Tesis_Ductor

Essays on Network Economics and Finance

Lorenzo Ductor Gmez

Essays on Network Economics and

Finance

Lorenzo Ductor Gomez

Supervisors: Prof. Maria Dolores Collado Vindel, Prof. Marco Jury van der Leij

A Dissertation Submitted to

the Departamento de Fundamentos del Analisis Economico

Universidad de Alicante

In Partial Fulfillment of the Requirements for the Degree of

Doctor of Philosophy

March 2012

A mis padres.

Acknowledgements

Cada palabra de esta Tesis Doctoral desde la primera hasta la ultima

se la debo a mis padres. Les estare eternamente agradecido por todo

el esfuerzo que han hecho para que este sueno sea una realidad. Estoy

tremendamente orgulloso y feliz de la familia que tengo, muchas gra-

cias Antonio y Ana Mara. Sin vosotros esto no habra sido posible.

Gracias a mis abuelos y a mi hermana por todo su apoyo desde que

comence mi carrera academica en Granada.

Many people have contributed to this Thesis. Above all, I am indebted

to my advisors Maria Dolores Collado Vindel and Marco van der Leij.

I am tremendously grateful to Maria Dolores, whose support, encour-

agement, patient and mentorship have been crucial for the develop-

ment of this dissertation.

I owe my deepest gratitude to Marco. He introduced me to this pas-

sionate world, which is becoming more and more popular, The Eco-

nomics of Networks. He also introduced me to his personal networks

and gave me the opportunity to visit him at the University of Cam-

bridge, where we started a joint article with Sanjeev Goyal and Marcel

Fafchamps. I am extremely grateful to Marco, Sanjeev and Marcel

for sharing with me part of their work, the Econlit Database, and for

their support, comments and advices during my Ph.D. studies. From

my experience with them, I could claim that peer effects and network

effects are present in intellectual collaboration.

I am very grateful to Yann Bramoulle, Jordi Caballe, Pierre-Philippe

Combes, Juan Carlos Conesa, Habiba Djebbari, Daryna Grechyna,

Gergely Horvath, Silvia Martinez and Francesco Serti for their hepful

comments.

Part of this dissertation was written at the University of Cambridge

and the Groupement de Recherche en Economie Quantitative dAix-

Marseille (GREQAM). I would like to thank the Faculty of Eco-

nomics at the University of Cambridge and the GREQAM for their

hospitality and for welcoming me as a Ph.D. student. Especially

thanks to Sanjeev Goyal in Cambridge and Yann Bramoulle in GRE-

QAM for their enthusiastic discussions. I learned a lot of things from

them.

I would like to show my gratitude to the postgraduate courses profes-

sors: Luisa Fuster, Gabriel Perez Quiros, Climent Quintana-Domeque,

Marco van der Leij and especially to Peter Kennedy for teaching us

how to teach and do Applied Econometrics. Rest in peace Peter, I

will always remembered you as an enthusiastic, passionate and one of

the best professor I have ever had. I thank the administrative staff

Marilo Rufete and Josefa Zaragoza for their help and support.

I would like to express my gratitude to my colleagues of the Quan-

titative Economics Doctorate. Particularly to Carlos Aller, Gustavo

Cabrera, Gergely Horvath, Danilo Leiva and Xavier del Pozo for their

support and friendship.

Finally, I will be eternally grateful to Daryna Grechyna for her support

and for staying with me in the worst moments of my Ph.D. studies.

This thesis would not have been possible without her encouragement.

I gratefully acknowledge financial support from the Spanish Minis-

tery of Science and Innovation (Programa Formacion del Profesorado

Universitario).

Contents

Introduccion ix

0.1 Primera Parte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . x

0.1.1 Coautora y Productividad Academica . . . . . . . . . . . x

0.1.2 Redes Sociales y la Produccion Academica . . . . . . . . . xiii

0.2 Segunda Parte . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv

0.2.1 Exceso del desarrollo financiero y el crecimiento economico xv

1 Co-authorship and Individual Academic Productivity 1

1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

1.2 Estimation framework . . . . . . . . . . . . . . . . . . . . . . . . 7

1.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

1.3.1 Definition of the variables . . . . . . . . . . . . . . . . . . 13

1.3.2 Descriptive analysis . . . . . . . . . . . . . . . . . . . . . . 17

1.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20

1.4.1 Does co-authorship lead to higher academic productivity? 20

1.4.2 Co-authorship and productivity across individual types . 23

1.5 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25

1.5.1 Not appropriate proxy variables. . . . . . . . . . . . . . . 25

1.5.2 Co-authorship tie duration. . . . . . . . . . . . . . . . . . 27

1.5.3 Research overlap and coauthors coauthor productivity. . . 29

1.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

1.7 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

v

CONTENTS

2 Social networks and research output 37

2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.2 Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.2.1 Empirical strategy . . . . . . . . . . . . . . . . . . . . . . 43

2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47

2.3.1 Definition of variables . . . . . . . . . . . . . . . . . . . . 47

2.3.2 Descriptive statistics . . . . . . . . . . . . . . . . . . . . . 51

2.4 Empirical findings . . . . . . . . . . . . . . . . . . . . . . . . . . . 53

2.4.1 Predicting future output . . . . . . . . . . . . . . . . . . . 54

2.4.2 Networks and career cycle . . . . . . . . . . . . . . . . . . 56

2.4.3 Network information across productivity categories . . . . 60

2.5 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 61

2.6 Exploring the channels . . . . . . . . . . . . . . . . . . . . . . . . 64

2.6.1 Career cycle effects and network variables . . . . . . . . . 64

2.6.2 Predictive value of output on networks . . . . . . . . . . . 65

2.6.3 Duration of predictive value . . . . . . . . . . . . . . . . . 67

2.6.4 Diversity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68

2.7 Concluding remarks . . . . . . . . . . . . . . . . . . . . . . . . . . 70

3 Excess Financial Development and Economic Growth 81

3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

3.2 Financial Development and Growth . . . . . . . . . . . . . . . . . 86

3.2.1 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . 86

3.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88

3.2.3 Estimation Results . . . . . . . . . . . . . . . . . . . . . . 89

3.3 Financial Development, Real Sector, and Growth . . . . . . . . . 91

3.3.1 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

3.3.2 Estimation Results . . . . . . . . . . . . . . . . . . . . . . 98

3.3.2.1 Impact of excess financial development on long-

run economic growth: Cross-sectional analysis . . 98

3.3.2.2 Impact of excess financial development on short-

run economic growth: System GMM . . . . . . . 101

3.4 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

vi

CONTENTS

3.5 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 106

3.6 Appendix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108

References 115

vii

Introduccion

Esta Tesis Doctoral esta compuesta de dos partes bien diferenciadas. La primera

parte consta de dos captulos que contribuyen a la literatura emprica de las

redes sociales, una rama emergente en la economa moderna. Las interacciones

sociales - representadas en redes o grafos - estan presentes practicamente en toda

actividad economica. Consecuentemente, la evaluacion de determinadas polticas

economicas debera realizarse teniendo en cuenta el impacto de estas interacciones

tanto en las acciones de los individuos como en el resultado economico. El estudio

de las redes sociales en economa ha ido adquiriendo una gran importancia desde

el ensayo de Granovetter (1985). Desde entonces se ha aplicado la teora de

redes sociales para analizar numerosos temas economicos como, por ejemplo: el

desempleo y la desigualdad salarial (Calvo-Armengol y Jackson, 2004), la difusion

del conocimiento y la innovacion (Bala y Goyal, 1998) o la provision de bienes

publicos locales (Bramoulle y Kranton, 2007), entre muchos otros. Vease Goyal

(2011) para un resumen de la literatura teorica y emprica de las redes sociales

en la economa y Jackson (2008) para una sntesis de los modelos y tecnicas

empleadas para analizar las redes sociales.

La primera parte de la presente Tesis se centra en las posibles externalidades

inherentes en las redes de coautores academicos. La comprension de estas poten-

ciales externalidades presentes en la colaboracion cientfica es de vital importancia

para la evaluacion de las polticas economicas cuyo objetivo son promover la co-

laboracin intelectual. Dichas polticas se han implementado presuponiendo una

relacion positiva entre la colaboracion cientfica y la productividad.

El primer captulo contrasta rigurosamente el impacto de la coautora en la

productividad de los autores academicos, utilizando como medida de productivi-

dad la calidad de la revista donde se ha publicado el artculo, su longitud y el

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0. INTRODUCCION

numero de artculos publicados en un determinado periodo. La relacion causal

entre la coautora y la productividad academica es identificada explotando infor-

macion de la red de coautores del autor en el pasado.

