Experimental economics made in Italy

Research in Economics 63 (2009) 213–215

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Editorial

Experimental economics made in Italy

On September 8–9, 2008 the VI LabSi International Conference entitled ‘‘Strategic Decision Making in Politics and Economics. Experiments, Theory, and Empirical Studies’’ was held in Salerno.1 Since 2002, with the first meeting in Siena, LabSi conferences have become a important point of reference for the network of Italian experimental economists, and their foreign links. As a matter of fact, almost a half of the 50 or so participants were non Italian scholars, not counting the keynote speakers of this edition: Tim Cason (Purdue University), Itzhak Gilboa (Tel Aviv University), John Kagel (Ohio State University) and Fernando Vega-Redondo (European University Institute).2 This volume presents a selection of the experimental papers presented at Labsi 2008, and provides an overview of the main themes dealt with at Conference (and also, to some extent, the main research interests of Italian Experimental Economists as a whole). Active research in Experimental Economics in Italy dates back to 1991, when Massimo Egidi founded the first Italian experimental lab, the Computable and Experimental Economics Lab (CEEL), in Trento.3 As they say, the rest is history, and we can now count ten additional fully functioning labs disseminated all over the country (here listed in strict alphabetical order): 1. 2. 3. 4. 5. 6. 7. 8. 9. 10.

Al.ex Laboratorio di Economia Sperimentale, Università di Alessandria. BeeLab Behavioural and Experimental Economics Lab, Università di Firenze. Center for Cognitive Economics, Università del Piemonte Orientale. Centro di Economia Sperimentale A Roma Est (CESARE), LUISS Guido Carli, Rome. Laboratorio di Economia Sperimentale (EELAB), Università di Milano Bicocca. Lab2, II Università di Napoli. Laboratorio di Economia Sperimentale (LES), Università di Bologna. Laboratorio di Economia Sperimentale, Università dell’Insubria. Laboratorio di Economia Sperimentale, Università di Salerno. LabSi Experimental Economics Laboratory, Università di Siena.4

1. Volume content We proudly open this volume with the contribution of one of the invited speakers of LabSi 08. John Kagel, acclaimed co-author (with Al Roth) of the first Handbook of Experimental Economics (Kagel and Roth, 1995), presents here a paper (joint with David Cooper), which follows up the path-breaking Cooper and Kagel’s (2005) AER paper on team playing in signaling games, where it was established a significantly better performance of (two player) teams, compared to that of individuals. The intriguing question raised by this literature is whether team’s observed better performance is due to (a) more sophisticated strategic thinking or (b) superior adaptive learning. In this respect, the paper provides strong experimental

1 Organizing Board: Giuseppina Autiero, Alessandro Innocenti, Annamaria Nese, Niall O’Higgins, Giovanni Ponti, Patrizia Sbriglia and Luigi Senatore. 2 For more information on past Labsi Meetings, please visit the website of the Centro Interuniversitario per l’Economia Sperimentale: http://www.economiasperimentale.it. 3 Trento is also the sole Italian University which hosts, within its Doctoral School in Economics and Management (CIFREM), two specific curricula built upon the Behavioral and Experimental Methods: i) Behavioral Economics and ii) Behavioral Management and Organization. 4 This list does not include research centers outside the country that do experimental research -so to speak- with a strong Italian accent, such as the Laboratory of Theoretical and Experimental Economics (LaTEx), at the Universidad de Alicante. 1090-9443/$ – see front matter © 2009 Published by Elsevier Ltd on behalf of University of Venice. doi:10.1016/j.rie.2009.10.002

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Editorial / Research in Economics 63 (2009) 213–215

