Social Status and Economic Behavior

Home , Social status

QUEENSLAND UNIVERSITY OF TECHNOLOGY

PhD Thesis

IF49: Doctor of Philosophy (Economics)

SOCIAL STATUS AND ECONOMIC BEHAVIOR

Candidate: Gevorg Ordyan

Principal supervisor: Professor Lionel Page

Associate supervisor: Dr Sébastien Massoni

2018

Acknowledgments

First I would like to thank my primary supervisor Prof Lionel Page for offering me the opportunity to conduct this PhD research. I was given a considerable freedom in pursuing my research interests, and at the same time directed wisely through many complications whenever I was stuck in confusing realities of behavioral science.

Next I would like to thank my associate supervisor Dr Sebastien Massoni for active day-to-day help in conducting my experiments, many insightful discussions and helping me to keep on track when problems started to feel unsolvable.

Also I wish to thank Harriet Smith and Gaurav Gogoi for their excellent research assistance in conducting my experiments, which take a lot of time and energy.

In addition I wish to thank the QUT Business School for providing all the necessary support, research funding and the strong scientific atmosphere, which are essential for doing a PhD.

And of course I would like to thank all of my friends at QUT, who also contributed a lot to my work.

Abstract

We have conducted three experimental studies investigating the impact of social comparison on economic behavior. Our first study is focused on risk-attitudes in social context. Outcome-based social preference models define the utility function as depending on income of peers. This predicts that choices over risky prospects should also depend on incomes of people with whom we compare ourselves. We test a particular model of contextual evaluation called range-frequency theory which predicts that social risk-attitudes should depend on one’s relative position between top and bottom members of the comparison group. We find a small decrease of risk- aversion when subjects have a low rank position within the comparison group, but changing one’s position between top and bottom members didn’t change risk- attitudes.

In our second study we try to understand the role of internal status hierarchy when a small group makes collective risky decisions. Theories and experiments on risk and uncertainty are traditionally focused on individual choices. In real life however risky decisions are often made by small teams or committees, and there is very little research trying to understand the process of conflict resolution when team members have different risk-attitudes. Inspired by related research from social psychology, we hypothesized that internal status hierarchy is a key driver of group dynamics. We found that low status male participants were less willing to change their opinion during the collective deliberation process. As a result the collective decisions over risky prospects were slightly shifted towards preferences of low status males.

In our third study we analyze the impact of multi-dimensional social status on bargaining. It has been long recognized in social science that human relations are shaped by their relative statuses based on several socially important hierarchies. It has also been hypothesized that people may overvalue the social dimension on which they hold a high status, and this might lead to conflicts in social relations. We

4 conducted an ultimatum bargaining experiment where players had different status positions on two very different dimensions. We tried to test if such an interaction results in conflict. Our result suggests that people are more likely to go for a compromise when faced with an opponent with opposite set of status positions.

Keywords

Social status, social decision theory, risk-attitudes, peer effects, risky investment, group decision making, ultimatum game, laboratory experiment, status hierarchy, status symbols

Statement for Original Authorship

The work contained in this thesis has not been previously submitted to meet requirements for an award at this or any other higher education institution.

To the best of my knowledge and belief, the thesis contains no material previously published or written by another person except where due reference is made.

Date: July 2018

QUT Verified Signature Signature:

Contents 1. Introduction and literature review ...... 14

6 1.1 Terminology: definitions of “social status” from sociology and social psychology ...... 15 1.2 Empirical research on status: sociology and social psychology ...... 19 1.2.1 Studies on macro-level ...... 19 1.2.2 Studies on micro level ...... 20 1.2.3 Signaling status: conspicuous consumption ...... 22 1.3 Insights from neuro-economics ...... 25 1.4 Insights from evolutionary biology ...... 27 1.4.1 Possible evolutionary origins of status-seeking behavior in humans 28 1.5 Modelling social status in economics ...... 29 1.6 Research questions addressed in this thesis ...... 31 2 Risk in social context: the impact of range position ...... 39 2.1 Introduction ...... 39 2.2 Testing range-frequency theory ...... 45 2.2 Experiment design ...... 46 2.3 Hypothesis development ...... 51 2.4 Participants and procedures ...... 53 2.5 Results and discussion ...... 54 2.5.1 Secondary results: gender effect ...... 59 2.5.2 Alternative models of social utility ...... 63 2.7 Conclusion ...... 67 3 Collective risky decisions: the role of status hierarchy ...... 69 3.1 Introduction ...... 69 3.2 The experiment ...... 74 3.3 Hypothesis development ...... 77 3.4 Participants and procedures ...... 81 3.5 Results and discussion ...... 81 3.5.1 Groups ...... 81

