Woman reading bill and holding mobile phone
VoxEU Column Microeconomic regulation

Loss aversion and consumer inertia: New evidence from phone subscriptions

Across a range of everyday markets, consumers make recurrent tariff choices in the face of a multitude of fees and plans, leading to concerns they may fail to make optimal choices of suppliers or contracts. This column uses data on approximately 60,000 mobile phone users in the UK to examine how people select their phone contracts. Even when provided with information that they could make savings by switching to an alternative plan, nearly two-thirds of customers do not act on this advice. People are most likely to change contract if they have incurred additional charges on top of their monthly plan, pointing to the role of loss aversion.

Consumers in the mobile telecommunications market have to select among a myriad of tariff plans available today. Most likely, some customers are not on the best contract for them and would be better off switching to a more appropriate tariff. However, with such a large number of contracts available, consumers are faced with confusion and might avoid switching altogether, harming themselves and the competitive process among firms along the way. Understanding what kind of information would help them switch is important for policymakers and regulators, such as Ofcom in the UK.

Information constraints to switching tariffs

The predominant thinking among regulators and competition authorities worldwide is that a lack of switching between tariffs is mainly an information and a computational problem. This has led, for example, Thaler and Sunstein (2008) to propose the RECAP (Record, Evaluate, and Compare Alternative Prices) regulation that would require firms to let customers share their usage and billing data with third parties, which could, in turn, provide unbiased advice about whether to switch to a competing provider. In a similar spirit, Grubb (2015), reviewing the evidence on why consumers in various markets struggle to choose the best price, puts forward as policy advice the provision or facilitation of expert guidance. Kling et al. (2012) demonstrate that simply making information available does not ensure consumers will use it, and suggest that by personalising the necessary market information, consumers would be able to overcome their ‘comparison frictions’ and switch more often to lower cost offers.

This line of thinking has given rise to many accredited schemes for third-party price-comparison sites covering telecommunications, but also several other industries (banking, electricity, credit cards, etc.). The aim of these schemes is to increase consumer confidence about how to find the best price for the service they wish to purchase, and to increase market transparency by providing or facilitating expert guidance. Sort of like an ‘expert’ friend whom you trust and could tell you how much you would save by switching to the best tariff for you. That would surely help solving the problem!

Our study

In a new study (Genakos et al. 2023), we present novel evidence explaining how people select their contracts in the mobile telecommunications industry. We use individual-level panel information of approximately 60,000 mobile phone users in the UK between 2010 and 2012. We got unprecedented access to Billmonitor.com, the leading mobile phone price comparison site in the UK, which was the first company to receive an accreditation award for mobile phone services. Consumers in our sample subscribe to monthly plans with a fixed payment component (the monthly rental) that includes several allowances (for call minutes, text messages, data usage, etc.). Upon registering with Billmonitor, consumers receive personalised information on the exact amount they could save by switching to the optimal contract for them. This information is calculated by an optimising algorithm devised by Billmonitor that is allowed to look into consumers’ past bills. In other words, in contrast to the literature so far, consumers in our setting have unbiased and personalised information available to them before making any choice.

Billmonitor is able to find better plans for many consumers who could save substantial amount of money by switching to alternative plans. Consumers with savings fall in two categories: (1) those who happen to exceed their allowance and pay extra fees, called ‘overage’ fees, and could save money by switching to a higher, more inclusive, plan; and (2) those who could also save money by switching instead to a lower, less inclusive tariff if their consumption is systematically lower than their allowance. If the only problem were information acquisition, then consumers of both types should switch with the same probability upon receiving their personalised information.

However, our conjecture, based on the pioneering work of Kahneman and Tversky (1979) that established prospect theory as an alternative tool to analysing choice under uncertainty, is that consumers will react differently due to loss aversion: individuals evaluate economic outcomes not only according to an absolute valuation of the saving outcomes, but also relative to subjective reference points. Paying more than the recurrent monthly rental (which serves as a natural reference point) is experienced as a loss. It should be a more ‘painful’ experience and should prompt consumers to switch with higher probability than they would if they could save the exact same amount by switching to a lower tariff, which is experienced as a gain relative to their reference point.

Loss aversion when responding to personalised contract information

Using the data on Billmonitor's customers, we evaluate what affects people switching contracts over time. We look at switching within operator, which is easy, almost seamless and not constrained by contractual clauses. We document three phenomena. First, we present evidence that even consumers with personalised, expert information on optimal contracts exhibit significant inertia: 62% of customers, who receive information that they could realise positive savings by switching to an alternative plan, do not act on this advice, forgoing savings of £186 per year on average. Second, in a switching probability econometric framework, we show that potential savings are still a significant determinant of switching, a finding that partly supports calls for unbiased advise by accredited third parties.

More importantly though, and in line with the loss-aversion conjecture, we find that, controlling for savings, switching is more likely to happen if the customer was charged overage fees. The psychological pain of paying over and above the expected fixed monthly fee is an even greater motivator to switch, in addition to the conventional economic reasoning that savings information should matter.

We show that this implies a kink at the reference point that is statistically and economically significant and robust to several alternative interpretations and specifications. In addition, we document a differential risk attitude of individuals who, on average, are risk averse in the domain of gains and risk seekers in the domain of losses, resulting in an S-shaped behaviour of their utility function that is consistent with prospect theory.

Policy implications

Our results not only shed light on an important ongoing academic debate on consumer inertia and information acquisition and utilisation, but also, from a practical point of view, put the use of price-comparison sites in a new perspective. We suggest that regulators hoping to rely on price-comparison engines to discipline market prices using shared data should first investigate what giving ‘good advice’ means in a context that includes loss aversion. Consumers also switch for behavioural reasons that have little to do with savings, but that still could be consistent with optimal individual behaviour. Just informing consumers about potential savings, may not prompt the healthy competitive switching that regulators would like to nudge. According to our results, savings are not necessarily the first thing even well-informed consumers are looking for. Rather, they often stick to a fixed reference point that leaves little room for bill shocks.


Genakos, C, C Roumanias and T Valletti.(2023), “Is Having an Expert “Friend” Enough? An Analysis of Consumer Switching Behavior in Mobile Telephony”, CEPR Discussion Paper 10861.

Grubb, M D (2015), “Failing to Choose the Best Price: Theory, Evidence, and Policy”, Review of Industrial Organization 47(3): 303-340.

Kahneman, D and A Tversky (1979), “Prospect Theory: an Analysis of Decision Under Risk”, Econometrica 47(2): 263–292.

Kling, J R, S Mullainathan, E Shafir, L C Vermeulen and M V Wrobel (2012), “Comparison Friction: Experimental Evidence From Medicare Drug Plans”, Quarterly Journal of Economics 127(1): 199–235.

Thaler, R and C Sunstein (2008), Nudge: Improving Decisions about Health, Wealth, and Happiness, Yale University Press.