evidence in favor of the latter, in that – just like for individuals, as well documented in several experiments – also teams fail to apply even the standard equilibrium refinements, including Cho and Kreps’ (1987) intuitive criteria and single round deletion of dominated strategies. This empirical evidence is reinforced by the extensive report of the actual communication exchanges between team members, made possible by a specifically designed chat interface. In that the authors warn that, when looking at experimental data, (learning) dynamic aspects need to be carefully considered: ‘‘to the extent that these [equilibrium refinements] do not predict well even under a ‘‘best case’’ scenario (teams rapidly develop strategic play and understand the underlying concepts well enough to generate strong positive transfer in related games), it implies that one must rely on learning models, and past empirical research with these models, to sort out between different equilibria . . . ’’. As if the focus on learning, one of the leading themes of theoretical and experimental research in the ’90s, had been abandoned -so-to-speak- a bit too early, considering our understanding on this matters. Signaling is also the key issue of Ponti and Carbone’s (2009) paper, which looks at the Chinos Game – a stereotypical framework of social learning – allowing for the possibility that subjects (although not necessarily all of them) deviate from profit maximizing behavior moved by distributional concerns. In the Chinos Game, players move sequentially and have to guess the total number of (privately known) coins hidden in everybody’s hands. Every correct guess is rewarded by fixed prize. Since players do not compete with each others – just like in typical social learning models – the unique equilibrium prediction implies players maximizing their winning probability (and, by doing so, perfectly revealing their private signal). Things change dramatically if players are also moved by relative comparisons (i.e. they hold social preferences). In this case, information shading may be justified as rational (equilibrium) response. This is because, if first movers are moved by envy – due to their disadvantageous position in the sequence – they may be willing to shade (at least, partially) their signal, with the aim of reducing late movers’ winning probability, even if this implies a reduction of their own expected payoff. This theoretical conjecture is validated empirically by way on an experiment specifically designed to measure subjects’ distributional concerns. By looking at the data, we see that subjects’ average behavior is better approximated (and best, for some experimental groups) by a structural model which includes the existence of distributional motives. Consolandi et al. (2009) reports on an experiment which tries to assess the role of ethical preferences in portfolio decisions. In the experiment, subjects have to choose repeatedly among the same four lotteries (framed as ‘‘stocks’’, within a portfolio choice problem), with fixed variance and increasing expected returns. In 3 out of 5 market periods, each one of the 3 assets of intermediate return (i.e. neither the highest, nor the lowest) are made artificially salient, by means of its inclusion in a purely fictitious Corporate Social Responsibility (CSR hereafter) index. Although the inclusion in the index has no effect on the return profile of the lottery, they remarkably find that subjects’ behavior seems to be sensitive on whether the lottery is made salient by its inclusion in the index. Specifically, the inclusion of a stock in the ethical index increased its share of about 30% in average and its exclusion decreased its share about the same magnitude. That the presence of opportunities to punish free riding behavior encourages cooperation in experimental public goods games is a well established result in the literature (Fehr and Gächter, 2000, 2002). What are less clear are the motives underlying the application of such sanctions. Specifically, why should individuals accept a reduction in their own utilities in order to reach a Pareto improving collective outcome? Nese and Sbriglia (2009) consider experiments aimed at throwing light on the processes underlying such behavior. The paper contains several innovative aspects, but above-all, in contrast to other papers on endogenous social norms which adopt a two-way choice (sanction/no sanction or reward/no reward), here the authors allow two alternatives to the basic public good game. This allows them to distinguish between the reaction of individuals to free-riding behavior through the application of sanctions or through a regime which avoids such free riding behavior but without the individual bearing the costs of sanctions, by paying rewarding participants according to their individual contributions. They find that, high contributors tend to react to free-riding by voting for a proportional reward system rather than one with costly sanctions, however, over the course of the repeated game, individuals chose the sanctions regime with increasing frequency. Moreover, when the sanctions regime was expected to prevail, contributions increased significantly. Finally, Nese and Sbriglia (2009) employed a survey to elicit the ethical attitudes of individuals towards cooperation, sanctions and free-riding behavior finding that the more cooperatively minded individuals tended to contribute more and progressively switched to the support of a sanctions regime in later rounds of the game. The subsequent contribution, from Farina et al. (2009) also uses attitudinal surveys to explain individual behavior in the laboratory. Indeed, here the focus is precisely on the relation between attitudes and behavior in experimental trust and dictator games. By distinguishing between individual attitudinal types – the trusting and the prudent, and the trustworthy and the untrustworthy respectively – the authors are able to throw further light on the motivations underlying behavioral trust and reciprocity observed in the laboratory. In particular, they find that a moderate association between attitudinal and behavioral trust similarly to Fehr et al. (2003) and in contrast to the seminal study of Glaeser et al. (2000). The distinction between individuals of differing attitudinal types allows the separate identification of strategic and otherregarding motivations which operate differently across differing attitudinal types. Finally, the disposition to trust on the one side, and the degree of compliance with civic values on the other, appear to independently motivate people. This finding does not match with experimental research indicating that ‘‘once it can be shown that it is reasonable to expect trustworthiness there is no longer any mystery about trust, since trust is typically a best reply to this expectation’’ (Bacharach et al., 2001, pp. 1–2). Indeed, individuals who self-report as ‘‘trustworthy’’ cannot be taken as necessarily endowed with a high disposition to trust. Rather, the findings support the ‘‘multiple self’’ view of individuals’ personality put forward by Elster (1986).