En el segundo captulo, en colaboracion con Marcel Fafchamps, Sanjeev Goyal

y Marco van der Leij, se evalua el poder informativo de la red de coautores de un

autor para predecir el rendimiento del individuo. Los resultados sugieren que los

reclutadores se beneficiaran de obtener informacion sobre la red de coautores,

siendo el factor mas informativo la productividad de los coautores de un autor.

La segunda parte de la Tesis se centra en el estudio de los potenciales factores

causantes de las crisis financieras. En particular, el tercer captulo coautorado

con Daryna Grechyna analiza el impacto del exceso del desarrollo financiero,

definido como el diferencial entre la tasa de crecimiento del sector financiero e

industrial, en el crecimiento economico. La existencia del exceso financiero es

justificada bajo la teora del rebasamiento de la informacion (informational

overshooting). Demostramos que para un crecimiento economico sostenible, el

crecimiento equilibrado en ambos sectores, financiero y productivo, es requerido.

Cuando el desarrollo financiero excede al desarrollo industrial en un 4.5% (medi-

dos en terminos de tasas de crecimiento); los recursos invertidos en la produccion

sobrepasaran la capacidad productiva de la economa, dando lugar a una cri-

sis financieraA continuacion, expongo de manera mas detallada el contenido de

cada uno de los captulos.

0.1 Primera Parte

0.1.1 Coautora y Productividad Academica

En este captulo, se analiza el impacto de la colaboracion academica en la pro-

ductividad del academico. Durante las ultimas decadas, la colaboracion cientfica

y las polticas gubernamentales con el objetivo de inducir la coautora de tra-

bajos han aumentado simultaneamente. Algunos ejemplos de estas polticas son

el EU-funded research networks (Commission of European Communities) y

a nivel nacional el Programa Ingenio 2010 (Ministerio de Educacion y Ciencia,

2006). En ambos programas, el proyecto de investigacion para el cual se solicita

x

0.1 Primera Parte

financiacion se ha de realizar en colaboracion con otros investigadores. Otro tipo

de polticas induciendo la colaboracion son las polticas internas de los depar-

tamentos, que exigen un numero mnimo de publicaciones al profesorado, pero

no descuentan esas publicaciones por el numero de academicos trabajando en el

artculo. Existen diversos factores, tanto positivos como negativos, a traves de

los cuales la colaboracion academica podra afectar a la productividad. Entre

los positivos podramos destacar los siguientes: el trabajo en equipo combina las

ideas y el talento de diferentes individuos y a su vez permite una division del tra-

bajo y una difusion del conocimiento que no podran darse sin la coautora. Sin

embargo, la colaboracion academica tambien podra afectar negativamente a la

productividad: colaborar conlleva unos costes de comunicacion, facilita el prob-

lema del polizon y ademas requiere del compromiso de cada uno de los miembros

del equipo, que deben de estar de acuerdo con las ideas, metodologas y el texto

del artculo. Por tanto, el efecto de la coautora en la productividad academica

depende de cuales de estos factores, positivos o negativos, predominan. La liter-

atura emprica sobre la relacion entre la coautora y la productividad academica

ha aumentado durante los ultimos aos. Sin embargo, no hay un consenso sobre si

el efecto de la colaboracion en la productividad es positivo, negativo o inexistente.

Laban y Tollison (2000), Presser (1980) y Zuckerman y Merton (1973) muestran

que los artculos cientficos coautorados son mas probables de ser aceptados para

su publicacin que los trabajos no coautorados. Chung, Cox and Krim (2009)

concluye que los artculos con mas autores son citados mas frecuentemente. Por

otro lado, hay autores que no han encontrado ninguna relacion entre ambas vari-

ables, como Medoff (2003), Lee y Bozeman (2005) y Acedo, Barroso, Casanueva

y Galan (2006). Mas sorprendente es la relacion negativa encontrada por Hollis

(2001). Utilizando un panel de 5277 artculos concluye que cuanto mas coautora

un individuo, mas baja es la productividad academica atribuida a ese individuo.

Un factor clave en la formacion de la coautora, ademas de las propias car-

actersticas de los autores, es el conjunto de oportunidad del autor, es decir, la

calidad de los proyectos que concibe el autor y los proyectos ofrecidos por sus

coautores. Por ejemplo, un autor puede decidir colaborar con otro porque carece

de ideas de gran calidad. Ignorar estos efectos de seleccion dara lugar a resultados

espurios y por tanto a conclusiones erroneas. Con la excepcion de Lee y Bozeman

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0. INTRODUCCION

(2005) ningun artculo ha intentado controlar por la potencial endogeneidad de

la coautora cientfica. El primer captulo de la presente tesis intenta cubrir este

vaco en la literatura utilizando para ello un panel de datos de economistas entre

1970 y 1999. Estos datos han sido obtenidos a partir del Econlit, una bibliografa

de revistas en economa compilada por los editores del Journal of Economic

LiteratureLa variable de productividad esta basada en el trabajo de Fafchamps,

Goyal y van der Leij (2010) y combina cantidad (numero y extension de los

artculos publicados) con calidad (ndice de impacto de la revista). Como las car-

actersticas del individuo y su conjunto de oportunidades estan endogenamente

relacionados con la coautora y la productividad, la colaboracion academica de

un individuo es instrumentada por los intereses comunes en investigacion entre

el autor y sus potenciales coautores en el futuro. Donde los potenciales coau-

tores son los coautores de los coautores acumulados en el pasado. Como destaca

Fafchamps, Goyal y van der Leij (2010), uno de los factores mas determinantes

para iniciar una colaboracion academica es que ambos autores tengan intereses

comunes de investigacion. Por otro lado, la colaboracion es improbable cuando

hay demasiado solapamiento entre las habilidades de ambos investigadores. De

ah, que incluyamos el cuadrado de esta variable para capturar potenciales no

linealidades entre la colaboracion y la variable de intereses comunes. Ambos

instrumentos captan la idea de homofilia, que consiste en la tendencia de los

individuos a interaccionar con otros de similares caractersticas.

Los resultados sugieren que tras controlar por la endogeneidad de la coautora

y la heterogeneidad individual no observada existe una relacion positiva entre la

colaboracion intelectual y la productividad academica. Sin embargo, este efecto

no es homogeneo y vara significativamente en funcion de las habilidades de los

autores. Los autores mas habiles obtienen un beneficio a traves de la coautora

tres veces superior a los individuos con menor capacidad. Los resultados empricos

indican que los academicos pueden mejorar su rendimiento a traves de la coautora

entre ellos. Para los gobiernos e instituciones, los resultados justifican la existencia

de polticas que estimulen la colaboracion intelectual.

xii

0.1 Primera Parte

0.1.2 Redes Sociales y la Produccion Academica

Este captulo analiza como el conocimiento sobre la red social de un investi-

gador -plasmada en las coautoras con otros autores - nos ayuda a desarrollar

una prediccion mas exacta de su productividad futura. El buen reclutamiento

requiere de una prediccion adecuada del potencial de un candidato. Los clubes

deportivos, departamentos academicos e incluso las empresas privadas utilizan el

rendimiento del candidato en el pasado como gua para predecir el potencial de los

solicitantes. En este captulo nos centramos en los investigadores academicos. Las

interacciones sociales forman un aspecto importante de la actividad investigadora:

los autores academicos discuten y comentan el trabajo de cada uno, evaluan el

trabajo de otros para su publicacion y colaboran entre ellos en proyectos de inves-

tigacion. La colaboracion cientfica conlleva el intercambio de opiniones e ideas

y facilita la generacion de nuevas ideas, que puedan resultar en una mayor pro-

ductividad en el futuro. Por lo que esperamos que, ceteris paribus, individuos

mejores conectados u ocupando posiciones mas centrales en la red tendran una

mayor probabilidad de recibir ideas y conocimientos de otros, aumentando as su

productividad en el futuro. La centralidad y la proximidad surgen de las conex-

iones creadas por los propios individuos. As estas conexiones pueden reflejar

caractersticas individuales inobservables, como la habilidad, sociabilidad y am-

bicion. Por ejemplo, una conexion entre un economista joven desconocido y un

economista de gran prestigio puede revelar caractersticas positivas del estudiante

(ambicion, esfuerzo, etc.) que de otro modo seran inobservables. Estas consid-

eraciones sugieren que la red de colaboraciones academicas esta relacionada con

la produccion de dos modos: uno, la red como conducto de ideas y, dos, la red

como senal de la calidad individual de los investigadores. Mientras que el primero

sugiere una relacion causal entre la red y la produccion academica, el segundo

sugiere que la red es meramente un reflejo de las caractersticas individuales.

