Sam Marsh, University of Sheffield

Suppose you run a pension scheme, and you conducted a valuation in the middle of a pandemic which showed the need for savage cuts — cuts to which the employers’ representative organisation acquiesced. How do you hide the fact that since then, the scheme has strengthened dramatically, as your very own data (see Figure 1) make clear?

Here’s how, in six easy steps.

1. Stop publishing data

Stop providing updates that indicate the strengthening of the scheme. USS were publishing monthly data, but stopped with their Aug 2021 figures. Why? USS reported in December 2021 that the monitoring framework ‘need[ed] to be reconfigured in line with the 2020 valuation’.

When USS presented their new monitoring reports confidentially to the Joint Negotiating Committee in early February 2022, UCU made it clear that there was significant public interest in them, and it would be absolutely unacceptable to keep them confidential until late May or early June, as USS had planned. The reports — with data going back to September 2021 — were finally published on 16th February. As Financial Times pensions correspondent Josephine Cumbo noted, that was just one day before the deadline for ‘hundreds of employers to respond to a consultation on cuts to pension benefits, which were based on the scheme’s position in March 2020, when its finances were less rosy’ of Universities UK’s consultation on the UCU proposals.)

2. Explain away improvements with broad-brush arguments

In the absence of putting out the full information, make broad-brush arguments that explain away any improvements. ‘Assets have increased, but so have liabilities’; ‘deficits are down, but future service costs are up’; ‘higher asset prices mean lower expected returns’. That kind of thing. (See, as examples, USS, 16 February 2022; or Kate Barker, Chair of the USS Board on 26 November 2021 in Figure 3.)

3. Get employers to back you up

Once Universities UK (UUK) had set their sights on cuts, they were happy to reinforce USS’s message. (While for a period UUK showed some interest in challenging the validity of the 2020 valuation, they swiftly pivoted to pushing all of the pain of the valuation onto scheme members through benefit cuts in April 2021 when USS rejected their request for a revisiting of the assumptions; see also USSbriefs82.) UUK’s actuaries, AonUK, skillfully stayed within their professional code to draft a document of support which didn’t contradict their previous work that had suggested there might well be significant improvements to the scheme’s health since the valuation date, and that USS was being overly prudent in its valuation assumptions.

4. Tweak assumptions to obscure the improvements

Did you previously allow for out-performance assumptions in your calculations of future service costs? It’s an easy tweak to ditch them in your monitoring data so as to push rates up, and almost no-one will notice! But do include a footnote (see Figure 5). (Removing the outperformance assumption means using a lower investment forecast to calculate the future service costs than if it were kept in. An outperformance assumption was used in the 2020 valuation, and also USS’s update to March 2021, but has inexplicably disappeared in these figures [1].)

5. Use subtle tricks to reframe your position

If contribution rates have reduced so much that you can’t tweak it away, reframe your earlier numbers so that people don’t spot it. Here, the sky-high 57% rate in the 2020 valuation has magically shrunk to 52.6%. See what USS did there? (If you can’t spot it, notice that the deficit recovery contributions have dropped from the 20% featured in the 2020 valuation to 15.6% here. This is achieved by exploiting the fact that the higher rates will be phased in over three years: a simple averaging then allows the shaving of 4.4% off the displayed rate, and an opportunity to obscure the improvements.)

6. Drown people in data

Drown people in data so that readers struggle to find the key results. Assume that people will have forgotten that the Joint Expert Panel’s first report in September 2018 noted, in relation to the ‘large volume of information’ placed on the USS website, that ‘[v]olume is not a substitute for good quality information that can be understood by all’ (p. 10).

That doesn’t work on me, of course. Figures 1, 7 and 8 display the crucial statistics and graphs you need. These show the deficit having reduced by around 80% and the scheme moving much closer to being fully-funded, now holding 97% of the assets the trustee thinks are required to cover the liabilities. The total contribution rate needed has also fallen to 25.8%-28.7% of total salary compared to the 31.2% identified in the 2020 valuation.

Bear in mind, too, that all of these are based on projections which, implausibly, assume negative real growth — as I documented in USSbriefs106 [2].

Incredibly, USS claim that ‘it’s difficult to reach any definitive conclusions as to the true direction of travel’ based on this data — a claim that appears to have been greeted with skepticism by Josephine Cumbo, one of the country’s top financial journalists.

To wrap up, all the indicators and statistics show a USS funding position that has dramatically improved since the valuation date, as Cumbo reported on 21 February 2022.

So, six easy steps. That’s how you do it!

Endnotes

[1] USS identify highly prudent investment forecasts, and use these to calculate the deficit; they then use the same forecasts to calculate future service costs and deficit recovery contributions, except they may choose to add in some ‘outperformance’ of the highly prudent rate. USS added +0.5% per year in the 2020 valuation, reduced this to +0.25% per year in their 2021 update, and now, in Figure 5, we see how they have ditched it altogether (with no explanation) in the monitoring data.

[2] See the updated graph (2 December 2021) of real-terms asset growth and forecasts for USS, 1995–2068, by Jackie Grant.

Editors’ addendum

For those interested in other uses of what might be described as strategic ignorance and as selective uses of data, we point you to USSbriefs88, where Nicky Priaulx and Gail Davies outline how the Universities and Colleges Employers Association (UCEA), with whom UCU are also in dispute — over pay, precarious working conditions, inequalities, and workload — report on staff turnover.

Minor corrections were made to this brief on 2 March 2022 to correct typographical errors (which, amongst other things, reinstalled both endnotes which had failed to be transferred during proofreading).

This paper represents the views of the author only. The authors believe all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSBriefs111; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to hide a dramatically strengthening USS pension scheme in six easy steps was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Compiled by the USSbriefs team*

*If you know of others, please contact us at USSbriefs@gmail.com

On 20 February 2022, Universities UK announced that ‘93 out of 97 employers responding to the consultation — representing over 92% of the active membership of the scheme and over 98% of those responding by weighting — do not support the UCU proposal to conclude the 2020 valuation. Three employers indicated conditional support for the UCU proposal, and just one employer provided support’. For background on UUK’s misrepresentations of UCU’s proposals, read Mike Otsuka’s two-part account (10 February 2022 and 12 February 2022).

On 15 February 2022, the Committee of University Chairs released a statement on USS and the UCU proposals which, as reported by the University of Manchester, was ‘helping to inform the consultation response the University has to provide to USS this week’.

Last updated Tuesday 29 March 2022, 13:47 pm GMT

Bradford, University of

Bristol, University of

Cambridge, University of

Durham, University of

Glasgow, University of

(i) short response

(ii) longer response

Institute of Development Studies, University of Sussex

Manchester, University of

Queen Mary University of London

Sheffield, University of

St Andrews, University of

Sussex, University of

UCL

Committee of University of Chairs statement

The compilers believe all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. This post is licensed under a Creative Commons 1.0 Universal Public Domain Dedication License.

Employers’ responses to UUK’s consultation on UCU proposals to avert UK-wide strike action was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Neil Davies, University of Bristol

Jackie Grant, University of Sussex

Chin Yang Shapland, University of Bristol

A pdf of this brief is also available here

How much risk, and what types of risk, is the Universities Superannuation Scheme (USS) taking? This is a critical question for universities across the UK and many of their employees. Will the fund have enough money to pay for all our pensions? Will it run out? Or is there a significant risk that we are collectively overpaying? You might have thought that these questions would also be of interest to the USS Trustee, that oversees the fund for members’, or The Pensions Regulator, that regulates it. However, curiously, while the USS has published a plethora of incoherent statistics and opaque analyses, neither it or TPR have bothered to publish estimates of the risk of the scheme defaulting.

In September 2021, David Miles and James Sefton, Professors of Financial Economics at Imperial College Business School, stepped into this vacuum, publishing ‘How much risk is the USS taking?’. The paper presents important, accessible and highly readable analysis which estimates how likely the USS is to default over time. Their work is particularly relevant to the current UCU dispute with 69 employers over the benefit cuts that Universities UK (UUK) is planning to implement on the basis of the 2020 USS valuation.

In this Brief, we assess the assumptions, replicate the results, explore further their model and consider potential extensions. We demonstrate that for a cautious model with reasonable assumptions for assets and asset growth, the fund has a less than 7% chance of defaulting for the duration that pensions promises are due, but a greater than 80% chance of being over funded by at least £100bn, and nearly 50% chance of having over £400bn.

We offer warm thanks to David Miles and James Sefton for sharing their code and data, for their helpful conversations and clarification. Their analysis is infinitely clearer, better and more credible than anything the USS has produced. We hope this Brief will be the beginning of more work in this area. All errors are our own.

Exploring the Miles and Sefton model

Here, we replicate Miles and Sefton’s results, and assess the plausibility of the assumptions they use given the context of the USS. As with most empirical analysis, their results are highly sensitive to the assumptions used as input to the models, particularly about the mean and variability of rates of return. Nevertheless, their model provides an excellent framework for investigating how different strategies might affect the USS’s risk of default.

Miles and Sefton test whether the funds available on the valuation date can cover all the pensions promised from those assets, as they fall due and without further contribution. Although they state this is “not an assumption that the scheme closes”, the assumption is consistent with a closed scheme and does not model the features of an open scheme. The USS is an open “immature” scheme (i.e. its members are relatively young), so this assumption is highly conservative, as it is likely to increase the risk of default in any given year because in their model the fund will not receive any additional revenue (i.e. no new USS pension members or additional years of accrual), except from the returns from its investments. A second assumption Miles and Sefton make is that the scheme has no “covenant support”. This horrific piece of pension jargon just means that our employers (universities), cannot top up the fund if it looks to be underfunded. This assumption is not realistic, as the USS has perhaps the strongest covenant of any private pension scheme in the country. This is a conservative assumption that likely increases the model’s risk of scheme default, because their model assumes employers cannot provide additional funds to address any deficit. Nevertheless, in our analysis below we make the same assumptions, while acknowledging that they are highly cautious, and we hope to revisit these assumptions in future analysis.

Asset allocation

The asset allocation refers to the proportion of the fund that is assumed to be invested in either equities, which earn a higher but more variable returns on average, and bonds, which are assumed to have a lower but completely certain return. Miles and Sefton take the initial assets as March 2020 of £66.5bn, with a portfolio weighted 75% equities to 25% bonds and a base case for returns on bonds and find “there is a 40% probability of the USS running out of funds before all pension promises have been met. About 30% of the time the USS runs out of assets by 2056.”

