Imagine a setback thermostat programmed at the factory that the consumer couldn’t modify. Who would want this device? You could give the customer a big enough discount to get her to accept the...
Better Data, New Conclusions
The authors respond to Roycroft’s reality check.
single definition across the studies, without going back to the customers and resurveying their income status—a step that wasn’t feasible for the purposes of our report.
As a practical matter, there’s no single definition of low income across the nation. Our analysis used the definitions that were used in each individual study.
Take the case of California’s statewide pricing pilot (SPP) where one of the definitions of “low income” is customers who are on the California Alternate Rates for Energy (CARE) rate, and another definition is customers earning less than $40,000. People on the CARE rate get a subsidized electricity rate which, given the tiered structure of rates in California, can range from a low of 20 percent to a high of 72 percent. To be eligible for the CARE rate, consumers simply have to declare that they meet its low-income guidelines. For households of one or two individuals, the income guideline is $31,300.
Not all low-income consumers who meet these income criteria are on the CARE rate; and some consumers who don’t meet the guidelines are probably on the CARE rate. CARE customers get a very hefty price discount that renders them insensitive to price fluctuations, and just relying on their price responsiveness would be misleading. And so we report both sets of price responsiveness estimates.
In Figure 1 of his article, Roycroft shows that low-income customers were found to be much less price-responsive than were other customers in the SPP. Our results show that when compared to average customers in that pilot, CARE customers exhibit a reduction of 3 percent in critical peak demand versus a reduction of 13 percent for the average customer. But when “low-income” customers (defined as income below $40,000) are compared to average customers, the former have a lower but similar response as the latter (11 percent vs. 13 percent).
For the PG&E Smart Rate program, the results cited in our report show that CARE customers reduced critical peak demand in the year 2008 by 11 percent vs. 17 percent for average customers; in 2009, the corresponding numbers were 8 percent and 15 percent.
These results are consistent with the results in Roycroft’s Figure 1, as are the Pepco results that we report. So the discrepancies aren’t as great as the Roycroft narrative might suggest.
Standing by the Study
While we appreciate Roycroft’s effort in bringing out once again the importance of the issue, we aren’t convinced that he has presented any new evidence that contradicts the conclusions in our article. We stand by them.
The debate on whether dynamic pricing will make low-income customers better off or worse off will continue. Our position remains that all customers should face the same prices and if, for social reasons, the government wants to protect the economic well-being of some subset of low-income households, it might consider offering them “energy stamps” similar to the kind of assistance that is offered through a food stamps type of program. (See Ahmad Faruqui, “Residential dynamic pricing and ‘energy stamps,’” Regulation, Winter 2010-2011, Volume 33, No. 4, pp. 4-5.) However, we are