The document analyzes the relationship between public (government and charity) spending on biomedical and health research in the UK and subsequent private pharmaceutical industry research and development (R&D) spending in the UK from 1982 to 2012. Using vector error correction modeling, it finds a complementary relationship between public and private spending, with a 1% increase in public spending leading to a 0.38-1.12% increase in private R&D spending over time. This suggests that public spending stimulates, rather than crowds out, private pharmaceutical industry R&D in the UK.

1. The economics of medical research:
Public/private spillovers and the rate of return
Jonathan Grant, Jon Sussex, Yan Feng, Jorge Mestre-Ferrandiz
(on behalf of Marco Hafner, Michele Pistollato and Peter Burridge)
Matt to provide image ….

2. Outline of the presentation
Context and objectives
What we did
What we found
Policy implications

3. Estimating the economic returns research

4. Two types of economic returns
‘Spillover’ or GDPgain
Direct and indirect impact on
the economy from medical
research
Estimates are disease
independent
Previously estimated GDP
gains to be equivalent to ROI
of 30% p.a. [range 26%-
34%], based on review of the
literature
Net health gain
Monetised health gains net
of the health care costs of
delivering them
Estimates are disease
dependent: c9% for
cardiovascular disease and
c10% for cancer

5. Public/private spillovers

6. Four areas of further research highlighted:
Replication: repeat estimate of health gains for other clinical areas ü
Time lags: improve understanding and estimates for the time it takes for
research to translate from bench to bedside ü
Attribution: improve understanding and estimates for attributing UK
health gains to UK research
Spillovers: develop contemporary, UK specific, estimates for biomedical
and health research

7. Outline of the presentation
Context and objectives
What we did
What we found
Policy implications

8. Main research question
What is the magnitudeof the effect of government and charity biomedical and
health research expenditure in the UK, separately and in total, on subsequent
private pharmaceutical sector research and development (R&D) expenditure
in the UK?
JonathanGrant
(Policy Institute,King’sCollege London)
PeterBurridge
(Department ofEconomics,University ofYork)
Yan Feng
(Office ofHealth Economics)
Marco Hafner
(RAND Europe)
Jorge Mestre-Ferrandiz
(Office ofHealth Economics)
Michele Pistollato
(formerly,Office ofHealth Economics)
Jon Sussex
(RAND Europe,formerly Office ofHealth Economics)

9. What we did
Reviewed theoretical, empirical and (UK) policy literature
Built 31-year time series of data for government, charitable and private
sectors’ R&D spend
Modelled interaction between private R&D spend, government and charity
research expenditure, plus control variables (global sales, dummy
variables)

10. Data collection: Variables included
Data period: 1982 – 2012
Key variables
Ø Government sector biomedical and health research expenditure in the UK
Ø Charitable sector biomedical and health research expenditure in the UK
Ø Private pharmaceutical R&D expenditure in the UK
Ø Global pharmaceutical sales
Ø Dummy variables for 1993 and 1993 onwards
We combined the government sector expenditure and charitable sector
expenditure as “public” expenditure for some analysis

11. Data collection: 10 disease areas
Time series broken down by 9 disease areas + “Other”:
Blood
Cancer
Cardiovascular
Central Nervous System
Gastroenterology
Infection
Respiratory
Skin
Vision

12. Data collection – public medical/health research
Government
Built on 2008 What’s it worth? database
Reviewed annual reports and funders’databases (e.g. MRC, NHS/DH,
Research Councils)
Charity
Reviewed annual reports and funders’databases (e.g. Wellcome Trust,
Association of Medical Research Charities)
AMRC allocated charities according to HRCS classification

13. Data collection – private industry R&D
Pharma R&D spend data available but not other medtech (pharma >95% of total
UK biomedical industry R&D)
Measurement issue: there is no direct measure of private R&D expenditure by
disease area
So we used a proxy: publications with authors giving UK industry addresses, by
disease area
Literature suggests 3 year average lag from research activity to subsequent
publication
We tried 0, 1, 2, 3, 4 and 5 year lags: most robust (“best”) model is when we
assume a 4 year lag between R&D expenditure and subsequent publication

