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Better Yellow Labels

Lowering air conditioning costs with location-targeted energy efficiency labels.

Information provision is a key element of energy-efficiency policy. Just think of the ubiquitous yellow EnergyGuide labels, which are required by law to be displayed on all major appliances sold in the United States. This information is supposed to help consumers make better decisions. However, current labels report only very coarse information based on national average energy prices and national average usage of the appliance.

The information can be particularly misleading with air conditioners. Within the continental United States annual cooling hours range from 310 in Maine to 2,771 in Florida, almost a 9:1 ratio. Residential electricity prices vary too, so the overall variation in operating cost for an air conditioner ranges across states by more than an 11:1 ratio. This means that the current labels are providing highly inaccurate information for many consumers.


Annual Cooling Hours by State

In a new EI@Haas Working Paper, available here, Gib Metcalf and I conduct an online experiment to measure the potential benefits from providing more accurate information. The control group was shown the current labels, while the treatment group was shown labels with state-specific information. For example, the label on the right below is for Iowa, a state with below average electricity prices and usage. Both groups were then asked to make hypothetical choices between air conditioners with different purchase prices and levels of energy-efficiency. 

Both Labels Smaller

             Current Label                                          State-Specific Label (Iowa)

We find that when presented with more accurate information, the average energy-efficiency of selected air conditioners stays about the same, but the allocation is much better. That is, consumers facing low expected operating costs invest less in energy-efficiency, while consumers facing high expected operating costs invest more. The figure below shows this graphically. The circles show the mean for each group and the range indicates the 95th percentile confidence interval.


The implied aggregate savings are substantial. We find that state-specific labels decrease lifetime cost by an average of $10 per purchase. Last year 4.4 million room air conditioners were purchased in the United States, so this is $44 million in annual savings.

We argue that this exceeds any reasonable estimate of the cost of implementing better labels. The Federal Trade Commission (FTC) already maintains label templates that manufacturers can download. Instead of one template per appliance, the FTC would provide 50 different templates, one for each state, accessible through a drop-down menu. Perhaps at the same time the FTC could automate the simple calculation required to fill in estimated yearly energy cost. All of this could be done at low cost, and be done not only for room air conditioners but for other appliances too.

Our results are consistent with an emerging view of consumers as “rationally inattentive” when it comes to making energy-related decisions (Sallee, forthcoming). When we ask them at the end of the experiment simple questions about the labels they demonstrate a remarkably low level of comprehension. Most do not know whether the labels they just saw were based on national or state energy prices, nor do they know how their state’s energy prices compare to the national average.

Daniel Kahneman refers to this kind of decision making in his book as WYSIATI, “What you see is all there is.” The content of the labels changes decisions, so it is not that people are ignoring this information completely. But they are not exerting the additional effort that would be required to understand what this information means or to spontaneously transform the information to take local conditions into account.

You see this not only in our experiment, but in actual choices as well. If choices were being made efficiently, you would expect to see consumers throughout the South buying energy-efficient air conditioners.  Instead, the highest levels of adoption of Energy Star air conditioners are actually in the Northeast and Midwest.


Share of New Air Conditioners Sold that are Energy Star

This suggests that other factors like political ideology may be important. But it also provides further evidence that the current labels are not working as well as they could. It  is not enough to simply say, as the label does, that “Your cost will depend on your utility rates and use.” We need to provide better information to help consumers connect the dots.

For more, see Does Better Information Lead to Better Choices? Evidence from Energy-Efficiency Labels (with Gilbert Metcalf), Journal of the Association of Environmental and Resource Economists, 2016, 3(3), 589-625.

Keep up with Energy Institute blogs, research, and events on Twitter @energyathaas.

