We decode conflicting data from ONS and BEIS, while questioning the accuracy of reported household costs. This paper delves into methodologies, revealing the disparity in how costs are captured, and explores the facts behind the contradictory evidence.
We navigate the perplexing case study of Northern Ireland's electricity costs. This paper not only dissects data discrepancies but also invites a re-evaluation of prevailing assumptions, inviting a fresh outlook on the intricate knot of factors influencing household energy expenses in Northern Ireland.
1. We present a Northern-Ireland-electricity-cost-puzzle. In our view, the household cost reported by the Office for National Statistics (ONS) is unlikely to be as accurate as otheres timates from the Department for Business Energy and Industrial Strategy (BEIS) or Utility Regulator (UR). We are unable, however, to confirm the source of bias affecting ONS’ data.
2. On the one hand, according to ONS, electricity costs in Northern Ireland (NI) are higher than any other UK region.
Figure 1: Weekly spend on electricity per household across UK Regions, 3-year period toMarch 2018
3. On the other hand, according to BEIS, electricity costs in NI are, in fact, lower than anyother UK region.
Figure 2: Electricity charges across UK regions, 2018 calendar year, average supplier,including VAT
4. The Utility Regulator’s (UR’s) Quarterly Transparency Reports (QTRs)1use the same method & process as BEIS and hence report a similar conclusion – that electricity costs inNI are in fact lower than other regions in GB.
5. In this paper, we explore reasons for this apparent contradiction.
6. The ONS methodology is based on household surveys, where households are asked howmuch they pay for electricity, using (if possible) last bill/payment. The relevant‘bill/payment’ period is also captured, which ONS then use to calculate a ‘weekly cost’.
7. In contrast, the BEIS methodology is based on information received directly from supplycompanies, via a quarterly survey. BEIS explain the methodology as follows:
“The suppliers provide figures for each tariff (unit costs, standing charges,split levels, discounts, dates of tariff changes and number of customers),splitting the tariff information by payment type and region. Data is receivedas part of a quarterly template, sent out to energy suppliers shortly after theend of each quarter. All information received from suppliers is qualityassured by BEIS prior to publication.”
8. Not only do the methodologies differ, the output also differs. The ONS methodologycaptures what households believe the household cost is, including the quantity of energyconsumed. In contrast, the BEIS methodology reports what suppliers say the charge per unitis, excluding the impact of consumption. Therefore, the reported values, from ONS and fromBEIS, may be consistent, if consumption-per-week explains the difference, as demonstrated in the following equation:
Weekly cost (WC) = Unit Cost (UC) * consumption-per-week (CPW)
9. Using the UC reported by BEIS and the WC reported by ONS, the balancing figure for CPWcan be derived, on the assumption that both UC & WC are consistent, by dividing WC byUC.
Table 2: Implied Consumption minus Reported Consumption. A puzzling difference.
10. The implied domestic consumption can now be compared to reported domestic consumption, using data published by UR2, as follows.
11. Clearly, there is a material inconsistency in, or misinterpretation of, the available data on electricity consumption and/or costs. The explanation(s) for this puzzle must be:
a. We somehow misinterpret the data,
b. BEIS understate electricity unit costs,
c. UR understate electricity consumption, and / or,
d. ONS overstate weekly electricity costs.
12. The average difference in Table 2 column D is 27%. But because we are not sure whether the difference is based on consumption errors or cost errors (or both!), we can call this puzzle, ‘the missing 1,000kwh per year’, or ‘the missing £150 per year’
13. Combining different sources of information is risky, and we cannot rule out the possibility that simplifying assumptions could explain this puzzle, at least in part. To test this, we address several assumptions underpinning Table 1 and Table 2.
14. Thus far, our presentation implies that the periods are directly comparable - however this is a simplification. Firstly, the ONS data is based on 36-month periods, ending in March each year. So, in Table 1 column A, the value for 2018 (£12.60) represents the average weekly cost for the 36-month period ending March 2018. Secondly, the BEIS data relates to 12- month periods ending 31st December each year. So, in Table 1 column B, the value we use for 2018 (15.63pence per kwh) relates to the 12-month period ending December 2018
15. However, comparing 3-year periods with 1-year periods, is unlikely to explain the puzzle in a material way. If it were a material factor, we would expect to see differences that were sometimes-over-sometimes-under, during a sufficiently long time-series of data3 Similarly, if mismatched periods (ending December rather than ending March) were driving the differences, we would expect temperature-corrected-consumption to be material – however using BEIS (UK level) data for over the relevant periods, temperature correction adjustments are usually smaller than 3%.
