1. The effects of Bologna Process on expenditure in HE systems of EU-15 countries
30 th Annual EAIR Forum
(Copenhagen 2008)
Tommaso Agasisti (Politecnico di Milano, Italy)
Carmen Pérez Esparrells (UAM, Spain)
Giusepe Catalano (Politecnico di Milano, Italy)
Susana Morales Sequera (UAM, Spain)

2. Agenda
Motivation
Objectives
Methodology and data
Results
Conclusions

3. The motivation Education => economic growth
Patterns of HE expenditure
Bologna process => process of convergence of educational policy (the objective: implementation of EHEA with common characteristics by the end of 2010)

4. The context Bologna Process: some characteristics
The idea has fully overcome the first expectations. Why?
Educational dimension => we are in the line of convergence
Economic dimension =>
convergence? (our research question)
Social dimension => the new challenge

5. Objectives
Analysis of indicators of financial resources invested in HE ( ) and verifying if is there a process of convergence in expenditure per student in HEIs.
Estimating if the wealth of countries (measured by GDP per capita) has influenced the process of convergence.

6. Data Dependent variable: Expenditure per student Determinants:
GDP per capita;
% population who attained tertiary education;
expenditure for HE as %GDP
public funds to HE as a % of total HE expenditure
“Bologna effect” (dummy)
SOURCE:
OECD data (Education at a Glance from 2002 to 2007)

7. Methodology Two approaches:
Regression analysis: fixed-effects and random-effects to detect a “Bologna effect”
Convergence analysis:
Absolute convergence (β-convergence)
Conditional β-convergence
σ-convergence

8. σ-convergence
The most important measure of cross-section analysis of dispersion that has been used: coefficient of variation (Barro & Salas-i-Martin).
σ-convergence occurs if dispersion among countries falls in time.
Coeficiente de variación: cociente entre la desviación típica y la media de la variable.
Desviación típica: raiz cuadrada de la varianza
Otros análisis utilizan la desviación típica de los logaritmos

9. β-convergence
A process of absolute convergence (β-convergence) exists if countries with lower expenditure per student in HEIs levels have grown to higher rates than countries with better levels.
β-convergence is calculated with the estimation of β in the following regression:
β-convergence will exist if parameter β is positive and statistically significant

10. Conditional β-convergence
In many situation an absolute convergence (β-convergence) cannot take place since there are different structural conditions between the different countries, so that they do not converge at a unique equilibrium point. There is absolute convergence if regions have the same starting level.
In these cases, we use what Sala-i-Martin (1996), Barro and Sala-i-Marti (1992) and Mankiw, romer and Weil (1992) denominated conditional convergence (including an other explanatory variable).

11. Results of regression analysis
Variable
Model 1 _ Fixed effects
Model 2 _ Fixed effects
Model 1 _ Random effects
Model 2 _ Random effects
GDP per capita
0.224
0.156
0.253
0.191
% Population who attained tertiary education
91.860
61.804
62.311
54.232
Expenditure for tertiary education as %GDP 4, 4, 4, 4,
Public funds for tertiary education as %total
3.456
1.676
-3.593
-4.471
Bologna Process (Dummy)
Constant
-4,
-1,
-5,
-1, N 69 r2
0.613
0.658
Rmse
700.4 F
20.211
19.201

12. Results of σ-convergence
Coeficiente de variación: cociente entre la desviación típica y la media de la variable.
Desviación típica: raiz cuadrada de la varianza
Source: authors’ elaboration

13. Results of β-convergence β *
( )
( ) **
( ) α **
( )
( ) **
( )
Adjusted R2
21.35%
-5.33%
34.46%
β(%)
4.7%
2.8%
8.2%
Notes: t-Statistic in parenthesis. The coefficients are statistically significant with a confidence of 90%(*) or 95%(**).

14. Results of β-convergence in the entire period
All the estimated parameters are statistically significant .
β positive informs about absolute convergence.
The goodness of fit is only 21.35%, which indicate scarce relation between both variables. However, in the period is 34%.

15. Results of conditional β-convergence
We also investigated whether convergence in the period has been affected by national wealth, in this case, GDP per capita.
In consequence, we ran a new estimation of the model including GDP per capita.
The inclusion of GDPpc is only significant in the period (after that period, the convergence is due to Bologna Process?)

16. Results of conditional β-convergence
Conditional convergence β *
( ) α
***
( ) λ
***
( )
Adjusted R2
43.75%
Notes: t-Statistic in parenthesis. The coefficients are statistically significant with a confidence of 90%(*), 95%(**) or 99%.

17. Results of conditional β-convergence
The introduction of GDPpc in the period increases significantly the estimated parameters and increases the goodness of fit until 43.75%.
In this period the growth rate of expenditure per student had been influenced by the level of GDPpc
Countries have converged to different stationary states; that is, GDPpc variable explains cross-country patterns of growth in expenditure per capita in this period, before Bologna Process.

18. Conclusions
The results of “financial” convergence for all the period ( ) are clear and they are going in the “right” direction. We show evidence of an approaching process in the composition of HEIs expenditure per student in EU-15 countries.
We have also identified that this convergence was more marked in the second part of the observation period, after Bologna process.
New research: with data until 2010
what’s happen in the long term?
Maybe, these difference in terms of expenditure per student will be less for a “natural” process 18

19. Conclusions
The role of private sector in explaining the convergence in expenditure per student