Como es sabido en la literatura emprica de interacciones sociales (Manski, 1993;

Moffit, 2001), identificar efectos de redes en un sentido causal es un reto ante

la ausencia de experimentos aleatorios. Este captulo contribuye a la literatura

emprica del estudio de las interacciones sociales. Tradicionalmente, los economis-

tas han estudiado como las interacciones sociales pueden afectar a la conducta

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de determinados grupos, prestando especial atencion a la dificultad de identificar

empricamente los efectos de pares o de red. Para un resumen sobre este trabajo,

vease Moffit (2001) y Glaeser y Scheinkman (2002). Recientemente, el interes

se centra en los mecanismos a traves de los cuales las redes sociales influyen en

la conducta y el rendimiento de los agentes economicos. Recientes artculos em-

pircos sobre la estimacion de los efectos de red son Bramoulle, Djebbari y Fortin

(2009), Conley y Udry (2008), Calvo-Armengol, Patacchini y Zenou (2008). La

dificultad de identificar estos efectos de la red ante la ausencia de experimen-

tos aleatorios radica en la endogeneidad de las conexiones de la red (dado que

no son aleatorias) y en los factores correlacionados con las caractersticas inob-

servables de un individuo y la probabilidad de establecer nuevas conexiones. En

este artculo tomamos un enfoque alternativo a la literatura: investigamos como

disponer de informacion reciente y pasada sobre la red de colaboraciones con-

tribuye a la prediccion de la productividad del investigador en el futuro. Primero,

estudiamos si las variables de la red de coautores proporcionan informacion rel-

evante sobre la productividad futura de un investigador, una vez conocemos las

publicaciones recientes y pasadas del individuo. Luego investigamos que vari-

ables son mas informativas y como vara su poder de prediccion a lo largo de la

carrera academica de un autor. Nuestra primera conclusion es que la incorpo-

racion de informacion sobre la red de coautores lleva a una modesta mejora en

la precision de los pronosticos sobre la produccion del individuo, mas alla de lo

que se puede predecir a partir del conocimiento de la produccion individual en

el pasado. Ademas, variables de la red, tales como la productividad de los coau-

tores, centralidad de cercana o el numero de coautores contienen informacion

valiosa sobre la productividad futura de un individuo. Tambien encontramos que

el efecto de senalizacion contenido en la red es cuantitativamente mas importante

que el del flujo de ideas o de informacion. Nuestra segunda conclusion es que

el poder de prediccion de la red de coautores es mas elevado para investigadores

jovenes y a su vez, decae sistematicamente a lo largo de la carrera academica

de un autor. Por el contrario, la productividad reciente y pasada mantiene su

poder de prediccion sobre la produccion academica del autor durante toda su

vida academica. Nuestra tercera conclusion establece que el valor predictivo de

la red de coautores no es monotono con respecto a la productividad pasada del

xiv

0.2 Segunda Parte

autor. Por un lado, las variables de la red no ayudan a predecir el rendimiento

de los individuos cuya productividad inicial se encuentra por debajo de la media.

Por otro lado, su poder de prediccion es alto para individuos que se encuentran

entre los mas productivos (top 1) y aquellos cuya productividad inicial es inferior

a la media.

Los resultados encontrados en la primera parte de la Tesis reflejan la impor-

tancia tanto de la coautora para aumentar la productividad academica, como la

de la red de coautores para predecir el potencial de un investigador.

0.2 Segunda Parte

0.2.1 Exceso del desarrollo financiero y el crecimiento economico

La segunda parte de la presente tesis analiza el exceso de credito o de instrumentos

financieros como uno de los principales factores causante de determinadas crisis

economicas. Intermediarios financieros cuyo objetivo son aminorar los problemas

de informacion asimetrica o facilitar las transacciones ejercen un impacto posi-

tivo en el crecimiento economico (Levine, Loayza y Beck, 2000). Sin embargo, las

recientes crisis sugieren que el exceso del desarrollo financiero podra perjudicar

al crecimiento economico bajo ciertas circunstancias. El enfoque tradicional y

optimista se ha centrado en una relacion lineal entre ambas variables. Estos estu-

dios concluyen que el desarrollo financiero facilita el crecimiento economico y la

convergencia de los pases. Algunos ejemplos son Levine, Loayza y Beck (2000),

Aghion, Howitt y Mayer-Foulkes (2005) y Michalopoulos, Laeven y Levine (2009).

Por otro lado, el enfoque actual, tras la crisis financiera de 2007, es mas pesimista

y se centra en las posibles fragilidades bancarias ocasionadas por el desarrollo de

instrumentos financieros. Por ejemplo, Arcand, Berkes y Panizza (2011), quienes

revaluan el trabajo de Levine, Loayza y Beck (2000), consideran una relacion no

monotona entre el desarrollo financiero y el crecimiento economico. Bajo este

supuesto, encuentran un lmite a partir del cual el desarrollo financiero tiene un

impacto negativo en el crecimiento economico. Beck, Chen, Lin y Son (2012)

evaluan la relacion existente entre la innovacion financiera en el sector bancario y

su fragilidad, el crecimiento y la volatilidad del sector real. Ellos muestran que la

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innovacion financiera esta asociada con una mayor volatilidad en el crecimiento

de las industrias que tienen una mayor dependencia de los recursos financieros

externos. Asimismo, encuentran una asociacion positiva entre la innovacion fi-

nanciera y el riesgo idiosincratico a las fragilidades bancarias, mayor volatilidad

en los beneficios del sector bancario y mayores perdidas bancarias durante la

reciente crisis de 2007.

Este artculo contribuye a la literatura del desarrollo financiero y el crec-

imiento, proponiendo el crecimiento del sector industrial (productos manufac-

turados y energa) y el crecimiento del sector financiero (servicios financieros),

como dos factores que simultaneamente determinan el crecimiento economico del

pas. Sugerimos que para lograr una senda de desarrollo economico sostenible,

es necesario el equilibrio del progreso tecnologico en ambos sectores, industrial y

financiero. El progreso tecnologico en el sector real expande las capacidades de

produccin de la economa, mientras que el sector financiero permite un uso efi-

ciente de estas nuevas capacidades. Analizando el estado del progreso tecnologico

y del desarrollo financiero durante las ultimas cuatro decadas contrastamos si el

exceso del desarrollo financiero, definido como la diferencia entre el progreso fi-

nanciero y tecnologico, propicia un detrimento en el crecimiento economico y

consecuentemente una crisis financiera. Para explorar el efecto del exceso del de-

sarrollo financiero en el crecimiento economico a corto plazo empleamos datos de

panel para 33 pases de la OECD. Consideremos un modelo de crecimiento donde

la variable dependiente es la tasa de crecimiento del PIB per capita. Nuestra

principal variable de interes es el diferencial en las tasas de crecimiento del sec-

tor financiero e industrial y su cuadrado, ambas variables son representativas del

exceso del desarrollo financiero. Tambien incluimos en el modelo el crecimiento

del sector industrial como variable de control, ya que estamos interesados en

variaciones del diferencial de ambas tasas no determinadas por el crecimiento del

sector industrial. Como puntualizo Kaldor (1967), la tasa de crecimiento del sec-

tor industrial es el factor mas importante para predecir el crecimiento economico.

Sin embargo, hay una variacion del crecimiento economico que permanece inex-

plicada. Nosotros nos centramos en explicar parte de esa variacion a traves del

sector financiero. El resto de variables de control en el modelo de crecimiento

son: el nivel del PIB per capita al comienzo de cada periodo de cinco aos, una

xvi

0.2 Segunda Parte

medida de la apertura comercial del pas, la inflacion, el gasto del gobierno en

proporcion al PIB y una variable representativa del capital humano. Dado que

todos estos factores son endogenos en el modelo y que por construccion el retardo

del PIB esta correlacionado con los errores del modelo, utilizamos el estimador

del metodo de los momentos generalizados (MMG) desarrollado para datos de

panel dinamicos por Arellano y Bover (2005) y aumentado por Blundell y Bond

(1998). Para evitar una infraestimacion del error estandar, implementamos la

correccion propuesta por Windmeijer (2005). Ademas, utilizamos como instru-

mento exclusivamente el segundo retardo de cada variable endogena para evitar

el sobre-ajuste de las mismas. Este estimador nos permite identificar el efecto

del exceso del desarrollo financiero en el crecimiento economico asumiendo que

las perturbaciones al crecimiento economico en el futuro no estan correlacionadas

con el desarrollo financiero en la actualidad. Este supuesto es factible dado que

utilizamos una ventana de cinco aos como periodo. Ademas, los tests de Hansen

y de Sargan apoyan la validez de los instrumentos. Los resultados demuestran

que el desarrollo financiero tiene un impacto positivo en el crecimiento en el corto

plazo. Sin embargo, cuando el diferencial entre el crecimiento del sector financiero

y el industrial alcanza el 4.45%, el efecto del desarrollo financiero en el crecimiento

economico pasa a ser negativo. Analogamente, utilizamos el diferencial entre el

credito privado en proporcion al PIB y la produccion industrial en proporcion

al PIB y su cuadrado como variables representativa del exceso del desarrollo fi-

nanciero. Bajo esta nueva especificacion, obtenemos que el desarrollo financiero

tiene un impacto negativo en el crecimiento economico, cuando el credito privado

en proporcion al PIB excede al ratio entre el PIB industrial y el PIB total, en un