3.5.2 Lottery choices ...... 82 3.5.3 Bargaining behavior on individual level: the impact of status ...... 83 3.5.4 Bargaining behavior depending on group type ...... 87 3.5.5 Status hierarchy and group dynamics ...... 89 3.6 Conclusion ...... 92 4 Bargaining with status-inconsistency...... 94 4.1 Introduction ...... 94 4.2 The experiment ...... 100 4.3 Hypothesis development ...... 105 4.4 Participants and procedures ...... 107 4.5 Results and discussion ...... 108 4.5.1 Status groups ...... 108 4.5.2 Ultimatum game rejections in double-status interactions...... 116 4.6 Conclusion ...... 118 5. General conclusion ...... 121 5.1 Summary ...... 121 5.2 General limitations ...... 127 5.3 Design limitations and directions for future research ...... 131 5.4 Policy implications ...... 135 Appendix A: Supplement to Chapter 2 ...... 137 A1: Experiment instructions ...... 137 Appendix B: Supplement to Chapter 3 ...... 142 B1: Experiment instructions ...... 142 B2: General knowledge quiz ...... 146 Appendix C: Supplement to Chapter 4 ...... 151 C1: Experiment instruction ...... 151 References ...... 164

List of Tables

Table 2.1: Endowments across 15 rounds in each of three treatments.

Table. 2.2: Summary of the experiment design

Table 2.3: Within-subject risky investment differences between treatments, combined for all 15 rounds.

Table 2.4: Within-subject differences in investments (in percentage) between treatments SOCIAL 1 LOW RANGE and INDIVIDUAL.

Table 2.5: Within-subject differences in investments (in percentage) between treatments SOCIAL 2 HIGH RANGE and INDIVIDUAL.

Table 2.6: Within-subject differences in investments (in percentage) between treatments SOCIAL 1 LOW RANGE and SOCIAL 2 HIGH RANGE.

Table 2.7: Male subjects: treatment differences in risky investments, combined for all 15 rounds.

Table 2.8: Female subjects: treatment differences in risky investments, combined for all 15 rounds.

Table 2.9: Pairwise correlations of risky invesments between three treatments (average investment of 15 decisions in each treatment is taken for each subject). Participants are consistent in their risk-attitudes across three treatments

Table 2.10: Treatment SOCIAL 1 LOW RANGE: growing monetary distances between the decision-maker and group average, across 15 rounds

Table 2.11: Distributions of risky investments across 15 rounds: treatment SOCIAL 1 LOW RANGE

Table 2.12: Treatment SOCIAL 1 LOW RANGE: growing monetary distances between the decision-maker and top member, across 15 rounds.

Table 3.1: Lotteries in each round: safe lotteries pay $3.5 to $5 with high chances, risky lotteries pay $8 to $10 with small chances.

Table 3.2: Summary of the experiment design

Table 3.2: Numbers of groups still bargaining at each step

Table 3.3: Frequencies of changing the initial opinion (by comparing the first and last choices), for each gender and status group.

Table 3.4: “Volatility of opinion” for each subject, in each status and gender group. Group means and standard errors are shown

Table 3.5: Pairwise differences in "Volatility of opinion" between Male_LOW_STATUS group and three other groups (1-sided t-tests)

Table 3.6: Frequency of difference between initial and last choices, depending on group type and status

Table 3.7: Percentages of groups having disagreements at each step (of those groups which initially had a disagreement)

Table 4.1: Four stages of the experiment

Table 4.2: Summary of the experiment design

Table 4.2: Numbers of subjects across status groups.

Table 4.3: Difference in proposals: High-to-Low vs Low-to-High interactions. Table 4.4: Distributions of offered candies in single-dimensional status interactions:

Table 4.5: Difference in proposals: DoubleHigh-to-DoubleLow vs DoubleLow-to- DoubleHigh interactions. Table 4.6: Distributions of offered candies in double- status interactions

Table 4.7: Distributions of offered candies in status-inconsistent interactions

Table 4.8: Proposals in status-inconsistent interactions compared to DoubleHigh vs DoubleLow interactions Table 4.9: Frequencies of proposals and rejection rates in double-status interactions. Table 5.1: Literature review: different methods used to impose status in laboratory experiments.

