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Loyalty taxes in retail electricity markets: not as they seem?


A common view in retail electricity markets is that retailers discriminate based on consumers’ loyalty: loyal consumers pay more and switchers can (and do) select the cheapest offers) when they switch. The premium is colloquially known as a “loyalty tax” or “loyalty premium”. Reflecting this understanding Australia’s governments, regulators and consumer advocates have encouraged consumers to switch electricity retailers. Using a sample of 47,114 household electricity bills we test whether consumers that had switched in the previous 12 months (“switchers”) pay less than consumers who remained with their retailers (“remainers”) in the previous 12 months. We find that the annual bills of switchers are expected to be AU$48 (4%) lower than remainers and that the median switcher could reduce their bills by 21% by selecting the cheapest offer. Classifying retailers into tiers however provides some nuance to the main conclusion: the third tier of retailers (the new entrants with market shares of less than 3%) impose higher loyalty taxes than the other two tiers (incumbents and mid-sized retailers). The middle tier of retailers impose the lowest loyalty tax, and in fact for many consumers they may reward loyalty. These findings suggest that the loyalty tax is (typically) smaller than widely considered, it varies across tiers of retailers and even engaged consumers typically do not select the lowest priced offer. This raises the question of whether switchers are motivated by lower bills as well as other factors or whether the main challenge is search difficulties.

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  1. See for example

  2. This is sometimes also referred to as “money left on the table” or “MLT” see (Mountain and Rizio 2019) and (Waddams Price and Wilson, 2010).

  3. Consumers were encouraged to use the Government’s price comparison site through the payment of AU$50 if they consulted the site, although they were not required to upload their bills in order to receive the payment. Consumers who had uploaded their bills agreed that the deidentified data in their bills could be used for research. Of the bills uploaded, 47,114 had the flexibility to choose their retailer and we were able to tell if consumers had switched to them in the last 12 months, or not.

  4. The Australian Energy Market Operator reported that 35% of all households switched retailer in 2018, however 14% of this 35% are counted as household moves (or new houses) so that the “net” switching rate (i.e. customers that switched from one retailer to another is 21%). Customers that select a retailer because they are moving into a new home or changing home are making an active choice of retailer and signing a new supply contract and for this reason and because we are not able to distinguish them other switchers, they are classified as “switchers”. It might however be the case that they engage in the search for retailers with less enthusiasm as those customers that actually switch from one retailer to another. We do not know what proportion of the switches in our sample are new homes/household moves but have no reason to believe it will be significantly different to the proportion in the population.

  5. We were able to do this for bills from all retailers in the market except from the retailer Powershop and so their customers’ bills are excluded from our analysis.

  6. The possible error from annualising consumption based on the average consumption in each bill was measured using data on the half-hourly consumption of 1524 household meters in the Citipower and Powercor areas of distribution covering one year, and provided to us by these distributors through C4Net. Our test involved selecting 86 days (the median billing period), annualising consumption for each of the 1524 meters based on the average consumption in this 86 day period and then comparing that to the actual annual consumption. This was done 221 times (for each of the 1524 households) to cover the 221 different possible combinations of 86 continuous days from March 2018 to December 2018 (the period during which most of the 47,114 bills were uploaded). The estimated annual consumption for each meter was then established by randomly selecting 10 of the 221 estimates and taking the average of these 10 estimates. This established a data series of estimates to compare to the actual annual consumption. The comparison was performed by regressing the estimated annual consumption for the 1524 households against the actual annual consumption. The coefficient of the estimate was 0.87 (and intercept of 322). The Multiple R-squared error of the regression was 96.7%, and the residuals were normally distributed. This procedure was repeated for billing periods of 30, 60, 90 and 120 days. Similar coefficients and multiple R-squared errors were produced. We conclude from this that our approach of estimating annual consumption from a single bill is unlikely to bias our analysis though of course it can not necessarily guarantee a high degree of accuracy in the estimation of the consumption in individual bills. Appendix A provides data on the number of bills classified by the month in which the mid-point of the billing period occurred, and also a histogram of the duration of the billing period of the bills in the sample.

