Luis E. VILA*, C. Delia DÁVILA** and José-Ginés MORA***

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

2. Outline • Introduction • Production of competences in H.E. • Proflex data • Modeling strategies • Some estimation results • Concluding remarks

3. 1. Introduction

4. 1 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

5. 1 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

6. 1 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)

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

8. 1 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)

9. 1 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

10. 2. Production of competences in HE

11. 2 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

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

13. 3. Proflex data

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

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

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

17. 3 HE input: Modes of teaching and learning

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

19. 4.Modeling strategies

20. 4 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, )

21. 4 Variance components model (two-level) Cij = f ( Rij , Sij ) + uj + ij Composite error terms: • uj : group noise N( 0, 2u ) : field, institution, country • ij: individual noise N( 0, 2 ) • Intragroup correlation = 2u / ( 2u +2 )

22. 5. Some estimation results

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

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

25. 6. Concluding remarks

26. 6 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 CTIsspecifically • Field of study, institution attended and country are crucial to development of CTI’s

27. 6 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

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