May Attallah

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May Attallah ANNÉE 2017 THÈSE / UNIVERSITÉ DE RENNES 1 sous le sceau de l’Université Bretagne Loire pour le grade de DOCTEUR DE L’UNIVERSITÉ DE RENNES 1 Mention : Sciences économiques Ecole doctorale Sciences Économiques et sciences De Gestion (EDGE) May Attallah Préparée à l’unité de recherche CREM (UMR 6211 CNRS) Centre de Recherche en Économie et Management Faculté des Sciences Économiques Thèse soutenue à Rennes Strategies of le 13 Octobre 2017 Information devant le jury composé de : Guiseppe ATTANASI Acquisition Under Professeur, Université de Lille 1 Rapporteur Uncertainty Kirsten ROHDE Professeur, Erasmus University Rotterdam Rapporteur Laurent DENANT-BOEMONT Professeur, Université de Rennes 1 Examinateur Fabrice LE LEC Maître de conférences, Université Paris 1 Panthéon-Sorbonne Examinateur Brice MAGDALOU Professeur, Université de Montpellier 1 Examinateur Olivier L’Haridon Professeur, Université de Rennes 1 Directeur de thèse This Ph.D. thesis should not be reported as representing the views of University of Rennes 1. The views expressed are those of the author and do not necessarily reflect those of the University. L’Université de Rennes 1 n’entend donner aucune approbation ni improbation aux opin- ions émises dans cette thèse. Ces opinions doivent être considérées comme propres à leur auteur. Acknowledgements During my PhD years, I have been supported by many people, I would like to express my gratitude to all of those who supported me through this adventure. First of all, I am deeply indebted to my supervisor Olivier L’Haridon. I would like to express my thanks to him for his support, his guidance and insights that have been essential to my dissertation and my career. I am proud to have written my thesis under his supervision. I would like also to thank Guiseppe Attanasi, Laurent Denant- Boemont, Fabrice Le Lec, Brice Magdalou and Kirsten Rohde for being part of my thesis committee. I would like to thank all the researchers of the CREM who helped me with their comments and discussions about my work. I would like to thank all the administrative staff of the CREM and of the University of Rennes 1. I have a special thought for Anne L’Azou and Cécile Madoulet. I would also like to thank my co-author Isabelle Vialle for her fruitful discussions and Elven Priour for teaching me Z-tree and helping me in conducting the experiments. Of course, I cannot forget ALL the PhD students who made the working atmosphere so pleasant and productive. Special thanks go to my officemates Guillaume Beaurain and Romain Gaté. I always enjoyed our teamwork (solidarité de bureau!), many stimu- lating and inspiring discussions as well as celebrating successes and coping with failures together. After sharing The office 108 for such a long time, they have become a family to me and I am deeply indebted to them. I would also like to thank Ewen Gallic and Xuan Tran and Vincent Malardé for always being helpful and fun to be around, making the last three years a very special time in my life. I also would like to thank my friend Nourhane who has always been there for me and supporting me till the end. Last, but definitely not least, I would like to thank my parents Atef and Seham and my sister Sally for unconditionally supporting all my pursuits. I owe a lot to them. iv v Résumé en français Stratégies d’acquisition d’information dans l’incertain La méthode expérimentale a été pendant longtemps l’instrument d’observation em- pirique privilégié des travaux d’économie comportementale. Cette méthode consiste à créer un environnement contrôlé afin de réaliser artificiellement une situation reflétant les conditions d’un comportement économique. Comme toute méthode expérimentale, elle se fonde sur la formulation d’hypothèses, l’observation empirique par l’expérience et l’interprétation des résultats. Elle est désormais devenue un élément à part entière de la boîte à outils de l’économiste. A ce titre elle peut être mobilisée pour évaluer des politiques publiques, notamment concernant le marché du travail. L’avantage premier de l’économie expérimentale repose sur ses capacités de contrôle de l’observation em- pirique : contrôle de l’assignation aléatoire des participants, contrôle de la nature des institutions, contrôle de la spécification des variables exogènes et contrôle des processus de détermination des variables endogènes. L’économie expérimentale et comportementale est une clé permettant de progresser notre compréhension des préférences individuelles, qui jouent un rôle primordial dans la prise de décision économique. La science économique traditionnelle repose sur les hypothèses de l’homo œconomicus, ou l’homme économique, un sujet économique ra- tionnel et purement intéressé. Cependant, il existe de plus en plus d’évidences provenant de différentes disciplines telles que la psychologie, la sociologie ou l’économie qui révè- lent les limites des modèles avec de telles hypothèses. De nombreuses études expéri- mentales en économie révèlent que les décideurs n’agissent pas comme prédit par les modèles de l’homo œconomicus. Ils