14. Biomedical and health care research expenditure 1982-2012
figures suggest that private R&D expenditure is subject
to more variation than public research expenditure.
UK R&D expenditures by disease areas (public and private)
Figures 3 and 4 illustrate the public (government and
the underlying data is available in Additional file 6 (in-
cluding public expenditure figures broken down by gov-
ernment and charity).
Similar to the aggregated data series reported in the
previous section, at the disease area level we observe, in
Fig. 2 Total UK research and development expenditure (government, charity and private), 1982–2012 (£m, 2012 constant prices)
Sussex et al. BMC Medicine (2016) 14:32 Page 11 of 23

15. Distribution of ln(public research)
UK R&D expenditures by disease areas (public and private)
Figures 3 and 4 illustrate the public (government and
charity) and private R&D expenditure figures by disease
area between 1982 and 2008 in the logarithmic form in
which they feed into the econometric models. Note that
Similar to the aggregated data series reported in the
previous section, at the disease area level we observe, in
each case, an overall upward trend in public research
and private R&D spending and with more variation,
expressed as upward and downward movements in the
2.8
3.2
3.6
4.0
4.4
82 84 86 88 90 92 94 96 98 00 02 04 06 08
BLOOD
4.0
4.4
4.8
5.2
5.6
82 84 86 88 90 92 94 96 98 00 02 04 06 08
CNS
4.8
5.2
5.6
6.0
6.4
6.8
82 84 86 88 90 92 94 96 98 00 02 04 06 08
CANCER
3.2
3.6
4.0
4.4
4.8
5.2
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Cardiology
2.8
3.2
3.6
4.0
4.4
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Gastroenterology
3.0
3.5
4.0
4.5
5.0
5.5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Infectious
2.4
2.6
2.8
3.0
3.2
3.4
3.6
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Skin
2.6
2.8
3.0
3.2
3.4
3.6
3.8
82 84 86 88 90 92 94 96 98 00 02 04 06 08
VISION
2.8
3.2
3.6
4.0
4.4
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Respiratory
6.8
7.0
7.2
7.4
7.6
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Other
LNPUBLIC
Fig. 3 Public (government and charity) research and development (log) expenditure by disease area, 1982–2008 (£m, 2012 constant prices)

16. Distributions of ln(private R&D)
time series, in the expenditure data for the private sector
than for the public sector.
Looking at specific disease areas in more depth, we
R&D expenditure series follows a slightly increasing
trend between 1982, interrupted in 1996 and followed
by a steady rise until 2008. For the majority of the years
2.8
3.2
3.6
4.0
4.4
4.8
5.2
82 84 86 88 90 92 94 96 98 00 02 04 06 08
BLOOD
4.5
5.0
5.5
6.0
6.5
7.0
82 84 86 88 90 92 94 96 98 00 02 04 06 08
CNS
3
4
5
6
7
82 84 86 88 90 92 94 96 98 00 02 04 06 08
CANCER
3.5
4.0
4.5
5.0
5.5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Cardiology
3.0
3.5
4.0
4.5
5.0
5.5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Gastroenterology
3.2
3.6
4.0
4.4
4.8
5.2
5.6
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Infectious
2.5
3.0
3.5
4.0
4.5
5.0
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Skin
1
2
3
4
5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
VISION
2
3
4
5
6
7
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Respiratory
6.0
6.4
6.8
7.2
7.6
8.0
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Other
LNPRIVATE
Fig. 4 Private research and development (log) expenditure by disease area, 1982–2008 (£m, 2012 constant prices)
Sussex et al. BMC Medicine (2016) 14:32 Page 12 of 23

