Análisis bibliométrico de la producción científica sobre Economía Experimental

UNIVERSIDAD DE SEVILLA DOBLE GRADO EN MATEMÁTICAS Y ESTADÍSTICA TRABAJO DE FIN DE GRADO Análisis bibliométrico de la producción científica sobre Economía Experimental TUTORA: Myriam González-Limón AUTORA: Cristina Maldonado Ruiz de Villegas ÍNDICE 1. Introducción ........................................................................................................ 3 2. Análisis de resultados ....................................................................................... 7 2.1. Análisis diacrónico de la producción ..................................................................... 7 2.2. Evaluación institucional ........................................................................................... 8 2.3. Análisis de la productividad de los autores ........................................................ 11 2.4. Análisis de co-autoría y co-citación de autores .................................................. 13 2.5. Análisis de ocurrencia de términos ...................................................................... 16 3. Conclusiones .................................................................................................... 18 4. Bibliografía ........................................................................................................... 20 Anexo ........................................................................................................................ 22 ÍNDICE DE TABLAS Tabla 1: Artículos por país ...................................................................................... 9 Tabla 2: Artículos por institución ......................................................................... 10 Tabla 3: Autores más citados (WoS) ................................................................... 12 Tabla 4: Autores más citados (SCOPUS) ............................................................ 13 ÍNDICE DE FIGURAS Figura 1: Esquema selección de artículos ............................................................ 5 Figura 2: Evolución de la producción científica................................................... 8 Figura 3: Producción científica en Europa ......................................................... 11 Figura 4: Red de co-citación WoS ........................................................................ 14 Figura 5: Red co-citación Scopus ........................................................................ 15 Figura 6: Red coautoría ......................................................................................... 16 Figura 7: Nube de palabras (1990- 2021) ............................................................. 17 Figura 8: Nube de palabras por periodos ........................................................... 18 1 Análisis bibliométrico de la producción científica sobre Economía Experimental Resumen La Economía Experimental (EE) es un método de trabajo de la Economía del comportamiento que desarrolla modelos teóricos de comportamiento humano en ámbitos económicos. Los experimentos económicos tienen ya una larga tradición, y han proporcionado resultados espectaculares y conclusiones ampliamente admitidas sobre la dinámica de mercados y el efecto de las instituciones económicas. Las nuevas tecnologías facilitan la realización y el análisis de estos experimentos. El objetivo principal de este estudio es la revisión sistemática de la producción científica sobre Economía Experimental, desde el año 1990 hasta finales de 2021, en las bases de datos de Web of Science Core Collection y Scopus. El análisis descriptivo de datos se realizó con el software Rstudio, mientras que el análisis de redes se hizo con el software Vosviewer. El estudio muestra, entre otras cosas, que la producción bibliográfica en este campo se ha intensificado exponencialmente; así como, que el país con más investigaciones es Estados Unidos y el autor más citado es Urs Fischbacher. Palabras clave Experimentos en Economía. Economía Experimental. Análisis bibliométrico. Producción científica. WoS. Scopus. VOSviewer. Bibliometric Analysis Of The Scientific Production In Experimental Economist Abstract Experimental Economics (EE) is a working method of behavioral economics that develops theoretical models of human behavior in economic settings. Economic experiments have a long tradition, and have provided spectacular results and widely accepted conclusions about market dynamics and the effect of economic institutions. New technologies facilitate the conduct and analysis of these experiments. The main objective of this study is the systematic review of the scientific production on Experimental Economics, from 1990 to the end of 2021, in the Web of Science Core Collection and Scopus databases. Descriptive data analysis was performed with Rstudio software, while network analysis was performed with Vosviewer software. The study shows, among other things, that the bibliographic production in this field has intensified exponentially; as well as, that the country with the most research is 2 the United States and the most cited author is Urs Fischbacher. Keywords Experiments in Economics. Experimental Economics. Bibliometric analysis. Scientific production. WoS. Scopus. VOSviewer. 