Table A2: Risky investments in INDIVIDUAL treatment (each subject’s average in 15 rounds) depending on age, gender, country of origin and time spent on the decision.

Table C2: Distribution of subjects across four status groups Table C4: Table C4: Ultimatum game offers in stage 2 are not correlated with age, gender or country of origin Table C5: Ultimatum game offers in stage 4 are not correlated with age, gender or country of origin

List of Figures

Figure 2.1: Screenshot of the experiment: treatment INDIVIDUAL

Figure 2.2: Screenshot of the experiment: treatments SOCIAL 1 and SOCIAL 2

Figure 4.1: Visual reactions task

Figure C2: Distribution of subjects across four status groups

Chapter 1

1. Introduction and literature review

“Man is by nature a social animal”

Aristotle

Human sensitivity to social comparison has long been recognized as an important determinant of behavior. All intellectual traditions in the area of social science give a key importance to issues such as “social justice”, “fairness” or “equality”. Plato’s “Republic” for instance develops the concept of “justice” and regards it as key to understand social processes. Later major works related to issues of social order and well-being, such as Thomas More’s “Utopia” or Karl Marx’s “Capital” give a central role to ideas of social justice and equality. Modern disciplines such as sociology, social psychology, economics or political science each develop their own theoretical and methodological approaches, and all of them have a strong focus on the issues of social comparison, social status and their impact on behavior. Issues such as equality of rights, equality of opportunities or income have always been and are a central topic in political debate. Academic studies focusing on these issues always find a strong resonance within wide circles of society. Modern success of Piketty’s “Capital in the 21st Century” is a good example showing that issues of social comparison and inequality never become irrelevant, even as the society as a whole gets richer.

Issues about social comparison or social status are very important and interesting questions discussed in all branches of behavioural science. All social

14 science disciplines focus heavily on why we care about social comparison, why we make these comparisons, how these social comparisons affect our behavior, what are the best ways of theorizing about and describing our social attitudes. These questions guide the three studies of this thesis. Using lab experiments we try to better understand how social comparison affects economic decisions, to fill certain gaps in related economic literature. Before formulating main research questions addressed in this thesis, we will give a comprehensive short review of relevant literature on this topic, focusing on definition of the term “social status” itself, and empirical findings related to this concept. This will enable to better formulate the research questions and their relevance for economics, and to do this in the light of broader knowledge from different scientific fields. Because there is no one coherent “theory of social status”, insights from different disciplines are brought together to give a general idea about this concept. Further, in the context of these findings, we will formulate the gaps in economic literature which naturally follow from findings about status in other disciplines.

1.1 Terminology: definitions of “social status” from sociology and social psychology

“Social status” is an abstract concept, and there are considerable differences in scientific methodologies and terminologies among different disciplines talking about social status and social comparison. It is important to focus on definitions and terminology used in these different scientific areas to reveal the main insights about “social status” from each discipline.

Usage of the term “social status” has originally started in sociology, so it seems natural to start the discussion about this concept with a brief look into sociological literature. Terms “social status” and “social class” are closely related and were introduced by Max Weber and Karl Marx who tried to analyze the new social reality in Western Europe emerging after Industrial and French Revolutions. As large masses of people were being included into active political life in big cities, a new social reality was emerging unseen in previous feudal order. Social science of the time had an objective to understand the key factors motivating people’s behavior. In his major work Marx defined the idea of “social class” to categorize people based on their relation to economic production. Assuming that production of material goods is society’s most important activity, it logically followed that an individual’s relation to that production process should define her social attitudes or “social consciousness”. How much you work or contribute to production of material goods and how much is your material income or your “share of the pie” should determine your place in the society or your “social class” (Marx, 1986). Weber’s concept of “status groups” is closely related but not so focused on relation to production process. “Social status”, according to Weber, can also be based on other aspects such as consumption behavior or mutual cultural and ethical values uniting certain groups (Weber, 1978).