  7. The assessment date (31 August 2019) is the median end-date of the billing period of the bills in our sample. There may be a difference between Available Savings at the time that consumers switched and when we examined their bills and performed the calculations. Specifically, a consumer that found the best deal when they switched may find that within a year further savings can be made by switching again. This could be because retailers increased prices to their existing customers after they switched (very few offers have fixed prices) or because more attractive offers become available between the time the customer switched and the time we examined their bills. The latter case will not bias our results since the saving is available to both the switchers and remainers. But to the extent that retailers raised prices to customers that switched to them in the first 12 months after switching, our estimate of the loyalty tax will understate the benefit that switchers gained at the time they switched.

  8. The Victorian Government suggests that seven out of ten Victorians can save money on their energy bills by using the government’s price comparison site and that typically, households can save $330 on energy bills annually (see This is a little below the average saving we estimate remainers could achieve if they found the cheapest publicly available.

  9. These results are available from the authors on request.

  10. Percentage discounts can be stated or applied. The discount can relate to usage or the total bill amount.

  11. Controlled load refers to separately metered and switched loads (generally electric hot water systems or slab or underfloor heating), often charged at a lower rate than the main load that operate during off-peak hours (e.g. overnight).

  12. In Victoria there are 42 different tariff structures that can be classified into 7 tariff type categories. All have daily charges (cents per day). In addition: “Flat” tariffs have a single consumption rate (i.e. cents per kWh charge); “Flexible” tariffs have three time-variant rates (i.e. rates that vary by time of day and for weekdays and weekends); “Seasonal-Flexible” have three time variant rates some of which differ across two seasons; “Multi-flat” has two or more rates that apply for blocks of consumption which can be measured daily, monthly or in some cases three monthly; “Time-of-Use” (also known as TOU) have two time-variant rates in weekdays; “multi-TOU” combine two time-variant rates with block structures for the peak rates; and “Multi-flexible” combine three time-variant rates with block rates for the peak periods. “Flat” tariff is the reference tariff structure in the regression.

  13. For example in a survey of 400 consumers Newgate Research (2017) suggested that more consumers switched plans offered by their existing retailer than switched retailers in 2016. In Britain, the Office of Gas and Electricity Markets has been tracking this for several years (see and finds slightly higher intra-retailer switches by tariff than switches to different retailers.

  14. These results are available from the authors upon request.

  15. This is colloquially known as “bait and switch” pricing (i.e. “bait” consumers with cheap offers but then later “switch” them onto more expensive offers). See for example


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We thank Amine Gassem without whose PDF parsing, website scraping and data science skills this research would not have been possible. We also acknowledge Stephanie Rizio’s research assistance and thank Stephen Littlechild for rigorous scrutiny, ideas and much interesting discussion.


This research was supported by a multi-year funding grant from the Government of Victoria for the establishment of the Victorian Energy Policy Centre.

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Correspondence to Bruce Mountain.

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Bruce Mountain and Amine Gassem are co-founders of a price comparison website, and the extraction and processing of bill data used in this study was performed using data extraction and pricing software used by that website.

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Appendix: Histogram of length of billing period and month of mid-date of billing period for all bills in the sample

Appendix: Histogram of length of billing period and month of mid-date of billing period for all bills in the sample

See Tables 10 and 11.

Table 10 Histogram of length of billing period
Table 11 Month of mid-date of billing period for all bills in the sample

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Mountain, B., Burns, K. Loyalty taxes in retail electricity markets: not as they seem?. J Regul Econ 59, 1–24 (2021).

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  • Retail choice
  • Search costs
  • Loyalty tax
  • Electricity

JEL Classification

  • C21
  • D11
  • D12