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UNIVERSIDAD AUTÓNOMA DE MADRID

FACULTAD DE CIENCIAS ECONÓMICAS Y EMPRESARIALES

Departamento de Economía y Hacienda Pública

TIPOLOGÍAS DE UNIVERSIDADES: RELEVANCIA DE LA TERCERA MISIÓN PARA LAS POLÍTICAS Y ESTRATEGIAS UNIVERSITARIAS EN EL NUEVO

MARCO DE LA EDUCACIÓN SUPERIOR

TYPOLOGIES OF UNIVERSITIES: ROLE OF THE THIRD MISSION IN UNIVERSITY POLICIES AND STRATEGIES IN THE NEW FRAMEWORK OF

HIGHER EDUCATION

TESIS DOCTORAL

Presentada por

Eva María de la Torre García

Dirigida por

Dra. Mª Carmen Pérez Esparrells

Dr. Fernando Casani Fernández de Navarrete

Madrid, octubre de 2016

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TIPOLOGÍAS DE UNIVERSIDADES: RELEVANCIA DE LA TERCERA MISIÓN PARA LAS POLÍTICAS Y ESTRATEGIAS UNIVERSITARIAS EN EL NUEVO

MARCO DE LA EDUCACIÓN SUPERIOR

TYPOLOGIES OF UNIVERSITIES: ROLE OF THE THIRD MISSION IN UNIVERSITY POLICIES AND STRATEGIES IN THE NEW FRAMEWORK OF

HIGHER EDUCATION

Eva Mª de la Torre García

Directores: Dra. Mª Carmen Pérez Esparrells Dr. Fernando Casani Fernández de Navarrete

Tesis presentada para optar al Grado de Doctor con Mención Internacional en la Universidad Autónoma de Madrid

Programa Oficial de Posgrado en Economía Departamento de Economía y Hacienda Pública

Universidad Autónoma de Madrid

2016

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A mis padres y hermanas

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AGRADECIMIENTOS / ACKNOWLEDGEMENTS

Estas páginas de la presente Tesis Doctoral están destinadas a agradecer a todas aquellas personas e instituciones que me han guiado y apoyado para que esta etapa de mi carrera académica llegue a buen puerto. Por supuesto, todas las opiniones y errores que este documento pueda contener son propios de la doctoranda.

En primer lugar, debo agradecer la inmensa labor de mis directores, la profesora Dra.

Carmen Pérez Esparrells y el profesor Dr. Fernando Casani Fernández de Navarrete.

Muchas gracias directores. Se me quedan cortas las palabras para haceros llegar el infinito agradecimiento que merecéis por vuestra dedicación, por la sesiones maratonianas que me habéis brindado sobre todas y cada una de las fases de este trabajo, por vuestro entusiasmo y aliento; en definitiva, por haber estado a mi lado en cada paso, en cada dificultad y en cada alegría. Por todo ello gracias directores, habéis sido una fuente de apoyo en multitud de formas y situaciones.

Gracias al profesor Dr. Tommaso Agasisti, por los seis meses de estancia que me brindó en el Politecnico di Milano. Su visión internacional del trabajo de investigación y su gran experiencia y pericia en las metodologías y el trabajo científico han significado grandes lecciones patentes en este documento. Asimismo, quiero agradecer también la desinteresada ayuda prestada por el profesor Dr. Martí Sagarra, con quien tuve la suerte de trabajar durante mi estancia de investigación, y cuyo entusiasmo y profundo conocimiento de las metodologías empleadas en la presente Tesis Doctoral han sido claves para la robustez de la misma.

No me resisto a agradecer también el apoyo y acogimiento recibido de los profesores del Departamento de Economía y Hacienda Pública, especialmente a su equipo directivo, Dr. Javier Salinas, Dra. Marta Rahona y Dr. Álvaro Salas, pero también al Dr.

Maximino Carpio con quien tuve la suerte de compartir mis primeras experiencias en investigación en el campo de la Economía de la Educación, y a los profesores Dr.

Miguel Angoitia, Prof. Santiago Barroso, Dr. Miguel Buñuel, Dr. Gilberto Cárdenas, Dra. Amparo de Lara, Dra. Jennifer Graves, Dra. Miriam Hortas, Dr. Pedro Morón, Dra.

Gabriela Sicilia, Dra. Paloma Tobes y Dr. Jesús Trello por sus ánimos, apoyo y consejos. Asimismo, quiero transmitir mi agradecimiento al resto de profesores del Departamento por la aceptación y bienvenida que me han brindado: Dra. Dolores Dizy,

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Dra. Marta Fernández, Dr. José Juan Franch, D. Hipólito Gómez, Dr. César Herráiz, D.

Luis Alfonso Rojí, Dra. Olga Ruiz, y Dña. Ana Ucendo y Dr. Francisco Utrera. Y no menos agradecida estoy a Dña. Reyes Zurbano, Secretaria Administrativa del Departamento, por todas sus gestiones, consultas y ayuda en asuntos de logística.

De igual forma, agradezco a los profesores Dra. María Jano y Dr. Salvador Ortiz del Departamento de Economía Aplicada por sus lecciones sobre técnicas de análisis multivariante; y a Jaime Villanueva y Cesar Pérez López del Instituto de Estudios Fiscales y a Giovanni Pirovano del Politecnico di Milano por sus orientaciones en el uso de los softwares de análisis estadístico.

Asimismo, quiero trasmitir mi agradecimiento a todos aquellos profesores que se reunieron conmigo en las fases iniciales de esta investigación. Gracias por vuestros consejos y vuestros puntos de vista sobre las ideas de investigación que os planteaba:

profesores Dr. Massimo Colombo, Dra. Chiara Franzoni, Dr. Massimiliano Guerini y Dra. Cristina Rossi del Politécnico di Milano; Dr. Mariano Regini y Dr. Matteo Turri de la Università degli Studi di Milano; Dr. Kristof de Witte de la Katholieke Universiteit Leuven; Dr. Benedetto Lepori de la Università della Svizzera Italiana; Dr. Cecilio Mar- Molinero de la Kent Business School; Dr. José María Gómez Sancho de la Universidad de Zaragoza; Dra. María del Mar Salinas de la Universidad de Extremadura; Dr. Daniel Santín de la Universidad Complutense; Dra. Angélica Mª Vázquez Rojas de la Universidad Autónoma del Estado de Hidalgo; y Dr. José Luis Zofío de la Universidad Autónoma de Madrid. Agradezco también a la Dra. Federica Rossi de la Birkbeck, University of London por la transmisión de sus profundos conocimientos de la tercera misión.

Tampoco me resisto a dedicar unas palabras de aliento a los otros muchos doctorandos y profesores ayudantes, algunos de ellos ya doctores, con los que me he cruzado en este camino, pero en especial a Martín Martín González y Adriana Pérez Encinas, quienes siempre me impresionan con sus proezas y me inspiran con su bondad, fuerza y perseverancia. También agradezco a Leyla Angélica Sandoval Hamón su inestimable ayuda en la solicitud de becas.

