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WP4 Impact & Sustainability

Studies made.

Name of the study Report
Social Cost-Benefit Analysis of HL-LHC CDS
Industrial Spillovers from the LHC/HL-LHC Programme at CERN CDS
Scientific Research at CERN as a Public Good: A Survey to French Citizens CDS
Scientific Research at CERN as a Public Good: A Survey to Swiss Citizens  
The Value of Human Capital Formation at CERN WILEY LIBRARY
Cultural Effects at CERN CDS
Initial guidelines for social cost-benefit analysis of the FCC programme CDS
Monte Carlo Simulations in Cost-Benefit Analysis  
Assessing the value of CERN’s free and open source software: the case of ROOT  
Structure for cost estimates and funding needs FCCIS M4.1


Financial Discount Rate (FDR): definition and estimation

When private or public investors decide to finance a project they implicitly face a cost that corresponds to sacrificing a return from another project. This amount is called “opportunity cost". Therefore, investors decide to commit their resources to a project only if its expected return is likely to exceed the opportunity cost. Inflows and outflows of a project are therefore discounted by means of a Financial Discount Rate (FDR). The FDR is the opportunity cost of capital and is valued as the loss of income from an alternative investment. It accounts for the time value of money: the idea that money available now is worth more than the same amount of money in the future because it could be earning interest, and the uncertainty about the future cash flow, that might be less than expected. Different approaches exist in the practice for the calculation of the financial discount rate: A commonly used approach consists of estimating the actual cost of capital relying on the real return on government bonds (the marginal direct cost of public funds) or the long-term real interest rate on commercial loans (if the project needs private finance), or a weighted average of the two rates. The latter approach is convenient when a project needs the financing both of public and private funds. Although being very practical and widespread, it does not reflect the actual opportunity cost of capital, because the best alternative investment could earn more than the interest rate paid on public or private loans. Moreover, there exists no credible approach to forecast reliably a financial interest rate over time horizons of decades that is typical of RIs. A second, more accurate, approach is to consider the return lost from the best alternative investment to determine the maximum limit value for the discount rate. In this case, the alternative investment is not the buying back of public or private debt, but it is the return on an appropriate portfolio of financial assets.

Social Discount Rate (SDR): definition and estimation

The Social Discount Rate (SDR) is used in the economic analysis of investment projects to discount economic costs and benefits. It reflects the opportunity cost of capital from an inter-temporal perspective for the whole society. In other words, it reflects the social view of how future benefits and costs are to be valued against present ones. A nil social rate of time preference derives from the assumption that equal weights are given to the benefits occurring at any point in time. A positive discount rate, on the other hand, indicates a preference for current over future consumption, whereas the opposite is true if the discount rate is negative. When a perfectly competitive economy is in equilibrium, the SDR coincides with the financial discount rate and with the financial market interest rate. However, this does not apply in the practice because capital markets are in fact distorted (e.g. due to “asymmetry of information”, namely the fact that a party of a transaction possesses greater knowledge than the other party). Different approaches have been proposed by the literature to estimate the SDR. The most popular one is the social rate of time preference (SRTP). This is the rate at which the society is willing to postpone a unit of current consumption in exchange of more future consumption. The logic of this approach is that the government should consider the welfare of both, the current and future generations and solve an optimal planning program based on individual preferences for consumption. A broadly used approach to estimate the SRTP is based on the following formula obtained from the Ramsey:

SRTP = p + e·g

where p is the pure time preference. e is the elasticity of marginal utility of consumption, i.e. the percentage change in individuals’ marginal utility corresponding to each percentage change in consumption. g is the expected growth rate of per capita consumption. Each term of the formula is discussed more in detail below. The two components of this formula (the one related to time preferences and the other related to consumption growth) reflect the two reasons why future consumption may have a lower value than in the present. First, present income or consumption is usually preferred because of uncertainty about the future and impatience. Second, future consumption may be valued less because of the probability of income and consumption grow through time. Indeed, if per capita consumption is growing, then the value of additional consumption in each year in the future is declining at a rate related to the rate of growth of per capita consumption and the elasticity of diminishing marginal utility of consumption. The pure time preference term (p) can be decomposed into two elements, one related to individuals’ impatience and myopia and the other one related to the risk of death or human race extinction. This latter component reflects the life chance and it is often simply measured as the ratio of total deaths to total population. The former component instead refers to the observation that individuals favour present over future consumption and this is reflected in a positive value of p. A positive value means that future generations would be worse off only because they are born at a later point in time, which would be unacceptable from the point of view of society. The consensus value in the economic empirical literature is to assume a value for p between 1% and 3%. See [6] for details. The elasticity of the marginal utility with respect to consumption (e) captures the dynamics of consumption over time. This parameter reflects the fact that if tomorrow consumers are a bit richer, marginal utility is decreasing. In other terms, it reflects how consumption should be transferred across different generations and it is a planning parameter for the social planner in that it reveals his preference for income inequality aversion. An approach to estimate the elasticity term is to consider the social judgement about how consumption should be transferred across people at different times. In this case, the elasticity tells us how much more worthwhile it is to transfer income from a rich person to a poor one. This can be determined by analysing the progressivity of national personal income tax rates. The following formula can be used to describe the elasticity:

