1. Evaluation of R&D public policy in the European Union: an Expert Knowledge-based and two-stage DEA Approach
Dª Mónica de la Paz-Marín
D. Carlos García Alonso
9th International Conference on Data Envelopment Analysis (DEA2011)
Thessaloniki, Greece
25th-27th august
Business Administration College
University of Córdoba
Spain

2. INDEX 1.- Why evaluate efficiency of R&D public policy?
I. PRESENTATION OF THE STUDY
1.- Why evaluate efficiency of R&D public policy?
2.- Our methodology.
3.- Our conceptual model: a Bayesian Network of an endogenous economic growth.
4.- The dataset.
5.- Data transformations: the Rules-Base.
6.- Selected scenarios.
7.- Data analysis: Two-stage EbCA-BN-DEA approach
II. ANALYSING THE RESULTS…
8.- DEA scores by selected scenarios.
9.- Conclusions.

3. DIFFICULTY OF MEASUREMENT (SOCIO- ECONONOMIC IMPACTS)
1. -Why evaluate efficiency of R&D public policy?
Lisbon Strategy
Barcelona target (3% of GDP of R&D intensity)
EU2020
Education
Employment
Environment
Health
Etc.
Socioeconomic alternatives
Why investing in R&D?
Accountability
Worldwide Economic crisis
Ageing of European Population
Unemployment
SCARCITY OF FUNDS
USE OF THIS FUNDS
EU STRATEGY
DIFFICULTY OF MEASUREMENT (SOCIO- ECONONOMIC IMPACTS)

4. 2.- Our methodology… Prior Expert Knowledge Acquisition Framework:1
Knowledge Base 2
Rules- Base
Framework:
A Bayesian Network
Satisfactory solution)
(Iterations until
Incorporate IK 3
Data collection/
preparation
Raw Data-base 4
Transformation of Data
Transformed
Database 5
Scenarios I/O
Guided Analysis Method: DEA 6
Evaluation of results
Results
Implicit knowledge (IK)? 7
Yes No
Decision Support System 8

5. 3.- Our conceptual model…(Bayesian Network of endogenous economic growth; ROMER, 1990)

6. R&D Public Policy; traditional scenario
3.- Our conceptual model…
(Bayesian Network of endogenous economic growth; ROMER, 1990)
From our bayesian network we extract a rules-base for transforming data and scenarios of analysis.
R&D Public Policy; socioeconomic scenarios

7. Last year available in official Database: 2008
4.- Our dataset…
Last year available in official Database: 2008
(Sources: Eurostat, WIPO, World Bank Database)
2. TIME LAG is taken into account: 2 years.
Period: 2006 for inputs (I) and 2008 for outputs (O)
3. TOTAL DMUs: 27 EU Member Countries.
4. Homogeneity in DMUs objectives:
Lisbon´s Strategy ( ).
International financial crisis: August, 2007.
And for future studies: new Strategy EU2020

8. ERDFUN2006=f(BUDPRI2006) 5.- Data transformation. The rules-base.
Bayesian Network:
ERDFUN2006=f(BUDPRI2006)
Variables:
ERDFUN: R&D Public Expenditure by source of funds
BUDPRI: Budget Priorities and EU Strategies
GERDT: Total R&D expenditure as % of GDP
Values:
ERDFUN Low: [0-0.55]; High: (0.55-1] CRISP SETS (fuzzy theory)
GERDT: R&D effort must reach 3% of GDP
How we measure Budget
Priorities of EU
ERDFUN/GERDT: 60% of R&D effort from Business Sector

9. RU1=IF ERDFUN/GERDT<=0.4 AND GERDT>=3 THEN ERDFUN’= ERDFUN*2
Rules for transformation of data
RU1=IF ERDFUN/GERDT<=0.4 AND GERDT>=3
THEN ERDFUN’= ERDFUN*2
(the real value is doubled)
RU2=IF ERDFUN/GERDT >0.55 AND GERDT<1.8
THEN ERDFUN’= ERDFUN/2
(the real value is penalised)

10. R&D and innovation process:
6.- Data Analysis Method: DEA.
R&D and innovation process:
A Two-Stage DEA approach
Traditional INPUTS of R&D public policy
Traditional
OUTPUTS of R&D public policy
Socio-economic
OUTPUTS
output-orientation: it is reasonable to assume that European countries aim to maximize the outputs with a given level of inputs
BCC MODEL: Variable returns to scale
Second-stage: Traditional outputs turn into new inputs in
various socioeconomic scenarios
First stage: Estimation of relative R&D efficiency scores (traditional R&D inputs and outputs)

11. Selected Scenarios 1.Traditional 2. Economic 3. Innovation
INPUTS: R&D public expenditure; R&D public personnel
OUTPUTS: Patents, scientific publications
1.Traditional
INPUTS: Patents, scientific publications
OUTPUTS: GDP growth, Real Labour Productivity growht per hour worked, High-Tech Exports, Unemployment
2. Economic
INPUTS: Patents, scientific publicati ns.
OUTPUTS: Trademarks, % SME with product innovations
3. Innovation
INPUTS: Patents, scientific publications.
OUTPUTS: Ph.D students, Life-long learning, Early leavers.
4. Education
OUTPUTS: Life expectancy, mortality rate (infant), age dependency ratio.
5. Health

12. % of output due to inputs
(public R&D expenditure and public R&D personnel)
Patents
10%
Scientific
Publications
90%

13. SCENARIO 1: TRADITIONAL (2X2)
INPUTS: R&D public expenditure; R&D public personnel
OUTPUTS: Patents, scientific publications.

14. SCENARIO 2: ECONOMIC (2X4)
INPUTS: Patents, scientific publications.
OUTPUTS: GDP growth, Real labour productivity growth per hour worked, unemployment, High-Tech Exports.

15. SCENARIO 3: INNOVATION DIFUSSION (4X4)
INPUTS: Patents, scientific publications.
OUTPUTS: Trademarks, Sales of new to market and new to firm innovations as % of turnover, SMEs introducing product or process innovations as % of SMEs.

16. 8.- DEA scores (selected scenarios)
EFFICIENT
LESS EFFICIENT

17. EFFICIENT
LESS EFFICIENT

18. EFFICIENT

19. 9.- Conclusions.
- We reduce uncertainty through a Bayesian Network as a conceptual model…
- We obtain a new model for decision-making support…
- We use DEA for validating our model and for assessing technical efficiency…
- Macroeconomic country data may not be sufficient to judge about inefficiencies without incorporating prior expert knowledge…
In future studies: random variables, Monte Carlo simulation, more scenarios, combination of alternative inputs/otuputs, etc.

20. Business Administration College (ETEA)
Thank you very much for your attention!
Questions??
Dª Mónica de la Paz-Marín
D. Carlos García Alonso
Business Administration College (ETEA)
C/Escritor Castilla Aguayo
University of Córdoba
14004-Córdoba
Spain