17. Distributions of ln(global pharma sales)
1993, with a decrease in sales between 1993 and 1994,
but is followed by a steady increase thereafter. Interest-
ingly, the global pharmaceutical sales series shows a par-
ticularly strong rise in sales starting in 1999 in most
disease areas.
of four specifications, one for each deterministic trend
specification. In this preliminary search the time-lag be-
tween funding and publication is treated as zero.
For each specification of the model we report six sta-
tistics: the cointegration rank, the statistics from the
8.0
8.5
9.0
9.5
10.0
10.5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
BLOOD
8.5
9.0
9.5
10.0
10.5
11.0
82 84 86 88 90 92 94 96 98 00 02 04 06 08
CNS
7
8
9
10
11
82 84 86 88 90 92 94 96 98 00 02 04 06 08
CANCER
9.2
9.6
10.0
10.4
10.8
11.2
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Cardiology
9.2
9.6
10.0
10.4
10.8
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Gastroenterology
9.2
9.6
10.0
10.4
10.8
11.2
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Infectious
8.2
8.4
8.6
8.8
9.0
9.2
9.4
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Skin
7.0
7.5
8.0
8.5
9.0
9.5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
VISION
8.5
9.0
9.5
10.0
10.5
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Respiratory
9.5
10.0
10.5
11.0
11.5
12.0
82 84 86 88 90 92 94 96 98 00 02 04 06 08
Other
LNSALE
Fig. 5 Global pharmaceutical (log) sales by disease area 1982–2008 (£m, 2012 constant prices)

18. Modelling aims
Is there a long run equilibriumrelationship between the level of:
Ø Government and charity biomedical and health research expenditure in the UK; and
Ø Private sector pharmaceutical R&D expenditure in the UK?
Are these complements or substitutes in the long run?
What is the long-run elasticity of private spend with respect to public spend?

19. Modelling methods
Vector Error CorrectionModel (VECM)
Various specifications
˗ Check the numberof long run equilibrium conditions
˗ Experiment with different specifications of the deterministic trend
˗ Experiment with different numbers of lags
˗ Check for the absence of residual autocorrelation up to 6 lags
Comparing the performance of the specifications
˗ AIC, Schwarz Criterion and log likelihood statistics
˗ Number of insignificant coefficientsin the Error Correction model
Trend model selection and determination of cointegratingrankfollowsthe parsimony
principle advanced by Pantula
Heterogeneity between different disease areas?
˗ We tried various approaches to handling heterogeneity explicitly
˗ The results were unstable andhard to interpret, so we have not differentiated between
diseases areas

20. Outline of the presentation
Context and objectives
What we did
What we found
Policy implications

21. What we found (1)
Long term equilibrium relationship between UK ‘public’biomedical and
health research expenditure, private sector R&D expenditurein the UK and
global sales
Complementary relationship between ‘public’and private R&D
expenditure, that is statistically significant at the 5% level in all
specifications
1% increase in ‘public’expenditureon R&D will eventually lead to
between 0.38% and 1.12% increase in private expenditureon R&D. Best
model suggests an elasticity of 0.81

22. What we found (2)
We analysed the impulse
response functions based on the
best model
44% of the response occurs after
one year, but the full response
takes many years

23. Outline of the presentation
Context and objectives
What we did
What we found
Policy implications

24. So what?
Crowding in, not crowding out. We have confirmed that government and
charity medical research spend stimulates additional private pharmaceutical
industry R&D in the UK
Given that in 2012 government+charity and private research spend were
respectively £3.43bn and £4.21bn,the elasticity of 0.81 means that a £1
increase in government+charity research spend produces an eventual £0.99
increase in private pharmaceutical industry R&D in the UK

25. Implications for the rate of return to public medical research
Substituting into the “What’s it worth?” RoR calculation, other parameters
unchanged, implies economic RoR in UK to UK public (govt+charity)
medical research is in the range 15%-18% (vs. 30%, range 26%-34%, for
original estimates)
Lower UK value than US could be due to smaller size and greater openness
of UK economy to the rest of the world
Added to estimates of the net monetary benefit of health gains arising from
cardiovascular research (9%) and cancer research (10%), total return
between 24% and 28%
UK-specific estimate still shows that investment in medical research
gives a very good rate of return

26. What next ….
Use current dataset to:
Ø Look at relationship between government
and charity
Ø Look at spillovers from private to public
Update social rate of return to R&D figures
(old, international and non-
biomedical/health)
Qualitatively understand how spillovers
actually manifest themselves and what are
the effective policy levers