We can reproduce these results. See Figure 1 below, which reproduces Figure 2 of Miles and Sefton. We also reproduce their results for all figures, including the more optimistic scenarios with reversion to the mean, and zero real rate of return on bonds. We provide code here, and Google sheets to reproduce Figures 2 and 3 and reproduce Figures 4 and 5 of their paper. R-code is also available on Github.

Measuring default risk and asset allocation

Next, how does the allocation to equities affect the risk of the scheme defaulting? This risk is presented in Figure 2. In this model we assume the Miles and Sefton base case values of real yields on index linked gilts as forecast from March 2020. What we see is that as the proportion of the fund that is invested in equities falls, the risk of default substantially increases [1]. For example, if the fund is only 25% invested in equities the risk of default by 2080 is nearly 100%. The last decade has seen many defined benefit pension funds reduce the amount they invest in company shares (equities), and increase the amount they invest in government bonds. This investment strategy is widely, and perhaps misleadingly, referred to as “derisking”. These models clearly show that the long term risk of default increases as the proportion of the fund invested in equities falls. In the jargon of USS “derisking” counter-intuitively increases the risk of default. The USS 2019 accounts, p20, report a USS reference portfolio with 60% equities. The USS 2020 valuation consultation, p23, assumes an allocation of 55% equities for a ‘strong’ covenant and 40% equities for a ‘tending to strong’ covenant. Increasing the proportion of the fund invested in bonds in this way, is likely to increase the risk of default. These results are consistent with analysis from the Government Actuary’s Department which also suggested that increasing the proportion of DB pension funds invested in bonds is likely to increase the risk of default. Therefore a key message from Miles and Sefton’s analysis, which is independent of input parameters, is that “derisking” or increasing the proportion of the fund invested in government debt likely increases the risk of default, and is very bad for members and employers.

Miles and Sefton evaluated the scheme as of 30th March 2020, at the depths of the COVID-19 induced financial panic. Worldwide stock markets fell by 19% in the 39 days prior to 30th March 2020. Clearly, since this date, many things have happened. Since March 2020, as can be seen in Figure 1 of USS Briefs 109 the value of worldwide stock markets have increased by 52%. Even the USS has seen a 33% increase in its assets with UUK’s actuary stating that March 2020 was a ‘poor date for the valuation’ and warned of ‘a danger that too much can be read into the conclusions of a valuation at that date.’ So again, this choice of date is likely to be a conservative assumption that increases the risks of the scheme defaulting, because if its initial assets are lower, then the long term risk of default increases.

Update to 2021

If the initial assets are instead set at £80.6bn (the value of the USS fund at March 2021) the risk of running out of funds before all pension promises have been met falls from over 40% to 25%, well within the 67% prudence level previously used by USS, even under the highly cautious assumptions of this modelling (Figure 3).

On the other hand, the risk of over funding by at least £50bn, for the duration over which pension promises are paid, is always over 50%. The risk of having over £200bn left in the fund when all promises have been paid is around 35%, while the risk of having over £400bn left over is over 20%. Thus, as Miles and Sefton point out, there are material risks of the scheme running out of money, but there are even larger risks of the scheme having far too much money, and ending up with a very large surplus. Both running out of money and having far too much money are very bad outcomes for members. If the scheme runs out of money, their pensions could be cut, and if the scheme has too much money, they will have paid far too much for their pensions. The employers will also have contributed far too much, and that money could have been used for university investment.

It is the job of the USS Trustee to balance these risks. Historically pension funds have aimed not for a 50:50 risk of being over or underfunded, but have aimed for a 65:35 risk of being overfunded — this is known as taking a “prudent” approach. It is always possible to decrease the risk of default by increasing the assets in the scheme, but this is expensive, and someone has to pay for it (i.e. active members and their employers). Again, it is the Trustees job to balance the costs of additional prudence on active members, against the benefits of reducing the risk of default.

The likelihood of running out of funds across a range of asset allocations, using the value of the USS’s assets at March 2021 is given in Figure 4. Figure 4 shows that the risk of default has fallen, as asset values have recovered from the COVID dip. This reduction in the risk of default is particularly large for strategies with higher equities allocations. For example, for asset allocation of 65% equities there is now only around a 25% chance the fund will run out of assets before all pension promises have been paid, and a similar 27% risk of having over £200bn remaining in the fund once all pensions have been promised.

Evidence based investment returns?

Finally, how do Miles and Sefton’s assumptions for investment returns compare to evidence? They assume equities have a real mean return of 4.5% (SD=17.15%). These estimates are low compared to estimates of historic returns in the published economics literature. For example, Jordà et al (2019) estimate that equities across 16 developed countries have mean real returns of 6.9% (SD=21.9%) [2]. They are also low compared with the historical real return of all USS investments which is around 7.8% over the last ten years and 8.1% over the last five years. Sefton and Miles justify this by assuming investment costs of between 0.25% and 0.5% of assets, and by assuming that future returns will be lower. The cited investment costs are high compared to even retail investments (e.g. index funds can charge as little as 0.03%), therefore it is unclear why USS costs should be so high [3]. Miles and Sefton argue that because the rate on UK government bonds is currently low, we should expect future returns to be lower. However, it is difficult to see how this claim is supported by empirical evidence. For example, Moody’s estimates the 15 year correlation between UK government bonds and worldwide equities to be 0.09, implying around 1% of the variation in future equities returns can be explained by UK government bond yields (i.e. the yield on government bonds does not meaningfully predict future long-term returns on equities).

Thus, the empirical basis for assuming the mean return on equities of 4.5%, rather than the just under 7% seen over the period 1870–2015 is unclear. While it is perfectly reasonable to use assumptions that differ from the historical averages, to be credible, these assumptions, and forecasts need clear empirical support. USS’s own rationale for proceeding with a 2020 valuation, on the grounds that a 2021 valuation requires such a significant reduction in return on equities, fails in this respect. Here we aim to explore reasonable assumptions to enlighten and progress the debate, but we look forward to further analysis.

With regard to assumptions on bond returns, we have used Miles and Sefton’s data throughout. It would be instructive to update bond returns to USS values in 2020 and 2021, and other projections, to better understand the dependency of the modelling on UK government bond yields. The Technical Appendix compares the Bank of England forward curves for March 2020 and 2021 with USS values of March 2020 and Miles and Sefton’s assumptions for March 2020. Initial analysis suggests broadly similar results for USS 2020 values.

The results when we use estimates from Jordà et al (2019) for the returns on equities, rather than the more pessimistic asset assumptions used by Miles and Sefton are presented in Figure 5. The risk of running out of funds before all pension promises have been met falls to around 9%. However, the risk of over funding by at least £200bn, that is once all pensions promises are paid, is almost 80%. The risk of having over £400bn left in the fund when all promises have been paid is around 65%.

It is worth considering other approaches to estimating returns on equities for a fund such as USS. We can model a simple globally diversified portfolio which invests in each country as a proportion of GDP, this results in real returns of equities of mean=7.09% and SD=13.52%, from the data in Jordà et al (2019). As described in the technical appendix, these values for mean return on equities are not dissimilar to the overall historical performance of the USS fund. This produces the distribution of Figure 6, where the risk of having insufficient funds to pay all pension promises as they fall due, with no further contributions, is less than 1% for an equities allocation of 75%. However the risk of over funding, with such assumptions, by at least £400bn is 85%. Given that the assumptions of the model are cautious, this risk of overfunding appears unacceptably high.

As a final example, Figure 7, we consider the USS reference portfolio from the 2019 accounts with 60% equities, a mean return on equities of 7%, and a standard deviation of 17%. The risk of default is a maximum of around 7% at 2100, a time period that covers the entire duration that pensions promises are due. The risk of default remains less than 5% up to 2060. The risk of overfunding though is clear, with an 80% chance of the scheme having over £100bn remaining, and nearly 50% chance of having over £400bn, having paid all pensions promises under ‘closed scheme’ style assumptions with no covenant.

Conclusions: where does this leave us?

These results demonstrate that even with prudent assumptions:

a) the risk of default, even over very long periods is low unless the scheme is substantially invested in bonds with a low real return

b) the risk of default using up-to-date asset values is even lower,

c) the USS’s preferred strategy of increasing the proportion of the fund invested in bonds is likely to increase the risk of default, and

d) the 50 year probability of default even using assumptions of a closed scheme is as low as 1% when using empirically based asset returns and updated USS asset values. This is far lower than conventional levels of prudence used in the pensions industry (i.e. 33%-35% as used in the 2014, 2017, and 2018 valuations)

Furthermore, even these figures are likely to overestimate the risk of default, as it does not allow for the fact that this scheme is an open immature scheme which has one of the strongest covenants in the UK. It also omits the proactive regulation of open DB schemes in the UK through the TPRs triennial valuations and interventions. It would be relatively straightforward to expand the model to include an intervention every three years that supplied cash top-ups to those paths of the simulation that fell below a given percentage confidence level of a funding target. We leave analysis of possible funding strategies and coherent funding metrics for future work.

Unlike the sum total of analysis published by the USS to date Miles and Sefton’s analysis and model provides a coherent framework and basis for assessing the risks that members care about — the risk that the scheme will default and the risk that we are all being overcharged. These results, and the results of Miles and Sefton suggest that we need an urgent detailed, open and reproducible exploration of the USS valuation methodology. This is the only way to restore members’ trust after the unnecessary, unjustified, unevidenced and catastrophically pessimistic USS 2020 valuation. This work highlights the significant risk of overfunding, and makes clear that changes are needed to the way risk is quantified in open DB pension schemes.

It is important to ask what bearing this has on the current valuation and planned cuts to benefits. We remind readers that the UCU formal dispute letter calls on employers to

1. Agree to revoke UUK’s proposed cuts to the Defined Benefit pension that were approved by JNC resolution on the 31 August 2021, and not to replace them with any alternative set of changes to benefits and/or increases in member contributions, unless this replacement has the agreement of UCU.

2. Publicly call on USS to issue an evidence-based, moderately prudent valuation of the financial health of the scheme as at 31 March 2021.

Given this exploration following from the work by Miles and Sefton, it is clear these demands are not only entirely reasonable, but absolutely necessary. We ask that all employers and scheme members call on UUK to agree to revoke their proposals, and produce a new valuation using a credible, evidence based approach to valuing the scheme’s financial health.