Debo también unas palabras de agradecimiento a los miembros del Observatorio de la Actividad Investigadora en la Universidad Española (IUNE) y miembros del Instituto

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Interuniversitario de Investigación Avanzada sobre Evaluación de la Ciencia y la Universidad (INAECU), y en particular a su director el Dr. Elías Sanz y a la Dra.

Daniela de Filippo. Gracias en primer lugar por los datos facilitados sin los cuales no podría haber llevado a cabo esta investigación, y en segundo lugar por sus valiosos comentarios críticos sobre los resultados preliminares de esta Tesis Doctoral.

Asimismo, aprovecho estas líneas para agradecer a mis jefes y compañeros/as del Consejo Social la confianza que depositaron en mi al darme la oportunidad de trabajar en un órgano de gobierno que me permitió tener una visión más completa y madura de qué significa la palabra ‘universidad’, así como las lecciones profesionales y personales que aprendí durante aquella etapa. Gracias a su Presidente, Manuel Pizarro, a su Vicepresidente Arsenio Huergo y al que fue su Vicepresidente segundo Julián Revenga.

Y gracias especialmente a las personas con las que compartí mí día a día en la Secretaría del Consejo Social: Jette Bohsen, Secretaria General, Isabel Bodega, secretaria administrativa y Sonsoles Contreras, apoyo técnico.

Guardo también un especial sentimiento de gratitud hacia todos aquellos que me han acompañado durante estos años y a la paciencia que han tenido con mis ausencias debido a las exigencias de la Tesis Doctoral. Gracias a mis amigas y amigos por las carcajadas que nos echamos y el cariño que me tenéis. Gracias por estar a mi lado, algunos desde hace casi 30 años, y otros desde que nos cruzamos en el Máster, en el Erasmus o en las habitaciones de los pisos que hemos compartido. Gracias también a los amigos que me acogieron en el Politecnico di Milano como una más y que compartieron conmigo los retos, frustraciones y alegrías típicos de los doctorandos.

Por último, pero no por ello menos importante, dar las gracias a mis padres, Jesús y Mª Paz, y a mis hermanas, Cristina y Araceli, a quienes esta Tesis está dedicada. Gracias por vuestro apoyo incondicional, inmenso amor y sabiduría que día a día me brindáis, siempre habéis sido el pilar que me respalda. También agradezco a mi pareja el apoyo, paciencia e inmensa comprensión de estos años, así como sus ganas de entender mi trabajo y mis inquietudes para aceptar los sacrificios que la carrera académica conlleva y reforzar mi determinación de dedicación a una profesión tremendamente ilusionante, gratificante y exigente.

A todos, ¡GRACIAS! En Madrid, a 10 de octubre de 2016

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INDICE

RESUMEN ... 23

INTRODUCTION ... 31

References ... 42

CHAPTER 1. THEORETICAL FRAMEWORK: THE THIRD MISSION OF UNIVERSITIES ... 49

1.1. The concept of third mission ... 49

1.1.1 Evolution of the knowledge and innovation processes and their related societal expectations from universities ... 50

1.1.2. A university third mission conceptual framework ... 54

1.1.3. Third mission as a source of institutional diversity ... 63

1.2. Measuring third mission: indicators proposed in the literature ... 67

1.3. The third mission in Spain... 76

1.4. Summary ... 91

1.5. References ... 99

CHAPTER 2. LITERATURE REVISION AND RESEARCH DESIGN ... 119

2.1. Literature revision... 119

2.1.1. Studying the efficiency of universities: accounting or not accounting for the third mission?... 119

2.1.2. Studying the determinants of universities efficiency ... 125

2.1.3. Defining typologies of universities: a review of the extant classifications of universities ... 130

2.2. Research questions and hypotheses ... 136

2.3. Empirical analyses to be performed ... 137

2.3.1. Sample and data sources ... 137

2.3.2. Studying the efficiency of universities: Data Envelopment Analysis ... 139

2.3.3. The relationship between the impact of third mission on universities’ efficiency and typologies of universities: complementing Data Envelopment Analysis with multivariate methods ... 141

2.4. Summary ... 144

2.5. References ... 148

CHAPTER 3. HOW INCLUDING THIRD MISSION INDICATORS DO CHANGE UNIVERSITIES’ EFFICIENCY SCORES: AN EMPIRICAL DEA ANALYSIS OF THE SPANISH PUBLIC HIGHER EDUCATION SYSTEM ... 167

3.1. Methodology: Data Envelopment Analysis ... 167

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3.1.1. The DEA Method ... 167

3.1.2. The use of DEA in the HE sector: strengths and caveats ... 173

3.2. Selection of the indicators ... 175

3.2.1 The production function in the HE sector ... 175

3.2.2 Selection of inputs and outputs ... 178

3.2.3 A composite indicator for measuring third mission ... 186

3.3. Empirical approach: exploring the effects of considering third mission on efficiency scores ... 188

3.4. Results ... 192

3.4.1 Main results: efficiency scores in various DEA specifications (with and without third mission indicators) ... 192

3.4.2 Heterogeneity: how efficiency scores with third mission indicators vary among universities ... 196

3.4.3 Output weights: the strategic profile of universities ... 201

3.5. Summary ... 206

3.6. References ... 210

CHAPTER 4. A STEP FURTHER. ASSESSING UNIVERSITIES EFFICIENCY USING PATENTS AS THIRD MISSION PROXY – A MULTIDIMENSIONAL SCALING ANALYSIS... 229

4.1. Methodology: Ordinal Multidimensional Scaling method and its complementation with other quantitative methodologies ... 229

4.1.1. Ordinal Multidimensional Scaling method ... 229

4.1.2. Combining MDS with other methodologies: the MDS-DEA method ... 237

4.1.3. Interpreting and MDS configuration: complementation of MDS results with Cluster and Property Fitting analyses ... 238

4.2. Indicators included in the MDS-DEA analysis ... 242

4.3. Combining DEA and MDS methodologies: results ... 247

4.3.1. Establishing the dimensionality of the MDS constructs and choosing the final set of ratios ... 247

4.3.2. Results: interpreting the final configurations ... 252

4.4. Summary ... 268

4.5. References ... 272

CONCLUSIONES FINALES... 279

ANEXOS...297

Annex I. Systems of indicators proposed for the third mission ... 299

Annex II. Third mission indicators proposed in E3M, SPRU and OEU projects ... 306

Annex III. Indicators used in the third mission literature ... 316

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Annex IV. List of universities included in the analysis and their acronyms. ... 324

Annex V. Additional results for the DEA analysis included in Chapter 3. ... 325

Annex VI. Robustness checks performed for the DEA analysis included in Chapter 3 332 Annex VI.I. Results of the analysis including as proxy of the facilities of universities the capital transfers (executed revenues) ... 332

Annex VI.II. Results of the analysis including as proxy of the facilities of universities the tangible and intangible assets ... 335

