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Does Focusing on Utility Rate Impact Actually Protect Consumers?

Jim Kennerly's picture
Policy Analyst NC Solar Center
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  • Mar 13, 2015 7:30 pm GMT

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utilities and rates

A common claim by many who are skeptical of the value of renewable energy and energy efficiency is that its deployment “raises rates” for all customers (especially residential customers) and thus should be pursued only with the greatest care (if at all).  What’s more, the argument implicitly equates “rates” to customer bills, especially when extended to renewables, efficiency and other distributed energy resources (DER) on the customer’s side of the electric meter.

Indeed, equating customer “rates” to “bills” is the cornerstone of the main arguments against change in the electric power sector, whether it is made against state policies encouraging renewables and efficiency, or pricing the impact of carbon dioxide, mercury, sulfur dioxide, nitrogen oxides and other pollutants from emitting sources. Overall, however, recent market shifts (and the impact that price elasticity of demand, a concept explored in Economics 101, has on those shifts) make abundantly clear that this assumption, going forward, may not be a reliable one.

The “Rates” Perspective on Utility Consumer Protection, and Its Origins

For a very long time, focusing on rates as a means to protect consumers against the market power of a regulated monopoly utility made sense. This is because raising rates meant, by default, increasing customer bills, sometimes substantially. As the graph below shows, during the second half of the 20th century, annual growth in electricity demand tended to be at least several percentage points a year, peaking in the 1950s at (typically) over 10% per year.

However, as the graph also shows, electricity demand has begun to stagnate in recent years. In fact, the stagnation has become so pervasive that at this point, the U.S. Energy Information Administration (EIA) has forecasted year-on-year future growth prospects to be not much greater than 1% per year through 2040.

The persistence of stagnant electric sales growth is the most crucial long-term threat to the energy status quo, and explains why many of the arguments against renewables and efficiency made by traditional energy interests (and official or unofficial consumer advocates) focus so much on the fear of “raising rates”. Put another way, it is natural for for-profit entities with significant interest in monopoly status that are not selling as much of their product to “try on” cost-effectiveness perspectives that undervalue increasingly affordable and differentiated alternatives to their service.

This perspective explains the increasing use of a cost-effectiveness test called the Ratepayer Impact Measure or “RIM” test. In essence, the RIM test systematically excludes many of the known (and, quite often, measurable) benefits and values of renewables, efficiency and DER from its results, or counts customer bill savings against the overall value of the program.

The RIM perspective, which has largely been phased out for energy efficiency, is used by electric utilities when arguing against retail rate net metering. These utilities tend to claim that the only benefit of solar PV is the avoided direct capacity and energy savings it provides, excluding non-energy benefits, societal benefits and other quantifiable cost savings. For example, despite the fact that the utility energy efficiency programs offered by California’s utilities are evaluated using the far more comprehensive Total Resource Cost (TRC) test, the California Public Utilities Commission’s 2013 study of net energy metering for solar was required by law to take the highly restrictive RIM perspective.

Separating Fact from Rhetoric: Using Data to Evaluate the “Raises Rates” Perspective

Given the available data, the most effective tool for more accurately teasing out the impact of rates upon customer bills is statistical analysis. To determine the impact of rates on average bills, I first measured the correlation between the two, with data from the EIA. Given that Alaska and Hawaii are (effectively) island grids with unusual generation sources (and thus have highly divergent “outlier” average monthly bills and prices from the lower 48), I excluded them from the analysis.

Correlation of Residential Rates/Prices to Monthly Bills (Source: EIA)


The table above illustrates the surprising result – with a coefficient of 0.025, there is, in essence, no correlation whatsoever between the rates customers pay, and their average monthly bills. Thus, and despite its prevalence as an argument against renewable energy and energy efficiency, it is thus not clear that “raising rates” has even any connection, let alone a positive or negative one, for a customer’s average monthly bill in a given state. 