[1] More specifically there is crossover point towards 2050 (in models using the base rate assumptions on bond returns) where the likelihood of default is around 10% for all asset mixes.

[2] Further, if we were to weight countries in the Jordà et al. dataset by their GDP, rather than using raw averages, the mean=7.43% and SD=20.52%. This would imply even lower rates of default. In addition, these figures implicitly assume that the USS is invested in a single country. Whereas in reality the USS can (and really should), have a globally diversified portfolio to minimise investment risk by investing in a broad range of countries. Using a simple strategy of investing in each country as a proportion of GDP results in returns of equities of mean=7.09 and SD=13.52 (see Figure 6).

[3] As an aside USS costs, particularly investment costs, are extraordinarily high, far higher than comparable large schemes reported in the TPR cost analysis. Therefore, there is no reason for USS costs to be as high as Miles and Sefton assume.

This paper represents the views of the authors only. The authors believe all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs110; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The USS Trustee’s risky strategy was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Neil Davies, University of Bristol

What’s going on with the valuation?

The good ship ‘Titanic’ University Superannuation Scheme (USS) is continuing full steam ahead towards its 2020 valuation. The 2020 valuation estimates the USS’s assets at the end of March 2020 and then estimates how much it would need to have had at that time to fulfil all its future pension payments promised for coming years. The USS has estimated that as of March 2020, it had between £9.8bn and £14bn less than it needed to pay our pensions over the next 80 or so years. As a result, the USS argues that the amount we pay for pensions each year needs to increase, and the future pensions we earn each year needs to be substantially cut.

Why was a 2020 valuation a bad idea?

The USS is not required to have a 2020 valuation by law — it is optional, at the discretion of the Trustees who run the scheme on behalf and for the benefit of USS members. The law only requires the USS to conduct valuations every 3 years, in the case of USS by March 2021. So, is it reasonable for the USS to conduct an optional valuation as of March 2020, and was it in members interests?

First, the 30 March 2020 was perhaps an inopportune moment to value the scheme’s investments (Figure 1). Worldwide stock markets fell by 19% in the 39 days prior to the valuation date. In the 3045 days included in Figure 1, there were only a handful of 39 day periods that experienced greater declines, all of which were in March and April 2020. Furthermore, since March 2020, as can be seen in Figure 1, the value of worldwide stock markets have increased by 52%. In March 2020, the USS estimated its assets were worth £66.5bn. Yet in June 2021, it estimated its assets had increased by £18.8bn, to £85.3bn, an increase substantially greater than the upper estimate of the deficit in March 2020.

The original rationale for an early, optional, 2020 valuation was agreed with employers as part of the resolution to the 2018 valuation. Employers’ original thinking behind this was that this would enable them to replace the 2018 valuation with a more favourable valuation in 2020. The motivation for this was to avoid increases in employer and employee contribution rates that were agreed as part of the 2018 valuation. Clearly, our employers’ cunning plan backfired somewhat. However, remarkably the USS further claims:

“Even if we had not planned to hold a valuation as at 31 March 2020, it is highly likely we would have concluded that a valuation was the most measured way to respond to and investigate the pandemic’s impact on the Scheme’s funding position.”

The financial turmoil that convulsed markets in March 2020 affected every one of the 5000+ pension schemes in the UK. Yet weirdly, the USS appears to be almost the only pension scheme in the country that voluntarily conducted a discretionary valuation, one not required by law, at the depths of the COVID induced financial panic. It is almost as if the Trustees of other schemes concluded that it might not be in members’ interest to conduct an unnecessary valuation at the outset of a once in a generation pandemic.

As part of the valuation process, the USS is supposed to keep its figures updated for what is called “experience”, i.e. how financial markets and the value of its assets and liabilities have changed since the valuation date. Great, right? Worldwide assets have increased by 50%. Even the USS has managed to increase its assets by 28%, and these assets now exceed any of its estimates of the amount needed to pay our pensions in 2020, even those that assumed no real return on its investments until 2102. So we just update the value of our investments to reflect today’s asset values, and there’s no problem right? Sadly not — the USS claims that future asset returns will be lower.

Now, pensions law requires that pension schemes need to have evidence to justify changes to their valuation methodology. For the 2020 valuation, after extensive discussion with The Pensions Regulator (TPR), the USS proposed assuming a maximum return of 0.2% above inflation. This is reduced from 0.92% above inflation in 2018. These reductions in the assumed rate of return are due to the USS making ad hoc and unprecedented changes to its assumptions about investment returns (especially the level of prudence it used, see here and here for details).

Previously, the USS has argued that falls in the rate of return on UK government debt (the “yield”) implies that the returns on a world-wide globally diversified portfolio will also be lower. However, between March 2020 and March 2021, the yields on UK government debt rose. In an increasingly desperate attempt to maintain the pretence that the scheme is underfunded, USS are claiming that they need to further reduce the assumed rate of return (the discount rate). In the USS Financial Monitoring Plan (a monthly update on scheme funding), they’ve assumed returns of up to -0.89%+CPI, and so the scheme’s deficit remains the same size despite the increase in its assets.

So the obvious question for the USS is: would it be reasonable and prudent to have not pressed ahead with an optional 2020 valuation at the depths of a pandemic, and submit a March 2021 valuation as required by law?

The USS has considered this, and published an entire “USS Brief” (imitation is the…), on the potential benefits and contribution rates of a 2021 valuation. In this document, the USS repeatedly claims that expected future investment returns have fallen since the depths of the COVID-19 pandemic in 2020. Indeed their expectations of future growth have fallen so much, they have offset much of the increase in asset values. The USS merely asserts that future asset returns will be lower. They present no credible evidence to support its claims about future asset growth. How pessimistic are the USS’s assumptions?

The changes the USS has made to its assumed rate of return above inflation are shown in Figure 2. Over the period 2011 to 2021, the USS has reduced the assumed rate of return from over 3% per year to 0.02% in 2021. It is these changes to the assumed rate of return that is driving the changes in the scheme. Are these changes justified? Are these changes to the assumed rate of return supported by evidence? Despite repeated requests from members of the board of Trustees, the Joint Negotiating Committee (JNC), and its stakeholders, the USS has consistently failed to provide credible evidence to support these changes. However, fortunately, economists have published lots of evidence about historical returns. For example, one reasonable source of evidence about investment returns is Jorda et al. (2019). They have compiled a dataset of investment returns to a range of assets, including equities and bonds for 16 countries between 1870 and 2015. Fortunately, unlike USS, Jorda et al. (2019) provide both their data and their statistical code. We can use this data to estimate the 30 year real returns of a USS-like portfolio of assets, this is shown in Figure 3.

The lowest return ever recorded in the dataset was 0.92% above inflation per year between 1890 and 1920. During this period around 40 million people died and much of Europe was blown to bits in the First World War, and 20 to 50 million people died from the Spanish flu pandemic. So is it reasonable to assume a maximum rate of return of 46 fold lower than the lowest return ever seen between 1870 and 1985?

Thus, the USS’s claims that members would be no better off in a 2021 than 2020 valuation are only true, if expected future returns are reduced from CPI+0.2% in 2020 to 0.02%+CPI in 2021. Even given a return of 0.02%, the estimated deficit would be far smaller than estimated in 2020.

So yet again the USS has failed to act in members’ interests or in the interests of the communities they serve — such as their students. Yet again, they have failed to provide credible evidence and analysis to support their claims. However, despite the continued pitiful performance (financial and actuarial) of the scheme, and the palpable fury of many members, there is little USS members can do within the scheme rules. The Board of Trustees that oversees the USS is entirely unaccountable to members. Members have no say in the appointment of many of the board members, and astonishingly the current chair of Trustees was appointed merely at the discretion of the 12 board itself.

If it is to regain any confidence and trust of members, the USS needs urgent governance reform. UUK has recently proposed a review on USS governance. However, given none of the Joint Expert Panel’s recommendations for reform of the scheme’s governance have been implemented by UUK or the USS board, members may be a little sceptical. As a result, members’ best hope maybe industrial action against their employers, and legal action against the USS Board of Trustees.

This paper represents the views of the author only. The author believes all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs109; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The case for a credible evidence-based 2021 USS valuation was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Neil Davies, University of Bristol

The USS valuation is based on assumptions. By changing these assumptions the USS can radically change the outcomes of the valuation such as the deficit and the costs of pension in future. One of the most important assumptions is the level of prudence on investment returns. Pension trustees are legally obliged to be prudent in the assumptions that underpin fund valuations, and adequately justify any changes to these assumptions from one year to the next. The USS 2020 valuation has brought these obligations into sharp focus, as a huge deficit and steep proposed increase in contributions have led both the union and employers to question the calculations (see USSbriefs106). In this brief, I argue that USS’s justification for adopting a new definition of prudence falls woefully short of what is required, and meet no professional standards for financial modelling, providing further evidence that a change in governance is urgently required in order to restore faith in the fund and avoid further turmoil in HE.

How (not) to value a pension scheme

Defined benefit pension scheme valuations are governed by The Occupational Pension Schemes (Scheme Funding) Regulations 2005 section 5. This dictates that the economic and actuarial assumptions must be chosen prudently, taking into account either the yields on assets held by the scheme and/or the market redemption yields on government or other high-quality bonds. Any change in the method of assumptions used from the last valuation must be justified by a change of legal, demographic or economic circumstances.

Thus regulations require the USS Trustee to prudently choose assumptions for the valuation. The Trustee argues that prudence is a qualitative judgement that cannot be expressed as a single number [1]. However, to comply with the regulations above the Trustee must be able to demonstrate that the process it followed to decide the level of prudence in the valuation was reasonable and was based on reasonable evidence that was relevant to the scheme. Critically, it is imperative that the Trustee does not reverse engineer its assumptions to produce a certain prespecified outcome.

Unfortunately, the Trustee has not published any evidence to date to justify many of the changes it is looking to make to the valuation methodology, including one of the most consequential: changes to the “level of prudence” on investment returns. The USS’s rationale for choosing the confidence level on investment returns is incoherent and unclear. The most plausible explanation is that they have reverse-engineered the confidence level to get a pre-desired discount rate of gilts + 2%, then arbitrarily increased this to derive the proposed discount rates for different levels of guarantees (covenant).

Valid statistical analyses have essentially three steps:

1. define assumptions and data;
2. use the assumptions and data to estimate a model;
3. and interpret results.