43.3%. Estos resultados son consistentes con nuestra hipotesis del exceso del de-

sarrollo financiero. La justificacion teorica de estos resultados se basa en la teora

del rebasamiento de la informacion ( informational overshooting ) introducida

por Rob (1991) y Zeira (1994) y aplicada posteriormente por Zeira (1999) para

explicar el aplastamiento del credito. De acuerdo a esta teora, la economa tiene

una capacidad productiva limitada y desconocido. Este lmite en la capacidad

productiva puede estar justificado por una tecnologa limitada, demanda limitada

o por la escasez de recursos. Los agentes racionales emplean toda la informacion

disponible para formar expectativas sobre este lmite. Mientras el lmite no ha

xvii

0. INTRODUCCION

sido alcanzado las expectativas son cada vez mas optimistas. Finalmente, las

expectativas son tan optimistas que la economa sobrepasa su capacidad: los

recursos invertidos en produccion son demasiados en comparacion a las posibili-

dades de produccion. Las expectativas, inversion y la actividad economica sufren

una cada, dando lugar a una grave situacion economica. Nosotros proponemos

considerar el progreso tecnologico del pas como principal fuente del crecimiento

de la capacidad de la economa. De hecho, la introduccion de nuevas tecnologas,

la invencion de nuevos bienes y materiales sirven como sustitutivo de los factores

escasos de produccion como por ejemplo, los recursos naturales y la fuerza lab-

oral. Por otro lado, se propone considerar el desarrollo financiero como factor

impulsor de la actividad economica. Conforme las posibilidades tecnologicas de

la economa aumentan, la demanda de servicios financieros tambien incrementan

(vease, por ejemplo, Aghion, Howitt y Mayer-Foulkes (2005) para una explicacion

basada en el modelo de crecimiento economico de Shumpeter). Por lo tanto, el

desarrollo financiero es un determinante crucial del crecimiento. No obstante,

cuando las nuevas tecnologas o instrumentos financieros son introducidas a una

tasa mas alta que las nuevas tecnologas de produccin, la velocidad a la cual la

economa se aproxima a su capacidad aumenta. Consecuentemente, el exceso

del desarrollo financiero podra disminuir el crecimiento economico debido a una

mayor probabilidad del rebasamiento de la capacidad productiva. Este modelo

no introduce ninguna friccion en el mercado, excepto la escasez de informacion.

La teora del rebasamiento de la informacion ha sido implementada para explicar

las crisis de la economa de mercado por Barbarino y Jovanovic (2007) y Bruno,

Rochet y Woolley (2009).

xviii

FIRST PART

Chapter 1

Co-authorship and Individual

Academic Productivity

1.1 Introduction

In recent decades, governmental policies aimed at inducing collaboration have

increased. These policies are based on the assumption that intellectual collabo-

ration results in productivity gains for the researchers. Some examples of these

policies are the EU-funded research networks (Commission of European Commu-

nities, 2006) and the national Spanish Ingenio 2010 Program (Ministry of Educa-

tion and Science, 2006). In both programs, researchers are required to collaborate

as a condition to obtain research funding.1 Other examples of policies inducing

academics to collaborate are internal departmental policies (such as evaluations

or rankings and employment or tenure decisions that require a minimum amount

of publications) that not fully discounted articles by the number of authors.

In addition, scientific collaboration between authors has substantially in-

creased over recent decades. Indeed, the data shows that the number of papers

written by more than one author stood at 42% during the 1970s, while the pro-

portion of co-authored articles was close to 60% in 1990s. Several authors have

1The purpose of the EU-funded network is to facilitate the sharing of research resourcesand, as a formally organized and coordinated structure, to sustain knowledge sharing amongpartners. The aim of the funding is to enhance the research potential of participants throughthe benefits of collaboration (Defazio et al., 2009).

1

1. CO-AUTHORSHIP AND INDIVIDUAL ACADEMICPRODUCTIVITY

provided explanations for this increase, including greater gains from the special-

ization and division of labor, falling communication costs and a greater pressure

to publish among others.2

Consequently, scientific collaboration is conditioned by a scientific policy that

has progressively stimulated intellectual collaboration (Melin & Persson, 1996).

If intellectual collaboration does not lead to gains in terms of research output, a

policy change is required.

A need has therefore arisen to study the effect of collaboration on academic

productivity and to answer these important questions: Does co-authorship lead to

higher academic productivity? Is the effect of co-authorship the same for every

individual? What are the channels through which collaboration might affect

individual productivity? This chapter contributes to these important questions.

By using data on economists over a 30 year period, from 1970 to 1999, I find that

after taking into account the endogeneity inherent in the co-authorship formation

process through an instrumental variable strategy, co-authorship leads to higher

individual academic productivity. However, this effect varies significantly between

low and high productive individuals.

On the one hand, co-authorship might positively affect individual productiv-

ity through several channels: teamwork may be more productive than working

alone as larger teams combine the ideas and talents of individuals (Chung et al.,

2009). Another advantage is the reduction of time devoted to a project, as teams

enable the researcher to start more projects with other authors at the same time.

Hudson (1996) proposes the division of labor as one of the main advantages of

coauthoring in economics. Apart from these advantages, coauthoring should lead

to higher productivity if knowledge spillovers i.e. network and/or peer effects

are present.3

2For instance, Laban and Tollison (2000) compare the incidence and extent of formal co-authorship observed in economics against that observed in biology, and discuss the causes andconsequences of formal co-authorship in both disciplines. Hudson (1996) analyzes the increase inco-authorship and the reasons for it. Goyal et al. (2006) studies the emergence of an economicssmall world and the increasing co-authorship as a factor explaining this phenomenon.

3Azoulay (2010) and Waldinger (2010) examine peer effects in science using the unanticipatedremoval of individuals as a natural experiment.

2

1.1 Introduction

On the other hand, intellectual collaboration might also decrease individual

productivity. According to Hudson (1996), the main disadvantages of a collab-

oration are that it involves compromises and communication and organization

costs. When working in a group, individual authors will have to agree on ideas,

texts, approaches or even conclusions proposed by others. These compromises

may lead to a reduction in risk taking that might affect the final quality of the

article. Hudson (1996) likewise suggests that there is a free-rider problem, which

in this context is related to the idea that the higher the number of authors writing

a paper, the easier it is for an author to contribute less to the project. Moreover,

another possible disadvantage is the negative externality through time devoted

by collaborators to other projects with other authors (Jackson & Wolinsky, 1996).

This is what I call congestion externality. The main idea is that if our coauthors

are busy because they are working on many projects at the same time, they have

less time to devote to our project. Therefore, we will have to devote more time

to the collaboration, hence we have less time to do other work.

The literature examining the relationship between co-authorship and aca-

demic productivity has increased over recent years. However, there is no agree-

ment as to whether this relationship is positive, negative or non-existent. Laban

and Tollison (2000) provide evidence that co-authored scientific papers are more

likely to be accepted for publication than sole-authored papers.4 Recently, Chung

et al. (2009) test the relation between intellectual collaboration and the quality

of the intellectual output using academic papers published in prestigious finance

journals. They find that papers with more authors are cited more often, in par-

ticular, papers with four authors are cited the most often.

In contrast to the above studies that suggest coauthoring and productivity

are positively related, Medoff (2003) show that collaboration does not result in

significant higher quality research in economics.5 In addition, using individual

panel data on 5277 journal publications, Hollis (2001) finds that output is nega-

tively related to co-authorship: the more an individual coauthors, the lower is the

4Presser (1980) and Zuckerman and Merton (1973) also find a positive correlation betweenco-authored articles and the probability of acceptance.

5More recently, Acedo et al. (2006) find very weak evidence that co-authored managementpapers are of higher quality than sole-author papers.