Annex VI.III. Results of the analysis including considering the subject mix of (enrolled and graduate students) students and publications ... 338

Annex. VI.IV. Results of the analysis using the average of each variable for 3 academic years: 2008-09, 2009-10 and 2010-11. ... 341

Annex VII. de la Torre, E.M., Casani F. and Pérez-Esparrells, C. (2015) ‘¿Existen diferentes tipologías de universidades en España? Una primera aproximación’, Proceedings of the Economics of Education Association, 10: 231 – 251. ... 344

Annex VIII. Results for the complete dataset ... 369

Annex VIII.1. Results for the complete dataset. MDS nine-dimensional construct. ... 371

Annex VIII.2. Results for the complete dataset. MDS ten-dimensional construct. ... 379

Annex IX. Results for the reduced dataset. Seven dimensions ... 387

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INDICE DE TABLAS

Table 1.1. The academic and post-academic perspectives on science and research. ... 52

Table 1.3. Correspondence table for the classifications of third mission activities proposed by the main theoretical frameworks. ... 61

Table 1.4. Main higher education stakeholders in European universities and their roles. ... 66

Table 1.5. Initially proposed and finally selected third mission indicators by E3M, SPRU and OEU projects. ... 71

Table 1.7. Third mission criteria of the Spanish system of remuneration incentives to researchers (sexenios) by field. 2015. ... 82

Table 2.1. Efficiency analyses of the transfer of knowledge activity of universities. ... 123

Table 2.2. Efficiency analyses accounting for the three missions of universities. ... 125

Table 2.3. Types of HEIs’ classifications. ... 130

Table 2.4. Typologies of universities identified by Van Vught et al. (2011) and Daraio et al. (2011) in accordance to their size. ... 131

Table 2.5. Comparison of the cluster analyses performed by Bonaccorsi and Daraio (2009), García-Aracil and Palomares-Montero (2012) and Schubert et al. (2014). ... 134

Table 2.6. Resources and production of the universities included and excluded in the sample. Academic year 2010-11. ... 138

Table 2.7. Concordance table for the aggregated fields of knowledge considered in this study with MEC – SIIU and IUNE databases. ... 142

Table 3.1. Definition of the variables used in the analysis. ... 179

Table 3.2. Descriptive statistics for the variables in the dataset* 2010-11. ... 185

Table 3.3. Correlations between the variables in the dataset. 2010-11. ... 186

Table 3.4. Total variance explained by the Principal Components. ... 188

Table 3.5. Correlation between the first Principal Component and the row variables. ... 188

Table 3.6. Input and output specifications of the DEA analysis performed. ... 190

Table 3.7. Description of the clusters identified in typologies of universities produced by de la Torre et al. (2015). ... 191

Table 3.8. Descriptive statistics on the efficiency scores by DEA specification. ... 193

Table 3.9. Average use of inputs and production of outputs for efficient and inefficient universities by DEA specification. ... 193

Table 3.10. Characteristics of the inefficient universities across all DEA specifications (I), the universities that become efficient when accounting for third mission (B) and efficient universities across all DEA specifications (E). Variation rates (%). ... 195

Table 3.11. Correlations between the DEA specifications. ... 195

Table 3.12. Efficient universities in the baseline model (bs) and universities that become efficient when accounting for the third mission. ... 197

Table 3.13. Average and variation rate of the efficiency scores by DEA specification for the whole sample, by cluster and by universities with and without university hospital (U.H.). ... 198

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Table 3.14. Pearson correlations between the efficiency gains and the subject mix of universities by DEA specification. ... 199 Table 3.15.1. Characteristics of the inefficient universities across all DEA specifications (I), the universities that become efficient when accounting for third mission (B) and efficient

universities across all DEA specifications (E). Variation rates (%). ... 200 Table 3.15.2. Characteristics of the inefficient universities across all DEA specifications (I), the universities that become efficient when accounting for third mission (B) and efficient

universities across all DEA specifications (E). Variation rates. ... 201 Table 3.16.1. Average weights assigned to inputs and outputs by DEA specification for: the whole sample, the inefficient universities across all DEA specifications (I), the universities that become efficient when accounting for third mission (B) and the efficient universities across all DEA specifications (E)... 202 Table 3.16.2. Average weights assigned to inputs and outputs by DEA specification for: the whole sample, the inefficient universities across all DEA specifications (I), the universities that become efficient when accounting for third mission (B) and the efficient universities across all DEA specifications (E)... 203 Table 3.17. Average variation of the weights (absolute value) of the teaching and research output of universities by DEA specification for the whole sample, by cluster and by universities with and without university hospital (U.H.). ... 205 Table 3.18. Pearson correlations between the variation of the weights of the teaching and research output of universities and their subject mix by DEA specification. ... 206 Table 4.1. Kruskal’s verbal classification. ... 235 Table 4.2. Definition of the 31 ratios calculated from the raw data and size control variable. . 243 Table 4.3. DEA specifications. ... 244 Table 4.4. Ranking of Spanish public universities according to the efficiency scores for two DEA specifications (baseline model – bs – and baseline model + patents – bs3p). ... 246 Table 4.5. Stress-1 and dimensionality (baseline model – bs – and baseline + patents model – bs3p). Complete dataset. ... 247 Table 4.6. Ratios included in the (baseline and extended) DEA-MDS analysis. ... 249 Table 4.7. Stress-1 and dimensionality (baseline model – bs – and baseline + patents model – bs3p). Reduced dataset. ... 251 Table 4.8.1. Results of ProFit analysis (baseline model – bs). Reduced dataset. ... 255 Table 4.8.2. Results of ProFit analysis (baseline + patents model – bs3p). Reduced dataset. .. 256 Table 4.9. Interpretation of the dimensions of the MDS construct for the baseline model (bs) and the baseline + patents model (bs3p). ... 262 Table 4.10. Mains statistics on the coordinates for the university vectors and overall efficiency by cluster. ... 265 Table 4.11. Main characteristics by cluster. ... 266 Table 4.12. Typologies of Spanish public universities. ... 266 Table 4.13. Correlations between AB123 efficiency levels and the other ratios included in the DEA-MDS analysis. ... 267 Table I.1. Indicators proposed by the SPRU project. ... 299 Table I.2. Indicators proposed by the OEU project. ... 301

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Table I.3. Indicators proposed by the E3M project. ... 303

Table II.1. Third mission indicators related to its technology transfer and innovation (TTI) dimension. E3M, OEU and SPRU projects. ... 306

Table II.2. Third mission indicators related to its continuing education (CE) dimension. E3M, OEU and SPRU projects. ... 312

Table II.3. Third mission indicators related to its social engagement (SE) dimension. E3M, OEU and SPRU projects. ... 313

Table III.1. Indicators used in the third mission literature. ... 316

Table V.1. Ranking based on the efficiency scores by DEA model. ... 325

Table V.2. Variation rate of the efficiency scores by DEA specification and university. ... 326

Table V.3.1. Weights assigned to inputs and outputs by DEA specification and university. Bs,bs3p, bs3i and bs3s DEA specifications. ... 327