e = ln(1 - t’)/ln(1-t)

where t’ and t are respectively the marginal and average income tax rates for an average taxpayer. One is the neutral value of the parameter: when e = 1, then 1 Euro of additional future consumption adds 1 Euro to social welfare. If e < 1, consumers are not so interested in future growth. If e > 1 consumers are interested in it. The expected per-capita consumption growth (g) is a welfare-related variable. From the point of view of inter-generational equity, this term implies that if future generations are expected to be wealthier that the ones of today, and thus if consumption rises over time, this would result in an increase of the discount rate in order to shift the priority to the poorer current generation. Usually very long-run growth rates of real per capita consumption are used to estimate future growth to smooth out possible short-term distortions. Empirical estimates for the rate of growth of per capita consumption are usually based on growth models, which take into account both the past long-term development path and expected future growth. A way to estimate g is to consider as proxy for consumption growth another welfare correlated indicator such as real per capita Gross Domestic Product (GDP) growth, consumption growth or personal income growth.

Information about the studies made.

Social Cost-Benefit Analysis of HL-LHC

The NPV of HL-LHC is positive and greater than the NPV of the counterfactual scenario. The ratio between benefits and costs of the difference is positive. The difference between the NPVs of the two scenarios is the benefit of the HL-LHC project since the alternative scenario is to continue the operation of the LHC until it reaches its end of life. The ratio between the HL-LHC and CFS total cost difference and the HL-LHC and CFS total benefit difference is 1.76. this means that every CHF spent on the HL-LHC project generates 1.76 CHF of benefits for the society. The main benefits stem from training and industrial spillovers. The probability of a negative NPV is negligible (6%), even under very conservative assumptions on the potentials for the generated benefits. In conclusion, the HL-LHC project yields significant socio-economic value, well in excess of its costs and in addition to its scientific output.

Discounted MCHF 2016 HL-LHC % CFS % Difference %
Total Cost 22,292   19,356   2,936  
Total Benefit 25,608   20,453   5,155 9.894
Human Capital 8,379 33% 6,302 31% 2,077 40%
Publications 613 2% 322 2% 290 6%
Technological Spillovers 10,187 40% 8,244 40% 1,943 38%
- Software 6,029 24% 5,591 27% 438 9%
- Hi-tech Suppliers 4,158 16% 2,653 13% 1,505 29%
Cultural Benefits 3,319 13% 3,028 15% 291 6%
Public Good Value 3,110 12% 2,557 12% 553 11%
NPV 3,316   1,097   2,219  
Benefit/Cost ratio 1.15   1.06   1.76  

Industrial Spillovers from the LHC/HL-LHC Programme at CERN

Qualitative and quantitative analyses about the impact of CERN on firms collaborating at the development of its research infrastructures provide evidence that the benefits associated with international large-scale research infrastructure projects go well beyond the value of basic research for the society. In fact, three different methodologies confirm that the benefits for firms working with CERN are substantial and involve different outcomes: from improved economic performance to a higher likelihood of developing new products. All in all, these analyses validate the assumptions underlying the CBA of the LHC/HL-LHC programme that reveal a significant contribution of industrial spillovers generated for the society.

Scientific Research at CERN as a Public Good: A Survey to French Citizens

CERN is funded through financial contributions of its Member States and taxpayers are the ultimate funders of CERN’s investment projects. In 2017, the contribution of France to CERN was EUR 144,476,712 (2017 prices).22 In the same year, the French population older than 18 years was 52,405,723.23 The ratio between these two quantities leads to a per-capita tax contribution of EUR 2.7. The most conservative estimation of the WTP is EUR 4 per-capita, which is a factor 1.5 higher than the actual contribution of French taxpayers today. Therefore, the main result of this report is that the willingness to pay of French citizens to support future investments in a particle accelerator is greater than what they currently implicitly pay as taxpayers. The awareness about CERN of the respondents is positively correlated to their willingness to pay. This points to a large potential for raising both awareness and potential willingness to pay.