It is of course possible to reverse this process:

1. define the results you want;
2. estimate your model for a range of assumptions;
3. and choose the assumptions that give you the result you want.

The latter approach — ‘reverse engineering’ — would normally be considered fraudulent for a scientific paper. Yet this appears to be the approach taken in the USS 2020 valuation, reverse engineering the discount rates by manipulating the input parameters used in the valuation — specifically the level of prudence on investment returns.

What is the “level of prudence” of investment returns?

The level of prudence in investment returns is the point in the distribution of returns that the valuation is conducted at. For example, consider Figure 1 below. These simulations use data from Jorda et al. (2019) to provide the mean and standard deviation of expected returns above inflation. The simulations are very simple, and can also be generated using a Google sheets toy example. For each year, the simulation draws a value of investment returns to bonds and equities using the distributions of investment returns estimated by Jorda et al, and the reference portfolio of the USS (a 64:36 equities:bonds ratio). The simulation draws 30 years of investment returns. This process is repeated 10,000 times to provide a distribution of annualised investment returns over 30 years.

The arithmetic mean annualised investment return is 4.7% and the median (50th centile or best estimate) return is 4.8% above inflation. While “prudence” is not defined in law, in the valuation, USS interprets the “level of prudence” in its investment forecasts as a point in the distribution of returns determining the discount rate to be used in the valuation. In the 2014 valuation, they used the 65th centile, i.e. the point such that 65 out of 100 simulated outcomes will give a higher return. In our simulation, the 65th centile has a return of 3.9% above inflation. So the discount rate based on this level of prudence is 3.9% above inflation. In 2017 and 2018, USS proposed using the 67th centile of investment returns; they increased the “level of prudence”. In our simulation, this gives a return of 3.8%, and thus a discount rate, of 3.8% above inflation. So as the level of prudence is increased, the discount rate falls, as we move to the left in Figure 1. In the 2020 valuation, the USS originally proposed using a range of different “levels of prudence” on the assets invested for pensions promised to members already drawing their pensions (73%) and active members still paying into the scheme (78% or 85% depending on the commitment of employers to the scheme). In our simulation, this equates to a discount rate of between 3.4% and 2.5% above inflation for active members. Thus, even though our assumptions about future returns have remained unchanged (the simulations are identical) USS can manipulate the discount rate by changing the level of prudence, or where in the distribution of returns it is choosing for its calculation.

As the expected discount rate decreases, the costs of providing benefits increase, very sharply. Moving from the 67th centile of expected returns to between 73rd to and 85th centile is estimated to cost the scheme, and ultimately members between c£7.4bn and c£9.5bn.

What evidence supports the USS’s proposed changes to the level of prudence?

We have recently submitted a complaint to the USS, signed by almost 4000 colleagues, which included two simple questions:

1. What process did the Trustee use to decide to recommend increasing the level of prudence from 67% used in 2018 to between 73% and 85% for the 2020 valuation?
2. What evidence did the Trustee use to inform its recommendation to increase the level of prudence for 2020 valuation?

The USS has recently published a note which attempts to explain and justify the changes they are looking to make to the level of prudence used in the valuation. They argue that comparing the “level of prudence” across valuations is challenging and that the “overall level of prudence” in the 2020 valuation is not obviously higher or lower than in 2018. However, as we have seen above, the level of prudence on investment returns is simple to compare and has been increased very substantially, at great cost to members.

The USS argues “Looking at prudence purely in terms of the assumptions for life expectancy and discount rates focuses only on valuation inputs. It is more instructive to view prudence through a wider set of lenses that also considers its impact on valuation outputs.” In the Pension Regulator’s (TPR) letter in response to the Section 76.1 report, they are also explicit that discount rates should be selected by choosing valuation input assumptions “However, in his initial advice on the discount rate assumptions (report date 24 May 2020), the Scheme Actuary advised the Trustee to limit the discount rates derived ‘mechanically’ by applying confidence levels on the FBB returns.”

However, this is precisely what the Trustee is not supposed to do — to reverse-engineer the assumptions and inputs for its models by considering the outputs (i.e. discount rates, the deficit, and contribution rates). Thus, they provide no external evidence to justify the changes in the level of prudence they are looking to make.

What is the impact of using 20 years expected returns rather than 30 year expected returns?

The documents the USS has provided for the 2020 valuation fall very fall short of the standards expected of academics and scientists, and are provided on a non-reliance basis, meeting no professional standards. For example, in the original proposal published by the USS in September 2020, no mention was made of a major change in their analytic models — specifically that they had changed from modelling 10 to 20 year returns to modelling 30 year returns. We only know they have made this change because of the documents they have published to justify how they are modelling prudence. They make a number of very curious claims about their modelling of investment returns:

“The 2017 and 2018 valuations used a combination of 10-year and 20-year return distributions, allowing for the time profile of expected returns relative to CPI, and the weighting of the return in each future period by the size of the corresponding benefit.

By contrast, the 2020 valuation uses a 30-year distribution of investment returns. Using a similar approach to the 2017 and 2018 valuations would result in confidence levels for both the discount rates for the 2020 valuation that were 73% — 79% (some 7% — 9% lower than confidence level for the pre-retirement discount rate indicated in Table 2) depending on the investment strategy.”

We can investigate the implications of this change using the simulations above. In Figure 2, the simulations have been rerun, but this time they include the distribution of returns at 15 years.

The average 15 and 30-year returns are very similar, around 5%. However, the distributions are very different, and the 15-year returns are much more variable. This is because, over time, year on year variation tends to get smoothed out, thus the variance of 30-year returns is much lower than 15-year returns. Therefore, the estimated returns at the 67th centile are 2.7% and 3.3% for the 15 and 30-year returns respectively. However, this creates a problem for the USS — the investment returns at a given level of prudence are higher for 30 year than 15-year investment returns, because 30 year returns are less variable than 15 year returns. To resolve this, USS argues we should keep the discount rate constant, and vary the level of prudence (i.e. which point in the distribution of returns should be used to derive the discount rate for the valuation). For example, in our simulations, the 73rd centile of returns in the 30-year returns is the same as the 67% centile of the 15-year returns. However, in doing this, they have fundamentally increased the level of prudence in the valuation — the expected investment returns at 15 and 30 years can only be equated if the level of prudence in 30-year investment returns is substantially and quantifiably higher. Yet, the USS has provided no evidence or justification for this increased level of prudence.

The USS also notes LCP’s “approximate analysis”, which has not been published or any meaningful details provided. Until the USS publish the details of this analysis, it must be disregarded.

Is 65% or 67% sufficiently prudent?

The USS used a 65% level of prudence in their 2014 valuation, and TPR did not challenge the valuation as being non-compliant with the legislation. In 2017 and 2018, USS increased the level of prudence to 67%, although no evidence was provided to justify this change. Therefore 65% and 67% level of prudence on investment returns were not challenged by TPR in 2014 and 2017 and 2018 respectively. Since then has there been any legal, demographic or economic circumstances that have changed sufficiently to justify this change in the valuation methodology? To date, the USS has provided no evidence to support this change. The most plausible explanation for why USS changed the level of prudence is that this is the level of prudence which gives a discount rate they, and TPR want. However, the discount rate is an outcome from the statistical model used in the valuation. Adjusting the input assumptions, such as the level of prudence, to achieve a desired discount rate is reverse engineering. This reverse engineering invalidates the valuation, as manipulating the level of prudence allows the Trustee to set the discount rate, deficit and future service costs to be whatever they want, irrespective of evidence.

USS lenses and contexts for comparing prudence

Despite arguing that the level of prudence is a qualitative judgement that cannot be expressed as a single number, USS prudence note details no less than five quantitative “lenses” and three “contexts” to justify their proposal to increase the level of prudence on investment returns for the 2020 valuation. I go through each of the arguments in the appendix. The key question is “do any of the arguments provided by USS provide quantitative evidence to justify increasing the level of prudence on investment returns in the 2020 valuation? The short answer is no. The lenses either suggest that prudence has been substantially increased (lens 1), or are outputs of the USS valuation model (lenses 2–5), or are qualitative statements about other factors in the valuation that are not justification for increasing the level of prudence on investment returns (e.g. the economic conditions at 30th March 2020, the strength of the covenant, and the size of the self-sufficiency deficit). None of these lenses or contexts are evidence to support increasing the level of prudence on investment returns, and suggests that the USS has reverse engineered assumptions of the valuation. See the appendix for full details.

Considering “the level of prudence” in the round

USS concludes by arguing that they do not use any single metric to define prudence, instead taking a holistic qualitative judgement on prudence. Prudence is widely defined as the excess over best estimate — it is the basis of all capital adequacy regimes for financial institutions. But equally, none of the comparisons they present, with the exception of lens 1, provides any meaningful quantitative comparison of the levels of prudence used in the valuation.

Das ist nicht nur nicht richtig; es ist nicht einmal falsch

It would be enormously helpful for stakeholders if the USS could provide a single overall measure of prudence used in the valuation, rather than making ad hoc changes to individual assumptions. The USS proposal to tweak the level of prudence on investment returns to make up for perceived underestimation of prudence elsewhere in the valuation is risible. There are many approaches for integrating statistical estimates into a single overall model to provide an overall indication of uncertainty or “prudence”. For example, multi-parameter evidence synthesis, meta-analysis, etc. But despite repeated criticism of its ad hoc approach to modelling, the USS has not been able to develop a coherent valuation methodology.

As a result, for the last six years, every valuation published by the USS has been met with incredulity from its stakeholders. I am 36 years old. I have no interest in seeing my pension underfunded, and would be happy for contributions to increase if necessary. Yet the USS has provided no acceptable reasoning for increased contributions, ludicrously basing its decisions based on qualitative judgements and arguments with scant theoretical or empirical justification.

None of the above will come as a surprise to members. Highly qualified USS members have been raising serious concerns about the valuation methodology for years. UUK have recently raised a series of similar concerns, only to be fobbed off with a contemptuous response from the Trustee.

How can we fix this mess?

All of this is indicative of a major ongoing governance crisis at the scheme. The JEP2 report made a number of simple recommendations to improve the scheme’s governance and valuation process. Naturally the USS has done nothing to integrate these recommendations into the scheme rules. I outline the problems with USS’s governance here and include some recommendations about how oversight and accountability of the Trustee must be urgently improved.

If USS governance is not radically improved, the USS Trustee stands little chance of recovering its members’ trust and will continue to fail in its fiduciary duties.

