Assessing the economic impacts of IT service shutdown during the York flood of 2015 in the UK

In this paper we focus on the ‘Christmas’ flood in York (UK), 2015. The case is special in the sense that little infrastructure was lost or damaged, while a single industry (IT services) was completely knocked out for a limited time. Due to these characteristics, the standard modelling techniques are no longer appropriate. An alternative option is provided by the Hypothetical Extraction Method, or HEM. However, there are restrictions in using the HEM, one being that no realistic substitutes exist for inputs from industries that were affected. In this paper we discuss these restrictions and show that the HEM performs well in the York flood case. In the empirical part of this paper we show that a three-day shutdown of the IT services caused a £3.24 m to £4.23 m loss in York, which is equivalent to 10% of the three days' average GVA (Gross Value Added) of York city. The services sector (excluding IT services) sustained the greatest loss at £0.80 m, where the business support industry which was predominantly hit. This study is the first to apply a HEM in this type of flood on a daily basis.

-Flegg, A.T. and T. Tohmo (2012) A comment on Tobias Kronenberg's "Construction of regional input-output tables using non-survey methods: the role of cross-hauling". International Regional Science Review 35.

Review form: Referee 2
Is the manuscript an original and important contribution to its field? No

Is the paper of sufficient general interest? Yes
Is the overall quality of the paper suitable? No Quality of the paper A paper that may be acceptable after major revision.

Can the paper be shortened without overall detriment to the main message? Yes
Do you think some of the material would be more appropriate as an electronic appendix? No

If there is supplementary material, is this adequate and clear? No
Are there details of how to obtain materials and data, including any restrictions that may apply? Yes

Recommendation?
Major revision is needed (please make suggestions in comments)

Comments to the Author(s)
The paper is about an interesting and timely topic. However, the paper should have more scientific rigour, especially in the language (e.g. Sec. 1.1). Major re-editing is expected during the review; other main issues include: -validation: has the method being validated? -limitation: in the abstract some limitation are mentioned, but this are not fully clarified in the main text -implication: why this study is important and how can contribute to decision-making/robustness (the typical "so what?")? -future studies: could this study be repeated? Does the "specialty" of the case study be a problem for reproducing or transferring it? Some specific notes are also detailed below. P1 L 29-30: alternative…method?; Hypothetical Extraction Method P2 L9: Boxing Day is on the 26 December? P2 L44-46: I think authors should specify the kind of losses are meant; for example, during 25-26 December all shops are closed and I don't see much shopping lost. If other type of transactions are meant, this should be clarified. P2 L53: Hypothetical Extraction Method P3 L25-27: I would remove the two sentences P3 L 41-42: I would remove the sentence P4 L 4-11: I would keep the Ericcson example much shorter, just focus on the loss P4 L 12-14: authors should specify the "international manufacturer", the loss and the type of incident P4 L37-54: "indirect losses" denote the wider disruption to services, including all urban networks (power grid, drainage, transport). The literature review can be expanded to include e.g. (Thieken et al., 2008); Aerts et al. (2013); Pregnolato et al (2016) P4 L 57-58: simplify the sentence e.g. "Post-disaster situations vary."; I would specify what it is meant by "normal" flood P8 L 11-19: I would integrate this sentences between brackets into the main text P13 L 14: see above, transactions during the 25-26.12 are limited because of shops closure P13 L 51-57: transform the "million decimals" (e.g. £0.075m) into a £75k; also at P14 P14 L21: clarify how you results are "in line" with other studies (e.g. compare numbers) P14 L42: just "Conclusion" P14 L 54-55: start the sentence with "Hypothetical Extraction Method" P15 L3-4: has the results being validated somehow? P15 L 7-24: could this be part of the discussion? P15 L 26-33: I would remove the entire paragraph Fig. 1: it would be useful to see also the £ lost in addition to % Fig 2: axis titles are missing. Again, not used the decimals of millions if none of the losses arrive to one million or more **** Aerts, J., Botzen, W., Bowman, M., Dircke, P. and Ward, P. (2013a) Climate Adaptation and Flood Risk in Coastal Cities. Taylor and Francis. Available at: http://ncl.eblib.com/patron/FullRecord.aspx?p=1576080 Thieken, A.H., Ackermann, V., Elmer, F., Kreibich, H., Kuhlmann, B., Kunert, U., Maiwald, H., Merz, B., Muller, M., Piroth, K., Schwarz, J., Schwarze, R., Seifert, I. and Seifert, J. (2008) Proceedings of the 4th International Symposium on Flood Defence,. Toronto, Canada, 6-8 May 2008. Pregnolato, M., Ford, A., Robson, C., Glenis, V., Barr, S. and Dawson, R. (2016) 'Assessing urban strategies for reducing the impacts of extreme weather on infrastructure networks', Royal Society Open Science, 3(5), pp. 1-15.