French citizens’ support to CERN can be compared to French public spending in other sectors. For instance, the per-capita contribution of French people (18+) to civil space exploration activities and associated scientific research carried out by France ranges between EUR 40 – 45 per year.

Scientific Research at CERN as a Public Good: A Survey to Swiss Citizens.

The great majority of Swiss citizens (74%) are willing to contribute for supporting investments in particle physics research. The bounded conditional average WTP of Swiss citizens is CHF 54.6 per person per year, which is very close to the unconditional average maximum WTP of CHF 57.2 per person per year. The higher percentage of respondents who answered “yes” to the bid asked determines such a convergence. This result holds true regardless whether people are informed about the actual annual contribution they pay to CERN as taxpayers. The contribution that Swiss taxpayers pay to CERN is CHF 6.3 per person per year. The WTP of Swiss citizens is higher than their actual taxation contribution of about a factor of 9.

In its home country, the great majority of citizens (78.8%) is aware of CERN, but public’s awareness remains lower than that of UNESCO, NASA, WHO.

The WTP is positively correlated with income, being a male, education, and being interested in scientific subjects, such as medicine, biology, astronomy, physics or geology. Having positive perceptions and thoughts such as “CERN permits to increases knowledge of universe” or “the research activity at CERN should increase in the coming decades” play a key role in determining public’s WTP.

The Value of Human Capital Formation at CERN

RIs are large-scale collective scientific enterprises which foster research, innovation and knowledge-sharing. They are important human and social capital incubators where bright minds concentrate and top-level human and social capital accumulates. The case study adopted in this paper refers to a world-famous European RI which hosts the largest accelerator used for research in particle physics: CERN. This RI is organized in the form of a large collaboration among different countries and involves a large number of universities and research institutes.

In a cost–benefit analysis framework, the contribution to human and social capital formation is one of the most important socio-economic benefits of a RI, in particular for early-career researchers who have the opportunity to develop competencies, skills and social connections in a unique learning environment. Based on the collection of primary and secondary data, we found that spending a period of study and/or work at CERN (particularly at LHC program and experiments) is particularly instrumental in developing scientific and technical skills which are attractive in the job market both inside and outside the academia. Such skills, according to ECRs perceptions, would translate into a salary premium ranging between 5% and 11% as compared with a scenario where they do not carry out such a unique experience. The total lifetime salary “premium” also varies depending on personal characteristics, future sector of activity and the total duration of stay.

Developing useful social connections is also important for job search and career advancement and may also contribute to this “premium”. Although this aspect was weakly perceived by the ECRs, its importance was particularly stressed by team leaders. The latter have a wider perspective on the future career of a different cohort of students and are more likely to understand the “value” of networking activities based on their tenured experiences and long-term views compared to ECRs. The “individual” dimension of social capital, which mainly acts through social connections established by ECRs working at the RI and the reputation of the infrastructure can also influence the labor market positively, for example, facilitating access to information, resources and career sponsorship.

We also found that the salary premium of spending a period at CERN can be ultimately compared with a year of doctoral studies in Europe. In both cases, students acquire scientific and technical expertise and other skills that are relevant for their careers and can build connections with other researchers and professionals in academia and in the industry. However, the acquisition of scientific and technical skills seems again to play a more significant role in the generation of the salary premium.

Initial findings reported by earlier studies have been confirmed and expanded by the new evidence collected with complementary methods. Since the employers value the additional skills and experience, training at CERN generates a measurable socio-economic added value and this is something that governments should also positively consider when deciding to invest public money in RIs. This methodology can be applied in the future to estimate the socio-economic return of other RIs in human and social capital accumulation. Estimates of the expected salary premium at CERN can be used as a benchmark for future cost-benefit analysis of post-LHC particle collider research infrastructure. Future research on this topic should also consider that students that are given the opportunity to train in prestigious work environments like CERN tend to be the most prepared and talented ones, also due to rigid selection processes at different levels. Hence, the salary effect discussed in this paper may partly stem from this initial bias, something that should be better considered in a future survey and research design by resorting, for instance, to techniques based on the counterfactual approach (see among others, Angrist & Pischke, 2008). Indeed, the comparison between our ECRs with a CERN experience and “similar” ECRs who did not have the same experience would minimise further bias generated by (self-) selection.