3


research output attributable to that individual. Greater collaboration appears to

lead to more frequent, longer, and better publications, but when the publica-

tions are discounted by the number of authors, the relationship between research

output and teamwork becomes negative. Estimates of the size of this effect are

that, on average, adding one more author is associated with a per capita output

reduction of between 7% and 20%. Hollis (2001) provides several reasons for the

negative relationship: Mismeasurement (i.e. wrong measure of productivity), free

rider problem, coordination costs, future but not current benefits from collabora-

tion, etc. Hollis also points out the potential endogeneity of team formation, that

is, the amount of co-authorship depends on the opportunity set the individual

faces. He defines the opportunity set as the projects conceived by the author,

and of projects offered to the individual as a coauthor. This study focuses on

the endogeneity of the co-authorship formation as the main econometric problem

driving this negative relationship.

Lee and Bozeman (2005) were the first to control for the possibility that co-

authorship is formed endogenously, that is, authors choose with whom to work.

For example, an author may choose to collaborate because some ideas are hard

to be tackled individually or because he or she prefers to work with authors that

have similar characteristics or intellectual skills. In particular, a high assortativ-

ity in the matching process is observed in the scientific network, which suggest

that less able authors mainly collaborate with authors of a similar type (less

productive). Ignoring this selection, its effect would be incorrectly attributed

to collaboration and biased coefficients obtained. Lee and Bozeman (2005) deal

with the endogeneity problem by using the extent to which a scientists ties are

cosmopolitan i.e. outside the proximate work environment as an instrument

for the number of collaborators. However, there is a potential correlation between

the instruments and productivity, as outside ties can directly affect productivity

by providing access to new ideas (Singh, 2007). Moreover, they assume that the

productivity of an author is only a function of the number of articles published in

a period. I consider that the productivity of an author not only depends on the

number of article published by the individual but also on the quality and length

of each article.

4

1.1 Introduction

In addition, the previous literature does not control for unobservable hetero-

geneity with the exception of Hollis (2001) who estimates a fixed effect model.

This study is the first to simultaneously control for time invariant unobservable

factors and for the potential endogeneity inherent in the co-authorship formation.

The main finding is that, once I control for the endogeneity of network formation,

co-authorship leads to higher academic productivity. This result is robust and

statistically significant. I also find evidence about the potential presence of peer

effects and congestion externalities in the scientific networks.

I attempt to overcome some of the endogeneity difficulties by drawing on

methods used for social network analysis. In order to control for endogenous

collaboration formation an instrumental variables regression is estimated. The

instruments are derived from the past network of the author and are based on

the common research interests between the author and her potential coauthors

(the past coauthors of is coauthors) and its quadratic term. As Fafchamps et al.

(2010) point out, one of the most important factor in determining the likelihood

of collaboration is some commonality of research interest between the authors.

On the other hand, collaboration is unlikely when there is too much overlap in

skills. Hence, the quadratic term of the common research interests is included to

allow for potential non-linear effects between collaboration and research overlap.

Both instruments capture the idea of homophily, which consists in the tendency

of individuals to associate with those who have similar characteristics. Fixed

effects are also considered to capture individual unobserved heterogeneity.

A panel data among economists over a 30 years period, from 1970 to 1999, is

used to implement this empirical strategy. This panel includes economists who

published in journals which are included in the list of EconLit, a bibliography of

economic journals compiled by the editors of the Journal of Economic Literature.

I use the proportion of co-authored articles to measure the extent of collaboration,

and a productivity measure created using the publication record of each author

in the Econlit database. This productivity measure is based on the work of

Fafchamps et al. (2010) and combine quantity (number and length of published

articles) with quality (journal rank).

5


This chapter also contributes to the empirical study of social networks, which

is a relatively new research area. In a recent work, Fafchamps et al. (2010)

examine the formation of coauthor relations among economists over a twenty

year period. Their principal finding is that a new collaboration emerges faster

among two researchers if they are closer in the existing scientific network. In

particular, being at a network distance of 2 instead of 3 raises the probability of

initiating a collaboration by 27 percent.6

More related to this chapter are the papers that attempt a joint estimation of

network formation and network effects. Various recent papers deal with this issue.

For example, Mihaly (2009) develops an empirical strategy based on a two-step

selection model a` la Heckman to control for endogenous friendship formation

with the aim of measuring the effect of peer interactions on student academic

achievement. Conti et al. (2009) estimate the effect of popularity (measured

as the number of friendship nominations received from schoolmates) on the la-

bor market returns controlling for the selection of friendship. Their empirical

strategy is to simultaneously estimate the outcome of interest together with the

friendship formation process. Similarly, this chapter estimates simultaneously the

productivity of an author and the amount of collaboration.

This work is also related to the articles that estimate social effects using net-

work data. Recently, Bramoulle, et al. (2009) provide the necessary and sufficient

conditions under which peer effects can be identified using network data. They

propose the use of peers peers (and peers peers peers) characteristics as in-

strumental variables for the peers behavior. In this chapter, I do not estimate

rigorously peer effects in academia though, I provide evidence of their exis-

tence. Instead, the main aim is to estimate the causal effect of co-authorship on

individual productivity. Using network data, we obtain exogenous variation of

co-authorship through variation on the research interests between the author and

the coauthors coauthors accumulated in the past. 7

6Mayer and Puller (2008) also study the formation of links. They argue that individuallevel heterogeneity reflected in differences in age, sex and race plays an important role in thematching process of individuals.

7Other recent papers on networks effect include Boucher et al. (2010), Calvo-Armengol etal. (2009), Lin (2010), Liu et al. (2012).

6

1.2 Estimation framework


This project seeks to estimate the causal effect of co-authorship on individuals

output. The primary equation of interest is the productivity or output function

which is given by,

log(qi,t) = Ci,t+ti,t+t2i,t+Hi,t+ni,t+q1i,t+q2i,t+Di,t+i+t+i,t, (1.1)

where qi,t is the productivity of an author i, which is obtained taking into account

the quantity and quality of the articles published by the author.8 As the distribu-

tion of output is skewed to the right, I apply the log transformation to minimize

the effect of highly productive individuals on estimates. The variable Ci,t is the

co-authorship variable obtained as the proportion of co-authored articles.9 The

time-varying factors are: years since first publication, ti,t, and its square, t2i,t, the

degree of research specialization of the author, Hi,t, average number of coauthors

papers, ni,t, average coauthors productivity, q1i,t and average coauthors coau-

thor productivity, q2i,t. Although, the average coauthors productivity, average

coauthors coauthor productivity and the average number of coauthors papers

might be endogenous to productivity, we can observe in the Bootstrap Hausman

Test provided in table 1.2 that their inclusion does not affect the co-authorship

coefficient. Moreover, their inclusion provides evidence of important network ex-

ternalities. In addition, as the panel data start for each individual with her first

publication in the sample and extends till the last observed publication of the

author or 1999, an author would have at least one article in her last year of

publication. To prevent any bias from this, all regressions also include a dummy

variable authors last year of publication, Di,t. An individual fixed effects, i, is

also included to account for all time-invariant unobserved factors, such as innate

ability, nationality, gender, school education, etc. Years dummies are included

for each year from 1981 to 1999, these year fixed effects, t, account for any pos-

8The precise definition of all the variables used in the estimation is provided in section 1.39For robustness, I also consider the average number of coauthors as the co-authorship vari-

able. See section 1.5

7


sible time trends in collaboration and individual performance.10 i,t is the time

varying error term. As both the productivity and the co-authorship variables

are likely to be correlated over time, I cluster standard errors by authors. The

main parameter of interest is , which captures the effect of co-authorship on

productivity.

One problem when defining the productivity variable is the lag of economics

publication. As pointed out in Fafchamps et al. (2010), there are considerable

delays between the moment a paper is submitted to a journal and the moment

it is published. Moreover, the time required for publication in economic journals

varies greatly both between and within journals. These two facts could lead

to a concentration of publications in some periods, which do not correspond to

the exact periods where the author was working on the projects. In order to

reduce this potential problem, all the variables, except experience, aggregate the

information for the last five years, from t4 to t (e.g. the dependent variable is thelog-average productivity from t4 to t). Although variables contain informationabout the last 5 years publications, the frequency used for estimation is annual;

I consider a year as a period.

Productivity might be affected by other important factors such as changes

in the degree of specialization, the time devoted by coauthors to other projects,

the quality of coauthors or the quality of coauthors coauthors. Some of these

time-varying factors are constructed from the scientific network. This network is

defined using authors as nodes and co-authorship as networks links. Following

Singth (2007), I assume a collaboration tie to last for 5 years.11 Therefore, the co-

authorship network is defined for each year from 1974 to 1999, and each network

contains links formed between t 4 to t. The proxy variables for these time-varying factors are the following:

- The years since first publication, ti,t, is included to control for the expe-

rience of the author. Experience in any field or job is one of the main factors

10The first authors observation in the estimation sample is 1980. See section 1.3 for moredetails.

11In the robustness section, the sensibility of the results to this assumption is tested.