Suggested citation: Davis, Lucas. “Better Yellow Labels” Energy Institute Blog, UC Berkeley, October 27, 2014,



Lucas Davis View All

Lucas Davis is the Jeffrey A. Jacobs Distinguished Professor in Business and Technology at the Haas School of Business at the University of California, Berkeley. He is a Faculty Affiliate at the Energy Institute at Haas, a coeditor at the American Economic Journal: Economic Policy, and a Research Associate at the National Bureau of Economic Research. He received a BA from Amherst College and a PhD in Economics from the University of Wisconsin. His research focuses on energy and environmental markets, and in particular, on electricity and natural gas regulation, pricing in competitive and non-competitive markets, and the economic and business impacts of environmental policy.

7 thoughts on “Better Yellow Labels Leave a comment

  1. Thank you for providing this information, which is consistent with our findings from the field. Savenia launched a retail labeling system in July of 2012 that adjusts cooling hours, energy costs and carbon footprint by zip for room air conditioners. Savenia Energy Rating Labels now cover over 30 product categories, and have reached millions of retail shoppers in 3 states. Our experience shows that shoppers choose the more efficient products, while retailers see a significant increase in sales volume, price point and customer loyalty as a result of marketing the information labels. Savenia zip-based labels are easily printed centrally or in the store through an online portal.

  2. A good study with good idea.

    Slightly off subject but does anyone know why electric and clothes dryers do not have energy labels? It has always amazed me how many people with natural gas, sometimes plumbed to the dryer, buy electric dryers apparently out of habit. Since natural gas is about 1/5 the cost of electricity per kwh a gas dryer is much cheaper to run. If dryers had energy labels on them it would be immediately obvious that gas one were much cheaper to use than electric.

    Does anyone know why dyers do not have energy labels?

    • This is what I’ve understood over time. Unfortunately, I don’t have any citations for the below:

      Clothes dryers were excluded from the original labeling program because (a) all of the dryers available were assumed to be equally effective in drying clothes, and (b) there was a great deal of controversy over the whole “fuel switching” issue. Labeling was intended to provide performance information among “like” products. Gas vs. electric was assumed to be outside the scope of the labeling program.

      For the same reason, there are no labels on residential ovens or stovetops.

  3. Thank you for this important work. It builds on a history of evaluation of the Energy Guide label that suggests it has long been sub-optimal in achieving its intended purpose: to inform consumers and encourage adoption of efficient technologies. (See, e.g., Thorne and Egan. 2002. “An evaluation of the Federal Trade Commission’s EnergyGuide appliance label: Final report and recommendations.” American Council for an Energy-Efficient Economy. Washington, DC.)

    Another important problem with the Energy Guide label is the categorization of products into “similar models.” The number of bins into which models are put makes a great deal of difference when comparing “similar” products. For example, a top-mount refrigerator may rate at the “uses more energy” end of the scale compared to other top-mount refrigerators, but may still use *less* energy than a highly-rated (even ENERGY STAR-qualified) side-by-side model. Consumers may therefore be left unaware that the choice of door configuration (side-by-side versus top-mount) has a significant impact on energy consumption and operating cost.

    Your work points out a critical element of effective public policy – program evaluation and the iteration of policy implementation based on evaluation outcomes. The energy policy community has done a very poor job of optimizing the information provided to consumers.

    • Thanks Dr. Payne for your comment and for highlighting this report by Thorne and Egan. Although they don’t focus on this issue of location-targeted labels, the report does include a lot of interesting qualitative evidence from interviews with consumers. Overall, consumers don’t seem to understand EnergyGuide labels very well. Based on their interviews, Thorne and Egan also endorse categorical ratings, basically giving each appliance from 1 to 5 stars based on its energy-efficiency. I think using stars would be too confusing because of the overlap with EnergyStar, but the broader point is very interesting and consistent with recent work by Sebastien Houde and Jim Sallee on this type of “notched” information provision.

      • The issue of categorical star rankings vs. the Energy Star logo was addressed in qualitative research conducted by ACEEE. The research found that, contrary to popular assumptions, the combination of categorical stars and the Energy Star was actually synergistic. Consumers did *better* in understanding both the categorical scale and the Energy Star logo when the two were present on the label together. This is another example of the need for empirical research, not the assumptions of policymakers, to guide policy implementation.

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