Table V.3.2. Weights assigned to inputs and outputs by DEA specification and university. bs3pi, bs3ps, bs3is and bs3c DEA specifications. ... 329

Table V.4. Variation of the weights (absolute value) of the weights assigned to the teaching and research outputs by DEA specification and university. ... 331

Table VI.I.1. Input and output specifications of the DEA analysis performed. ... 332

Table VI.I.2. Ranking based on the efficiency scores by DEA model. ... 333

Table VI.I.3. Descriptive statistics on the efficiency scores by DEA specification. ... 334

Table VI.I.4. Correlations between the efficiency scores by DEA specifications. ... 334

Table VI.I.5. Correlations between the efficiency scores for the DEA analyses accounting and not accounting for the facilities of universities. ... 334

Table VI.II.1. Input and output specifications of the DEA analysis performed. ... 335

Table VI.II.2. Ranking based on the efficiency scores by DEA model. ... 336

Table VI.II.3. Descriptive statistics on the efficiency scores by DEA specification. ... 337

Table VI.II.4. Correlations between the efficiency scores by DEA specifications. ... 337

Table VI.II.5. Correlations between the efficiency scores for the DEA analyses accounting and not accounting for the facilities of universities. ... 337

Table VI.III.1. Input and output specifications of the DEA analysis performed. ... 338

Table VI.III.2. Ranking based on the efficiency scores by DEA model. ... 339

Table VI.III.3. Descriptive statistics on the efficiency scores by DEA specification. ... 340

Table VI.III.4.1. Pearson correlation between the efficiency scores by DEA specifications.... 340

Table VI.III.4.2. Spearman correlation between the efficiency scores by DEA specifications. 340 Table VI.IV.1. Input and output specifications of the DEA analysis performed. ... 341

Table VI.IV.2. Ranking based on the efficiency scores for each university by year and DEA model. Average of the academic years 2008-09, 2009-10 and 2010-11. ... 342

Table VI.IV.3. Descriptive statistics on the efficiency scores by DEA specification. Average of the academic years 2008-09, 2009-10 and 2010-11. ... 343

Table VI.IV.4. Correlations between the efficiency scores by DEA specifications. Average of the academic years 2008-09, 2009-10 and 2010-11. ... 343

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Table VI.IV.5. Correlations between the efficiency scores for the DEA analyses for 2010 and

for the average of the academic years 2008-09, 2009-10 and 2010-11. ... 343

Table VIII.1. Principal Component Analysis. Eigenvalues and variance accounted for by component (bs and bs3p). Complete dataset. ... 369

Table VIII.2. DEA-MDS analysis. Stress-1 and dimensionality (bs and bs3p). Complete dataset. ... 369

Table VIII.1.1.1. Results of ProFit analysis (bs). Complete dataset. Nine dimensions. ... 373

Table VIII.1.1.2. Results of ProFit analysis (bs3p). Complete dataset. Nine dimensions. ... 374

Table VIII.2.1.1. Results of ProFit analysis (bs). Complete dataset. Ten dimensions. ... 381

Table VIII.2.1.2. Results of ProFit analysis (bs3p). Complete dataset. Ten dimensions. ... 382

Table IX.1. Principal Component Analysis. Eigenvalues and variance accounted for by component (bs and bs3p). Reduced dataset... 387

Table IX.2. DEA-MDS analysis. Stress-1 and dimensionality (bs and bs3p). Reduced dataset. ... 387

Table IX.3.1. Results of ProFit analysis (bs). Reduced dataset. Seven dimensions... 391

Table IX.3.2. Results of ProFit analysis (bs3p). Reduced dataset. Seven dimensions. ... 392

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INDICE DE FIGURAS

Figure 1.1. Conceptual framework for analysing the third mission. SPRU project ... 55

Figure 1.2. The OEU third mission theoretical framework. ... 57

Figure 3.1. Input oriented efficiency vs. output oriented efficiency in DEA ... 171

Figure 3.2. Constant returns to scales (CRS) vs. variable returns to scale (VRS) DEA variants ... 172

Figure 3.3. Production process in HE. ... 176

Figure 3.4. Simplified production process in HE. ... 177

Figure 4.1.1. Example of a monotone function. ... 234

Figure 4.1.2. Example of a monotone regression (ordinal MDS analysis). ... 234

Figure 4.2. Graphic representation of the results of an MDS analysis together with the ProFit analysis for Spanish universities. ... 241

Figure 4.3.1 Elbow diagram (baseline model – bs). Complete dataset. ... 248

Figure 4.3.2 Elbow diagram (baseline + patents model – bs3p). Complete dataset. ... 248

Figure 4.4.1 Elbow diagram (baseline model – bs). Reduced dataset. ... 251

Figure 4.4.2 Elbow diagram (baseline + patents model – bs3p). Reduced dataset. ... 252

Figure 4.5.1. Dendrogram for cluster analysis of variables (baseline model – bs). ... 253

Figure 4.5.2. Dendrogram for cluster analysis of variables (baseline + patents model – bs3p).253 Figure 4.6.1. Multidimensional Scaling configuration in Dimensions 1 and 2 (baseline model – bs). ... 258

Figure 4.6.2. Multidimensional Scaling configuration in Dimensions 1 and 2 (baseline + patents model – bs3p). ... 259

Figure 4.7.1. Multidimensional Scaling configuration in Dimensions 2 and 3 (baseline model – bs). ... 260

Figure 4.7.2. Multidimensional Scaling configuration in Dimensions 2 and 3 (baseline + patents model – bs3p). ... 261

Figure 4.8. Dendrogram for the cluster analysis on the university vectors. ... 264

Figure VIII.1.1 DEA-MDS analysis. Elbow diagram (bs). Complete dataset. ... 370

Figure VIII.1.2 DEA-MDS analysis. Elbow diagram (bs3p). Complete dataset. ... 370

Figure VIII.1.1.1. DEA-MDS analysis. Dendrogram for cluster analysis of variables (bs). Complete dataset. Nine dimensions. ... 371

Figure VIII.1.1.2. DEA-MDS analysis. Dendrogram for cluster analysis of variables (bs3p). Complete dataset. Nine dimensions. ... 372

Figure VIII.1.2.1. Multidimensional Scaling configuration in Dimensions 1 and 2 (bs). Complete dataset. Nine dimensions. ... 375

Figure VIII.1.2.2. Multidimensional Scaling configuration in Dimensions 1 and 2 (bs3p). Complete dataset. Nine dimensions. ... 376

Figure VIII.1.3.1. Multidimensional Scaling configuration in Dimensions 2 and 3 (bs). Complete dataset. Nine dimensions. ... 377

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Figure VIII.1.3.2. Multidimensional Scaling configuration in Dimensions 2 and 3 (bs3p).

Complete dataset. Nine dimensions. ... 378 Figure VIII.2.1.1. DEA-MDS analysis. Dendrogram for cluster analysis of variables (bs).

