of light bulb use. Therefore, delivering decision support systems that tackle mere “salience” (not necessarily about price, but also about quantity or other critical variables) can be tremendously effective. Put differently, previous evidence that real-time pricing has conservation effects may be driven by mere “salience” rather than actual price-responses.
A third behavioral consideration is consumers’ increasing awareness about issues surrounding privacy. Information systems researchers have provided fascinating examples of how easily behavioral profiles can be obtained from intelligent measuring devices based on non-intrusive load monitoring (NILM). Probably most illustrative, researchers were able to accurately infer the content (TV channel, movie) of media use based on information acquired from the metering device (Greveler et al., 2012). Thus, given that privacy concerns are increasingly important to consumers, a task for behavioral scientists should be the search for alternatives that are equally suited at lower risks of privacy breaches. One way to still enable time-of-use pricing would be to locally and temporarily store energy use on the smart metering device and transfer aggregated data. Perhaps less prohibitive, feedback to the energy provider at a longer time-interval (e.g., 60-minute instead of 5-minute intervals) may be harmful in terms of economic efficiency, but should be weighed against lower risks of privacy breaches. Finally, an – perhaps better – alternative is to attempt the use of disconnected technology that addresses psychological mechanisms (e.g., salience, loss-aversion, etc.) of energy use. In the Swiss trial (Tiefenbeck et al., 2016), for example, the researchers use a completely disconnected device that outside of a study does not require any data storage and transmission at all.
A final behavioral consideration is the (subjective) fairness perception of time-varying pricing schemes. Although people have the choice to opt-out of the flexible tariff, a utility company will be held accountable and judged against the fairness of the default plan they provide to its customers. This may impose a constraint on time-varying price tariffs by default. By design, prices should increase if many people demand energy. Price surges should, therefore, directly follow peaks in demand. Raising prices when “need” of energy is particularly high (e.g., a heatwave), can pose a strong justice conflict. This is especially true when it is hard to disentangle the willingness and the ability to pay for energy (Sandel, 2012). One of the inaugural works in behavioral economics has elegantly shown how fairness perceptions can impose an important constraint on efficiency (Kahneman, Knetsch and Thaler, 1986). Recent discussions of the legitimacy of surge pricing – for instance as the company UBER has implemented during a crisis in Sydney – shows that the topic is still highly relevant. However, in many instances, surge pricing (hotel prices, airplane fares, etc.) is not criticized for lack of fairness. If – however – energy is interpreted as a basic need for citizens, it can be assumed that fairness concerns may emerge and provide a strong negative effect on the perceived legitimacy of default setting that favor flexible rates.
In sum, Schneider and Sunstein provide a suitable approach to increase the domestic uptake of flexible price tariffs. Defaults have proven effective and the authors tap into important reasons why current choices may not reflect individual or social optima. However, the additional behavioral considerations discussed here show how behavioral science is able to provide a multi-faceted discussion about the suitability of behaviorally informed policy.
References (in order of appearance):
Choi, J. J., Laibson, D., Madrian, B. C., & Metrick, A. (2004). For better or for worse: Default effects and 401 (k) savings behavior. In Perspectives on the Economics of Aging (pp. 81-126). University of Chicago Press.
Johnson, E. J., & Goldstein, D. (2003). Do defaults save lives?. Science, 302(5649), 1338-1339.
Ebeling, F., & Lotz, S. (2015). Domestic uptake of green energy promoted by opt-out tariffs. Nature Climate Change, 5(9), 868-871.
Jessoe, K., & Rapson, D. (2014). Knowledge is (less) power: Experimental evidence from residential energy use. The American Economic Review, 104(4), 1417-1438.
Wolak, F. A. (2011). Do residential customers respond to hourly prices? Evidence from a dynamic pricing experiment. The American Economic Review, 101(3), 83-87.
Azevedo, I.L., Morgan, M. G., Lave, L. (2011). Residential and regional electricity consumption in the US and EU: how much will higher prices reduce CO2 emissions? The Electricity Journal, 24 (1,) 1040-6190.
Levitt, S. D., & List, J. A. (2009). Field experiments in economics: The past, the present, and the future. European Economic Review, 53(1), 1-18.
Bolderdijk, J. W., Steg, L., Geller, E. S., Lehman, P. K., & Postmes, T. (2013). Comparing the effectiveness of monetary versus moral motives in environmental campaigning. Nature Climate Change, 3(4), 413-416.
Tiefenbeck, V., Goette, L., Degen, K., Tasic, V., Fleisch, E., Lalive, R., & Staake, T. (2016). Overcoming Salience Bias: How Real-Time Feedback Fosters Resource Conservation. Management Science.
Greveler, U., Glösekötterz, P., Justusy, B., & Loehr, D. (2012, January). Multimedia content identification through smart meter power usage profiles. In Proceedings of the International Conference on Information and Knowledge Engineering (IKE) (p. 1). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp).
Sandel, M. J. (2012). What money can’t buy: the moral limits of markets. Macmillan.
Kahneman, D., Knetsch, J. L., & Thaler, R. (1986). Fairness as a constraint on profit seeking: Entitlements in the market. The American Economic Review, 728-741.