8


influencing productivity. Moreover, more experienced authors are more likely to

initiate a project with someone else as they have more contacts and therefore

more collaboration opportunities.

- The degree of research specialization, Hi,t, control for the potential effects

of specialization on productivity. Specialization allows a scientist to become an

authority on a given subject (Hackett, 2005).12 On the other hand, studying

a wide range of topics may facilitate the generation of new ideas and enable a

researcher to tackle projects that require a broader view as the researcher has a

more diverse knowledge.13 Moreover, specialization might also affects the amount

of collaboration as overly specialized authors may not be able to tackle projects

that requires knowledge on different fields. Therefore, they may be more willing

to collaborate than authors who have a more diverse source of knowledge.

- The average number of articles published by the coauthors of an author

i, ni,t, is a proxy for the time devoted by is coauthors to other projects with

other authors i.e. congestion externality. The time allocated by is coauthors

to other projects with other authors might reduce the opportunities of author

i to initiate new projects, as he or she will have to devote more time to the

collaboration. Thus, authors who work with busier coauthors are likely to have

lower productivity.

- The average coauthors productivity of author i, q1i,t, captures the coauthors

quality - in terms of productivity.

- The average coauthors coauthor productivity of author i, q2i,t controls for

the quality of these nodes at distance 2.

The main important econometric problem is the endogenous co-authorship

formation. Authors choose with whom to work and these associations may be

influenced by unobservable characteristics. For example, an author may choose

to collaborate because some ideas are hard to be tackled individually. Then, the

12Leahey (2006) found that specializing improves productivity.13Belmaker et al. (2010) found that both over-specialization and over-generalization are

detrimental to academic success.

9


difficulty of the project can lead to greater cooperation and may also lead to

higher output. In this case ignoring the selection of co-authorship would lead to

spurious results. To correct for this type of bias, an instrumenting strategy is im-

plemented. Equation (1) is estimated by an efficient two-step generalized method

of moments (GMM), instrumenting Ci,t by: the common research interests be-

tween author i and the nodes at distance 2 (the coauthors of is coauthors) that

author i accumulated from t 10 to t 6, w2i,t6, and its quadratic term.14 Bothvariables capture the degree of homophily in terms of research interests between

author i and the potential coauthors that author i may have in the future. These

instruments control for the endogeneity of co-authorship by capturing the extent

of matching on the overlap in research interests of author i with her potential

coauthors.

The identification of the co-authorship parameter, , comes from past varia-

tion in the research overlap between author i and her potential coauthors, w2i,t6.

Therefore, to get rid of the individual fixed effects, i, equation (1) is transformed

using first differencing instead of within transformation. As applying the latter

would create a spurious correlation between the average of the instrumental vari-

ables and the productivity.15 Given the small variability of the instruments from

one period to another, I consider the instrumental variables in levels to avoid the

problems of weak instruments.16 The assumption imposed by the efficient two-

14Following Baum et al. (2003): The efficient GMM estimator minimizes the GMM cri-terion function J=N*g*W*g, where N is the sample size, g are the orthogonality or momentconditions and W is a weighting matrix. In two-step efficient GMM, the efficient or optimalweighting matrix is the inverse of an estimate of the covariance matrix of orthogonality con-ditions. The efficiency gains of this estimator relative to the traditional IV/2SLS estimatorderive from the use of the optimal weighting matrix, the overidentifying restrictions of themodel, and the relaxation of the i.i.d. assumption. For an exactly-identified model, the efficientGMM and traditional IV/2SLS estimators coincide, and under the assumptions of conditionalhomoskedasticity and independence, the efficient GMM estimator is the traditional IV/2SLSestimator .

15As the average of the instrument include the period from t 4 to t, positive values of thecommon field overlap in this period will be associated with positive co-authorship and positiveproductivity, creating an spurious correlation between the average of the instrument and theproductivity variable.

16This alternative was proposed by Arellano and Bond (1991), who developed a GMM esti-mator using lagged levels of the endogenous variables internal instrument as instrument forthe equation in first differences. Instead, I use the lagged level of the common research interestbetween an author and her potential coauthors external instrument as instrument for the

10


step GMM estimator is that the variation in the error term is uncorrelated with

past research overlap between author i and her potential coauthors. Formally,

the two-step efficient GMM estimator relies upon the following orthogonality

conditions:

E(i,tw2i,t6) = E(i,t1w2i,t6),

E(i,t(w2i,t6)2) = E(i,t1(w2i,t6)2).

These instruments have a theoretical justification. They capture the idea of

homophily, which consists in the tendency of individuals to associate with those

who have similar characteristics. Homophily has been documented across several

characteristics, such as age, race, gender, religion and occupations, e.g., Fong and

Isajiw(2000), Baerveldt et al. (2004), Moody (2001), McPherson et.al. (2001),

Conti et al. (2009) and Mihaly (2009). I focus on the homophily between re-

searchers in term of common research interests, measured by the research overlap

and based on the idea that authors tend to collaborate with individuals who

have some commonality in research interest. The homophily effect between re-

searchers in term of research overlap has been documented by Fafchamps et al.

(2010). They show that the emergence of a new collaboration tie is decisively

shaped by the commonality of research interest between the authors. They also

find that the relationship between the research overlap index and the probability

of collaboration follows an inverted-U curve. For example, the likelihood of form-

ing a collaboration is highest when the research overlap index is 0.660. I include

the squared of the research overlap variable to capture this potential inverted-

U curve relationship. The main idea behind the inverted-U curve relationship

is that authors need some common research interest to initiate a collaboration.

Hence, research overlap cannot be too small. On the other hand, when research

overlap is too strong collaboration is unlikely as there is too much overlap in skills

(Fafchamps et al., 2010).

These instruments are valid as long as the above orthogonality conditions are

satisfied. These assumptions are plausible as these potential coauthors are not

difference in the amount of collaboration.

11


nodes at distance 2 from the current network (obtained from nodes accumulated

from t 4 to t) but from the past network (obtained from nodes accumulatedfrom t 10 to t 6). Moreover, I do not expect that the research overlap wouldaffect the future productivity of an individual through other channels, other than

co-authorship, as this variable is only related to the matching process.17

1.3 Data

The data used for this chapter are the same as the one used by Goyal et al.

(2006), Fafchamps et al. (2010), Van der Leij and Goyal (2011) and Ductor et

al. (2011). The data come from the EconLit database, a bibliography of journals

in economics compiled by the editors of the Journal of Economic Literature.18

From this database, I use information on all articles published between 1970 and

1999 by less than 4 authors.19 The panel data start for each individual with her

first publication in the sample and extends till the last observed publication of

the author or 1999.

As already pointed in Ductor et al. (2011) a significant fraction of economists

in the EconLit database publish very infrequently and may not publish a single

piece over a five year period. To rule out such authors, I restrict attention to

individuals who at every point t, have published at least one piece in the previous 5

years i.e, active authors. Similarly, the model is estimated in first differences and

the instruments are based on information from t 10 to t 6. As a consequence,the first six observations of each author are lost. Moreover, as the co-authorship

network combines 5 years of publications, I loose the first 5 years of the sample as

starting values.20 The analysis therefore only considers articles published between

1980 and 1999. Finally, authors who has never collaborated during their academic

17See section 1.5 for the robustness of the instrument to a potential internal validity threat.18See Goyal et al. (2006) for more details.19As Van der Leij and Goyal (2011) pointed out, in the EconLit database 77% of the articles

were written by 2 authors, 19% by 3 authors, and 4% by 4 or more authors. Moreover, Van derLeij (2006, pp. 53-56) show that the co-authorship network statistics are practically unaffectedwhen (for a subset of the data) articles with 4 or more authors are included.

20Authors whose first article is published before 1974 are not considered since they do nothave a defined network from the first year of their career.

12

1.3 Data

career are excluded from the sample, since their variables are constant over the

time frame and a first differences model is estimated. These authors represent a

13.25% of the active authors population.21

1.3.1 Definition of the variables

In this section, all the variables used to estimate the outcome equation (1) are

described.

Co-authorship, Ci,t. The amount of co-authorship by an author i during a

period t is measured as the ratio between the number of co-authored articles and

the total number of articles published by the individual during the period t 4to t. Notice that collaboration that does not result in journal publications is not

observed. Moreover, neither informal collaboration as valuable comments from

colleagues in conferences and/or seminars nor the amount of effort devoted by

each author to the project is observed.22

Productivity, qi,t. The productivity of an author i at period t is measured as

follows:

qi,t =Sj=1

pagesj qualityjNumber of authorsj

,

where S is the total number of articles published by author i from t 4 to t.The variable quality is a measure of the quality of the journal proposed by

Fafchamps et al. (2010). They construct this measure based on the work of

Kodrzycki and Yu (2006) hereafter KY who construct an impact index for a

large number of economics journals. They complement KY work by predicting

the impact index of journals not included in the list compiled by KY. To do

21On average the productivity of authors without a co-authored article during all their careerlife is 1.26, whereas the average productivity for authors with at least one collaboration is 5.35.These sole-authors are also younger on average. Given their characteristics, their exclusionmight lead to a downward bias in the value of co-authorship.