Complete dataset. Ten dimensions. ... 379 Figure VIII.2.1.2. DEA-MDS analysis. Dendrogram for cluster analysis of variables (bs3p).

Complete dataset. Ten dimensions. ... 380 Figure VIII.2.2.1. Multidimensional Scaling configuration in Dimensions 1 and 2 (bs).

Complete dataset. Ten dimensions. ... 383 Figure VIII.2.2.2. Multidimensional Scaling configuration in Dimensions 1 and 2 (bs3p).

Complete dataset. Ten dimensions. ... 384 Figure VIII.2.3.1. Multidimensional Scaling configuration in Dimensions 2 and 3 (bs).

Complete dataset. Ten dimensions. ... 385 Figure VIII.2.3.2. Multidimensional Scaling configuration in Dimensions 2 and 3 (bs3p).

Complete dataset. Ten dimensions. ... 386 Figure IX.1.1 DEA-MDS analysis. Elbow diagram (bs). Reduced dataset. ... 388 Figure IX.1.2 DEA-MDS analysis. Elbow diagram (bs3p). Reduced dataset. ... 388 Figure IX.2.1. DEA-MDS analysis. Dendrogram for cluster analysis of variables (bs). Reduced dataset. Seven dimensions. ... 389 Figure IX.2.2. DEA-MDS analysis. Dendrogram for cluster analysis of variables (bs3p).

Reduced dataset. Seven dimensions. ... 390 Figure IX.3.1. Multidimensional Scaling configuration in Dimensions 1 and 2 (bs). Reduced dataset. Seven dimensions. ... 393 Figure IX.3.2. Multidimensional Scaling configuration in Dimensions 1 and 2 (bs3p). Reduced dataset. Seven dimensions. ... 394 Figure IX.4.1. Multidimensional Scaling configuration in Dimensions 2 and 3 (bs). Reduced dataset. Seven dimensions. ... 395 Figure IX.4.2. Multidimensional Scaling configuration in Dimensions 2 and 3 (bs3p). Reduced dataset. Seven dimensions. ... 396

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RESUMEN

En la década de los 80, en paralelo con el avance del paradigma económico neoliberal y el proceso de la globalización, tuvo lugar el comienzo de una tendencia crítica que cuestionaba la utilidad de la actividad universitaria en términos de su contribución al desarrollo cultural, social y económico, llevando a la exigencia de una actividad universitaria más aplicada. Esta visión crítica que comenzó en el mundo anglosajón prendió rápidamente en el mundo académico, político y en la sociedad en general, desembocando en una ampliación del concepto de universidad a través de la inclusión de la tercera misión en las misiones fundamentales de las universidades.

En la presente investigación se realiza, en primer lugar, un estudio teórico de la tercera misión, quedando ésta definida como la ‘relación de la universidad con el mundo exterior no académico: industria, autoridades públicas y sociedad’ (Schoen et al., 2007, p.127), que se manifiesta en forma de experiencias de colaboración ‘entre instituciones de educación superior y sus comunidades (local, regional, nacional, global) para el intercambio mutuo y beneficioso de conocimiento y recursos’ (Driscoll, 2008, p. 39) y para el beneficio de la economía y la sociedad (Molas-Gallart et al., 2002). Asimismo, se consideran tres dimensiones de esta tercera misión: la transferencia tecnológica y la innovación, la formación continua y el compromiso social.

La revisión de literatura realizada en dicho estudio teórico da lugar a varias conclusiones. En primer lugar, la tercera misión es un concepto relativamente nuevo (al menos si lo comparamos con la docencia y la investigación) y como tal los académicos no han logrado consensuar una definición única, cuáles son las actividades que recoge o cuáles son los indicadores apropiados para su estudio y evaluación. Asimismo, existe un generalizado desequilibrio hacia las actividades de transferencia de conocimiento, que en el caso español quedaría explicado por el fomento de la misma en diversas leyes y programas implantados desde 1983; quedando también camino por recorrer para que la tercera misión tenga un peso equiparable a las misiones de educación e investigación y esté integrada en las estructuras y estrategias universitarias.

Posteriormente, se vuelve a revisar la literatura para constatar que los estudios de eficiencia de las universidades no suelen considerar esta tercera misión, y que los pocos que sí la consideran, no examinan el impacto de la tercera misión en la misma, ni su

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importancia para unas universidades y otras dependiendo de las características de éstas.

Estos estudios tampoco analizan si los indicadores de la tercera misión son adecuados o no en el contexto de los análisis de eficiencia. Así, la presente tesis doctoral se plantea las siguientes preguntas de investigación e hipótesis:

- (P1). ¿Cómo varía la evaluación de las Instituciones de Educación Superior (IES) como consecuencia de la introducción/exclusión de indicadores de tercera misión en el análisis?

- (P2). ¿Cómo varía la clasificación de las IES en tipologías como consecuencia de la introducción/exclusión de indicadores de tercera misión en el análisis?

- (P3). ¿Está la (in)eficiencia relacionada con una tipología de IES concreta?

- (H1). La no inclusión de la tercera misión en los análisis de eficiencia (y desempeño) de las universidades implica ignorar una parte importante de su actividad.

- (H2). El sesgo introducido por la no inclusión de la tercera misión en la evaluación de las universidades está relacionado con su especialización por áreas de conocimiento (subject mix) y por misiones universitarias (mission mix).

- (H3). La tercera misión tiene un papel fundamental en la definición de tipologías de universidades.

- (H4). Las universidades más eficientes muestran cierto grado de especialización en alguna misión en particular.

Para alcanzar estos objetivos de investigación, se ha construido una base de datos para 47 universidades públicas españolas presenciales con información procedente del Sistema Integrado de Información Universitaria (SIIU) del Ministerio de Educación, y el Observatorio de la Actividad Investigadora en la Universidad Española (IUNE).

Dicha base de datos recoge información sobre las principales características institucionales de las universidades, así como sobre su desempeño en docencia, investigación y tercera misión. Dichos datos hacen referencia al curso académico 2011- 12 (los datos más recientes disponibles en el momento de la recogida de los mismos, noviembre de 2014).

A partir de esta base de datos, se realizan ocho análisis DEA (Data Envelopment Analysis) utilizando las siguientes proxies: (i) los estudiantes matriculados, (ii) el

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personal académico total Equivalente a Tiempo Completo, (iii) los estudiantes graduados, (iv) el número de publicaciones, (v) el número de patentes concedidas (nacionales e internacionales), (v) los ingresos de tercera misión y (vi) el número de spin-offs establecidas. En primer lugar se desarrolla un modelo DEA básico en el que no se incluyen las proxis de la tercera misión. Los resultados de este análisis básico son comparados con aquellos obtenidos en posteriores análisis DEA en los que se añade: (i) una proxi de tercera misión diferente en cada análisis; (ii) dos proxies de tercera misión alcanzando todas las posibles combinaciones de las mismas; (iii) un indicador compuesto construido a partir de las tres proxis de tercera misión consideradas mediante el Análisis de Componentes Principales. De esta forma, se estudia la adecuación de los indicadores seleccionados. Además, posteriormente se analizan las diferencias en los resultados de los ocho análisis DEA efectuados en base al mission mix y el subject mix de las universidades de la muestra.