Later developments in sociology have built upon these ideas, trying to develop the concept of “social status” into a useful tool for understanding socio-economic relations. For example, Treiman’s empirical work has tried to understand people’s perceptions about social status. In his book “The Division of Labor and Occupational Stratification” Treiman compares data from 53 countries analyzing the “prestige rankings” of different professions. He concludes from empirical surveys that people’s subjective understanding of “social status” is usually based on one’s occupation, level of education and income (Treiman, 2013). This empirical

16 observation generally agrees with initial definition of social class based on one’s relation to production of material goods. Because one’s education and occupation define what she gives to society (in terms of economic production) and one’s income defines what she takes from society (her “share of the pie”). So occupation, education and income together are a good indicator one’s “relation to economic production” or one’s “social status”, and largely define power relations between individuals.

Such a definition of social status naturally results in a hierarchical element: people may have more or less important positions within the society in terms of their overall contribution to public good. People also have different skills, different rights and different access to resources. Such a differentiation results in hierarchical ordering of people according to their bargaining power. If this asymmetry is recognized and accepted by everyone then the status is informally “awarded” to the individual by the society (Berger et al., 1972). Understanding the existing status hierarchy is thus essential for navigating through the complex network of social relations. Further studies in sociology are often focused on relations between the status (usually based on education and income) and social behavior, such as political attitudes, voting preferences etc.

To conclude, sociology has developed the concept of social status as a theoretical tool to understand individual’s relation to the society. “Social status” then can be understood as an indicator of individual’s relation to the society, reflecting her contribution to public good, importance of her skills for society, her rights and obligations.

On the other hand, social psychology has emerged in parallel as an interdisciplinary science between sociology and psychology. Over time this discipline has developed its own terminology and empirical methods to talk about social status. Recent developments in social psychology are trying to give new definitions to the

17 term “social status” itself. This endless process of redefinitions and conceptual clarification can result in misunderstanding of terminology and make social psychology literature confusing to understand.

In social psychology literature there is a certain amount of confusion over the term “social status ”. Many studies follow a certain terminological framework which gives separate and distinct definitions to terms “status ” and “power”. For example, in their big review on this issue Magee and Galinsky clarify this terminology and give a general definition of social hierarchy as a rank order of individuals with respect to a valued social dimension. Furthermore, they stress the “status” and “power” hierarchies as the most important dimensions of social stratification. “Social status” is defined as the amount of informal respect enjoyed by the individual, but not just the position on any important hierarchy (Magee and Galinsky, 2008). In practice this means that high status individuals contribute more to public good. This specific definition is commonly accepted in social psychology (Blau, 1964; Fiske, 2010; Kemper, 2006; Hall et al., 2005; Henrich and Gil-White, 2001). This specific terminological framework is further developed to describe social relations by many social psychologists (Anderson and Kilduff, 2009a; Fiske and Berdahl, 2007; Ridgeway, 2001; Zelditch Jr, 1968). “Power” on the other hand defines the level of administrative (or other kind of) control over resources (Dépret and Fiske, 1993; Galinsky et al., 2003; Georgesen and Harris, 1998; Gruenfeld et al., 2008; Keltner et al., 2003; Overbeck and Park, 2001). So someone can have high power or access to resources but low status or esteem within the society. Having high power does not automatically mean higher esteem by others. Social psychologists stress this difference between “status” and “power”, so it is important to clarify this terminology to understand the literature in this discipline (Blader and Chen, 2012).

1.2 Empirical research on status: sociology and social psychology

Despite being intellectually not-well organized and messy in terms of theoretical formulations or empirical findings, research about status in psychology and sociology has found very interesting insights about human behavior and its relation to social rankings.

1.2.1 Studies on macro-level

Empirical studies about social status often use the quantitative measure of socio-economic status (SES), which is usually the normalized average of individual’s relative income and/or level of education in her country. Such studies try to understand the link between SES and different aspects of behavior or well-being. It is important to understand that SES is analyzed in relative terms: having high or low SES is always relative to a social comparison group. It appears that relative SES often explains many aspects of behavior. First of all, political attitudes and voting behavior are highly dependent on relative distribution of income and individual’s SES within the country. Besides this well-known impact of SES on political behavior, there have been other important findings.