22One important effect of co-authorship may be to raise likelihood of publication for a givenquality of the research rather than to improve the quality per se. In that case the marginalpublished single authored paper may be a better paper than the marginal published co-authoredpaper. This may lead to a downward bias in the value of co-authorship.

13


this, they regress the KY index on commonly available information such as the

number of published articles per year, the impact factor, the immediacy index,

the Tinbergen Institute Index, an economics dummy, interaction terms between

the economics dummy and the impact factor, and various citation measures.

They then use the predicted value obtained from this regression as impact index

for journals not included in the KY list.23 The actual KY impact index is used

whenever available.

The pages variable measures the length of the article and is given by the

number of pages of the article divided by the average number of pages of the

articles published in the same journal.24 Thus, I assume that longer than average

papers are more valuable pieces of research.25

Observe that research is discounted by the number of authors, n, to account

for the individuals contribution to the sum of output, giving 1n

credit to any

single author.

Field concentration. To measure the degree of specialization of an author I

use the Herfindahl index. Formally, this index is defined as

Hit =Ff=1

(xit,f )2,

where xit,f is the total fraction of articles published by author i in the field f from

t 4 to t, and F is the number of fields. To construct this variable, articles arecategorized into 121 subfields according to the first two digits of the JEL codes.

Articles with multiple JEL codes are divided and assigned proportionally to each

of the corresponding fields. This measure takes value from 1/F , reflecting the

23Since most of the journals that KY omitted are not highly ranked, their predicted qualityindex is quite small.

24As pointed out by Sauer (1988) if journal editors act as value maximizers to allocate space,longer articles are more valuable than those of lesser length (on average).

25The number of pages for each article has been truncated from above at fifty pages. Themain idea is not to give so much extra value to literature review articles as in general a literaturereview paper is much longer than the average article. For robustness, I also use as productivityonly the journal quality index divided by the number of authors working in the article. SeeSection 1.5

14

1.3 Data

maximum degree of diversity, to 1 if the author write all her articles in the same

field. Higher values of this index indicates a higher degree of specialization of the

author.

Average number of articles of the coauthors. This variable is computed as

the average number of papers published by the coauthors of author i from t 4to t, excluding the papers published with author i. Although, there is a strong

correlation between this variable and the average coauthor productivity, peer

effects or network effects are expected to affect mainly the quality of the article.

This may be because the effects come through the flow of information from one

node to another in the co-authorship network. Thus, while the average number

of papers of the coauthors of author i captures how busy the coauthors of an

author are, the average productivity of the coauthors (which take into account

the quality of the article) is more related to the presence of knowledge spillovers.

Average Coauthors productivity. To control for the quality of coauthors, the

average productivity of coauthors from t 4 to t is included in equation (1). Tocompute this variable, the productivity of each coauthor is calculated, excluding

the papers published with author i.

Average Coauthors coauthor productivity. This variable is computed in the

same way as the average coauthors productivity but looking at the productiv-

ity of the coauthors of coauthors, and excluding the papers published with the

coauthors of author i.

Instrumental variables. The first instrument measures the common research

interests between author i and the nodes at distance 2 that this author has had

from t 10 to t 6, w2i,t6, i.e. the research overlap between author i and herpotential coauthors.

To obtain the proxy for research overlap between author i and her potential

coauthors, I use a measure that is very close to the one proposed by Fafchamps

et al. (2010). They construct an index of research overlap between any two

15


researchers.26 I extend their definition to construct an index of research overlap

between an author and all her potential coauthors.

To do this, Fafchamps et al. (2010) categorize articles into 121 subfields

according to the first two digits of the JEL codes. Articles with multiple JEL

codes are divided and assigned proportionally to each of the corresponding fields.

Then, the cosine similarity measure is considered to be a measure of field overlap

between i and all her nodes at distance 2 accumulated from t 10 to t 6,N2t6(i).

27 This measure is computed as follows: Suppose that xit6,f is the fraction

of articles written by i in field f in the period from t 10 to t 6 (such thatf x

it6,f = 1) and x

N2t6(i)t6,f is the fraction of articles written by the potential

coauthors of author i in field f from t 10 to t 6. Then, the research overlapindex is,

w2i,t6 =

f x

it6,fx

N2t6(i)t6,f

f

(xit6,f

)2f

(xN2t6(i)t6,f

)2 .

This variable takes values from 0, if i and her potential coauthors did not

write any paper in the same field, to 1 if i and her potential coauthors wrote in

exactly the same fields and in exactly the same proportion.

The second instrument is the squared of this variable and it is introduced

to account for the potential inverted-U relationship between field overlap and

collaboration documented by Fafchamps et al. (2010).

The instruments are measured using publications from t 10 to t 6 toavoid spurious correlation with the productivity variable and its lag, which are

measured from t 4 to t and from t 5 to t 1, respectively.26See Fafchamps et al. (2010) for more details.27The research overlap measure capture not just having worked in similar research areas

but also overlap in research topics. For instance, if a researcher has worked on, say, develop-ment economics and microeconomic theory (2 separate categories in JEL codes), she may bemore likely to work with another researcher who has also focused on development and micro(Fafchamps et al, 2010).

16

1.3 Data

1.3.2 Descriptive analysis

Figure 1.1 plots the relationship between the total average output measure and

career time using all individuals in the data set.

Figure 1.1: Total Average Productivity across Career time. Full sample2.

42.

52.

62.

72.

82.

9

6 8 10 12 14 16 18 20Career time

95% CI Fitted valuesTotal Average Productivity from t4 to t

The sample consists of articles published from 1974-1999. All authors are considered exceptthose whose first article was published before 1974.

As it can be observed, there is a rapid increase in total average output between

the first publication and the ninth year after the first publication and then a

steady decline starts and continues to the sixteenth year. The negative trend in

the output after 9 years is due to fewer articles and lower quality. The increase on

productivity after the fifteenth year of experience is a consequence of the definition

of the panel in which an author has at least one publication in the last year of

her career. Moreover, the proportion of authors who have more than 15 years of

experience is high 40% of the total authors population, thus, these authors will

have at least one publication during the 16 and 21 years of their career leading

to the positive trend observed in figure 1 at the end of their academic life.28

28For robustness, the main regressions are also performed extending the panel until the foursubsequent years after the last publication. The main results remain under this panel durationand are available upon request from the author. I also consider a different specification includinga cubic term of the experience, the results are quantitatively the same.

17


Figure 1.2 shows the relationship between the total average output and career

time using only the sample of active authors. Notice that active authors tend to

publish more regularly as there is a rapid increase in the total average productivity

until the thirteenth year after the first publication, and then it practically remains

constant. The steady decline in the total average productivity in this sub-sample

is not observed.

Figure 1.2: Total Average Productivity across Career time. Active Sample

44.

55

5.5

66.

5

6 8 10 12 14 16 18 20Career time

95% CI Fitted valuesTotal Average Productivity from t4 to t

The sample consists of articles published from 1974-1999. Only active authors areconsidered.

The non-linear relationship between experience and productivity is evidence to

include the square of experience in the regressions. This quadratic term captures

the decreasing return to experience or academics life-cycle effects.

Figure 1.3 graphs co-authorship against experience for the sample of active

authors. Observe that as the authors become more experienced, the average num-

ber of coauthors per article increases. This informally suggests that individuals

with more contacts are more likely to collaborate. On average, economists in

the first years after obtaining their Ph.D. have not established many contacts,

thus the opportunities to publish a paper with other colleagues are scarce. Then,

as the author becomes more experienced and known by others, the likelihood of

collaborating increases.

18

1.3 Data

Figure 1.3: Total Average number of coauthors on Career time. Active Sample

.65

.7

.75

.8

.85

5 10 15 20Career time

95% CI Fitted valuesTotal Average Number of Coauthors from t4 to t

The sample consists of articles published from 1974-1999. Only activeauthors areconsidered.

Table 1.1 provides a summary statistics of the different variables used in the

estimation. Column 1 provides summary statistics for authors with average life-

time co-authorship between 0 and 0.5 (low-average co-authorship). Column 2

provides statistics for authors with average lifetime co-authorship between 0.5

and 1 (high-average co-authorship).