3 1. Introducción La Economía Experimental (en inglés Experimental Economics, EE) es una disciplina muy reciente que pretende "usar el laboratorio para generar datos controlados y con ello contrastar hipótesis" (Brañas y Espinosa, 2011). Brañas y Barreda (2011) consideran que el laboratorio nos permite situar a decisores humanos en una situación análoga a la que dicha teoría describe y ver como se comportan. Vernon Smith (2005) manifiesta que la EE aplica métodos de laboratorio para estudiar las interacciones de los seres humanos en los contextos sociales gobernados por reglas explícitas o implícitas. Según Ufano (2005) un experimento económico es una recreación de una situación real en la que un grupo de individuos toma una decisión con contenido económico. Más concretamente, se puede tener en cuenta los tres ingredientes básicos que debe poseer un experimento: i) un entorno, ii) unas instituciones formales y iii) el comportamiento específico de cada individuo (Smith, 1994). De manera paralela a la Economía Experimental surgió la Economía del Comportamiento (ambas disciplinas están estrechamente relacionadas); "la Economía Experimental es una herramienta (un método de trabajo) de la Economía del Comportamiento que tiene como objetivo el desarrollo de modelos teóricos del comportamiento humano en ámbitos económicos y sus consecuencias para el funcionamiento de los mercados y las instituciones" (Brañas y Espinosa, 2011). Según Fatás y Roig (2004), se pueden mencionar dos acontecimientos que generaron la aparición de los métodos experimentales: - Primero, la publicación del libro “Teoría de juegos y comportamiento económico” en 1944 por Von Neuman y Morgenstern, que sentó las bases de lo que actualmente se conoce como la "teoría de juegos" principal herramienta para para estudiar el comportamiento de los agentes en decisiones estratégicas. - Segundo, la realización de los experimentos de mercado en la Universidad de Harvard en 1948 por Edward H. Chamberlin, utilizando como demandantes y oferentes a estudiantes que podían vender y comprar unos productos ficticios en el mercado; contrastó si se cumplía la predicción de que los mercados se equilibran al precio de mercado resultante. Su mercado arrojó un resultado bastante sorprendente: se vendía una cantidad notablemente mayor de la predicha por los modelos teóricos. Vernon Smith, un estudiante de aquellos que participaron en los experimentos de 4 Chamberlin, no quedó muy convencido de la interpretación que su profesor había hecho y quince años más tarde publicó dos trabajos en los que mostraba que, con información pública, los precios que se iban publicitando ponían de acuerdo a compradores y vendedores que iban realizando transacciones hasta alcanzar los precios de equilibrio. En tono humorístico Smith (2005) dice que es verdad que en muchos experimentos los sujetos están confundidos y toman decisiones que no esperamos; pero ¿no es cierto acaso que en el mundo real también existen personas confundidas? Posteriormente, comenzó a analizar el efecto de pequeños cambios institucionales en los resultados y con ello nació la Economía Experimental (por lo que recibió el Nobel de Economía en 2002); la importancia de las instituciones radica en que generan una estabilidad que hace posible tomar las decisiones con un mínimo de racionalidad. Desde los trabajos de Rabin (1993) y posteriormente otros, como los de Fehr y Schmidt (1999), Bolton y Ockenfels (2000), Fehr y Fischbacher (2003) o Charness y Rabin (2002), se han explorado nuevas funciones de utilidad o funciones objetivo donde los sujetos no sólo piensan en sus propios beneficios, sino que además pueden considerar el bienestar de otros jugadores. El influyente trabajo de Laibson (1997) ha hecho reflexionar sobre cómo la gente toma decisiones intertemporales sobre cuestiones de capital importancia para una economía como son el ahorro, la planificación de las pensiones o el cuidado del medio ambiente. "También hay otros aspectos del comportamiento, como la aversión al riesgo, la aversión a las pérdidas o el exceso de optimismo, que pueden tener consecuencias de gran importancia para las decisiones de los individuos y el funcionamiento de los mercados" (Brañas y Espinosa, 2011). La economía experimental ha evolucionado hacia la generación de nuevas teorías y modelos y ha ampliado los campos de estudio para incluir, por ejemplo, las diferencias de género en el comportamiento económico (Croson y Gneezy, 2009) o la Neuroeconomía (Glimcher, Camerer, Poldrack y Fehr, 2008). Asimismo, la obtención de resultados a partir de los datos generados se ha visto beneficiada por el desarrollo de técnicas econométricas