1. Study of the socio-economic impact of CERN HL-LHC and FCC-hh
Workshop on
“The economic impact of CERN colliders:
technological spillovers, from LHC to HL-LHC and beyond”
May 31st, 13:30 – 15:30
Intercontinental Hotel, BERLIN
Are CERN suppliers different?
A quasi-experiment
Andrea Bastianin
(University of Milan)
with
Paolo Castelnovo (University of Milan), Massimo Florio (University of Milan) and
Anna Giunta (Center Manlio Rossi-Doria, Roma TRE University)

2. Outline
Introduction
Data
Methods
Results
Conclusions

3. Introduction

4. Aim Distinguishing features of the study:
Identify the determinants of the probability of being a CERN supplier.
Evaluate the impact of CERN procurement on profitability.
Distinguishing features of the study:
Reliance on counterfactual: what would happen to the profits of firms that are not CERN suppliers, if they were suppliers?
Data about CERN potential suppliers: firms registered with CERN procurement that have not delivered any order yet.
Relevance:
A growing body of research investigates the economic impacts of Public Procumement of Innovation (PPI).

5. Overview of results
“Play by the rules”: CERN’s procurement practices drive the probability of becoming suppliers.
“Variety matters”: more diversified firms are more likely to get orders.
“Innovate”: high-tech firms are more interesting for big- science labs and hence get orders more easily.
“Science pays back”: CERN’s effect on profits is positive and statistically significant.

6. Data (with a focus on potential suppliers)

7. Details about CERN activity codes

8. Details about CERN activity codes

9. Details about CERN activity codes

10. Details about technological intensity of activity codes
Classification of activity codes according to their technological intensity based on CERN experts’ assessment of a sample of 300 LHC orders > 10,000 CHF1.
23 two-digits hi-tech activity codes in classes 3-5.
Lo-Tech
Hi-Tech
(1)
very likely to be "off-the-shelf" orders with low technological intensity
(3)
mostly "off-the-shelf" but usually high-tech and requiring some careful specification
(2)
"off-the-shelf" orders with an average technological intensity
(4)
high-tech orders with a moderate to high specification activity intensity to customize products for LHC
(5)
products at the frontiers of technology with an intensive customization work and co-design involving CERN staff.
1 Details in: Florio, M., S. Forte and E. Sirtori (2016). “Forecasting the socio-economic impact of the Large Hadron Collider: A cost–benefit analysis to 2025 and beyond.” Technological Forecasting and Social Change, 112, 38–53.

11. Who would like to collaborate with CERN?
Potential suppliers
Who would like to collaborate with CERN?
Potential suppliers: firms registered with CERN Procurement Office that never delivered any order.
In the period: 2553 firms.
A total of 7919 self-reported activity codes have been recorded spanning 59 CERN’s 2 digits codes.

12. Potential suppliers by country of origin (% of firms recorded as potential suppliers)

13. Potential suppliers by self-reported activity codes Share of Hi-tech and Lo-tech activity codes

14. Estimation sample
Actual suppliers (AS): 867 firms that have delivered LHC- related orders (> 10,000 CHF) between 1991 and 2008.
Potential suppliers (PS): 2553 firms registered with CERN Procurement Office that never delivered any order in the period.
Matching CERN data and ORBIS data (with company accounts data):
886 firms (26% of original sample).
AS = 323 firms (36% of total).
PS = 563 firms (64% of total).

15. Estimation sample: timing
1991
1995
2008
2010
Registration
Procurement
ATE estimation
2014

16. Data: controls
Controls xt are a set of time-invariant and time-varying variables belonging to two classes.
Firms’ characteristics:
Size: Log Total Assets (average over );
Reliability: Liquidity ratio (average over );
R&D intensity: Intangible Assets as % fixed assets (average over ). See e.g. Chan et al. 2001, JoF;
Location: country fixed effects;
Common idiosyncratic shocks: time fixed effects (for registration year).

