1. Luis E. VILA*, C. Delia DÁVILA** and José-Ginés MORA*** The development of competences at Latin-American universities: A multi- level production function approach *University of Valencia **University of Las Palmas de Gran Canaria *** Institute of Education, University of London Ljubiana, Slovenia 24-26, September 2009

2. Outline 1.Introduction 2.Production of competences in H.E. 3.Proflex data 4.Modeling strategies 5.Some estimation results 6.Concluding remarks

3. 1. Introduction

4. H.E. & the economy’s potential for innovation Individual and aggregate productivity gains, and therefore growth and development, emerge from using in production newly available knowledge – Schultz (1975)  ‘allocative ability’ – Lucas (2009)   in  (t)/  t =  (t) H.E. contributes to build innovation potential in the economy via the supply of new H.E. graduates 1

5. Innovative role of graduates Fresh HEGs bring into the workplace (among other forms of human capital) their capacity to innovate: – Ability to mobilize in their jobs already-available knowledge/resources not utilized previously – Ability to continuously create or adapt, and use, newly-available knowledge or resources while developing tasks and responsibilities in their jobs 1

6. Competences to innovate (CTIs) Innovative behavior implies a sequence of activities Detection, acquisition, evaluation, reallocation Specific competences are needed to perform well in the activities leading to productive innovation We will call them “competences to innovate” (CTIs) 1

7. Detection of opportunity Innovative behavior and competences E.S. Resource reallocation 1 Idea creation or acquisition Evaluation of new ideas Alertness to new opportunities Come up with new ideas, solutions Mobilization of capacities of others Willingness to question ideas ActivitiesCompetences

8. Objective and approach To explore production function relationships between the development of innovation-related competences and the prevalence of diverse teaching and learning modes in H.E. studies Production of education theoretical framework Model-based general approach Data Proflex (  8,700 graduates from 33 Latin- American universities in 9 countries) 1

9. Motivation Stronger emphasis in ‘the right T&L modes’ would - ceteris paribus- increase the contribution of Higher Education to economic growth and general wellbeing in Latin-American countries by improving aggregate innovation potential through a more effective development of CTIs by graduates 1

10. 2. Production of competences in HE

11. Literature: two main views of learning Early Childhood Development: Knowledge acquisition as a cumulative process starting in early childhood at the household Education Production Function: Cognitive achievement as the result of applying diverse combinations of educational inputs to students 2

12. Competence development ( C ) Educational resources ( R ) Programme characteristics Teaching & learning modes A model for competence production E.S. Higher education studies Prior investments Dedication, effort Ability Student resources ( R ) 2

13. 3. Proflex data

14. Table 1 Field of study (ISCED 2000). Descriptive statistics N = 8301 records 3

15. Questions on competences in Proflex A. “How do you rate your own competence level?” B. “What is the required level of competence in your current work?” C. “What was the contribution of the programme completed to your competence development?” Answers to A  Individual’s human capital Answers to B  Human capital needed for job Answers to C  ‘Value added’ by H.E. study 3

16. Output: Contribution of Higher Education to competence development 3

17. HE input: Modes of teaching and learning 3

18. Table 2 Other variables in the analysis. Descriptive statistics. 3

19. 4. Modeling strategies

20. Estimating education production functions Ability is unobserved  Stochastic frontier models Competences correlated  Orthogonal factors Subjective evaluation  Relative measures Group effects / endogeneity  Multi-level models. – T&L modes =  (Programme, X) – Student effort =  (Modes, Programme, Z,  ) – Programme = (Prior achievement, Y,  ) 4

21. Variance components model (two-level) C ij = f ( R ij, S ij ) + u j +  ij Composite error terms: u j : group noise N( 0,  2 u ) : field, institution, country  ij : individual noise N( 0,  2  ) Intragroup correlation =  2 u / (  2 u +  2  ) 4

22. 5. Some estimation results

23. Table 3 : 2-level model (individual/field) for development of CTIs 5

24. Table 4: 3-level model (individual/institution/country) for development of CTIs 5

25. 6. Concluding remarks

26. Summary of main results Acquisition of CTIs in H.E. appears to depend on the prevalence of some pro-active T&L modes Each CTI is more efficiently developed through a specific combination of T&L modes Most prevalent modes in Latin-American H.E. contribute little, if any, to develop CTIs specifically Field of study, institution attended and country are crucial to development of CTI’s 6

27. Problems remaining / Further research No longitudinal information on decision rules of –Students: programme choice, behavior –Institutions: study design, admission –Policy makers: regulation and funding Costs of teaching & learning modes unknown Other sources of CTIs beside higher education Actual CTIs utilization not yet addressed 6

28. The development of competences at Latin-American universities: A multi-level production function approach Thank you very much for your attention luis.vila@uv.es