Happiness and well-being

Studies on happiness and well-being show a dependence of well-being on relative income within the social reference group but not on the absolute level of income (Diener et al., 1999). Easterlin’s trend-setting paper showed that in the USA, at any given time the rich are happier than the poor, but GDP growth over years does not lead to growth of overall mean happiness of the society (Easterlin, 1974). Many

19 further studies have been published in line with this result. For example, an analysis of large socio-economic survey data for UK has shown that self-report happiness and life-satisfaction depends on people’s relative rank of income and education in the country. So the level of life-satisfaction is not directly related to absolute income, but rather to relative income (Boyce et al., 2010a).

Big differences of relative SES within countries or communities are also correlated with a number of social problems such as higher crime rates, lower trust levels within communities and many other social problems. In their book “The Spirit Level” Wilkinson and Picket analyze the social impact of inequality in Western societies. They bring a large number of empirical observations linking socio-economic inequalities to many major social problems, ranging from child well-being in schools, crime rates within communities, frequency of mental health problems, drug-addiction problems and many other, with a main conclusion that high income inequality strengthens these problems (Wilkinson and Pickett, 2009). Many other studies show similar results: higher income inequality leads to decrease in social capital, less engagement in communal activities, higher levels of isolation between different layers of society (Kawachi et al., 1999; Hemenway et al., 2001).

1.2.2 Studies on micro level

Definition of social status based on income and education is a general measure of status. However, in more local contexts people are often related to each other in more specific terms. Many studies have tried to understand the impact of such a “local” status on micro or individual level. An important research direction related to local status started with Festinger’s social comparison theory (Festinger, 1954). Festinger’s seminal paper proposed that people constantly compare themselves with others to understand their own abilities and to form opinions. This theory has grown

20 into a broad research area focusing on how people compare themselves to others and how this comparison impacts feelings and behavior. Such an approach is conceptually close to sociological understanding of status: social comparison is key to understand one’s own position within the society and adjusting social behavior. Many interesting findings have been made within this body of literature. For example, a psychological study finds that the local status, defined as the respect that a person enjoys within the circle of her colleagues and family has a strong impact on her subjective well-being (Anderson et al., 2012). Other studies have shown that people who feel threatened start to compare themselves with others who are worse off. Such a downward comparison results in positive affect. For example, Taylor, Wood and Lichtman (1983) found that women who suffer from cancer often compare themselves with other patients with worse conditions, and such a comparison is psychologically comforting (Taylor et al., 1983). A meta-analysis of 23 studies found that people with dangerous medical conditions often compare themselves with ones who are worse off (Tennen et al., 2000). Scholars have further tried to understand the reasons of such selective social comparison. A meta-analysis on the issue concludes that downward comparison with an individual who is worse off leads to positive affect (Gibbons and Gerrard, 1997). So even on more local settings people constantly compare themselves to others and such comparisons can have a strong impact on their emotions and behavior.

Social status and health

Many studies have found a correlation between relative SES and health. For example, an interesting technic developed in psychology is used to measure one’s subjective perception of self-status: the person is shown a simple 10-step ladder and is asked to indicate a position on this ladder as a “general indicator” of her social status, in terms of education and level of income within her country (Adler et al., 1994).

Some studies have found a positive relation between such a subjective SES and health (Adler et al., 2001; Singh-Manoux et al., 2003; Cohen et al., 2008) . Here it can be argued that having higher income shall be linked to better access to healthcare. However these studies claim to have controlled for objective access to healthcare. So subjective feeling of one’s place on social hierarchy has been linked to health. In next paragraphs we will illustrate insights from biologically oriented literature to shed more light on this relationship.

1.2.3 Signaling status: conspicuous consumption

As mentioned earlier, individual’s position on the socio-economic hierarchy largely determines her general relation to society. Understanding social hierarchy and your place on it is crucial for successful social interactions. Thus people need to signal their own status to others, and also understand other people’s statuses. Such a signaling of status is done by demonstrable status symbols. Generally, as a demonstrable status symbol can be anything which gives a clear idea about individual’s level of income and education, her affiliation to certain social groups and her place in society in general. Many aspects of behavior can be well understood if we look at them as signaling of status. For example, consumption behavior has always been an important way to signal one’s social standing (Mazzocco et al., 2012). In his famous monograph “The theory of the leisure class” Thorstein Veblen defined the term “conspicuous consumption” to describe the overinvestment in luxury goods (Veblen, 1965). Such a demonstrative overconsumption was typical among members of economic elites in the early 20th century Western Europe. Products and services that people buy always have a double role: besides the main material function any visible product also has a second role