Why might this be the case? Much of the answer can be traced to a concept from Economics 101 – the price elasticity of demand, or the responsiveness of customer behavior and demand to changes in price. This is an extremely significant concept to account for as utility rates rise, because it proves that the higher the rates you pay for electricity, you are much more likely to take this as a “price signal”, and “receive” the signal by reducing energy use. 

Indeed, the role of elasticity is supported by official U.S. government data.  For example, according to the EIA, the long-term price elasticity of electricity demand is -0.4, which means that for every 1% electricity rates go up over 25 years, residential usage will decline 0.4%. For example, if utility rates go up 50% over 25 years (2% per year), this means that customers will, all other usage remaining the same, respond by using 20% less energy on average each month than they otherwise would have with the same rates.

But Do Lower Rates Really Drive Lower Bills?

What is most compelling in terms of consumer protection, though, is to explore the other side of price elasticity – the degree to which keeping rates low actually results in higher demand and customer bills. To understand the relationship between rates and consumer protection, it is thus more appropriate to introduce a third variable – average customer usage by state.

Correlation of Residential Usage to Monthly Bills (Source: EIA)

 In evaluating these two variables, the results in the table above show a more clear inverse (negative) correlation (-0.75) is thus easier to establish between usage and rates in the lower 48, and thus the existence of price elasticity. In other words – the lower a customer’s rates are, the more electricity they use.

Going Deeper: From Correlation to Inference

However, as the maxim goes, “correlation is not causation”. Indeed, the degree to which a given state tends to rely on electricity for heat (instead of natural gas or fuel oil) plays a significant role as well. Thus, it is important to also use multiple regression to evaluate the significance of not just average rates and prices, but also the market penetration of electric heating, which can be estimated from the results in EIA’s Residential Energy Consumption Survey (RECS).


Dependent Variable

Independent Variable

Estimated Impact (& Range) on kWh Usage

P-Value (Statistical Significance Level)

Below Significance Threshold (p=0.05)?

Average Monthly Usage

Average Rates/Prices

-41.2 (-27.6 to -54.6)



Average Monthly Usage

Elec. Heating Market Penetration

5.6 (+4.0 to +7.1)



 Source: EIA Form 826 and Residential Energy Consumption Survey Data


Controlling for the (also statistically significant) impact of electric heat market penetration, for each cent per kWh utility rates rise, it is possible to infer from the data that average usage will fall an average 42 kWh. Overall, these 2 variables explain nearly 80% of the variation in usage, which provides yet more significant evidence that the impact of residential customers’ price elasticity of demand is real.

Key Takeaways

To be sure, as with all complex subjects in life, other factors can and do influence these results, often in a manner that is “statistically significant”. In addition, it would be preferable to have deeper, customer-specific data before jumping too far to such a significant conclusion.

However, these regression results make clear that having lower rates is far more closely associated with using more, not less, electricity, and thus having a higher electric bill. Thus, it appears that focusing overly on avoiding “raising rates” to putatively protect average consumers is unlikely to provide regulators and consumer advocates the result they desire, and may in fact thwart their purpose altogether.

Photo Credit: Utility Rate Impact/shutterstock

Jim Kennerly's picture
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Bruce McFarling's picture
Bruce McFarling on Mar 14, 2015

Another dimension is the variability of rates.

For instance, in a related area, I was a discussant for a paper at an Economics conference that simulated the impact of the opening up of some Texas consumer electricity markets to competition. One thing that was striking to me that when a plot was done of the projected costs of the incumbent regulated electricity supplier and new entrants competing against the incumbent, all of the advantage in rates were for periods when the price of power was lower … when higher fuel costs drove prices up, the rate from the regulated supplier was the effective ceiling on the rates of the entrants, so prices collapsed together.

Or, in other words, “this ‘deregulation’ will save you money, except when it is most important to you to be able to save money, then it will offer no effective benefit”.

It seems there is likely to be a similar (though not identical) situation with renewable energy resource impacts on rates, since during fuel cost spikes, windpower will reduce rates due to merit-order curve effects reducing hours of generation of the least fuel efficient peaker plants, while the relative impact on rates of solar PV in an “RIM” sense will be lower.


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