16. Separately, a further assumption in Table 2 is that consumption values, are directly comparable. This is not a perfect assumption because the UR data is based on connections whereas the ONS data is based on households. If there are more connections than households, then the consumption per connection (Table 2 column B) is understandably lower than the consumption per household (Table 2 column A).
17. However, in our view, we do not think this is a material issue. UR’s connection numbers are very similar to the number of households reported by ONS. In effect, the connection-tohousehold ratio is small. For example, in 2017 the ratio is 1.01 (i.e. a 1% difference). Although the ratio is larger for previous years. Hence, overall, we don’t think this distinction is material enough to explain the puzzle
Table 3: Connection-to-household ratios are small and hence don’t explain the puzzle
18. Table 1 column B refers to ‘unit costs’ but if these exclude fixed charges, it could contribute towards the puzzle. However, the BEIS methodology confirms that “unit costs reflect the prices of all suppliers and include standing charges”.
19. Further, Table 1 column B does not reveal whether these ‘unit costs’ relate to credit customers, direct debit customers, or prepayment customers (or some combination thereof). If the BEIS data is based on, say, direct-debit customers (who pay less) instead of creditcustomers (who pay more) this may help explain the puzzle. However, again, the BEIS methodology is reasonably clear:
“The average bill is equivalent to the total revenue divided by the total number of customers. For each tariff, the total number of customers in a year is equivalent to the average number of customers across the four quarters. For each tariff, total revenue is equivalent to the average number of customers multiplied by the sum of the bills in each of the four quarters.”
20. To support this, BEIS publish the average unit cost for each payment type (which BEIS refer to, loosely, as ‘tariffs’) and each region. The charge for credit customers is higher than direct debit customers (by 7% in 2018, and less in preceding years). However, the BEIS publication shows the overall average unit cost across all payment types, which is what we use in Table 1 column B to represent an ‘average customer’. In any case, even if we used the charge for credit customers, (more expensive than direct debit and prepaymentcustomers) it would not fully explain the puzzle.
Figure 3: Payment methods do not seem to explain the puzzle
21. One part of the BEIS methodology is worthy of further investigation, in terms of what ismeant by “No allowances are made for introductory offers or non-cash benefits that may beavailable from suppliers.” However, if we assume that introductory offers and non-cashbenefits would reduce the BEIS unit costs, this would increase the puzzle, rather than explain it.
22. Lastly, BEIS confirm that its data is inclusive of VAT, ruling out a potential 5%understatement.
23. UR’s Quarterly Transparency Reports are, in terms of both connection numbers and consumption per quarter, based on information from Northern Ireland Electricity Networks (NIEN). NIEN’s consumption data is based on estimates, because not all electricity meters will be read simultaneously on 31st December each year. Therefore, it is possible that NIEN underestimate true consumption, particularly for the quarter ending 31st December, when consumption can be higher than the previous three quarters (assuming NIEN’s estimates are impacted by smoothing).
24. On this basis, underestimation is more likely for quarters ending 31st December, but by the same logic, it is also more likely that other quarters are overestimated. On an annual basis, the net effect should result in no systematic bias.
25. It is therefore unlikely that underestimation would occur every year for four consecutive years. To explain the puzzle in Table 2, NIEN’s estimates would need to systematically and materially underestimate true consumption. Underestimation is more likely to occur when sales of electricity increase, as has been the case in NI (unlike mainland GB, where electricity sales have fallen by 11% (England and Wales) to 22% (Scotland).
Figure 4: An index of electricity sales (volumes using TWh) to consumers, 2002 = 100
26. Nonetheless, in the absence of anything more tangible, systematic underestimation by NIEN(and hence within UR’s QTRs) seems, in our view, unlikely.
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