22 of signaling one’s wealth or taste (taste is a proxy of overall level of education). This demonstrative consumption has been linked to a need for status (Charles et al., 2009). People can invest much more than needed buying luxurious goods just to signal their wealth (Corneo and Jeanne, 1997) For example, Nelissen and Meijers showed in an experiment that participants wearing a prestigious branded t-shirt were more likely to get help when asking for it and get more money when calling for charitable donations, compared to participants with no-brand t-shirts (Nelissen and Meijers, 2011). In another experiment, Dubios et. al. show that subjects who feel low-powered choose bigger sized food and drink packages, when in a public place. They interpret this as a relation between product size and status demonstration: bigger size is associated with higher status (Dubois et al., 2011). This relationship between status and product size in Western societies has been demonstrated by other studies (Baudrillard, 1998; Schubert et al., 2009; Dannenmaier and Thumin, 1964). People who feel having low status often prefer over-sized products (Rucker and Galinsky, 2008; Rucker and Galinsky, 2009; Dubois et al., 2010).

This status-signaling role of consumption goes well beyond of material products (Drèze and Nunes, 2008; Mandel et al., 2006; Ordabayeva and Chandon, 2010). For example, listening to a certain genre of music can signal affiliation to certain social groups, and such a demonstration of taste can often be independent of actual musical preferences. People may attend a 3-hour opera concert and applaud even if they dislike this genre of music and sitting for 3 hours is close to a physical torture for them. This may happen if it’s important to show publicly that you accept the values of certain high-status groups. Status can be signaled via lifestyle. For example, members of royal families in many ancient empires were not expected to work, and working would be regarded as unacceptable.

An interesting idea about status signaling is often articulated in status literature and goes back as far as to Adam Smith. It is argued that the more unequal a society is, the more important it is to demonstrate your affiliation to upper status groups in order to be well-accepted and successful. Such a demonstration is done using all sorts of status symbols. This insight can help to understand many “strange” aspects of behavior. For example, an analysis of car-buying preferences in different US states finds a link between inequality and car price. It found that in more unequal states people actually spend more to buy a “prestigious” car, in terms of price and brand (Bricker et al., 2014). Wilkinson and Picket report a similar finding for US states: higher inequality leads to increased consumption of aggressive looking cars such as SUVs. They interpret this as a status-signaling effort. In more unequal societies it’s more important to signal your strength, which in case of cars means bigger size, more powerful engine or other non-important qualities (Wilkinson and Pickett, 2009).

Such “irrational” aspects of behavior can be well understood if we look not to actual physical characteristics of products but to their status-signaling role. Choosing the “right” kind of physical products, listening to “right” kind of music, using the “right” language are all means of showing your material position on income hierarchy, your level of education, your affiliation to certain social groups and your overall place within the society.

Signaling of status takes place in any human society. Even in pre-historic hunter-gatherer societies people demonstrate their valuable skills using status symbols. For example in many hunter-gatherer societies, superior hunting skills are a source of high status because good hunters have an important role for tribe’s overall survival (Ellis, 1994). Such a high status is further demonstrated with specific jewelries or body painting. This signaling takes place despite the egalitarian rules of

24 food-sharing. High status men in such societies often have privileged access to females with resulting reproduction benefits (Von Rueden, 2014)

1.3 Insights from neuro-economics

Neuro-economics uses brain scanning technologies to understand the biological process in the brain related to economic decision making. A number of recent studies focus specifically on how brain reacts to information about social rankings and social comparison. Many such studies find specific processes and areas in the brain focused on the analysis of social hierarchies and corresponding behavior. For instance, a recent study measured brain responses to unfair offers in an ultimatum game. Participants were awarded a high, medium or low status before the game, based on their performance in a simple reaction-time task. Results of EEG scanning show that neural response to unfair offers was decreased if the subject had a lower status. So the small or “unfair” offer was perceived as more just if the receiver had a lower status and this difference was detected on the level of brain processes (Hu et al., 2014). Zink et al conducted a similar fMRI study where subjects are awarded ranks (golden stars) based on relative performance in simple reaction time tasks. After establishing the status hierarchy subjects are paired into dyads to play simple games. Players are shown photos of the other player and her status (which is higher or lower). When subjects look at pictures of higher-status peers then a stronger activation is detected in brain regions responsible for attention and social cognition (Zink et al., 2008). Breton et al (2014) conduct a similar experiment with imposed status hierarchy and found a similar activation of attention when subjects are shown faces of high-status subjects (Breton et al., 2014). It is known from other psychological

25 literature that high status people attract more attention and this was also measured on the level of brain neurophysiology.