Los resultados de este análisis confirman las dos primeras hipótesis, pues muestran cómo la eficiencia del Sistema Universitario Público Español (SUPE) presenta un nivel de eficiencia bastante homogéneo para el análisis básico, lo cual implica un pequeño margen de mejora de los niveles de eficiencia relativa de las universidades analizadas.

No obstante, cuando se incluye la transferencia de conocimiento en el análisis, sí se da un aumento sustancial de la eficiencia relativa media del SUPE, así como una reducción de su variabilidad, debido, en gran parte, a la consideración de una parte importante de la actividad de las universidades y no sólo a la inclusión de una variable adicional en el modelo (un output adicional para el mismo número de inputs). Asimismo, el incremento de eficiencia al incluir en el análisis la transferencia de conocimiento no se distribuye por igual entre las 47 universidades públicas presenciales: las universidades más penalizadas por la no inclusión de la transferencia de conocimiento en el análisis de eficiencia son aquellas con un fuerte perfil técnico-científico o con una especialización relativa mayor en la transferencia de conocimiento; y las menos penalizadas son aquellas universidades más orientadas a la enseñanza superior, así como aquellas que disponen de al menos un hospital universitario. No obstante, si se hubiera dispuesto de los datos necesarios para incluir proxis de formación continua y compromiso social en el análisis, o para aproximar mejor la tercera misión en el caso de las ciencias de la salud, serían otras universidades con otras características las que habrían mejorado sustancialmente su eficiencia relativa.

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En la última sección empírica de la investigación, se aplica la técnica de Escalado Multidimensional (MDS) simultáneamente sobre: (i) las puntuaciones de eficiencia de cada universidad e información sobre sus características (atendiendo a las diferentes fuentes de diversidad institucional), y (ii) su desempeño en cada una de las tres misiones; una metodología muy innovadora en el ámbito de la educación superior. Los resultados permiten identificar seis tipologías implícitas de universidades públicas presenciales:

- Universidades orientadas hacia la eficiencia global,

- Universidades no orientadas hacia la eficiencia en investigación, - Universidades orientadas hacia la eficiencia en investigación,

- Universidades orientadas hacia la eficiencia en investigación y transferencia de conocimiento,

- Universidades sin una clara orientación a la eficiencia en alguna de las misiones universitarias, y

- Universidades orientadas hacia la eficiencia en transferencia de conocimiento.

Los resultados confirman las hipótesis tres y cuatro: la tercera misión tiene un papel fundamental en la definición de tipologías de universidades; y las universidades eficientes muestran cierto grado de especialización en alguna misión en particular, pues se observa que en el caso del SUPE las universidades más eficientes son aquellas que muestran una orientación más fuerte que el resto de universidades hacia la eficiencia en su actividad investigadora, las cuales además pueden centrarse, bien en las misiones tradicionales, o bien en la transferencia de conocimiento.

Los resultados de la revisión de literatura y de los diferentes análisis descritos tienen varias implicaciones en materia de gestión y política universitaria, tanto para España como para otros sistemas de Educación Superior (ES). En primer lugar, para el caso específico de España, esta investigación relaciona los niveles de eficiencia técnica universitaria con la especialización por misiones y otras características de las universidades, ayudando a los gestores universitarios en la identificación de sus semejantes (benchmarking) y de las áreas de mejora de la institución en lo que a eficiencia técnica se refiere. Además, los resultados constatan que existe heterogeneidad en el SUPE, una información esencial para la futura revisión de su sistema de financiación con el objetivo de potenciar un comportamiento estratégico de las

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universidades que favorezca la diferenciación entre ellas: (i) asignando los fondos necesarios para que aquellas universidades con potencial para competir internacionalmente mejoren su posición en los rankings globales y (ii) fomentando simultáneamente la especialización en el resto de instituciones mediante una asignación de los fondos que potencie el desarrollo de aquellas misiones en las que cada universidad sea más eficiente.

En cuanto a las implicaciones de los resultados para el SUPE y otros sistemas universitarios de otros países, éstas se derivan, en primer lugar, de la ampliación del concepto de medición de la eficiencia, pues este debe incluir la tercera misión para evitar sesgos en los resultados. Estos resultados tienen consecuencias directas en el caso de los sistemas de financiación, pues podrían desincentivar la tercera misión en caso de no considerarla, ya que penalizaría las estrategias que la respaldan.

Finalmente, se pone de manifiesto la necesidad de completar las bases de datos oficiales sobre universidades, incluyendo información fiable y comparable sobre su actividad en este campo; pero también se destaca la necesidad de poner a disposición de los investigadores dicha información para que así produzcan resultados empíricos relevantes para la gestión universitaria y para el diseño de políticas públicas.

Por último, el análisis realizado da lugar también a preguntas adicionales a abordar en la futura actividad investigadora de la doctoranda, como sería el estudio de las potenciales economías de alcance entre las tres misiones universitarias (un ámbito aún por explorar en el ámbito de la tercera misión) o el estudio longitudinal de las variaciones en las tipologías de universidades en relación con las políticas y programas implantados en España a lo largo del tiempo.

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INTRODUCTION

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INTRODUCTION

In the 80s, and parallel to the neoliberal economic paradigm and the globalisation process, a critical trend emerged regarding the role of university systems in the socio- economic development of their local, regional and national environment (Laredo, 2007).

More specifically, such movement questioned the usefulness of Higher Education Institutions (HEIs) in terms of cultural, social and economic development (Bornmann, 2013), demanding more applied university results, i.e. claiming the provision of knowledge in ways, quantities and forms in which society can absorb it and use it to its benefit (Jongbloed et al., 2008).

This critical trend started in Anglo-American countries and was rapidly embraced by academia, politicians and society at large, leading to the broadening of the university concept. However, it has not entailed a distinct modification of the objectives and processes of universities in all cases (Rodriguez-Pomeda and Casani, 2008, p.366).

Thus, by the 90s the activities related to the valorisation of research and innovation, life-long learning and outreach that universities were already carrying out on their own initiative and in a disorderly way, were finally formally included among the fundamental tasks of universities, and became their ‘third mission’ (Laredo, 2007).

Some authors consider the ‘allocation’ of this third mission to universities as the second academic revolution (Etzkowitz, 2000), being the first one the inclusion of research duties among university tasks (Jencks and Riesman, 1968). Both revolutions were consequence of the evolution of the knowledge production process (Sánchez- Barrioluengo, 2014).

Although the scientific community has not reached a consensus on the definition of the third mission yet, in this work we define it as the university’s ‘relationship with the non- academic outside world: industry, public authorities and society’ (Schoen et al., 2007, p.127) which take the form of collaboration experiences ‘between institutions of higher education and their larger communities (local, regional/state, national, global) for the mutually beneficial exchange of knowledge and resources’ (Driscoll, 2008, p. 39) and for the benefit of the economy and society (Molas-Gallart et al., 2002).