This paper represents the views of the author only. The author believes all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs108; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

[1] This is incorrect. Many statistical models synthesise errors from multiple parameters to provide an overall indication of uncertainty or “prudence”. Widely used examples include meta-analysis and Multi-Parameter Evidence Synthesis (MPES). Indeed, USS also argue “Prudence in the valuation means that there is much greater than a 50% confidence level that our funding target, the technical provisions (TP) liability, will be achieved” i.e. prudence is the probability of meeting the funding target in excess of 50%.

CORRECTION: On April 12th, the value in this sentence was corrected from 3.5%: “So the discount rate based on this level of prudence is 3.9% above inflation.”

The USS’s prudence: evidence-based or reverse-engineered? was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Deborah Mabbett, Birkbeck, University of London

Another valuation cycle has come round, and USS has produced some new large numbers for the scheme’s deficit, ranging from £14b to £18b. These figures suggest that USS will not be able to pay the pensions it has promised without a substantial increase in contributions (see USSbriefs106). But assessing whether a pension scheme is adequately funded or not is an exercise shrouded in uncertainty. It requires guesses to be made about future investment returns, inflation and pensioner longevity. The regular valuation makes these guesses and throws up an answer, but that answer is only as good as its assumptions and methodology.

Deep uncertainty about the future makes everyone uncomfortable, from contributors to scheme administrators to the regulator. Many in the pensions sector respond to this uneasy predicament by embracing the valuation as the next best thing to godliness, but the valuation process has profound limitations. One of the striking things about the pensions sector is that these limitations are quite well-known and discussed in the professional community, yet valuations dominate the governance of defined benefit (DB) pensions (see USSbriefs57; USSbriefs44).

The regulator, scheme administrators and employers have been caught up in pernicious processes of financialisation (see USSbriefs16; USSbriefs50). This often vague term has a well-defined meaning in the context of pensions, with distinct and traceable consequences. Valuation uses three financial techniques: discounting, mark-to-market pricing, and measuring risk by volatility. Discounting is used to turn the future flow of pension payments into a present value for liabilities, assets are valued at their current market prices, and the risk that assets will be insufficient to meet liabilities in the future is estimated from the past volatility of asset prices.

Challenging assumptions

These techniques allow the uncertain future of pension obligations and fund performance to be reduced to a snapshot in which available information about the future is efficiently compressed into the present, and expressed as a fund surplus or (more often) deficit. It is a remarkable achievement to render an uncertain future calculable in this way, and many users find the performance convincing. Critics have to work hard to chip away at the authority of a valuation. In some aspects, they have succeeded (see USSbriefs106). It is well-known that mark-to-market pricing can be procyclical, exacerbating economic instability. Regulators remain attached to market values, not because those values are right but because they are beyond the control of regulatees. Any deviation from market prices at the valuation date is seen as ‘manipulation’. Consistent models driven by ‘objective’ forces are preferred to messier human processes of debate and negotiation, despite known issues with the quality of the information.

A different problem affects the discount rate. This has to be chosen by those doing the valuation exercise. The choice is biased to low values (inflating the present value of future liabilities) because this is seen as suitably precautionary. ‘Conservative’ assumptions are favoured. Benchmarks for conservatism are drawn from the choices made by other valuers. Few have the courage to stand up for a higher discount rate, even though the precautionary bias that supposedly protects the pension scheme can damage the sponsoring employer. This damage is set aside because the whole exercise is oriented to protecting already-accrued pension rights, with no voice for those who might hope to gain a pension in the future.

Managing the wrong risks

But it is in the estimation of risk that the valuation process descends wholesale into a world of smoke and mirrors. It may seem obvious that the risk that has to be assessed is the risk that the resources of the scheme will not be sufficient to pay pensions as they fall due. But that is not the risk that the valuation process focuses on. It is concerned with ‘valuation risk’: the risk that the current value of the fund is less than its discounted liabilities. This risk is measured as the volatility of the value of the fund, and it can be reduced by investing in assets with less volatile prices: this is ‘de-risking’. This reduction in risk brings with it a reduction in expected investment returns. The effect is that the risk that scheme resources will be insufficient is actually increased by so-called de-risking (see USSbriefs49).

Numerous distinguished economists, statisticians and practitioners have drawn attention to the perverse effects of managing valuation risk rather than managing the meaningful risk that scheme resources might be insufficient to fund the future flow of pension payments. As I document in Reckless Prudence, my recent article in the Review of International Political Economy, the problem was highlighted in the 2000 Myners Review of institutional investment in the UK, and reiterated by John Kay in his 2012 review. The Bank of England has also weighed in, but to little effect. Somehow, attention to valuation risk has proved impossible to dislodge. The Pensions Regulator is totally committed to the central place of valuations in pension fund governance, and over the years it has rewritten the Myners principles to make managing valuation risk a central task of pension fund trustees. Employers are also compelled to pay attention to valuations, particularly since the adoption in the early 2000s of corporate accounting principles that mean that pension fund deficits or surpluses must appear on sponsors’ balance sheets.

Re-negotiating risk

In short, defined benefit pensions have fallen into a trap. The trap was designed and built by financial economics, but financial economics has moved on, leaving behind a set of interlocking practices that no policy actor seems to be able to unpick. One response has been to deem the whole setup beyond rescue, and advocate defined contribution (DC) pensions instead. However, as many have noticed, these place a lot of risk on scheme members (see USSbriefs17).

Looking outside the UK, it seems that better solutions can be found when employers and unions keep a firm grip on their schemes and manage to resist wholly financialised governance. Collectively-managed schemes can adopt different approaches to risk management, often involving more flexibility in pension commitments. Paradoxically, the commitment to DB pensions in the UK is exceptionally rigid, closing off pragmatic negotiated risk management. Intergenerational unfairness is one consequence when DB schemes are replaced by DC: those with accrued rights are strongly protected, while new employees are pushed into an insecure DC world.

The current regulatory structure for DB pensions can make it difficult for employers and unions to wrest control of their schemes from the tight grip of scheme administrators and the Pensions Regulator. One escape route is to abandon DB in favour of a ‘collective defined contribution’ (CDC) scheme. The scheme negotiated by Royal Mail and the Communication Workers Union provides some defined benefits, but it avoids much of the burden of DB regulation because there is also scope to vary accrued rights and pensions in payment. In a CDC scheme, valuations are still done, as the trustees have to ensure that successive generations of members are treated fairly. Valuations inform this decision-making, but do not dictate it. They do not determine investment strategy; nor do valuation deficits or surpluses have real effects through corporate accounting. There is a lot to discuss and negotiate in a CDC scheme, and there will no doubt be a temptation to reduce this decision-making to financial formulae, which should be resisted.

Co-operation remains key

The lesson for USS is that employers and unions must continue to cooperate to defend the scheme and challenge the approach taken by scheme administrators and the regulator. A decade after the financial crisis, financialised governance remains immensely influential, despite deeper understandings of its flaws. Trapped in the bizarre play-acting of a defined benefit pension scheme, it is natural to want to leave the theatre. But there is no way that people are better off on the street. Collective governance can bring great rewards despite the difficulty of mediating divergent interests. The challenge for the next decade is to grow out of dependence on the nostrums of finance and reinstate collective control.

Deborah Mabbett was a member of the Joint Expert Panel on USS, which produced two reports in 2018–19. This paper is written in a personal capacity and represents the views of the author only. It draws on her article ‘Reckless prudence: financialization in UK pension scheme governance after the crisis’ published in the Review of International Political Economy, 2020. The author believes all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs107; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Fighting financialisation in USS was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Sam Marsh, University of Sheffield

On 3 March, after months of delays, USS revealed the outcome of their 2020 valuation. We’re used to their numbers being off the scale but this time they have outdone themselves. To keep benefits unchanged will, they say, require a contribution of somewhere between 42% and 56% of salary, much of which will be used to cover a huge deficit of around £15bn-£18bn. These figures have the potential to savage pay and conditions and rock the higher education sector. Luckily, they seem to be built on sand.

Checking the health of a defined benefit scheme like the USS, which has to pay out a secure income for each of its members between retirement and death, requires a complex calculation founded on assumptions about the future. Predicting how long people will live often gets the attention, but arguably more important is the prediction of the likely rate of return that contributions invested to pay for the benefits will deliver, the so-called ‘discount rate’. Small tweaks in this assumption can easily turn surpluses to deficits, affordable contribution rates to those of the kind USS served up. So how do USS’s assumptions stack up?

Unreasonable assumptions…

By looking at annual reports as far back as we could find them, Jackie Grant and I have collated and plotted figures for the annual investment growth achieved by USS since 1995 and compared them with the forecasts used in the past decade of valuations (see Figure 1). The results are striking.

While the portfolio has grown steadily in real terms over the past two decades — a period that takes us through the dot-com bubble, the credit crunch, and the market-shock caused by the pandemic — the forecasts used in successive valuations have become progressively more pessimistic. With the exception of the 2018 valuation, each time investments have outperformed the forecasts, the forecasts have been downgraded further. The huge deficits announced by USS result from an assumption that we’ve reached the limit: the investments can no longer be relied on to grow in real terms. Capitalism is dead.

…unacceptable consequences

If that sounds unlikely to you, you’re not alone. Even USS don’t actually think the returns will be so low. In their valuation documents they put the chances of that at around 10–20%. My reading of their figures suggests it may be even lower. This extreme prudence, much higher than USS have used in the past, is the overwhelming source of the low discount rates which lead to the soaring costs and eye-watering deficit estimates (see USSbriefs67). Throw in a valuation date of 31 March 2020, when panic in the markets drove down the value of assets significantly, and you have a recipe for a crisis. (The scheme’s investments have since rebounded significantly, up around 20% from 31 March 2020 to now, a year later.)

Yet again we have a valuation which strains under close scrutiny being used as a basis for contribution increases or benefit cuts. Where in the past this has pitted the scheme’s members against employers in hugely damaging industrial action, hundreds of hours of discussion over the past years and months have brought the two parties together, and both are now calling out the most recent valuation as unjustified. Universities UK, the representatives of the employers, have written to the Chair of the USS trustee board asking for a review of the valuation. What happens next is anyone’s guess.

How did we get here…and what’s next?