29-Jan-2019
Dear Dr Meng The Editor of Proceedings A has now received comments from referees on the above paper and would like you to revise it in accordance with their suggestions which can be found below (not including confidential reports to the Editor).
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Once again, thank you for submitting your manuscript to Proc. R. Soc. A and I look forward to receiving your revision. If you have any questions at all, please do not hesitate to get in touch. This paper has been reviewed by two reviewers and a number of major issues have been raised, ranging from case study data, model validation, knock-on effects to implications for decision making. The authors are suggested to provide a detailed response to all the comments from the two reviewers.
In addition, I would recommend the authors to avoid footnotes and use proper references instead. I also strongly recommend the case study data should be made easily accessible, for example through a repository with DOI rather than contacting the authors.

Reviewer(s)' Comments to Author:
Referee 1 Comments to the Author(s): The manuscript is well structured and written, I had no problem understanding everything. The figures and tables are informative and appropriate. I agree with the authors in that the York flood provides an interesting challenge, because the outage lasted only three days. Then, the authors scale final demand by 3/365 to arrive at 3-day effects. But in here they assume that final demand is temporally uniform across a whole year, which is unlikely to be the case, especially in the UK where heating, food choices etc are highly seasonal, and therefore food suppliers and utilities would experience seasonal variations of IT use. This issue is especially aggravated by the fact that the flood occurred during the Christmas season, where an unusually high use of IT could be expected caused by the Christmas shopping sprees.
A major shortcoming is though that the method does not account for downstream effects, and the authors acknowledge this in Section 2.2, where they discuss Oosterhaven's criticism. My question here would be: What has Oosterhaven himself recommended to include downstream effects? And what -if anything at all -could the authors take on board? After all, an IT outage will have major effects on any business that relies on these IT inputs. My gut feeling is that the backward effects may be rather uninteresting compared to the major forward effects. This is my main concern regarding the manuscript. The paper is about an interesting and timely topic. However, the paper should have more scientific rigour, especially in the language (e.g. Sec. 1.1). Major re-editing is expected during the review; other main issues include: -validation: has the method being validated? -limitation: in the abstract some limitation are mentioned, but this are not fully clarified in the main text -implication: why this study is important and how can contribute to decision-making/robustness (the typical "so what?")? -future studies: could this study be repeated? Does the "specialty" of the case study be a problem for reproducing or transferring it? Some specific notes are also detailed below. P1 L 29-30: alternative…method?; Hypothetical Extraction Method P2 L9: Boxing Day is on the 26 December? P2 L44-46: I think authors should specify the kind of losses are meant; for example, during 25-26 December all shops are closed and I don't see much shopping lost. If other type of transactions are meant, this should be clarified. P2 L53: Hypothetical Extraction Method P3 L25-27: I would remove the two sentences P3 L 41-42: I would remove the sentence P4 L 4-11: I would keep the Ericcson example much shorter, just focus on the loss P4 L 12-14: authors should specify the "international manufacturer", the loss and the type of incident P4 L37-54: "indirect losses" denote the wider disruption to services, including all urban networks (power grid, drainage, transport). The literature review can be expanded to include e.g. (Thieken et al., 2008); Aerts et al. (2013); Pregnolato et al (2016) P4 L 57-58: simplify the sentence e.g. "Post-disaster situations vary."; I would specify what it is meant by "normal" flood P8 L 11-19: I would integrate this sentences between brackets into the main text P13 L 14: see above, transactions during the 25-26.12 are limited because of shops closure P13 L 51-57: transform the "million decimals" (e.g. £0.075m) into a £75k; also at P14 P14 L21: clarify how you results are "in line" with other studies (e.g. compare numbers) P14 L42: just "Conclusion" P14 L 54-55: start the sentence with "Hypothetical Extraction Method" P15 L3-4: has the results being validated somehow? P15 L 7-24: could this be part of the discussion? P15 L 26-33: I would remove the entire paragraph

Is the paper of sufficient general interest? Yes
Is the overall quality of the paper suitable? Yes Quality of the paper A good paper worth publishing in Proceedings.