Note that for authors with a low average lifetime co-authorship the mean

productivity is 4.35. While for those authors with a high lifetime average co-

authorship, the mean productivity is 5.76. Moreover, these authors have higher

average coauthors productivity and higher overlap in research interests with

nodes at distance 2. Also note the high variability of the productivity and network

variables, whose standard deviation is generally much higher than the mean.

19


Table 1.1: Summary statistics of the data

Variables Low-co-authorship High-co-authorship

mean st.d. mean st.d.

Avg. Productivity from t-4 to t 4.35 10.12 5.76 11.16Avg. Pages from t-4 to t 8.62 5.25 9.25 5.32Avg. Quality from t-4 to t 4.19 8.79 5.18 9.24Experience 9.60 4.45 9.85 4.53Co-authorship .23 .27 .77 .27Avg. Coauthors Productivity 11.16 35.87 21.62 41.10Avg. Coauthors Coauthor Prod. 9.11 27.25 19.25 33.10Avg. Coauthors papers 1.81 3.39 3.28 3.41Research overlap with Coauthors Coauthor .14 .25 .37 .33Degree of Specialization .36 .24 .35 .23Number of Observations 39585 39585 68501 68501Number of Authors 6726 6726 10652 10652

a.These statistics correspond to the active author sample and publications from 1974 to 1999.The first four years of each author are not considered as the productivity and co-authorship

variable are not defined.

1.4 Results

This section presents the results from estimations of the outcome equation (1).

1.4.1 Does co-authorship lead to higher academic produc-tivity?

The main question of interest is whether co-authorship affects productivity once

it is discounted by the number of authors. In this section, I estimate equation

(1); I provide results of the estimated model without controlling for the selection

of co-authorship, then estimates controlling for the potential endogeneity of co-

authorship formation are provided.

Column 1 of Table 1.2 shows results of the first-difference specification in

which the independent variables include co-authorship, years since first publi-

cation, a dummy variable authors last year of publication and year dummies.

Column 2 provides estimates from a first-difference regression of equation (1) con-

trolling for unobservable individual heterogeneity and time varying factors but

not for the endogeneity of co-authorship. The implication of these regressions is

20

1.4 Results

that co-authorship lead to lower academic productivity. This result is consistent

with Hollis (2001) who finds a negative effect of co-authorship on productivity.

To correct for the possible bias of the co-authorship measure, the instru-

menting strategy described in section 1.2 is implemented. Column 3 of Table

1.2 presents the results controlling for the co-authorship formation process but

not for the time varying factors. Column 4 shows the results from estimating

equation (1) controlling for the endogeneity of co-authorship and controlling for

the degree of specialization of the author, average number of coauthors paper,

quality of coauthors and quality of the coauthors of coauthors.29 Observe that

the coefficient of co-authorship becomes significantly positive after instrument-

ing, which shows that the individual productivity increases as authors substitute

sole-authorship by teamwork. In other words, for example, the total productivity

of two authors collaborating on two published papers is greater than the total

productivity expected by each of the two authors writing a sole-authored paper.

One possible explanation for the change of the sign of the co-authorship variable

after instrumenting could be that authors have some periods where they have

better ideas than in other periods. On the one hand, collaboration is more likely

in periods where the author has a lack of ideas, since he or she is more willing to

accept any co-authored project of any quality. On the other hand, in periods of

good ideas, the author will only share ideas that require skills of other researchers.

Then, good ideas not requiring specialization will be sole-authored, resulting in

a positive correlation between sole-authorship and productivity (Hollis, 2001).

Once the instrumental variable strategy is implemented, only exogenous varia-

tions of the co-authorship variable through variations on the common research

interest between author i and her potential coauthors are considered. As the dis-

tribution and fields of specialization of these potential coauthors affects the range

and diversity of dispersed knowledge that an author can access (Bonacich, 1987),

having more diverse contacts might help an individual to create new knowledge

combinations, driving the benefits from exogenous co-authorship.

We can observe some evidence of the presence of knowledge spillover: the

higher the productivity of coauthors, the higher the productivity of the author is.

29First stage estimates are presented in Table 1.8.

21


Table1.2:

Th

eeff

ectof

co-auth

orship

onacad

emic

pro

du

ctivity

(1)F

D(2)

FD

(3)F

D-IV

(4)F

D-IV

Co-au

thorsh

ip.3991

.5099

2.6906 2.6021

(.0288)(.0281)

(.5270)(.6570)

Exp

erience

.1395 .0790

.00

27.04

73(.0421)

(.0385)(.06

07)

(.0590)

(Exp

erience)

2.0042

.0034

.0022

.0016

(.0003)(.0003)

(.0005)(.0005)

Degree

ofS

pecialization

1.5103

1.4294

(.0325)(.0466)

Avg.

Nu

mb

erof

Coau

thors

Pap

ers.0162

.0193

(.0023)(.00

41)

Avg.

Coau

thors

Pro

du

ctivity

.0009 .0019

(.0002)(.00

03)

Avg.

Coau

thors

Coau

thor

Pro

d.

.0001 .0002

(.0002)(.00

04)

Au

thors

Last

Year

ofP

ub

lication.2911

.2010 .26

92 .18

31

(.0126)(.0125)

(.0178

)(.0172)

Year

Du

mm

iesYES

YES

YES

YES

Nu

mb

erof

Au

thors

1083510835

1083

510835

Nu

mb

erof

Ob

servations

7079470794

7079470

794

Cragg-D

onald

Wald

Fstatistic

.

24.30

38.40H

ansen

J-test

(p-valu

e)

3.6

42(.05

63)

0.1

99(.65

56)

En

dogen

eityT

est(p

-value)

5.465

(.0000

)36.0

62(.00

00)

Bootstrap

Hau

sman

Test

(p-valu

e)

0.15(.7016)

b.Th

esam

ple

consists

ofactive

auth

orsan

dp

ub

lications

from1980-1999.

Year

fixed

effects

were

inclu

ded

inth

ean

alysis,

bu

tare

not

reported

here

tocon

servesp

ace.T

he

Bootstrap

Hau

sman

Test

evaluates

ifth

ed

ifferen

ceof

the

co-auth

orship

para

meter

estimated

by

mod

el(3)

and

model

(4)are

signifi

cantly

diff

erent

fromzero.

Stan

dard

errorsin

paren

thesis

adju

stedfor

clusters.

Sign

ifica

nt

at1%

level,S

ignifi

cant

at5%

level.

22

1.4 Results

On the other hand, the quality of authors at a higher distance in the co-authorship

network does not affect individual performance. The negative sign of the average

number of coauthors papers reflects the congestion externality idea. For example,

the busier the coauthors of author i are, the less time they devote to research

projects with this author and the lower the output of author i is. The above

network variables might be endogenous to productivity. However, their inclusion

does not affect significantly the effect of co-authorship on academic productivity.

Using the Bootstrap Hausman test, we cannot reject the null hypothesis that both

estimators of the co-authorship parameter are equivalent.30 Career time has a

negative impact on productivity for authors with more than 6 years of experience,

consistent with the academics life-cycle effects.31 Specialization have a negative

effect on productivity consistent with the findings of Belmaker et al. (2010).32

Regarding the empirical validity of the instruments. I reject the null hypoth-

esis of weak instrumental variables. Thus, the instrumental variables are suffi-

ciently correlated with the troublesome variable, co-authorship. The Hansens-J

test is used for testing the null that the overidentifying moment conditions are

true. From Table 1.2, we cannot reject the null that the instruments are valid.

Moreover, it is clear from the endogeneity test that the variable co-authorship

cannot be treated as exogenous.

1.4.2 Co-authorship and productivity across individual types

I am also interested in the relationship between co-authorship and productivity

across different types of individuals. As already pointed out by Ductor et al.

(2011), it is expected that the benefits from a collaboration differs across individ-

uals. Access to ideas is an opportunity and it takes ability and effort to publish a

high quality article. Thus, it is reasonable to suppose that the potential benefits

from a collaboration vary with the abilities and efforts of a person (Ductor et al.,

30See Appendix A3 for a description of the test.31As a consequence of the empirical strategy, the first observation of an author corresponds

to the seventh year after the publication of her first article.32I also consider other specification including field fixed effects using the 121 JEL codes. The

results not presented for the sake of brevity are available upon request from the author. Allthe results are qualitatively the same under this specification

23


2011). The main hypothesis is that more able researchers can exploit the benefit

from co-authorship to greater extent.

In order to analyze the potential difference of co-authorship between the differ-

ent types of individuals, I divide the sample into two different groups depending

on the productivity of the first publication. Summary statistics reported in Ta-

ble 1.9 suggest that the first publication of an author is a very good predictor

of the future potential performance of an author. Then, the estimation strategy

is performed in each sample. Table 1.3 presents the effect of co-authorship on

produc

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