If you’re wondering how we got here, let me finish with a little history. In the noughties, choppy equities markets caused large swings in the funding position of the USS. By 2013, a tighter regulatory environment and a change of mood saw the USS board employ the then head of the Pensions Regulator, Bill Galvin, as its CEO. The appointment might have been seen as an attempt to bring the scheme into line with the prevailing direction in the pensions industry. The task was to reduce the scheme’s reliance on growth-seeking assets and follow the orthodoxy of ‘de-risking’ which, among other things, would enable an orderly winding-up of the scheme if necessary (see USSbriefs73). Under Galvin, USS introduced three tests (Test 1 being the most famous) that were sold as a way to manage the scheme’s risks, magically controlling both the contribution rates and the reliance on the employers in a way that should allow an easy exit from the scheme at any point employers were brave enough to try it. A ‘self-sufficiency’ funding level would remain within reach at all times.

But it was too good to be true. If employers believed that USS’s executives had found a way to tame the markets and the scheme, then they were sold a fiction. The reality since Galvin’s appointment has been a succession of valuations in which benefit cuts and contribution increases that employers believed would stabilise the scheme were deemed insufficient almost as soon as they were implemented, requiring more pain at the next valuation. The control over contributions and funding levels that employers might have expected turned out to be an illusion.

The irony is that an approach closer to how the USS had been managed historically may well have led to a decade in which members and employers would have received the stability they craved. That stability would have come from recognising one of the huge strengths of the scheme: that its stable cash-flow position while it remains open to accrual allows it to ride out bumps in the markets that closed schemes feel acutely. By attempting to mimic the management of closed schemes, Galvin’s approach introduced the very instability employers were hoping to avoid.

What we are seeing now is the culmination of the mindset that Galvin brought to the scheme, which brings only a spiral of decline, unaffordable contribution increases and industrial strife. That approach has been a demonstrable failure on its own terms, and members, employers and the sector are paying a heavy price. We urgently need a rethink on how the USS is managed. I’m very glad our intense negotiations with employers over the past year or more have brought us to close alignment on these issues, and that Universities UK are starting to call for change. Better late than never.

This paper represents the views of the author only. The authors believe all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs106; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How extreme prudence and misguided risk-management sent the USS into crisis was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Andrew Chitty, University of Sussex

Please see two additional notes at end of this Brief.

Until now the standard test for Covid-19 has been the PCR (Polymerase Chain Reaction) test. But in the last six weeks a new type of test, a Lateral Flow test made by Innova which gives a result in 30 minutes, has been rolled out for mass testing in Liverpool and Merthyr Tydfil, and in hospitals and care homes. This test has also been offered to students at over 100 universities before they leave at the end of term. However the new tests have been widely criticised as insufficiently accurate for purpose (BMJ 17 Nov 2020, Guardian 1 Dec 2020, Guardian 3 Dec 2020, BMJ 4 Dec 2020, Telegraph 4 Dec 2020, Financial Times 11 Dec 2020).

This brief addresses the question of just how much two negative Lateral Flow tests tell you about your likelihood of being Covid-negative, specifically in relation to students who have been tested in the last two weeks. The answer will be that it all depends on the results from other students who were tested in the same programme.

We begin by summarising the typical course of a Covid-19 infection, explaining how a PCR test works, and distinguishing the sensitivity and the specificity of a test. Then we summarise the findings of the two published studies of the sensitivity of the Lateral Flow tests, and finally come back to our title question.

1. The typical course of a Covid-19 infection

For what follows, we shall assume that the typical course of a Covid-19 infection for an individual is:

(1) Day 0: the individual is infected.

(2) Day 3: the individual becomes infectious to others (and remains so for about 12 days thereafter).

(3) Also on day 3, the individual also becomes ‘Covid-positive’ (and remains Covid-positive for somewhere over 15 days thereafter).

Here for an individual to be ‘Covid-positive’ on a given day means that they would test positive for the Sars-CoV-2 virus in a well-administered PCR test on that day. Conversely for them to be ‘Covid-negative’ on a given day means that they would would test negative in such a test on that day.

From the last two assumptions we can say that being Covid-positive on a given day corresponds roughly to being infectious on that day.

All this is for an individual who becomes symptomatic. However a recent study (Ra et al, 22 Sep 2020) has found that the viral loads of individuals who contract the virus but remain permanently asymptomatic are similar to those of individuals who become symptomatic, and one expert has commented that ‘many studies have shown the same thing’ (Tang, 22 Sep 2020). We can conclude that all of the above, except for assumption (3), also holds for individuals who remain permanently asymptomatic.

2. How the PCR test works

Next we need to say something about how the PCR test works. A letter to the BMJ explains that viral culture is ‘the gold standard for confirming the presence of viable, infectious virus’ but is both difficult and expensive to perform. A PCR test is the nearest widely available equivalent:

PCR tests work by repeatedly doubling the amount of genetic material in the original sample, until there is a detectable quantity of it. Each doubling is referred to as a “cycle”; and the number of cycles or doublings before there is a detectable quantity of genetic material is called the “cycle threshold” (CT or Ct). The larger the amount of viral RNA there is in the sample, the smaller the number of cycles that are required before it can be detected. And since the number of cycles is the CT value, the lower the CT value, the more virus there was in the original sample, and the more likely it is thought to be that the case was actually infectious, rather than still carrying leftover RNA, which is not clinically significant. (English, 23 Aug 2020)

So the ‘viral load’, meaning the quantity of viral RNA in an original sample taken from a patient, can be expressed as a Ct value, with a low Ct indicating a high viral load and vice versa. Therefore we can label the vertical axis in figure 1 with Ct values that reduce as we go up the axis. The top horizontal line represents a Ct value of about 15. The bottom horizontal line represents a Ct value of about 40, in that normally a PCR test is stopped after 40 cycles (PHE, Oct 2020, p. 6).

This means that, to be accurate, for an individual to be ‘Covid-positive’ on a given day means that their sample would show a detectable quantity of Sars-CoV-2 genetic material after 40 or fewer cycles in a well-administered PCR test on that day, and likewise for ‘Covid-negative’.

3. Sensitivity and specificity

We have to distinguish two kinds of accuracy of a test for Covid-19:

(1) The sensitivity of a test is the percentage of those who have the virus who test positive using this test. By contrast those who have it but test negative are ‘false negatives’. So:

Sensitivity (%) + false negatives (%) = 100%

(2) The specificity of a test is the percentage of those who do not have the virus who test negative using the test. Those do not have it but test positive are ‘false positives’. So:

Specificity (%) + false positives (%) = 100%

With regard to our question, what matters is the sensitivity of the test rather than its specificity. The higher the sensitivity, the lower the percentage of false negatives, and the lower the chance, if you have a negative result, that you have the virus. A specificity of less than 100%, i.e. the existence of false positives, raises a different problem which we will not pursue.

We have defined sensitivity relative to ‘those who have the virus’. By this standard the PCR test is not 100% sensitive, since it does not detect the virus for the first 3 days after infection (see section 1 above). Even after day 3 it is not 100% sensitive. However as we saw in section 1, being Covid-positive on a given date corresponds roughly to being infectious on that date. So it is reasonable to pose our title question in terms of ‘Covid-negativity’, as opposed to ‘not having the virus’.

For the same reason, it is reasonable to measure the sensitivity of other tests relative to PCR results. This is how the sensitivity of the Innova Lateral Flow test has been measured in the two field studies on it done so far: the one by Porton Down and the University of Oxford, and the other by the University of Liverpool.

4. The sensitivity results of the two studies

The Porton Down and University of Oxford study (8 Nov 2020) used 6,967 patients. It had four phases, of which the two field studies were phase 3b and phase 4.

In phase 3b the tests were “either by laboratory scientists at PHE Porton Down or by fully trained research health care workers at the testing site” and the sensitivity was measured for different viral loads. Sensitivity varied dramatically, from 100% for highest viral loads (CT < 18) down to about 33% for lowest detectable viral loads (CT > 35). (See figure 2). Across all individuals with a detectable viral load the sensitivity was 76.8%.

In phase 4 the sensitivity was measured for different kinds of testers. Across all individuals with a detectable viral load the sensitivity was 79.2% when the test was used by “laboratory scientists”, 73.0% when it was used by “trained health care workers” and 57.5% when it was used by “self-trained members of the public given a protocol”.

The Liverpool study (dated 26 Nov 2020, published 11 Dec 2020) used 3,199 patients tested with “military supervised self-administered swabs”. Again it found that the sensitivity varied dramatically, from 85.7% (12 out of 14 cases) for highest viral loads (CT < 20 ) down to 11% (1 out of 9 cases) for lowest detectable viral loads (CT > 30). Across all individuals with a detectable viral load the sensitivity was 48.9%.

Both studies were available to the government before it published its Community testing: a guide for local delivery (30 Nov 2020). Its statements there summarise — in a very broad brush way — the results of the studies

In field evaluations, such as Liverpool, these tests still perform effectively and detect at least 50% of all PCR positive individuals and more than 70% of individuals with higher viral loads in both symptomatic and asymptomatic individuals.

and then a little later:

In the field evaluation in Liverpool, compared to PCR tests, these tests picked up 5 out of 10 of the cases PCR detected and more than 7 out of 10 cases with higher viral loads, who are likely to be the most infectious.

5. Discussion

Let’s apply these results so as to answer our title question.

First, for our purposes we should ignore the differences between the sensitivities found for different viral loads, since for each of these viral loads we are looking at a group who are equally Covid-positive. So the sensitivity figures that matter are just those figures across all individuals with a detectable viral load.

Second, the two studies found that sensitivities across all such individuals varied greatly according to who was administering the test. Summarising the above results we get the following sensitivities:

Military supervised self-administered swabs (Liverpool): 48.9%
Self-trained members of the public given a protocol (Porton Down / Oxford): 57.5%
Trained health care workers (Porton Down / Oxford): 73.0%
Laboratory scientists (Porton Down / Oxford): 79.2%

At universities, where a lot of scientific expertise is typically available, it seems reasonable to assume that the standard of testing will be towards the upper end of this spectrum. So we shall assume that the sensitivity of Lateral Flow tests in this context is about 70%.

Third, the sensitivity of two successive tests will be greater than that of a single one. If there is a 70% chance of getting a false negative from one test, there is only a 30% x 30% = 9% chance of getting two false negatives in a row. So the sensitivity of a double test is 91%. That is, a double test will pick up 91% of Covid-positive individuals.

