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"Behaviour and Beliefs in Long-Distance Interactive Online Experiments between Moscow, Tomsk and Samara" -- laboratory's recent presentation on XX April International Academic Conference at Higher School of Economics

The Senior Research Fellow of the Laboratory Heike Hennig-Schmidt presented the results of the experiment "Behaviour and beliefs in long-distance interactive online experiments between Moscow, Tomsk and Samara “ during XX April International Academic Conference On Economic and Social Development

Extended Abstract. 

Authors: Alexis Belianin, Gregory Chernov, Heike Hennig-Schmidt, Olga Kuznetsova, Marina Ryzhkova, Gari Walkowitz

Our study analyzes how preferences on cooperation are distributed over three regionally distinct subject pools in Russia: Moscow (Central region), Samara (Volga region) and Tomsk (Western Siberia).  Russia is a large and diverse country, and not much is known about interregional differences, especially those reflected in behaviour. Idiosyncratic social norms related to fairness, equity, reciprocity or competition may exist within a region that would differ from those in another region and may predetermine interregional economic and civic relations, and the development of the respective social institutions.

Why to expect behavioural differences? Local norms can differ because people acquire ideas, beliefs and preferences from observation and interaction with other members of their own social group. Learning from peers can lead to stable social norms because people socially learn what is undesirable or even gets punished in their own environment. These arguments have been put forward and have been supported in between-country studies (e.g. Bornhorst et al. 2010, Boyd and Richerson 1985, 2004;Henrich et al., 2001; Boyd et al., 2003, Herrmann et al., 2008, Gächter and Herrmann 2011, Goerg et al. 2016, Lönnqvist et al. 2015, Richerson et al. 2016), in within-country analyses (e.g., Chmura et al. 2016, China; Michailidou and Rotondi, 2018, Italy; Zhang 2015, 2018, Italy) as well as in within-city contexts (e.g., Falk and Zehnder 2013, Zurich, Switzerland, Bigoni et al. 2018, Bolgna, Italy; Bogliacino et al. 2018, Bogota, Columbia; Karaja and Rubin 2017, small village in Romania; Lei and Vesely 2010, Hong Kong).

Trust and cooperation is important social capital as it has been shown to be associated with stronger economic performance (Knack, Keefer 1997), and to increase allocative efficiency by mitigating monitoring costs and contract enforcement problems (Herrmann et al. 2008). Also, larger behavioural differences actually exist within countries compared to differences between countries (Vieider, 2015; l’Haridon et al. 2017, Falk et al 2018). With respect to Russia, these findings suggest that sustainable development of the Russian economy and society can be ensured by stabilizing and fostering cooperation and trust among Russian citizens, developing institutions supportive to these factors, and improving the understanding the behaviour of fellow citizens and foreigners. Evidence from Russian survey data (georating 2012) suggests heterogeneous levels of general trust between the three cities of our experiment, as well as of general and local solidarity and the intention of joint problem-solving.

As workhorse for our behavioral experiment we use the Ultimatum Game in a 3x3 between-subject design with 302 participants. Subjects interact online in real time with either subjects from their own subject pool, or with participants of each of the other two subject pools. 

Our research questions relate to (1) Heterogeneity in the levels of offers and Minimal Acceptable Offers (MAO) across cities; (2) Familiarity with own subject pool in that standards of offers and MAOs exist within the cities such that the distributions of Senders’ offers/Responders’ expectations on offers as well as Responders’ MAOs and Senders’ beliefs about MAOs do not differ ; (3) Alignment of actions and beliefs across subject pools such that beliefs on counterparts’ behaviour in other cities match actual behaviour in those cities.

We found more heterogeneity across cities with regard to Responders‘ MAO and Senders‘ expectations than in Senders’ offers and Responders’ expectations. In particular, Senders’ offers in Samara are higher than those in other cities as are Responders’ expectations on offers in Samara. Comparing Senders’ offers and Responders’ expectations show a rather good calibration in all treatments. As to MAO, levels differ between all three cities. Moreover, Muscovite Responders demand from their fellow-city Senders significantly lower amounts than they minimally demand from the other cities. Within their own subject pools, Senders’ anticipations and actual MAOs are well calibrated in Samara and Tomsk. Across subject pools, Senders’ anticipations and actual MAOs are well aligned, except for Moscow.