échouent souvent à maximiser leur propre profit parce que les choix humains ne sont pas seulement motivés par l’intérêt matériel, mais aussi par des motifs tels que l’altruisme, la réciprocité et l’aversion à l’inégalité. Ces motifs sont appelés “préférences sociales". Outre la dimension des préférences so- ciales, il existe deux autres dimensions au sein des préférences individuelles qui ont un impact sur la prise de décision: les préférences face au risque et les préférences vi temporelles. La plupart des décisions économiques majeures sont prises dans des con- ditions d’incertitude et affectent le futur ainsi que le présent (par exemple, le problème classique d’allocation de consommation/épargne). Les décisions optimales dépendent donc des attitudes vis-à-vis du risque ainsi que des préférences temporelles. Le premier chapitre1 aborde ce sujet. Conformément à la critique de Henrich et al. (2010b), la majorité des données et des recherches en sciences comportementales est basée sur des échantillons tirés de populations occidentales, formées, industrialisées, riches et démocratiques (en anglais l’acronyme WEIRD-bizarre- correspond à Western, Educated, Industrialized, Rich, and Democratic). Néanmoins, malgré le caractère re- streint de leurs échantillons, les chercheurs tirent souvent des conclusions concernant les préférences humaines en général. Dans ce chapitre, je propose une synthèse et un point de vue sur la représentativité des résultats issus des expériences de laboratoire et de terrain qui visent à expliquer les préférences sociales, les préférences concernant le risque et le temps. Il existe effectivement des différences incontestables entre les popu- lations d’étudiants et d’autres populations plus générales. En comparant les étudiants avec des échantillons représentatifs des adultes, je remar- que que les étudiants constituent en quelque sorte une limite inférieure de la pro-socialité dans les mesures expérimentales de confiance, d’équité ou de coopération. Dans les ex- périences testant la propension à coopérer, il existe deux types de variabilité dans les résultats : une variabilité inter-pays et intra-pays. Il existe une relation inverse non parfaite entre les normes de coopération et le niveau de développement d’un pays. En outre, les facteurs sociodémographiques (par exemple : âge, urbanisation, statut étudi- ant) déterminent le comportement de coopération au sein d’un même pays. Donc, les étudiants se comportent différemment des autres participants et les résultats expérimen- taux basés sur un échantillon d’étudiants ne sont pas souvent généralisables. De même, pour les étudiants et les sujets issus des populations WEIRD, la relation entre confiance et réciprocité est positive et forte, pourtant elle est inexistante pour les adultes non étudi- ants et les sujets issus des populations non-WEIRD. En outre, les expériences mesurant 1Publié dans L’Actualité Économique (2016) vii l’équité et l’altruisme confirment que les étudiants sont un échantillon spécial. La dif- férence entre l’offre moyenne du jeu de l’ultimatum et de celui du dictateur dans les pays en voie de développement est bien plus faible que celle des étudiants. Nous con- statons donc que les résultats des étudiants sont souvent atypiques en regard des résultats sur des populations plus générales. Par ailleurs, nous avons vu que les étudiants avaient également un comportement beaucoup plus homogène dans ces jeux que les participants en population générale. La généralisation des résultats issus des études expérimentales est donc sensible, non seulement car les comportements sont potentiellement différents, mais également plus diversifiés en population générale. La comparaison des mesures des attitudes vis-à-vis du risque et du temps entre les populations d’étudiants et les autres populations est plus délicate puisqu’il y a des dif- férences de méthodologie, de protocole et de contexte entre les travaux. D’une part, les attitudes sont très hétérogènes, même au sein d’une population supposée homogène, comme celle des étudiants. D’autre part, les mesures réalisées des attitudes vis-à- vis du risque ou de l’impatience reposent souvent sur des méthodes très différentes les unes des autres. En plus de l’hétérogénéité individuelle et de l’hétérogénéité des populations d’origine, ces différences de méthodes de mesure créent un troisième type d’hétérogénéité, méthodologique, rendant difficile toute comparaison. Dans ce domaine également, cette analyse montre qu’une forte hétérogénéité est la règle, et de ce fait, il est nettement plus difficile de conclure à une spécificité des étudiants ou de toute autre population particulière pour ce qui concerne les attitudes face au risque ou les préférences temporelles. Les chapitres suivants se focalisent sur les attitudes vis-à-vis du risque car elles con- stituent des déterminants importants de la prise de décision individuelle. La distinction entre risque et incertitude est donc importante. Celle-ci a été proposé pour la première fois par Knight (1921) dans son livre “Risk, Uncertainty and Profit".
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