Fourth, at this point we need to introduce a new concept: the negative predictive value (or predictive value of a negative test). This is the likelihood that someone who gets a negative Lateral Flow test result is Covid-negative. The negative predictive value is calculated in two stages:

(1) Multiply the prevalence of Covid-positivity in the population being tested by the percentage of false negatives in the test. This gives the chance that someone who gets a negative test is nevertheless Covid-positive.

(2) Subtract this percentage from 100%. This gives the chance that someone who gets a negative test is Covid-negative.

So the negative predictive value depends both on the sensitivity of the test and on the prevalence of Covid-positivity in the population being tested. The only exception is the special case where the sensitivity of the test is 100%, in which case the negative predictive value is automatically 100% as well.

The Liverpool study gives a negative predictive value of 99.23%. The method it used to calculate this figure must be as follows:

Total tested (excluding voids): 3026
Total PCR positive: 45
Covid-positive prevalence in the tested population: 45 / 3026 = 1.49%
Total of those who are PCR positive who are also Lateral Flow positive: 22
Sensitivity of Lateral Flow test: 22 / 45 = 48.9%
Percentage of false negatives: 100% - 48.9% = 51.1%
Chance that someone who tests negative is Covid-positive: 51.1% x 1.49% = 0.77%
Negative predictive value: 100% - 0.77% = 99.23%

Let us do this calculation again with the same prevalence but assuming that the sensitivity is 91% (corresponding to two successive tests each with a 70% sensitivity):

Covid-positive prevalence in the tested population: 1.49%
Sensitivity of Lateral Flow test: 91%
Percentage of false negatives: 100% - 91% = 9%
Chance that someone who tests negative is Covid-positive: 9% x 1.49% = 0.13%
Negative predictive value: 100% - 0.13% = 99.87%

So someone who took part in this reimagined version of the Liverpool study and got two negative tests would have had a 99.87% chance of being Covid-negative, or only a 0.13% chance of being Covid-positive. This seems impressive. However we need to remember that even if this person did not know their test result they would still only have a 1.49% chance of being Covid-positive. Knowing that they had a double negative test reduced their chance of being Covid-positive, but from an already low base.

From all this it follows that if I am a student who has had two negative Lateral Flow tests, then to find out how likely it is that I am Covid-negative I need to know not only the sensitivity of a double test (which we have assumed to be 91%) but also the prevalence of Covid-19 among the population tested at my university. For this I need to ask my university:

(1) How many students it tested in the period 30 November to 11th December (excluding void tests)

(2) How many of these students tested positive either on the first or second test.

I can then take the number who tested positive, multiply it by 100/91 (assuming a sensitivity figure for the double test of 91%), and divide the result by the total number tested to work out the prevalence of Covid-positivity in the population tested. From this I can use the above method to work out the chances that I am Covid-positive or Covid-negative, on the day of the second test.

Of course for this calculation to be valid I have to be a typical member of the tested population in the key relevant respects. But this seems plausible if I have been part of a university testing programme, for here the tested population are all students at my own university, they are all like me asymptomatic, and they are all like me sufficiently health-conscious to take the test. If I am untypical then this will change my chances. For example, if I am much more cautious about social distancing than the average member of the tested population then my chance of being Covid-positive will be reduced, and so on.

An immediate conclusion is that universities need to publish the above two pieces of data immediately. Without it students will be unable to form an assessment of how likely it is that they are Covid-negative after getting two negative Lateral Flow test results — whether now or on returning to university in January and February 2021.

___

Additional note 1 (21 December 2020)

The main text has now been clarified by adding the italicised words to this sentence:

‘So someone who took part in this reimagined version of the Liverpool study and got two negative tests would have had a 99.87% chance of being Covid-negative, or only a 0.13% chance of being Covid-positive.’

Also, the main text says that to get the negative predictive value of a Lateral Flow test we have to “multiply the prevalence of Covid-positivity in the population being tested by the percentage of false negatives in the test” then “subtract this percentage from 100%”. However this is not fully accurate. To show this, see the Liverpool results (adapted from the second table in the report):

Sensitivity = true positives / all PCR positives = 22/45 = 48.89%
Specificity = true negatives / all PCR negatives = 2979/2981 = 99.93%

The correct figure for the negative predictive value is true negatives divided by all negatives, which is 2979/3002 = 100% - (23/3002) = 99.2338%.

Instead the figure reached by using the method in the main text is 100% - (prevalence x percentage of false negatives), which is 100% - [(45/3026) x (23/45)] = 100% - (23/3026) = 99.2399%.

As can be seen, the difference is very small. It will always be small as long as the prevalence as measured by the Lateral Flow test is low, i.e. as long as the total of Lateral Flow positive tests (here 24) is small compared to the total number tested (here 3026).

Correspondingly the method given in the last paragraphs of the main text for calculating the negative predictive value of a Lateral Flow test is also not fully accurate. The method given was in effect:

‘Divide the number who tested positive by the total number tested, divide this by the sensitivity, multiply the result by the percentage of false negatives, then subtract the result from 100%.’

This can be summarised in the formula:

NPV = 1 - [(number of positive tests / total tested / sensitivity of test) x (1 -sensitivity)]

A fully accurate calculation of the negative predictive value of a test needs to take into account its specificity, as well as its sensitivity and the prevalence in the population tested. For a further explanation see Boskey, 11 December 2020, and for the exact formula see Positive and negative predictive values, Wikipedia.

Additional note 2 (28 December 2020)

The main text says ‘we can say that being Covid-positive on a given day corresponds roughly to being infectious on that day’, but this is inaccurate. The literature suggests that although the periods of infectiousness and of PCR-positivity start at about the same time (namely about two days before the onset of symptoms) the period of infectiousness is much shorter. This is because the PCR test detects not only ‘live’ infectious virus but also ‘dead’ noninfectious viral genetic material that can remain in the body for some time after the immune system has cleared the infectious virus. For a summary see Deeks et al 22, September 2020, and for a graphical representation the final figure in Jefferson et al, 5 August 2020.

This paper represents the views of the author only. The authors believe all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs105; the authors will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

What do two negative Lateral Flow tests tell you about your likelihood of being Covid-negative? was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Matthieu Burnay, Queen Mary University of London
Harriet Evans, University of Westminster
Perry Keller, King’s College London
Eva Pils, King’s College London
Tim Pringle, SOAS University of London
Sophia Woodman, University of Edinburgh

1. Introduction

We are a group of academics with many years of experience of teaching on China, including Hong Kong, in the fields of law, political sociology, labour relations, human rights, and gender politics. We are deeply concerned that, in their eagerness to maintain fee income from Chinese international students as near to pre-Covid levels as possible, some UK universities have signed up to a China-based system for providing access to online teaching to students who choose to study for their UK degrees from their homes in the PRC. We are concerned this system potentially endangers our students and invites censorship of the curriculum in our universities.

2. UK HE and the Great Fire Wall of China

As has been widely reported, many UK universities that have become dependent on steep international fees from Chinese students faced a sharp fall in their incomes this academic year if applicants failed to enroll on their courses (see #USSBriefs94). In the event, the fall has apparently been less precipitous than forecasted, although reliable data is not yet available, due in part to last minute marketing of courses to students in China. But a significant proportion of these students are joining courses from their homes in China, due to a variety of factors, including worries among students and parents about the UK’s shambolic approach to coronavirus control and late issuance of letters students need to apply for UK visas. The Chinese Ministry of Education has announced that, unlike in the past, it will recognize UK degrees that involve online study.

But studying online for a UK degree from inside China presents specific challenges. The ‘Great Fire Wall’ restricts access to the internet outside China, imposing mechanisms to filter content and block ‘blacklisted’ sites, including major platforms such as Google, Youtube, Facebook, Twitter; news providers such as the Guardian and the New York Times; and transnational activist networks, among others. The ‘virtual private networks’ (VPNs) that UK universities routinely provide to their staff and students to access much of their content from off campus are blocked as part of a generalized Chinese government ban on VPNs and other forms of encrypted communication. Students in China joining some UK university courses (such as pre-sessional English programmes) during the summer reported significant connection problems.

3. Over the wall: the Alibaba ‘solution’

In this context, institutions representing UK universities are rolling out a dedicated service to enable students studying for UK degrees from China to access their course materials. This has been piloted over the summer at a number of UK HEIs, and is a joint project by promoters of all things digital in UK HE Jisc and Ucisa, the British Council (which is involved in marketing UK HE) and Universities UK. The service provides access to UK universities’ online platforms for students within China via a government-approved VPN enabled by Chinese internet and e-commerce giant Alibaba. UK universities want to ensure that students in China can have reliable access to course materials, including recorded lectures, readings and live activities, and are able to participate in their courses, posting comments on discussion boards and submitting assignments.

From the publicly available information, this service, which has been piloted in a number of UK universities over the summer, and is now being rolled out at some of our institutions, will allow students to access their UK university’s content via a login to a dedicated Alibaba Cloud service on its Cloud Enterprise Network. Although the documentation on the Alibaba service describes this being routed via the company’s ‘virtual private cloud’ on servers in locations outside China, this does not mean that Chinese government surveillance and censorship mechanisms would be avoided, because all traffic would initially be routed through Alibaba’s servers in China.

4. Censorship, surveillance and students at risk?

As well as claiming that it will provide ‘fast and reliable access’ to course materials, the documentation states that the Alibaba ‘solution’ would be ‘fully legal and compliant with Chinese laws and regulations’. These laws allow for extensive censorship of public content on social media and news websites, as well as of personal communications, based on broad and vague criteria. While parameters for what is forbidden are set by the authorities, responsibility for deleting and blocking related content, activity and users rests with social media platforms and services, including Alibaba. China’s 2016 Cybersecurity Law makes companies that fail to carry out these responsibilities subject to massive fines, prosecution and even cancellation of business licenses. This legal responsibility implies that Alibaba could face legal sanctions if it failed to block course content on prohibited topics such as protests in Hong Kong or the detention camps in Xinjiang.

The Alibaba scheme could also put students at risk, as their engagement with their courses can be monitored through Chinese government electronic surveillance systems. This is the case not only for students studying for their degrees remotely from China, but also potentially other students who are in the UK but in the same courses, whose engagement could potentially be monitored via the access of the students joining course activities remotely. This is no idle fear in a context where there have been significant tensions among students over support for the 2019 protests in Hong Kong, for example.