Such brain-imaging studies indicate that certain brain areas are actively involved in analyzing information about social rankings. Chiao et al. use semantic distance tests to demonstrate that people are faster at detecting bigger differences between two numbers and bigger differences between social ranks (Chiao et al., 2004) . An fMRI study further shows that same brain regions are responsible for analyzing the difference between numbers and difference between social ranks (Chiao et al., 2009). In another experiment Boksem et al. create a similar status hierarchy based on results in a simple task. They conduct an EEG analysis and find a stronger activation of a certain type of brain signal -called MFN, among low status participants. This brain signal is often related to self-evaluation of behavior in a social context. Authors interpret such an activation of self-evaluation among low status subjects as a tendency to avoid social-evaluative threat. They link such behavior to the need of low-status subjects to be accepted by the social group and thus an increased activation of self-monitoring of behavior among low-status subjects (Boksem et al., 2011). Similar findings are reported by Muscatell et. al.: university students with low social status show stronger activation of brain areas which are used understand the thoughts of others about our behavior (Muscatell et al., 2012). Chan et. al. (2018) find that higher SES is associated with lower levels of age-related brain decline in older ages (Chan et al., 2018). Many other neuroeconomic experiments find specific brain regions and brain signals related to analysis of social hierarchy and relative social status. Navigation through complex social hierarchical structures requires mental abilities found in these neuro-economic studies.

These studies are generally related to the “social brain hypothesis”, which argues that the need to understand the complex social relations within the group has

26 been the main evolutionary driver behind our big brains (Tomasello, 2009). So the intellect was needed first of all to analyze the social relations within the group and behave accordingly in order to survive. For example, Dunbar argues that the size of the neo-cortex in monkey and hominid species is directly related to the average size of the tribes for each species (Dunbar, 1998). This correlation can indicate that the intellectual abilities are first of all needed to understand the relations within the tribe. Furthermore, social hierarchies are always an important part of such relations.

1.4 Insights from evolutionary biology

Evolutionary biology seems completely unrelated to socio-economic issues discussed above. However, this science can give a deeper understanding of why are people naturally so sensitive to social comparison. According to main approach of evolutionary psychology, many aspects of human behavior can be better understood if we look at their evolutionary origins (Buss, 2015; Barrett et al., 2002). Humans evolved from their primate ancestors for several million years. Principles of random mutation and natural selection of fitness-maximizing treats can explain many of our instincts and emotional predispositions. Simple instincts such as fear of height or higher-level emotions such as love for own child lead to higher chances of survival and genetic self-replication (Scott-Phillips et al., 2011).

These universal principles work for all species including humans. It turns out that having a small relative advantage over competitors leads to increased chances of survival (Garay, 2008). For example, when the lion attacks a group of zebras, each zebra must try to run a little faster than other zebras. So even if lion runs faster than zebras, it will catch the slowest one and others will survive. In this example running

27 faster compared to other zebras (but not necessarily compared to lion) will increase the survival rate. Now suppose an accidental genetic mutation creates a female instinct to prefer the fastest- running male (compared to others). Then the offsprings of this female will inherit its father’s fast-running capabilities and will be more likely to survive. This simple example shows that a preference to mate with the relatively stronger will have reproductive benefits. This is a classical evolutionary explanation of behavior: proximate mechanisms such as a desire to mate with the high-status male helps to reach the ultimate goal of successful self-replication of the genotype. This is a blind “mechanical” process which results in certain fitness-maximizing preferences for a given environment. From this viewpoint, the desire to acquire higher status can be regarded as proximate mechanism supporting the ultimate goal of successful reproduction. Relatively high status can lead to greater access to food (Barton et al., 1996) and reproductive success (D'amato, 1988). Many animal species have such attitudes to prefer the relatively stronger partner (for females) or compete to become the dominant within the group (for males). Such an importance of relative advantage of force exist among non-human primates where bigger-sized monkeys are perceived as higher status (Rivers and Josephs, 2010).