Several studies promulgate the benefits of the third mission, and particularly the benefits of the collaboration between universities and other organisations of the

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productive fabric and the third sector (Geuna and Muscio, 2009). However, in most European Higher Education (HE) systems, third mission is still a minor mission and is hardly incorporated into university structures and strategies (E3M, 2012).

Since third mission duties are relatively recent, most countries are still structuring this type of activities in order to boost them and increase their visibility. The Spanish case is not different: following the international trend, the University Reform Law (Ley Orgánica de Reforma Universitaria – LRU, 1983) assigned third mission tasks to universities, but in a negligible way.

Despite being a decentralised system in which the regions (Comunidades Autónomas) have important competences in HE (for example university funding), the Spanish HE System is predominantly homogeneous, in the sense that the homogeneous conception of Spanish universities is embedded in the legal framework that coordinates the system at national level: university legislation assigns the same rights and duties to all universities, only considering some differences for private universities (a laxer regulation) and for the universities directly controlled by the Ministry of Education (Universidad Internacional Menéndez Pelayo and Universidad Internacional de Andalucía – IUMP and UNIA, respectively). Thus, all Spanish universities, whether public or private, have the obligation to develop three functions: teaching, research and third mission; which has been termed in the literature as the 'one-size-fits-all-model'.

This model hinders the differentiation of the HE system, because institutional strategies of all universities must be designed on the basis of these three missions (Sánchez- Barrioluengo, 2014).

In 2016, the Spanish HE system had 86 universities: 50 public and 36 private. Public universities include the national university of distance education (Universidad Nacional de Educación a Distancia – UNED) and the two aforementioned international universities. These last two universities do not have their own academic staff but they do provide HE degrees, having focused so far on post-graduate degrees (short courses and masters). With regard to private universities, they are mostly young universities, with 28 universities established since 1990, while in the case of public universities 39 were established along the 20th century.

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The third mission in Spain has not been gradually developed, but its evolution is the result of the impact of several laws and policies on research, knowledge transfer and HE; including HE funding policies aiming at complementing universities’ regional funding (based mainly on teaching and research performance and costs) and also comprising assessment and incentive policies for universities’ academic staff. In fact, the third mission in Spain was initially endorsed by the Fundamental Law of Universities Act (Ley Orgánica de Universidades, 2001 – LOU) and its subsequent modification (LOMLOU, 2007). Later on, in 2008, the University Strategy (Estrategia Universidad 2015 – EU2015) and the Campus of International Excellence programme (Campus de Excelencia Internacional – CEI) were launched. The objectives of the EU2015 and the CEI programme may be summarised as follows: (i) to modernise the Spanish HE System; (ii) to increase the system differentiation; (iii) to encourage market-orientation and a better fit between universities’ products and societal needs;

and (iv) to foster strategic management and behaviour of Spanish universities. These policies were complemented with funding schemes and entailed the definition of funding priorities for developing a renewed relationship between HEIs and society especially focused on technology transfer and innovation.

Accordingly, public spending on the third mission, particularly knowledge transfer, increased. Knowledge transfer is the third mission dimension most developed in Spain;

however, its processes are not fully mature yet, since incentives for researchers to transfer knowledge to society are still weak and there are several (managerial and cultural) barriers that remain. Consequently, in Spain there is still room for improvement in the area of knowledge transfer and valorisation (Vilalta, 2013) and the available evidence on the efficiency achieved is not conclusive (Rodríguez-Pomeda and Casani, 2008). Later on, the economic crisis led to funding constrains also for knowledge transfer, and although universities have changed their R&D funding structure to compensate for the reduction of national and regional public funds (Perez- Esparrells et al., 2015), the technology transfer performance of Spanish universities has been considerably affected.

Notwithstanding, the Spanish HE system (particularly the Spanish public universities), is one of the main elements of the Spanish R&D and innovation system. According to the National Statistics Institute (Instituto Nacional de Estadistica – INE), in 2014 Spanish universities accounted for 28.13% of the national R&D expenditure, although

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they only produced 4.15% of the R&D revenue, because their fundamental objective is still the production of basic research results, and not the commercialisation of knowledge. Consequently, most of the Spanish R&D funds come from companies (46.1%) and public administrations (41.36%)1. Additionally, universities have 16.11%

of the full-time staff in the sector (92.15% hired by public universities) and 14.80% of its researchers2 (91.27% employed by public universities).

Regarding life-long learning courses and outreach, although nowadays almost all Spanish universities are engaged in these activities, the extent and forms of such engagement is especially heterogeneous, since there has been no government programme aiming specifically at their development. In other words, in the Spanish case, the third mission of universities is undoubtedly oriented towards technology transfer and innovation as a consequence of the public programmes and policies implemented over the years.

To sum up, the third mission is a relatively recent concept, which has led to an intense debate: there is still not consensus on its exact definition, on the activities that it encompasses, on whether it is a mission itself or a set of activities complementary to teaching and research, or on the ‘label’ used to name it (in the literature there are different ‘labels’ to refer to the third mission such as third stream, regional engagement or transfer of knowledge). The only aspects in which there is consensus among academics are: (i) its relevance, (ii) its potential benefits not only for society at large but also for the HE sector, and (iii) the difficulty of defining and implementing a system of indicators to characterise it3.

With regard to the third mission indicators, in most countries it is the technology transfer and innovation the dimension for which there is more (and more reliable) data.

Therefore, it is not surprising that this is also the more studied dimension in the (national and international) literature. However, the existing data on this dimension is also considered insufficient by many authors (see for example Rossi, 2014), and the

1 According to INE, in 2008 Spanish public administrations provided a higher percentage of R&D funds (45.57%) than companies (44.95%).

2 This percentage has dropped along the economic crisis: in 2008 universities hired 56.11% of the researchers of the Spanish R&D sector (INE).

3 Various projects have attempted to define a system of indicators for the third mission with different levels of success: some of them have led to annual surveys for the collection and publication of data and others have been abandoned in the pilot phase.

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data availability varies considerably among countries depending on their respective degree of development of the third mission (Laredo, 2007). Consequently, the knowledge transfer process is not fully understood yet (Berbegal-Mirabent et al., 2013) as well as its explanatory factors or its impact on university performance (Kim et al., 2009). For the case of life-long learning and social commitment, the lack of data on them entails that these activities have been less studied than knowledge transfer.

In this work we go a step further: we study the impact of the third mission on the efficiency of universities (using as proxy the knowledge transfer), since the inclusion of the third mission entails greater amount and diversification of the output of universities but the same inputs or resources. There are already several studies on the efficiency of universities and other HEIs, a relevant topic in industrialised countries for several reasons: (i) it is a relevant source of economic growth (World Bank, 2002; Johnes, 2008; European Commission, 2010); (ii) there is generalised trend of decreasing funds for universities (Estermann and Bennetot Pruvot, 2011); (iii) there is increasing population of HE students (Agasisti and Perez-Esparrells, 2010); (iv) there is increasing competition in the sector in terms of institutional performance and prestige (Van Vught, 2008); (v) performance-based funding systems are spreading (Hicks, 2012); and (vi) there is growing demand for transparency and accountability on the results of institutions receiving public funds (Gómez-Sancho and Mancebón-Torrubia, 2012).

