Final Report for Department for Business, Innovation and Skills and Department for Culture, Media and Sport
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- Results from public mobile model
- £ million 2006
- 10-year NPV (2012–2021)
- Wi-Fi offload model Consumer surplus
- Producer surplus
Producer surplus calculations Producer surplus is calculated using the formula 
We have calculated annual values for 2011 so that they can be compared with the findings of the 2006 Europe Economics study. We have also compared our figures against the results of the 2006 study, updated for the change in RPI between 2006 and 2011. Finally, we provide calculations of the net present value of the cumulative producer surplus over a ten-year period from 2012 to 2021. The net present value is calculated by assuming a discount rate of 3.5% per annum (i.e. the same as in the consumer surplus calculation). Results from public mobile model Figure B .19 shows our results for 2011, and compares them against the results of the 2006 study (a) as originally presented, and (b) increased in line with the percentage change in RPI from 2006 to 2011. We also show a net present value (NPV) for the ten-year period from 2012 to 2021. The discount rate used for both the consumer and the producer surplus is the UK Government’s social discount rate of 3.5% per annum plus the percentage change in RPI. Figure B.19: Range of surplus from public mobile [Source: Analysys Mason, 2012]
Note: all results have been rounded to 3 significant digits. Figure B .20 below shows the consumer surplus generated by public mobile over the forecast period, before discounting.
As discussed in Section B.3.2 above, we have also calculated a sensitivity for the consumer surplus in 2011 based on assumptions about the elasticity of demand for mobile voice and data. The resulting values for consumer surplus in 2011 are as follows: Data: £5.5 billion Voice: £2.0 billion Total: £7.5 billion. This alternative calculation therefore results in a much lower consumer surplus than the range of £24.2–28.2 billion presented above. We consider that this sensitivity gives a very pessimistic estimate of the consumer surplus since the choke prices are based on the assumption that the demand curve is straight and has a slope equal to the elasticity for the marginal subscriber, whereas in reality we expect most subscribers to be less price sensitive than the marginal subscriber and hence the demand curve to be concave (as shown in Figure B .11) and to lie above the straight line that we have assumed. However, this sensitivity does serve to underline the degree of uncertainty surrounding the consumer surplus from public mobile. Figure B .21 shows the producer surplus generated by public mobile over the forecast period, before discounting and before considering the effects of Wi-Fi offloading (discussed in Section B.4).
Since our own assumptions about the future growth in mobile data usage are towards the low end of third-party estimates, we have also modelled a sensitivity using the higher data growth forecasts from the Cisco Visual Networking Index (see Figure B .22).
In this sensitivity, the discounted producer surplus drops to just £15 billion over the forecast period, and the annual surpluses become negative after 2020. We believe that this scenario is unlikely, as it implies a business model that is unsustainable for mobile operators, forcing them to increase data prices in order to cap demand. Wi-Fi offload model Consumer surplus Previous studies, including the 2006 study,131 have attempted to estimate the consumer surplus from the use of Wi-Fi to access a fixed broadband connection at home, but these have suffered from a lack of data on willingness to pay for this service and have been forced to assume a largely arbitrary value for willingness to pay. In our opinion, the average household’s willingness to pay for Wi-Fi access to fixed broadband is low, since the occupants could relatively easily use a wired connection instead. However, we believe it is appropriate to treat as a consumer surplus the amount that smartphone owners save by using Wi-Fi networks rather than their mobile operator’s network for data transfers in their homes and their places of work (we refer to this as passive offloading). It also seems appropriate to treat passive Wi-Fi offloading of data from laptops and tablets that have a mobile broadband connection in the same way, since owners of such devices have indicated that they are willing to pay for mobile data capability and Wi-Fi enables them to pay less than they otherwise would. Our approach to Wi-Fi consumer surplus therefore takes these factors into account. For 2011, we have assumed an average cellular data price of £0.04 per MB (benchmarked from a UK operator), although we expect this to decline over the forecast period. We assume that between 1% and 5% of data traffic was actively offloaded in 2011, depending on the device type; this rises to a maximum of 15% by 2021. Figure B.23: Approach used to estimate the consumer surplus from Wi-Fi offloading [Source: Analysys Mason, 2012] 
Producer surplus The 2006 study estimated the producer surplus from Wi-Fi using the accounting method previously described and considering the accounts of one hotspot provider (The Cloud, which has subsequently been acquired by BSkyB) and one supplier of hotspot equipment (Redline UK). Looking forward, we believe that most of the producer surplus from Wi-Fi will accrue to the mobile operators which, in the absence of Wi-Fi, would need to construct more base stations to handle data traffic. Our producer surplus Wi-Fi offload model considers the amount of data traffic that is likely to be offloaded from the cellular network through either home or office Wi-Fi networks (passive offloading, as discussed in the previous section on consumer surplus) or through public hotspots owned by either the mobile operator or a third party (active offloading). Figure B .24 shows the methodology used to calculate the producer surplus from Wi-Fi offloading. The increase in producer surplus from Wi-Fi offloading therefore comes from a reduction in capacity cellular sites required, as less traffic will be carried over the cellular network. Figure B.24: Approach used to estimate the producer surplus from Wi-Fi offloading [Source: Analysys Mason, 2012] 
There are a number of benchmarks for the proportion of data offloaded per device, ranging from around 60% to 80%.132 We have assumed an average of these benchmarks in 2011, with a gradual increase over the forecast period. We estimate that around 90% of smartphone data traffic is generated indoors; as a result of this, all benchmarks show that the vast majority of offloaded traffic is passively offloaded indoors. Passive offloading does not result in any cost for the operator, but active offloading requires the operator to either build its own Wi-Fi hotspots, or buy wholesale data from a third-party hotspot operator (such as BT Openzone). The opex and capex associated with setting up and running these Wi-Fi hotspots is generally significantly lower than for a cellular site. We have modelled the cost to the operator of Wi-Fi offloading by considering a mixture of both of these scenarios. We have estimated the current number of operator-owned hotspots133 and projected forward based on our forecasts of traffic growth.134 The remainder of offloaded traffic will be carried over third-party hotspots, for which the cellular operator will pay the hotspot provider a wholesale fee for offloaded data.135 The outputs of traffic reduction due to Wi-Fi offloading, as well as the resulting capex and opex are subsequently fed back into the main mobile model and lead to an increased producer surplus. The base case assumes that 95% of indoor smartphone traffic will be offloaded from the cellular network by 2021, although we have also considered a scenario where this figure is only 75%.
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