Investments in Higher Education and the Investments in Higher Education and the Economic Performance of OECD Member Economic Performance of OECD Member

CountriesCountries

Faculty of Architecture & Town PlanningTechnion – Israel Institute of Technology

5th Bi-National Regional Science Workshop

Tel Aviv, 29-30/4/2007

Amnon FrenkelEran Leck

The contribution of Universities and Academic The contribution of Universities and Academic Research to the EconomyResearch 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.

Econometric Growth Regression StudiesEconometric 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).

Research ObjectiveResearch 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

ModelsModels Indirect model - Two-stage, least-squares regression model

Direct model - Multivariate regression models.

Investigation UnitInvestigation Unit The 30 OECD countries + Israel.

Data BasesData Bases

• Electronic database of the World Bank (WDI) Electronic database of the World Bank (WDI)

• Science and Technology Indicators of the OECDScience 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 LABORSTA (International Labor Organization Bureau of Statistics)Statistics)

Two-stage - least-squares regression modelTwo-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)

Stage 1Stage 1

Human Capital Quality as a Function of Human Capital Quality as a Function of higher education investmentshigher education investments

X variablesNStandard Error R2

Y variables

%employees in the computer field

Expenditure per student in research universities

172.5547E-07** 0.59

Expenditure per student on R&D

205.82154E-07** 0.51

Number of researchers in R&D per 100,000 residents

236.85726E-07** 0.54

%of employees in the scientific and technological fields

Expenditure per student in research universities

184.39093E-07* 0.29

Expenditure per student on R&D

198.66423E-07** 0.41

Number of researchers in R&D per 100,000 residents

258.60061E-07** 0.35

Regression Results – Stage 1Regression Results – Stage 1

** Significant at the 1% level * Significant at the 5% level

Percentage of employees in the computer field as a function Percentage of employees in the computer field as a function of total expenditure per student in research universitiesof total expenditure per student in research universities

ISR

CHE

POL

NLD

ITA

DEU

FIN

CANAUS

R2 = 0.59

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

0 5000 10000 15000 20000

Expenditure per student in research universities

% e

mpl

oyee

s in

the

com

pute

r fie

ld

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

Percentage of employees in scientific and technologicalPercentage of employees in scientific and technological fields as a function of the expenditure per student on R&Dfields 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

ISR

UK

TUR

NLDIRE

DEUFINR2 = 0.41

0.0%

0.5%

1.0%

1.5%

2.0%

2.5%

3.0%

3.5%

4.0%

0 1000 2000 3000 4000 5000Expenditure per student on R&D

% o

f em

ploy

ees

in th

e sc

ient

ific

and

tech

nolo

gica

l file

ds

Stage 2Stage 2

Economic Performance as a Function of Economic Performance as a Function of Human Capital QualityHuman Capital Quality

Regression Results – Stage 2Regression Results – Stage 2Y variables N

Standard Error R2

Z - v

aria

ble

s

Foreign direct investments as a percentage of GDP % employees

in the computer field

273.29**0.35

Expenditure on communication and information technology as a percentage of per-capita GDP2616608.60** 0.65

Ratio of high-technology exports to total exports

% of employees in the scientific and technological fields

272.63**0.32

GDP per capita, constant 1995 US$ 26143543.95** 0.32

Foreign direct investments as a percentage of GDP 273.60**0.37

Expenditure on communication and information technology as a percentage of per-capita GDP 2519438.67** 0.49

** Significant at the 1% level * Significant at the 5% level

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

ISR

TUR

CHE

SVK

PRT

NLD JAPITA

IRE

DEUFIN

DNK

R2 = 0.32

0

5000

10000

15000

20000

25000

30000

35000

0.00% 1.00% 2.00% 3.00% 4.00% 5.00%% of employees in the scientific and technological fileds

GD

P p

er c

apita

, con

stan

t 199

5 U

S$

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.

01 11 i1SCI_ENG EX_STUD_R&D +

02 12 i2GDP_CAP SCI_ENG+

The SUR Model:

(1)

(2)

OLS and SUR Results for Model B

SCI_ENG )E1( GDP_CAP )E2( SCI_ENG )E1( GDP_CAP )E2(

Intercept 0.020783 8570.1 0.019925 5424.0)0.003671(** )3905.8(* )0.003613(** )3826.1(

EX_STUD_R&D 3.73E-06 4.09E-06)1.28E-06(* )1.25E-06(**

SCI_ENG 399905.9 505897.5)124100.1(** )121248.6(**

N 19 19

Adj R2/Weighted R2 0.28 0.33

Independent Variable

Dependent VariableOLS Estimation Simultaneous SUR Estimation

0.48

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

Multivariate Model Multivariate Model

Multivariate models describing the association betweenMultivariate models describing the association betweenhigher education variables and per-capita GDP (PPP)higher education variables and per-capita GDP (PPP)

ModelIndependent variablesBetaStandard

Error R2N

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

0.5621Expenditure 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.

GDP per capita PPP as a function of the expenditures on R&D and GDP per capita PPP as a function of the expenditures on R&D and instruction in research universities (log-linear model) instruction in research universities (log-linear model)

** Significant at the 1% level. * Significant at the 5% level.

ModelIndependentvariables

BetaStandard Error

R2Model

E

(Constant)6.9660.36**

0.8621Expenditure on instruction in higher education institutions as a percentage of GDP

0.3930.15*

Expenditure on R&D in research universities (per student)

0.3900.05**

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.

Point Elasticities - per-capita GDP in relation Point Elasticities - per-capita GDP in relation to the expenditure on R&D in research universitiesto 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).

AUT

DNK

FIN

FRA

DEU

IRE

NLD

SVK

SWE

TUR

UK

USA

ISR

R2 = 0.89

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

0 2000 4000 6000 8000

Expenditure per student on R&D (in PPP $)

Ela

stic

ity o

f GD

P p

er

cap

ita in

re

latio

n

to th

e e

xpe

nd

iture

pe

r st

ud

en

t on

R&

D

Simultaneous ModelSimultaneous Model

Schematic Description of the model

R&D Expenditure Percentage of

Employees in the computer

field

GDP Per Capita

Instruction Expenditure

01 11 21 i1COMP EX_STUD_R&D + EX_I_TEA + 1)

02 12 i2GDP_CAP COMP+ 2)

03 13 3EX_STUD_R&D GDP_CAP i 3)

04 14 4EX_I_TEA GDP_CAP i 4)

OLS and SUR ResultsOLS and SUR Results

COMP )E1( GDP_CAP )E2( EX_STUD_R&D )E3( EX_I_TEA )E4( COMP )E1( GDP_CAP )E2( EX_STUD_R&D )E3( EX_I_TEA )E4(

Intercept 0.000804 14282.5 -535.1 0.209549 -0.00151 11112.7 -1541.8 0.22950.00279 )2585.3(** 1084.4 0.261501 0.00263 )2406.7(** 1036.2 0.2612

EX_STUD_R&D 2.46E-06 2.71E-06)5.898E-07(** )5.54E-07(**

EX_I_TEA 0.003487 0.005251)0.002428( )0.002195(*

COMP 672738.8 980382.2)221691.4(** )201861.0(**

GDP_CAP 0.143563 0.000035 0.191017 0.000034)0.049065(** )0.000012(** )0.046696(** )0.000012(**

N 18 18 18 18

Adj R2/Weighted R2 0.51 0.31 0.30 0.30

Independent Variable

OLS EstimationDependent Variable

Simultaneous SUR Estimation

0.72

• 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.

ConclusionsConclusions