17. Data: controls CERN’s procurement practices:
Variety: Dummy = 1 if self-declared no. of activity codes > 1.
Proximity: Dummy = 1 if firm is located in Switzerland or France.
Procurement policy: Dummy = 1 for very poorly balanced countries (if Ind. Ret. Coef. for supplies < 0.3 when firm registered).
Firms in very poorly balanced Memeber States have a priority in tendering preocedures for orders > 100,000 CHF.
Two alternative proxies of technological intensity:
Dummy = 1 if at least 1 of the self-reported act. code is classified as hi-tech.
Categorical variable with share of self-reported hi-tech act. codes (0%, 1%-25%, 26%-50%, …, 100% of total registered activity codes).
return coefficient = ratio between a country’s percentage share of the value of all contracts during the preceding four calendar years and its percentage contribution to the CERN Budget over the same period
Limited tendering is used to improve the return coefficient for supplies to Member States with a very low return coefficient (i.e. below 0.3). In this case, firms from other Member States cannot participate in these tendering procedures. Alignment is used to give priority to firms in poorly-balanced countries. For instance, if the lowest bid is posted by a firm in a well-balanced country, CERN negotiates with the two lowest bidders in poorly-balanced Member States, provided that their bids fall within 20% of that of the lowest bidder. If one of these two firms aligns its price to that of the lowest bidder in the well-balanced country, it gets the contract. When technically feasible, contracts can also be split among multiple bidders to give an advantage to firms in poorly-balanced countries, even if they have not posted the most economically convenient bid.
Non-competitive tendering is used also when: (i) for reasons of standardization or strategic constraints the product or service must be supplied by a certain firm (single source procurement); (ii) when there exist only one firm proposing a given product or service (sole source procurement).

18. Methods

19. A program evaluation look at CERN procurement
Aim of program evaluation (PE): to measure the impact of treatment on outcome for a set of statistical units.
Units = firms;
Treatment = being a LHC supplier;
Outcome = Earnings Before Interest and Taxes, EBIT in 2010 ( profits)
Characteristics of the methodology:
There is a vast literature on the econometrics of PE
James J. Heckman won the Nobel in 2000 for his contributions to the PE.
The survey by Imbens and Wooldridge (2009, JEL) cites over 300 references.

20. Propensity score matching in a nutshell
Use a model for binary dependent variables (i.e. logit) to estimate the probability of being supplier:
p(xt) = Pr(treatment | xt).
where xt are control variables capturing firms’ characteristics and CERN’s procurement practices.
Match actual and potential searching “intersections” in their estimated propensity score, p(xt).
Estimate Average Treatment Effect (ATE)
where ATE is difference in EBIT between actual and potential suppliers, once the characteristics that determine assignment to treatment have been accounted for.
Aim of steps 1-2 is to approximate a sample design, where firms are randomly assigned to treatment.

21. Results

22. Estimates of the probability of being CERN suppliers Logit model for binary dependend variables

23. Estimated probability of being CERN suppliers (Model 1) Lo- VS Hi-Tech firms in well/poorly balanced and very poorly balanced countries

24. Estimated probability of being CERN suppliers (Model 1) Lo- VS Hi-Tech firms in well/poorly balanced and very poorly balanced countries
Base case
DProbability

25. Estimated incremental probability of being CERN suppliers With respect to lo-tech firms in a well or poorly balanced country (Model 1)

26. Estimates of the CERN effect With Hi-tech dummy

27. Conclusions
LHC procurement is associated with an increase in the profitability of firms.
Positive impact on EBIT.
As a side effect propensity score matching delivers some insights on the CERN procurement strategy.
The way ahead…
Quantile approach: Estimate the entire distribution of ATE for EBIT and other measures.
Work on the timing of the analysis.
Add robustness checks: change x and implement different matching algorithms.

28. Additional Results

29. Estimates of the probability of being CERN suppliers Logit model for binary dependend variables – without time fixed effects

30. Estimates of the «CERN effect» With Hi-tech categorical variable – with time fixed effects

31. Propensity score estimation: controls, xt
concept
variable
FIRM
Size
Avg. log Total Assets in
Reliability
Avg. Liquidity ratio
R&D intensity
Avg. Int. Ass. as % fix. asset
Location
country fixed effects
Common shocks
time fixed effects (for registration year)
CERN
Variety
1 if No. activity codes>1, 0 if = 1
Proximity
1 for Switzerland & France
Procurement policy
1 if very poorly balanced
Tech. Intensity
1 if Hi-tech (at least 1 act. code)
Share Hi-tech Act. Codes (Categorical)