1. Investments in Higher Education and the Economic Performance of OECD Member Countries Faculty of Architecture & Town Planning Technion – Israel Institute of Technology 5th Bi-National Regional Science Workshop Tel Aviv, 29-30/4/2007 Amnon Frenkel Eran Leck

2. The contribution of Universities and Academic Research to the Economy Direct Benefits: Enhancement of GDP, employment, and labor productivity Enlargement of the pool of skilled scientists and engineers Indirect Benefits: Capital investments Creation and adoption of technological innovations Nelson, 1986; Jaffe, 1989; Adams, 1993; Fischer and Varga, 2003. Types of contributions of higher education to economic growth (Martin et al., 1996): Increasing the stock of useful knowledge Promoting knowledge spillovers Training highly skilled graduates Creating methodologies and new scientific tools Increasing the capability for scientific and technological problem-solving.

3. Econometric Growth Regression Studies Criticism : Difficulties in finding reliable indicators of technological change Econometric difficulty in drawing conclusions from non-experimental data The models do not explain the association between higher education (or basic research) and economic performance in a direct way Chatterji (1988); Adams (1990, 1993); McMahon (1993); Guellec and van Pottelsberge de la Potterieu (2001); Sianesi and Reenen (2003).

4. Research Objective To investigate the association between higher education investments and economic growth in OECD countries. Hypothesis – a positive and significant relationship exists between higher education investments and the economic performance of developed countries Models Indirect model - Two-stage, least-squares regression model Direct model - Multivariate regression models.

5. Investigation Unit The 30 OECD countries + Israel. Data Bases Electronic database of the World Bank (WDI) Electronic database of the World Bank (WDI) Science and Technology Indicators of the OECD Science and Technology Indicators of the OECD Electronic databases of UNESCO and the OECD Electronic databases of UNESCO and the OECD LABORSTA (International Labor Organization Bureau of Statistics) LABORSTA (International Labor Organization Bureau of Statistics)

6. Two-stage - least-squares regression model Stage 1 - higher education investments in technological and scientific research – X (input) contribute to the training of a skilled, technological labor force – Y (output). [1] Y= f(X) Stage 2 - Skilled labor force - Y (input) is translated into higher productivity and growth rates - Z (output) [2] Z= f(Y)

7. Stage 1 Human Capital Quality as a Function of higher education investments

8. R2R2 Standard Error N X variables 0.592.5547E-07**17 Expenditure per student in research universities % employees in the computer field Y variables 0.515.82154E-07**20 Expenditure per student on R&D 0.546.85726E-07**23 Number of researchers in R&D per 100,000 residents 0.29 4.39093E-07* 18 Expenditure per student in research universities % of employees in the scientific and technological fields 0.418.66423E-07**19 Expenditure per student on R&D 0.358.60061E-07**25 Number of researchers in R&D per 100,000 residents Regression Results – Stage 1 ** Significant at the 1% level * Significant at the 5% level

9. Percentage of employees in the computer field as a function of total expenditure per student in research universities Strong and statistically significant relationship exists between the per student expenditure in research universities, and the percentage of employees in the computer field in the country

10. Percentage of employees in scientific and technological fields as a function of the expenditure per student on R&D fields as a function of the expenditure per student on R&D The more the country invests in universities’ R&D, the greater will be the percentage of employees in the computer, scientific, and technological fields

11. Stage 2 Economic Performance as a Function of Human Capital Quality

12. Regression Results – Stage 2 Regression Results – Stage 2 R2R2 Standard Error NY variables 0.353.29**27 % employees in the computer field Foreign direct investments as a percentage of GDP Z - variables 0.6516608.60**26 Expenditure on communication and information technology as a percentage of per-capita GDP 0.322.63**27 % of employees in the scientific and technological fields Ratio of high-technology exports to total exports 0.32143543.95**26GDP per capita, constant 1995 US$ 0.373.60**27 Foreign direct investments as a percentage of GDP 0.4919438.67**25 Expenditure on communication and information technology as a percentage of per-capita GDP ** Significant at the 1% level * Significant at the 5% level

13. GDP per capita as a function of the percentage of employees in scientific and technological fields A positive and significant link exists between the percentage of employees in scientific and technological fields and the GDP per capita

14. The linkage between the two stages Does a significant and positive association also exist between higher education and economic performance? A simultaneous equation model is formulated, using the seemingly unrelated regression (SUR) method.

15. The SUR Model: (1) (2) OLS and SUR Results for Model B Figures in parentheses are the standard error ** Significant at the 1% level * Significant at the 5% level the second index in each parameter represents the equation number

16. Multivariate Model

17. Multivariate models describing the association between higher education variables and per-capita GDP (PPP) ModelIndependent variablesBeta Standard Error R2R2 N A (Constant) 5208.4 2458.09* 0.7418 Total expenditure per student in research universities 1.08 0.33* Number of researchers in R&D per 100,000 residents 2.07 0.72** B (Constant) 4772.2 3192.37 0.6321 Expenditure on R&D in research universities (per student) 2.8 0.65** Expenditure on instruction in higher education institutions as a percentage of GDP 9070.3 2922.88* C (Constant) 4872.1 3546.33 0.5621 Expenditure on instruction in higher education institutions as a percentage of GDP 8097.4 3247.66* Expenditure on R&D in higher education institutions as a percentage of GDP 22882 6501.27** ** Significant at the 1% level. * Significant at the 5% level.

18. GDP per capita PPP as a function of the expenditures on R&D and instruction in research universities (log-linear model) ** Significant at the 1% level. * Significant at the 5% level. Model Independent variables BetaStandard Error R2R2 Model E (Constant) 6.966 0.36** 0.8621 Expenditure on instruction in higher education institutions as a percentage of GDP 0.393 0.15* Expenditure on R&D in research universities (per student) 0.390 0.05** A one percent increase in expenditure on R&D (per student) in research universities and a one percent increase in expenditure on instruction in higher education institutions (measured as a percentage of GDP) may contribute to a rise of 0.78% in the GDP.

19. Point Elasticities - per-capita GDP in relation to the expenditure on R&D in research universities A clear spatial dimension, with Western European countries (e.g., Sweden, Germany, the Netherlands, UK, Austria, Finland) presenting much higher point elasticities than Eastern European countries (Hungary, Poland, Slovakia and Turkey). Smaller countries (Sweden, Israel, the Netherlands, Austria, and Finland) have higher point elasticities than do big countries (Unites States, France).

20. Simultaneous Model Schematic Description of the model R&D Expenditure Percentage of Employees in the computer field GDP Per Capita Instruction Expenditure 1) 2) 3) 4)

21. OLS and SUR Results

22. The findings of the simultaneous model support our hypothesis regarding a two-stage process between higher education investments and economic growth. Higher education investments and scientific and technological research make a significant contribution to the economic performance of OECD countries The two main activities of universities – teaching and research--were found to be connected to the ability of OECD countries to enhance their per-capita GDP Small countries see a vital need to constantly reassess the degree of innovation of their economies in order to sustain economic competitiveness. Small countries must think imaginatively in order to overcome their own limitations, whether in size or resource. Investments in a technologically skilled labor force become a feature of paramount importance in national and strategic economic planning. Conclusions