cada vez más sofisticadas y adaptadas al campo de estudio, habiendo dado lugar a lo que Andersen, Harrison, Lau y Rutström (2010) han denominado la econometría del comportamiento, así como por la introducción de innovaciones como los experimentos virtuales, que combinan las ventajas de los experimentos de campo y los de laboratorio (Fiore, Harrison, Hughes y Rutström, 2009). Por otro lado, la investigación en el ámbito académico ha estado siempre muy vinculada a la gestión bibliográfica; no en vano, las fuentes bibliográficas y su correcta gestión son fundamentales para llevar a cabo una investigación de calidad. Los mayores desafíos que enfrentan los investigadores incluyen la síntesis significativa del big data de la literatura científica a través de métodos analíticos 5 libres del sesgo subjetivo del investigador. Figura 1: Esquema selección de artículos El desarrollo de la revisión sistemática de la literatura reciente se basa en un protocolo de búsqueda porque requiere un gran rigor metodológico para neutralizar el sesgo. Según Salvador, Marco y Arquero (2018) en una revisión sistemática es esencial publicar de manera completa y transparente el método utilizado 1. Hemos comprobado que no existe una publicación de revisión sistemática de la literatura que utilice los términos de “Economía Experimental”. 2. Se ha llevado a cabo la búsqueda de artículos, de una manera imparcial y lo más completa posible, a través de las palabras clave que mejor se adaptaban a la pregunta del estudio. Términos y ecuación de búsqueda: - Econom* (econometric, economic) Experiment* (experimental, experiments) Econom* and Experiment* - La ecuación de búsqueda en Scopus ha consistido en: TITLE(Econom* and Experiment*) OR KEY("econom* experiment*" OR "experiment*econom*") 6 - La ecuación de búsqueda en WoS: (TI=(Econom* and Experiment*)) OR AK=("econom* experiment*" OR “experiment*econom*") 3. "A partir de los 90 se ha producido una intensificación en la publicación de artículos experimentales en las más prestigiosas revistas científicas de economía. Esto viene a indicar que los experimentos se consolidan como otra herramienta válida de la profesión. Con todo ello podríamos decir que la economía experimental se encuentra en la actualidad en una época dorada (Brañas y Barreda, 2011). Hemos tomado, por tanto, la fecha de 1990 como punto de partida de la búsqueda y hasta 2021. 4. Las búsquedas se han realizado en las bases de datos de WoS y Scopus. Solo artículos de revista. WoS - Busca siempre los términos en inglés (aunque la publicación esté en otro idioma porque todo está indexado en inglés) - Se ha descartado la búsqueda por tema (topic), utilizando la búsqueda avanzada de los términos seleccionados en el título y en las palabras clave de autor. - No es necesario introducir el operador AND para recuperar varios términos de búsqueda (como en Google), aunque se pueden utilizar los booleanos. - El * permite recuperar variaciones de palabras (aunque de forma automática lo hace con varios tipos de sinónimos). - Con la ecuación de búsqueda: (TI=(Econom* and Experiment*)) OR AK=("econom* experiment*" OR “experiment*econom*"). - Solo artículos desde 1990 y en inglés: 1.222 documentos (17 de marzo de 2022). SCOPUS - Con la ecuación de búsqueda: TITLE(Econom* and Experiment*) OR KEY("econom* experiment*" OR "experiment*econom*"), salen 2.363 documentos. - Solo artículos desde 1990 y en inglés: 1.329 documentos (17 de marzo de 2022). - Los artículos duplicados se han eliminado de la base de datos de Scopus a través del gestor bibliográfico EndNote y con una comprobación manual. 5. WoS nos permite seleccionar un grupo de resultados y realizar un análisis según criterios como el autor, el año de publicación, el idioma, la organización, etc. De 13 Cabe destacar a John A. List (University of Chicago) como el autor más prolífico con 15 artículos. AUTHOR ARTICULOS CITAS TOTAL PUB (SCOPUS) H INDEX(SCOPUS) Ortmann, A 2 498 66 19 Hertwig, R 1 491 215 55 Perc, M 2 345 462 95 Szolnoki, A 2 345 159 65 Starmer, C 3 113 55 27 Cardenas, J 1 102 60 25 Samuelson, L 1 86 135 36 Bonetti, S 1 78 7 4 Ben-Ner, A 1 76 51 23 Kramer,A 1 76 19 12 Levy, O 1 76 13 9 Alm, J 2 75 172 37 Donovan, C 1 74 30 12 Rosenberg, N 1 57 62 23 Mckee, M 2 52 75 26 Tabla 4: Autores más citados (SCOPUS) En la tabla 4 tenemos el mismo estudio para los 236 artículos de Scopus, cuyo autor más citado está muy por debajo de los más citados de WoS. 2.4. Análisis de co-autoría y co-citación de autores El análisis de co-citación se define como la frecuencia en la que dos autores son citados simultáneamente; un detalle importante de la co-citación es el hecho de que cuántas más veces dos artículos se citan a la vez más probabilidad hay de que tengan un contenido relacionado. Se ha realizado utilizando el software VOSviewer. (Para el análisis de co-citación tenemos que volver a trabajar con las bases de datos por separado). 