Can the paper be shortened without overall detriment to the main message? No
Do you think some of the material would be more appropriate as an electronic appendix? No

If there is supplementary material, is this adequate and clear? Not applicable
Are there details of how to obtain materials and data, including any restrictions that may apply? Not applicable

Recommendation?
Accept as is

Comments to the Author(s)
The authors have responded well to my comments. The paper can be published.

Review form: Referee 2
Is the manuscript an original and important contribution to its field? Yes

Is the paper of sufficient general interest? Yes
Is the overall quality of the paper suitable? Yes Quality of the paper A good paper worth publishing in Proceedings.

Can the paper be shortened without overall detriment to the main message? No
Do you think some of the material would be more appropriate as an electronic appendix? No

If there is supplementary material, is this adequate and clear? Yes
Are there details of how to obtain materials and data, including any restrictions that may apply? Not applicable

Recommendation?
Accept as is

Comments to the Author(s)
The article is ready for publication Decision letter (RSPA-2018-0871.R1)

Dear Dr Meng
On behalf of the Editor, I am pleased to inform you that your manuscript entitled "Assessing the Economic Impacts of IT Service Shutdown during the York Flood of 2015 in the UK" has been accepted in its final form for publication in Proceedings A.
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Best wishes
In addition, I would recommend the authors to avoid footnotes and use proper references instead. I also strongly recommend the case study data should be made easily accessible, for example through a repository with DOI rather than contacting the authors.
Thank you very much. We have removed the footnotes and replaced them with proper references.