Cultural Effects at CERN

Cultural activities attract a significant number of visitors. The rate of participation of both in-person and virtual visitors are indicators of the size of the cultural impacts. Previous studies have found that the positive effects of LHC programme related activities amounts to about EUR 2.1 billion between 1993 and 2025. The cost benefit analysis of the LHC/HL-LHC combined programme reports an estimated value of cultural goods from 1993 to 2038 of EUR 3.3 billion. Our analysis confirms the positive impacts found in these previous studies. In addition, revised baselines have been created to estimate the impacts of websites, social media and media-rich contents such as videos. The table below shows the total discounted benefits generated by LHC/HL-LHC-related cultural goods generated by four different communication channels.

Cultural channel Discounted benefit (CHF)
Youtube 1,907,771,970.07
Social media 2,724,419.03
Permanent exhibitions 3,929,352.19
Websites 427,043,508.99
Travelling exhibitions 48,104,515.43
Total benefit 2,389,573,765.71

These findings should be considered as conservative. There are many other communication channels such as for instance TED and CERN events, which have not been included in this analysis. Further investigations are needed to quantify their impacts. YouTube platform turns out to be the channel with the highest single media-related impact. Its benefits may still be underestimated if considering, that we only focused on the views of the videos by registered users. However, a video can also be disseminated also trough other channels, such as social media and websites.

Initial guidelines for social cost-benefit analysis of the FCC programme

Past CBAs of the LHC programme have identified a set of benefits that are expected to arise from it. The long time-horizon of the FCC programme adds complexity to the design of a CBA. The CBA of the LHC/HL-LHC delivers a foundation for an evaluation of the societal costs and benefits of different FCC scenarios. The result of the CBA is summarized by the NPV associated with a given RI. The uncertainty of this random variable can be estimated with MC simulations that can also be used to assess how the CBA’s results are affected by changes in the underlying assumptions such as the timeline of costs and benefits. While the NPV can be positive, negative or zero, the results of CBAs do not inform on the scientific utility of a RI. In facts, the CBA cannot be used to influence the ranking of different RI based on their discovery potential, on the contrary it does provide a ranking of RI based on the net benefits they generate for the society.

The CBA model developed in the frame of the LHC/HL-LHC programme assessment is thus both methodologically appropriate and also necessary for the FCC programme. It could be accompanied by a technological forecasting analysis that might help improving the estimation of benefits for firms and other economic agents. One methodology that can be used for this purpose is for instance the Delphi method. This involves multi-round forecasting challenges where experts provide initial forecasts and then adjust their initial guesses based on feedbacks they receive. This process is iterated until a satisfactory level of consensus is reached and final forecasts are constructed from the aggregation of individual forecasts.

Monte Carlo Simulations in Cost-Benefit Analysis

This report is a guide make to understand how the Monte Carlo simulations have been performed and have been used so far in our studies.

Assessing the value of CERN’s free and open source software: the case of ROOT

The value of free and open source software that is released into the public domain by research infrastructures is widely acknowledged by earlier literature, while there is no a unique metric for estimating such value.

The case of ROOT, which has been presented in this report, suggests that an approach relying on the production cost side, as with the COCOMO, may underestimate the value of knowledge that is embedded in free and open source software. Our estimation – relying on the users’ perspective – shows that the value of resources (time) saved that is thanks to the release of ROOT as a free and open source tool is much higher than its production cost (as well as the cost of an alternative commercial software). In addition, a multivariate analysis reveals that females, students and employees at University are the ROOT users (responding to our survey) that experienced the greatest benefits in terms of time-saving, quality and completeness of the task carried out. Further research is needed to interpret this result, possibly with a larger balanced sample.

As part of our conclusions, it is worth pointing out that findings from this case study should be cautiously taken into account and not considered as the final evaluation of the economic impacts of ROOT but rather as a starting point on how to fine-tune the measurement of benefits arising from free and open software. It should bear in mind that the estimation presented in this report was based on a small and non-random sample of ROOT users and empirical analysis were illustrated with the main purpose to showcase an approach for measuring the value of knowledge creation at CERN. Further researches would be needed to extend this approach to a large number of ROOT users as well as to properly estimate the impact of ROOT outside CERN. In this regard, an issue that one should bear in mind for the future is that while interviews with experts were suggesting a substantial use of the ROOT in many fields other than physics, such as for instance finance and industry sectors, our sample seems not confirming this profile by instead highlighting the use of ROOT primarily from researchers, students and post-docs working in research field. As a consequence, when we estimated the value of time saved by those users, we relied on an average salary for early career researchers which probably underestimated the higher salary profile of ROOT users (e.g. those working in finance and industry fields). Also, our estimation remains conservative for a number of prudent assumptions which have been used for the purpose of calculation.

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