Repression in China is targeted, and depends on identifying people regularly accessing content or online activities seen as problematic (particularly those engaging in any form of collective action national or local authorities find problematic), and focusing monitoring on such ‘suspect’ people. Using the Alibaba Cloud service, UK universities will not be able identify what kinds of monitoring and censorship happen when and to whom. Given the Chinese government’s demonstrated AI capacities, this monitoring could include automated profiling of student use of materials or interaction with the teaching to infer political reliability or political inclinations. By providing the Alibaba service to their students, UK universities could be complicit in enabling such profiling, and in our view this would be a failure in our duty of care to our students.

5. China and the chilling effect

There are broader concerns about the potential chilling effects for teaching of China-related material in UK universities, both short term and long term. This is not an idle concern: in recent years, controversies have erupted as the Chinese government has sought to pressure academic publishers to censor ‘politically sensitive’ content, including Cambridge University Press. It also comes in the context of the newly passed National Security Law in Hong Kong, which criminalizes a broad range of previously acceptable speech, and exerts extraterritorial powers that have raised deep concerns among scholars working on China-related issues. In such an environment, content deemed potentially offensive to the Chinese government may be at risk from (self-)censorship, either because teachers opt to eliminate it or because institutions decide that certain ‘problem’ courses are no longer viable. Documentation for staff at a number of universities offering this service has made vague references to ‘problematic’ content that may result in some teachers preemptively removing any China-related material from their courses.

Some institutions have effectively started justifying such censorship of courses for Chinese students studying remotely, asking teachers to provide ‘alternatives’ to ‘problematic’ China related content for these students. Such moves presume that all Chinese students will be offended by or want to avoid such content; in our view this is a mistaken assumption based on stereotyped notions of Chinese students. Some of our students from China choose to study at UK universities precisely because they will encounter a different range of approaches and opinions to those they have encountered in universities in mainland China, and some specifically want to hear about alternative analysis of developments in their own country at a time when such debate is being closed down at home. Pro-government, nationalist students may be vocal, but there are many others with a variety of viewpoints. One indication of this in the UK context is a finding from a representative sample of mainland Chinese students studying for undergraduate and postgraduate taught degrees at UK universities. The Bright Futures survey, conducted in 2017–18, found that 71% of respondents said they ‘never’ participated in activities of the Chinese Students Association (which is supported and funded by the Chinese authorities) and a further 22% said they participated once a month or less.

6. Alternative solutions and academic freedom

Given the concerns outlined above, we do not believe that UK universities have done enough to find alternatives to the Alibaba service that might mitigate some of the risks we describe. Other academic institutions, including joint-venture universities with campuses in China, have apparently negotiated exceptions to the ban on foreign VPNs. For obvious reasons, these universities do not publicize the ad hoc solutions they have been able to find, as these would technically be violations of Chinese law. In the current context other possibilities for UK HE might include approaching the Chinese Ministry of Education to negotiate access for students in China to UK university VPNs, or to a collectively managed joint UK-university ‘VPN concentrator’ located in China. Another part of a solution could be a joint-UK university project to mirror UK university server content in locations nearer to China (such as Singapore, South Korea or Japan) that would allow for faster access to content via VPNs. These solutions could address some of the key surveillance concerns, but would nonetheless still be subject to censorship demands by Chinese authorities.

Universities should not plead that they cannot consider alternatives on cost grounds, since the Alibaba service is reportedly costly (although rates have not been made public), with prices likely reaching £100,000 per institution annually depending on data volume. With a model of payment by data volume, UK universities are in the invidious (and likely unworkable) position of distinguishing between ‘study-related’ and other usage of the service. More importantly, no saving of expenditure or maintaining of pre-Covid income levels can justify the ‘costs’ of exposing our students to the risk of persecution as a result of taking UK university courses, or of inviting Chinese government censorship into our university systems.

Unfortunately, there is little sign that the leaders of the sector are considering the complexity of the risks involved. On 15 October 2020, UUK issued a report entitled ‘Managing risks in internationalisation: security related issues’. Deplorably, this report suggests that universities are, or should become, guardians of UK national security, but fails to recognise the nature of the risks to academic freedom that staff and students in the UK are actually facing. The report certainly makes no mention of the concerns we outline above, despite UUK being a co-sponsor of the Alibaba scheme. Addressing itself exclusively to ‘senior leaders’ in universities, the report also suggests a top-down, managerial approach to addressing the risks of academic internationalisation, without giving sufficient thought to the need to involve academic staff. Self-governance is an important dimension of academic freedom. One reason we are publishing this piece is that we have had little or no say in how our institutions are making policy in this area, despite the evident relevance of our expertise, and the gravity of the concerns we raise. At this moment, we believe UK universities need to commit to strong defense of academic freedom, ensure that this applies equally to staff and students and prevent this key value of our universities being undermined by ‘technical’ or market considerations.

This paper represents the views of the authors only. The authors believe all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs104; the authors will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Internet access deal allows Chinese government censorship in our UK university (virtual) classrooms was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.

Andrew Chitty, University of Sussex

In the last week the first outbreaks of Covid-19 among students have been reported at four UK universities: Oxford Brookes, Edinburgh Napier, Bath and Manchester Metropolitan. But there are already anecdotal stories of multiple cases at many more universities, and there is a natural suspicion that these official reports are only the tip of an iceberg.

To know how likely more outbreaks are, we need to know how many students arriving at UK university towns and campuses will be Covid-positive. This Brief introduces a spreadsheet ‘Estimated numbers of students arriving at UK universities this term with Covid-19’, which estimates this number for students arriving today (21 September) and on the next two Mondays (28 September and 5 October).

It also estimates. for each of these three dates, how many of those who are Covid-positive will be currently asymptomatic, and also crucially how many will be both currently asymptomatic and infectious.

The spreadsheet focuses on students because they are far more numerous than staff, because unlike they are moving into new large shared households, and because they tend to socialise much more widely than staff. So they are the most likely vectors for transmission once term begins.

Its headline results (rows 197–198 of the spreadsheet) are that as of today, 21 September:

• about 14,000 students due to return to UK universities (6 in every thousand) are currently Covid-positive
• of these about 9,000 (4 in every thousand) are currently asymptomatic
• of these in turn about 3,500 (1.5 in every thousand) are infectious
• this last number could be as low as 2,000 or as high as 6,500

The above numbers will increase by about 50% by next Monday (28 September) and will more than double by the following Monday (5 October). This is ignoring the almost inevitable increases in infection rates over the next fortnight among students who have already arrived at university towns and campuses.

The crucial number is that of those who are both currently asymptomatic and infectious. These are by far the most likely to pass the virus to others, since they have no way of knowing that they are carrying it. In the conditions of student life, the chances of any one of them starting an outbreak are very high. With somewhere between 1,000 and 4,000 students already in this category today, it is hard not to conclude that the HE sector will suffer multiple large outbreaks of Covid-19 within weeks. These outbreaks will have serious consequences for the long-term health of students themselves, and possibly severe consequences for the short-term health of the staff they may inadvertently pass the virus to in seminars and at library desks; of the other members of the local community they may pass it to while shopping or on public transport; and of the relatives they may pass it to when going home during term.

The spreadsheet does not attempt to predict the spread of infection once students have arrived. However Brooks-Pollock et al (14 September) have modelled the progress of Covid-19 infection at Bristol University in the course of the autumn term based on students home location and the prevalence of the disease in their home location in July. They estimate that, with the university following Public Health England guidelines but continuing to teach face to face, prevalence would grow with a doubling time of 9 days, corresponding to a daily growth rate of 8%, and that by the end of term 5,700 students (20% of the whole student population, with a confidence interval of 3,900 to 7,400 students) would be infected. By way of preventing this outcome, they find that ‘reducing face-to-face teaching is likely to be the single most effective intervention’.

By now it is too late to prevent the migration of between one and two million students to university towns and halls of residence over the course of this month, and the inevitable multiple outbreaks that will result. But universities have it in their power, following the various leads of Birkbeck College, University College London, Queen Mary University of London, St Andrews and Manchester, to mitigate this outcome to the maximum by taking all teaching online this term, and by simultaneously announcing that they will release all students who decide they do not want to move into university accommodation from their contracts. No doubt this will cause financial losses. But in the face of a disease that is lethal for some and, regardless of age, causes prolonged debilitation for a significant proportion, universities’ duty of care to their students, their staff and their local communities must take absolute precedence.

Note on methodology

Essentially the spreadsheet takes

• the estimates of English case prevalence on 10 September from the Office for National Statistics
• the relative recorded incidences in the four UK nations in the 14 days to 10 September from the government Coronavirus website
• the estimates of age group prevalence in England on 10 September from the Office for National Statistics
• the rate of growth of symptomatic prevalence from 10 to 18 September from the Covid Symptom Study, and
• the numbers of students at each UK university from HESA 2018–19 data

From these it estimates, at each university, on each of the above three dates, among students at the university:

• total cases of Covid-19 on that date
• currently symptomatic cases
• currently asymptomatic cases
• currently asymptomatic and infectious cases

For currently asymptomatic and infectious cases the spreadsheet gives midpoint, lower bound and upper bound estimates.

The spreadsheet ignores non-UK students on the grounds that those who have come from countries where there is any significant prevalence will be asked to self-quarantine on arrival.

For more details see the notes on the spreadsheet.

— — — — — — — — — — — — — — — — — — — — — — — — — —

Additional notes 22 September 2020

(1) In the Note on Methodology I have now replaced the words ‘the relative recorded incidence across different age groups on 7–14 September from Public Health England’ by ‘the estimates of age group prevalence in England on 10 September from the Office for National Statistics’. The previous text was included by error.

(2) I understand from colleagues at the University of Manchester that the university’s present policy is to offer a large proportion of face-to-face (in-person) teaching this term. I apologise for this error. The section ‘What will semester 1 look like?' on the Manchester page to which I linked in the Brief was difficult to interpret.

(3) It has been pointed out to me since the publication of the Brief that Jess Enright (Glasgow) has been running calculations on github similar to the ones introduced in this Brief, using a set of sources overlapping with my own.

— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —

This paper represents the views of the author only. The author believes all information to be reliable and accurate; if any errors are found please contact us so that we can correct them. We welcome discussion of the points raised and suggest that discussants use Twitter with the hashtag #USSbriefs103 ; the author will try to respond as appropriate. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How many students arriving at UK universities this term are Covid-positive? was originally published in USSbriefs on Medium, where people are continuing the conversation by highlighting and responding to this story.