Reviewer(s)' Comments to Author:
Referee 1 Comments to the Author(s): The manuscript is well structured and written, I had no problem understanding everything. The figures and tables are informative and appropriate. I agree with the authors in that the York flood provides an interesting challenge, because the outage lasted only three days.
Thank you for acknowledging the potential interest of our selected case. We strongly agree that the York flood offers an interesting and special case that invalidates existing flood modelling techniques because of little damage caused to the physical infrastructure.
The HEM method has a long tradition, with major contributions from Dietzenbacher and van der Linden 1993; Miller and Lahr 2001; Dietzenbacher and Lahr 2013.
Thank you for your comments. We agree with you and appreciate the significant contributions made by existing literature to the HEM method. We have incorporated your mentioned references in our revised manuscript (please see line 185, 159 and 161).
With regard to the regionalisation of the We appreciate the reviewer's comments considering the possible effects from crosshauling. We do agree cross-hauling would have significant impacts in trade flow estimates. However, in our study, trade flows were not used, because our HEM method only focuses on technological coefficients (A) and final demand (F). Given that the UK national IO table is the type B IO table we used here, domestic and import matrix are separated, and therefore, our LQ approach to estimate A and then F for York is only based on the domestic matrix, excluding trade. Therefore, whether or not trade is precisely estimated does not matter in terms of our purpose in this case. If our purpose here would have been to construct a solid city-level IO table, it is definitely necessary to consider CHARM or use a gravity theory-based model. However, it requires other city-level trade data as sample data which are not available. To clarify the method, we added a brief description in section 5 to discuss methods you mentioned and stated why it is not applied in this study, as shown below: "The Location Quotient has been widely applied in the regionalisation of IO tables, and the Augmented Flegg Location Quotient is a variant of the Location Quotient family. This is adjusted to be based on the Flegg Location Quotient to take regional specialisation into account, which make it possible to scale national input coefficients upwards (Flegg et al. 2016;Jahn 2017). However, location capitals is largely based on the following assumptions: 1. Assuming identical productivity per employee in each region; 2. Assuming consumption per employee of the same products, which basically refers to identical consumer preference; and 3. Excluding cross hauling between regions as consumption would be fully satisfied by local supply if the region is the exporter for the given commodity (Riddington et al. 2006;Zheng et al. 2019 (UK IO table), which would not be affected by trade estimates. Therefore, the cross-hauling problem in the LQ approach was omitted in this case." Then, the authors scale final demand by 3/365 to arrive at 3-day effects. But in here they assume that final demand is temporally uniform across a whole year, which is unlikely to be the case, especially in the UK where heating, food choices etc are highly seasonal, and therefore food suppliers and utilities would experience seasonal variations of IT use. This issue is especially aggravated by the fact that the flood occurred during the Christmas season, where an unusually high use of IT could be expected caused by the Christmas shopping sprees.
We thank the reviewer for these constructive comments. We agree with you that our assumption of averaged final demand cannot fully represent the seasonal variations in consumption. Therefore, in the revised manuscript, we have added an upper boundary of the results following the same methods but employed different numbers for final demand during the three-day IT outages. Due to the lack of daily sales data for York, we assumed the same monthly trend in household expenditure as the UK. According to data from the Office for National Statistics (2016), household expenditure on food, drink and tobacco, clothing and footwear and other household goods during December are 16%, 42% and 31% higher than those of other non-Christmas months during 2015. Therefore, we adjusted the original three days' final demand that was calculated from the IO table. The upper boundary of economic loss considering the Christmas consumption peak is estimated as £4.23 million (please see lines 393-402. While we believe such estimates still contain uncertainties, we believe the provision of an estimate range in economic loss is the best we can do due to the lack of detailed seasonal transaction data at hand.
A major shortcoming is though that the method does not account for downstream effects, and the authors acknowledge this in Section 2.2, where they discuss Oosterhaven's criticism. My question here would be: What has Oosterhaven himself recommended to include downstream effects? And what -if anything at all -could the authors take on board? After all, an IT outage will have major effects on any business that relies on these IT inputs. My gut feeling is that the backward effects may be rather uninteresting compared to the major forward effects. This is my main concern regarding the manuscript.
We thank the reviewer for these insightful comments. For disasters lasting longer, we agree with the criticism raised by Oosterhaven. What he basically argues is that the extraction behaviour in the HEM method is based on the assumption that all downstream demand for a sector's intermediate sales simply and completely disappear after the incident. In contrast, he thinks such downstream demand for intermediate sales will not disappear, but rather, seeks for substitutions elsewhere. Also, the HEM neither measures the higher order backward impacts of this disappearance, nor the forward impacts of the secession of these sales upon the purchasing industries. This is, of course, the case for any disaster that lasts longer and when sectors have enough time to adapt. However, we suggest his criticism does not hold for our special York flood case. This is because the IT outage resulting from the flood only lasts for three days, which provide insufficient time for economic sectors to adapt or seek for other substitutions. This is also because of the special nature of IT services produced by local carriers that invalidate the replacement or substitutions of imports.
Thank you very much. We have changed them into italics accordingly.
-Section 2.1: Please insert and discuss also Schulte in den Bäumen et al. 2015 Thank you very much. We have inserted and discussed the recommended reference on line 111-116.