14 Figura 4: Red de co-citación WoS Se presenta en la figura 4, donde se han considerado un total de 3,700 autores relacionados con las referencias citadas en los 1,222 artículos pertenecientes a WoS. La unidad de medida ha sido los autores. Se definió un mínimo de 35 citas para cada autor, esta restricción permitía una mejor visualización de la red ya que solo 82 autores cumplen esta restricción. El método de normalización asociado fue asociation strength. como resultados se generaron 6 clusters con un total de 81 autores, en los que el tamaño del nodo está relacionado con el número de citas recibidas por el autor y las líneas que los interconectan muestran las relaciones de co-citación que fueron citados en el mismo documento. • Cluster 1 (en rojo): 26 items • Cluster 2 (verde): 19 items • Cluster 3 (azul oscuro): 16 items • Cluster 4 (amarillo): 8 items • Cluster 5 (morado): 6 items • Cluster 6 (azul claro): 6 items 15 Figura 5: Red co-citación Scopus Repetimos el análisis con los 236 artículos restantes de Scopus, con la limitación de 35 citas mínimas solo tenemos 17 autores clasificados en 3 clusters con 12, 4 y 1 autores respectivamente. (Figura 5) Llama la atención que en ambos diagramas (figuras 4 y 5) destaca el autor Smith, V.L., siendo éste el más co-citado. VOSviewer permite hacer el análisis de coautoría y co-ocurrencia de palabras con ficheros extraídos de gestores de referencia, por lo que para el análisis de coautoría se han fusionado ambas bases de datos en Endnote y se ha seleccionado en el software el análisis de coautoría con autores como unidad de medida y el método de conteo “full counting”. 16 Figura 6: Red coautoría Para cada uno de los autores se calcula the total link strength de la coautoría. Se muestra el mayor set de autores interrelacionados para la mejor visualización, con 66 autores clasificados en 8 clusters. El tamaño de los círculos se corresponde con la proporción de artículos que tiene el autor en el corpus aunque también se podría poner en función del número de coautorias. Los autores que más han trabajado en colaboración con otros autores son los correspondientes al cluster 1 (en color rojo) y los items más cercanos a ellos (Huber, Camerer, Imai, Dreber y Johannesson). 2.5. Análisis de ocurrencia de términos Para la ocurrencia de términos se han extraído las del campo “Author Keywords” de los artículos y se ha realizado una figura que consiste en una nube que presenta las palabras de un texto en diferentes tamaños según su aparición. La figura se ha hecho con la librería “wordcloud” del software RStudio. 17 Figura 7: Nube de palabras (1990- 2021) Las palabras más utilizadas eran economic, experiments, results, experimental… coincidiendo con los términos que se habían empleado en la ecuación de búsqueda y que, por lo tanto, hemos descartado. Los términos más destacados son “analysis”, “energy”, “social”… 18 Figura 8: Nube de palabras por periodos Con el objetivo de estudiar si ha habido un cambio en las corrientes temáticas a lo largo de los años se puede analizar esta ocurrencia de palabras en diferentes periodos de tiempo. En la figura 8 aparece a la izquierda la nube de palabras correspondiente a los artículos de los años 1990 hasta el 2000, y a la derecha, desde 2016 hasta 2021. En el primer periodo, las palabras más repetidas son “design”, “simulation” y “development”. En los últimos años han aumentado las ocurrencias en términos como “water” o “solar”. 3. Conclusiones Desde el 90 han aumentado exponencialmente los artículos de Economía Experimental (EE) en las más prestigiosas revistas de Economía, lo que nos lleva a pensar que la EE se encuentra en la actualidad en un buen momento. Con el objetivo de hacer una estimación de la producción en los próximos años se han ajustado los datos a una curva exponencial, con un 𝑅2= 0.94, con la que se prevé un crecimiento más pronunciado en los próximos años. La bibliometría se ha convertido en una de las técnicas de estudios más fiables e imparciales para sintetizar un gran volumen de literatura. En este trabajo llevamos a cabo la revisión sistemática de la producción científica sobre EE en las bases de datos de Web of Science Core Collection y Scopus. 19 Entre otros datos interesantes del estudio, se observa que los países con más investigaciones en la materia son USA, Reino Unido y Alemania; y que las instituciones con mas aportaciones, y proporcionalmente con más citas por trabajo, son el Instituto de Tecnología de California y la Universidad de Londres. En cuanto a la productividad de los autores, destaca Urs Fischbacher, cuyo artículo “z-Tree: Zurich toolbox for ready-made economic experiments” ha sido citado más de 4000 veces en la base de datos WoS. Los términos que más aparecen en la bibliografía, excluyendo los usados en la ecuación de búsqueda, son “analysis”, “energy” o “social”. En los artículos correspondientes a los últimos años han aumentado las ocurrencias en palabras como “water” o “solar”. 20 4. 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