Editorial / Research in Economics 63 (2009) 213–215

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The final paper included in this volume, by Botti et al. (2009) compares behavior in the field with that in the more traditional laboratory setting in order to assess whether the behavior of subjects is consistent in the two settings. The authors implement an experiment to mimic (with experimental rewards and subjects) the rules of a well-known Italian TV quiz Affari Tuoi (the national version of the international format Deal or No Deal) under two different settings: a traditional lab setting; and, a framed lab, in which the experiment was replicated in the Italian public television studio where the show was actually recorded and in the presence of an audience. They do not find any significant evidence of framing effects: students behave in a similar way in the two lab settings, responding essentially to incentives. Comparing the risk attitudes shown by experimental subjects in the two lab treatments with those exhibited by the contestants in the field, they find that contestants in the TV show are generally more risk averse than students in the lab. Last, but not least, Bigoni (in press) takes seriously Cooper and Kagel’s (2009) call for further research on (bounded rationality driven) learning models, and reports on an experiment specifically designed to look at firms’ behavior and market dynamics when information about the market structure and opponents’ behavior is difficult to acquire and process. Her results suggest that belief learning seems to be the key element, but she also find that subjects tend to imitate their opponents’ most successful behavior (just like in Fernando Vega-Redondo’s (1997) Econometrica paper, another invited speaker of Labsi 08) driving towards a more competitive outcome.5 We would like to thank all the conference participants, the University of Salerno who, in addition to the site, provided financial and administrative support to the Conference, and last but most important, the numerous support staff, above-all Antonia Gregorio, Francesco loMagistro and Max D’Amico all of whom were instrumental in making the conference such a success. And, with that, it only remains for us to wish you a pleasant and, we hope, informative read. References Bacharach, M., Guerra, G., Zizzo, D.J., 2001. Is Trust Self-Fulfilling? An Experimental Study. Mimeo. Bigoni, M., 2009. What do you want to know? Information acquisition and learning in Cournot games. Research in Economics (in press). Botti, F., Conte, A., Di Cagno, D., D’Ippoliti, C., 2009. Lab and framed lab versus natural experiments: Evidence from a risky choice experiment. Research in Economics 63 (4), 282–295. Cho, I.K., Kreps, D.M., 1987. Signaling games and stable equilibria. Quarterly Journal of Economics 102 (2), 179–221. Consolandi, C., Innocenti, A., Vercelli, A., 2009. CSR, rationality and the ethical preferences of investors in a laboratory experiment. Research in Economics 63 (4), 242–252. Cooper, D.J., Kagel, J.H., 2005. Are two heads better than one? Team versus individual play in signaling games. American Economic Review 95, 477–509. Cooper, D.J., Kagel, J.H., 2009. Equilibrium selection in signaling games with teams: Forward induction or faster adaptive learning? Research in Economics 63 (4), 216–224. Elster, J., 1986. Introduction. In: Elster, J. (Ed.), The Multiple Self. Cambridge University Press, Cambridge, UK. Farina, F., O’Higgins, N., Sbriglia, P., 2009. Suit the action to the word, the word to the action: Eliciting motives for trust and reciprocity by attitudinal and behavioural measures. Research in Economics 63 (4), 253–265. Fehr, E., Fischbacher, U., Von Schupp, B., Wagner, G.G., 2003. A nation-wide laboratory. Examining trust and trustworthiness by integrating behavioral experiments into representative surveys. Cesifo Working Paper n. 866. Fehr, E., Gächter, S., 2000. Cooperation and punishment in public goods experiments. American Economic Review 90 (4), 980–994. Fehr, E., Gächter, S., 2002. Altruistic punishment in humans. Nature 415, 137–140. Glaeser, E.L., Laibson, D., Scheinkman, J.A., Soutter, C.L., 2000. Measuring trust. Quarterly Journal of Economics 65, 811–846. Kagel, J.H., Roth, A.E. (Eds.), 1995. Handbook of Experimental Economics. Princeton University Press, Princeton, NJ. Nese, A., Sbriglia, P., 2009. Social norms in repeated public good games. Research in Economics 63 (4), 266–281. Ponti, G., Carbone, E., 2009. Positional learning with noise. Research in Economics 63 (4), 225–241. Vega-Redondo, F., 1997. The evolution of Walrasian behavior. Econometrica 65 (2), 375–384.

Niall O’Higgins Università di Salerno, Italy Giovanni Ponti Universidad de Alicante, Spain Università di Ferrara, Italy

5 Although included in this Special Issue, due to space constraints, this paper will be published in the next issue of Research in Economics.