Referee 2 Comments to the Author(s):
The paper is about an interesting and timely topic. However, the paper should have more scientific rigour, especially in the language (e.g. Sec. 1.1). Major re-editing is expected during the review; other main issues include: -validation: has the method being validated?
Thank you for this question. The Hypothetical Extraction Method has a long tradition in disaster risk analysis. It was first proposed to estimate the relative importance of certain industries for an entire economy by Paelinck et al. (1965) andStrassert (1968), and later re-formulated by Meller and Mafan (1981) and Cella (1984). The method was used to estimate the effects of the extraction on other industries and on the wider economic system when an industry is hypothetically eliminated from the economic system, and the difference between the output level of the other industries before and after the extraction reflects the linkages between the extracted industry and the rest of the economy. While the method was once criticized by Oosterhaven (2017)   Thank you for your question. As we discussed in the Introduction, traditional ways of flood and disaster modelling become less useful for special cases, such as the York flood case here, for several reasons. Most important is that existing flood and disaster modelling efforts rely heavily on quantifying the damages to infrastructures as a direct and tangible consequence of flooding. However, not all flooding events will cause damage to infrastructure and this makes it difficult to implement standard ways of disaster modelling. It appears challenging to estimate the flood induced indirect and intangible costs from soft services shutdowns, such as the IT service here. Therefore, our contribution to decision-making/robustness here is to provide an alternative and feasible tool for these 'special' cases, which have similar features in short duration and impact on soft services that both invalidate traditional disaster modelling tools.
-future studies: could this study be repeated? Does the "specialty" of the case study be a problem for reproducing or transferring it?
Thank you for your questions. While we acknowledge that the York flood case is a special case compared with other major flooding events, it is not a unique case in the context of a growing number of disastrous events. The York flood case is special in the sense that it lasted only for a short period of time -three days and affected only soft services -IT services, which cannot be replaced immediately. Traditional risk assessment tools are invalidated by such a case because they either measure the direct economic loss resulting from the damages to physical infrastructures, or measure the indirect economic loss cascading along the downstream and upstream production chains. Therefore, our aim here is to provide an alternative tool -the Hypothetical Extraction Method -for these special disaster events with similar features when other risk modelling tools can no longer be used.
Some specific notes are also detailed below. P4 L 12-14: authors should specify the "international manufacturer", the loss and the type of incident Thank you very much. We have clarified and specified the loss and the incident type according to your suggestions.
P4 L37-54: "indirect losses" denote the wider disruption to services, including all urban networks (power grid, drainage, transport). The literature review can be expanded to include e.g. (Thieken et al., 2008); Aerts et al. (2013); Pregnolato et al (2016) Thank you very much. We have inserted and discussed the recommended reference in the Literature Review section.
P4 L 57-58: simplify the sentence e.g. "Post-disaster situations vary."; I would specify what is meant by "normal" flood Thank you. We have simplified the sentence to clarify what we meant by 'normal' floods.
P8 L 11-19: I would integrate these sentences between brackets into the main text Thank you. We have made the change accordingly.
P13 L 14: see above, transactions during the 25-26.12 are limited because of shops closure Thank you. We have made the change according to your comments.
P13 L 51-57: transform the "million decimals" (e.g. £0.075m) into a £75k; also at P14 Thank you very much. We have changed the unit according to your comments.
P14 L21: clarify how you results are "in line" with other studies (e.g. compare numbers) We agree that 'in line' is not an appropriate word here. We have clarified and changed it into 'These results reveal the heavy reliance on the supply of IT services in their operations and production. This, on the other hand, also acknowledges and confirms the recent remarkable growth in IT outsourcing in the UK.' P14 L42: just "Conclusion" Thank you. We have made the change accordingly.
P14 L 54-55: start the sentence with "Hypothetical Extraction Method" Thank you. We have made the change accordingly.
Thank you for your comments. To validate our results and consider the extensive transaction volumes that may occur during the Christmas period, we have added an upper boundary tof the results following the same methods but have employed different numbers for final demand during the three-day IT outages. Due to the lack of daily sales data for York, we assumed the same monthly trend in household expenditure as in the UK. According to data from the Office for National Statistics (2016), household expenditure on food, drink and tobacco, clothing and footwear and other household goods during December are 16%, 42% and 31% higher than those of other non-Christmas months during 2015. Therefore, we adjusted the original three days' final demand that was calculated from the IO table. The upper boundary of economic loss considering the Christmas consumption peak is estimated as £4.23 million. P15 L 7-24: could this be part of the discussion?
Thank you very much for this comment. We have expanded this part of the discussion to provide an upper bound, as well as the discussion on p7 under section 2.2 Applications of the hypothetical extraction method (HEM).
P15 L 26-33: I would remove the entire paragraph Fig. 1: it would be useful to see also the £ lost in addition to % Fig 2: axis titles are missing. Again, do not use the decimals of millions if none of the losses arrive to one million or more Thank you for this comment. In order to provide both the broad and sector-specific picture regarding the economic losses, we decided to keep the Figure1. However, we integrated the paragraphs in section 4 Results to make them more consistent. ****