However, those studies analysing specifically the impact of the third mission on the efficiency of universities are scarce.

But the third mission is also a source of institutional diversity. The scientific community has studied the factors and evolution of HEI’s heterogeneity for long. Researchers have been particularly active in this area in the last decade, as a consequence of the emergence of four factors with strong impact on HEI’s heterogeneity: (i) the rise of the global rankings (and their consequent analysis and criticism – see for example Harvey, 2008); (ii) the increasing strategic behaviour of universities (Casani et al., 2014); (iii) the greater prominence of university evaluation agencies (Turri, 2014); and (iv) the European Higher Education Area (Bologna Declaration – European Ministers of Education, 1999) and the European Research Area (Ljubljana Process – Council of the European Union, 2008). In the case of the global rankings and the evaluation agencies, the difficulty added by the heterogeneity of universities lies in the non-comparability of the units tested (Stella and Woodhouse, 2006). As for the case of universities and

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national and supranational governments, institutional heterogeneity renders it difficult to know the HE sector in depth, as well as predicting the results of institutional strategies (Van Vught et al., 2010) and public policies (Daraio et al., 2011).

Spanish universities are usually considered as homogeneous because of their nature of public services providers: they have similar characteristics and productive structures and a fair average quality and overall efficiency (which are also rather homogeneous), and basically there are only regulation differences for public and private institutions.

Consequently, institutional diversity has been traditionally recognised mostly with regard to the size of universities and their subject mix (being especially relevant health sciences and technical fields of knowledge). Private universities would also be distinguished because of their usually strong teaching orientation.

Thus, although all Spanish universities legally share the same missions and although the Spanish HE system is essentially homogeneous, different profiles or typologies of universities can be identified according to different criteria, e.g. size, age or subject mix among others. In this work we add to the sources of institutional diversity traditionally considered in the definition of typologies of university, the relative weight that each institution assigns to teaching, research and third mission respectively (‘mission mix’) and the technical efficiency of universities in each one of their three missions. In addition, we also take into account that, in the particular case of the third mission, universities tend to develop it differently depending on their subject mix and the characteristics of their socio-economic environment and stakeholders: these factors influence the potential of the institutions for the development of the different activities included under the label of the third mission, as we will explain throughout the thesis.

In this way, the typologies of Spanish public universities that we identify are based on their institutional characteristics, their specialisation by missions (mission mix) and their efficiency in each mission: these dimensions contain highly relevant information for designing university policies and strategies aimed at increasing the differentiation and the overall efficiency of the system, and also encouraging market orientation – thus, following the international trend of the last decades.

In summary, this study attempts to answer the following research questions and hypotheses for the Spanish case:

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- (R1). How does the evaluation of HEIs’ efficiency vary when including/excluding third mission indicators?

- (R2). How does the classification of HEIs into typologies vary when including/excluding third mission indicators?

- (R3). Is (in)efficiency related to a particular typology of university?

- (H1). Not accounting for the third mission of universities in the analysis of their efficiency (and performance) disregards an important part of their activity.

- (H2). The bias in the assessment of universities for not accounting for their third mission is closely related to their ‘subject mix’ and to the ‘mission mix’, two key factors for the development of universities’ strategies.

- (H3). Third mission plays a fundamental role when defining typologies of universities.

- (H4). Efficient universities show a certain degree of specialisation in a particular mission.

In order to answer the aforementioned questions and test our hypothesis, this Doctoral Thesis is organised as follows:

In Chapter 1 we present the theoretical framework. We revise in depth the definition of the third mission: its origins, characteristics, dimensions and implications. Likewise, we study alternative systems of indicators proposed in the literature for the characterisation of the third mission, their success and risks, as well as the difficulties in their definition and the attention that they pay to the different dimensions of the third mission. Finally, Chapter 1 also includes a description of the degree of development of the third mission for the Spanish case and of the different regulations, policies and initiatives that have fostered it over time, as well as the barriers and difficulties still prevailing. To complete the analysis of the Spanish case, we also include a brief account of the quantitative Spanish data sources available.

In Chapter 2 we describe and justify the empirical study presenting: (i) relevance of the research questions; (ii) the pertinence of the hypotheses risen; (iii) the respects in which the analysis proposed is innovative; and (iv) the methodologies chosen to answer the questions and test the hypotheses. To this end, we carry out a comprehensive revision of

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the literature related, identifying the gaps or areas that have not been addressed by the scientific community yet in order to guarantee the novelty of the Doctoral Thesis. We define the research questions and hypotheses on the basis of such revision and we choose those methodologies that we consider to better fit our research objectives.

In Chapter 2 we also select our sample. This Doctoral Thesis focuses on the study of the Spanish public HE system including in the sample 47 out of the 50 public universities:

we exclude from the sample the atypical institutions in order to ensure the robustness of the efficiency analysis carried out in Chapter 3, i.e. the Universidad Internacional Menéndez Pelayo (UIMP), the Universidad Internacional de Andalucía (UNIA) and the Universidad Nacional de Educación a Distancia (UNED). The 47 public universities in the sample providing on-campus education gather approximately 90% of the academic staff of the Spanish HE system, about 81% of the graduates, and around 95% of the research and knowledge transfer results. Four out of these 47 universities are polytechnic universities and 15 of them have at least one university hospital.

The two data sources employed are the Integrated University Information System (Sistema Integrado de Información Universitaria – SIIU) and the Observatory of the Research Activity at the Spanish Universities (Observatorio de la Actividad Investigadora en la Universidad Española – IUNE). On the one hand, SIIU is the new platform of the Spanish Ministry of Education for the collection, processing and analysis of data on the Spanish HE system. SIIU collects extensive data on teaching and university funding. On the other hand, the IUNE Observatory was established in 2012 by the Universidad Carlos III and the Universidad Autónoma de Madrid within the framework of the Research Institute for Higher Education and Science (Instituto Interuniversitario de Investigación Avanzada sobre Evaluación de la Ciencia y la Universidad – INAECU), which belongs to the Alliance 4 Universities (Alianza 4 Universidades 4U) 4. The IUNE Observatory gathers quantitative data from various administrative data sources and builds its own bibliometric indicators from the Web of Science (ISI). It provides information on universities’ research and innovation results.

The data in this work refer to the academic year 2011-12 or the natural year 2011: the

4 The Alliance 4 universities (Alianza 4U) is a strategic association of the Universidad Carlos III de Madrid, Universidad Autónoma de Madrid, Universidad Autónoma de Barcelona and Universidad Pompeu Fabra established in 2008 with the objective of enhancing the internationaliszation of its members.

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