1.4.1 Possible evolutionary origins of status-seeking behavior in humans

Humans have unique qualities (among the animal kingdom) to be prosocial and compassionate for other people beyond their genetically related family (Bowles and Gintis, 2011). However, instincts to achieve and enjoy relative advantage over others or becoming depressed when left behind still exist. Psychological findings about social comparison and its impact on behavior can be explained in the light of evolutionary psychology. Robert Sapolsky’s research about non-human primates

28 has revealed how the relative place within the dominance hierarchy has a direct impact on production of different hormones, such as serotonin (related to feelings of enjoyment), cortisol (related to stress) and others. These hormones impact animal’s behavior and are related to their stress levels (Sapolsky, 2017). A male baboon beating every other male and reaching an unchallenged position on the top of a stable dominance hierarchy will have increased levels of serotonin hormone. Serotonin is linked to feelings of happiness among humans as well. On the other hand, male baboons who are beaten up and fall down the dominance hierarchy have higher levels of cortisol hormone. This hormone is related to stress among humans. Physiologically, the hormonal reaction of a low-ranked male baboon is close to that of a human with extreme depression (Sapolsky, 2004; Sapolsky, 1990)

These findings from biology can shed light on human sensitivity to social comparison. Why we feel good when we are richer than others or feel bad when relatively underachieving? Such desires and feelings can be at least partly explained by the status-seeking instincts we inherited from our ancestors. In 21th century, being relatively rich or poor in a developed country doesn’t have much impact on physical survival. In most countries no-one dies from hunger or cold nowadays. But still, feeling relatively poor can trigger a chronical stress response which can result in stress-related diseases such as diabetes, cardiovascular diseases etc (Wilkinson, 2001; Williams, 1989). Psychological and epidemiological findings discussed in previous sections can be explained and understood in the light of knowledge from evolutionary biology.

1.5 Modelling social status in economics

The literature reviewed above shows that social comparison has a profound role in human behavior. On all levels of behavioral research, from pure biological to sociological, importance of social hierarchies and individual’s place on it has been well recognized. We deliberately gave a review on neuroeconomic and biological literature to show that “social status” is not just an abstract or empty theoretical construct created by social scientists but has deep biological roots.

Neo-classical economic modelling of behavior, based on axioms of rationality, has often neglected the impact of social comparison on behavior. In many economic models agents are supposed to maximize only their own well-being measured in absolute terms. Income or well-being of others are often considered unimportant, as formalized in the principle of “indifference from irrelevant alternatives”. Many economic models often don’t take into account the idea that “prestige” or social status have an impact on social relations.

This limitation is actively changing. A number of economic models are considering social comparison as part of the maximized utility. Famous outcome- based social preference theories by Fehr & Schmidt, Bolton & Ockenfels and others model the impact of status on income hierarchy. Such models assume that utility depends not only on one’s self-income but also on income of others. These models include downward and upward comparison parameters which define agent’s attitudes towards others who have more or less income (Fehr and Schmidt, 1999b; Bolton and Ockenfels, 2000). This means that economic choices of agents should depend on social comparison or status on income hierarchy. Basically, these social preference models are simple mathematical formulations of the idea that social comparison affects behavior. People are not just maximizing their individual income but maximize a utility depending also on relative income of others. Many sociological and psychological findings discussed above can be mathematically

30 modelled using outcome-based social preference models. For example, Boyce et. al. have found that people’s satisfaction with salary depends on their relative income within the community (Boyce et al., 2010a). This fact can be captured by outcome- based social preference models which assume that utility depends on income of others.

Besides income, people also hold status on other dimensions. Status can be based on important skills (such as hunting skills in hunter-gatherer tribes or intellectual skills in an academic environment). Some economic models try include such a non-monetary informal status. For example, Besley and Ghatak construct an economic model of incentives in organizations, where employee’s utility also depends on informal status incentives such as prestigious job titles or non-monetary awards (Besley and Ghatak, 2008).

1.6 Research questions addressed in this thesis

When status or relative social position become part of the maximized utility, a large number of interesting research questions begin to arise, relevant for economics. We address some of these questions in this thesis.

Study 1: Risk in social context

In our first study we investigate the impact of social comparison on risk- attitudes. As described in findings above, higher levels of inequality can lead to higher levels of crime, fuel political unrest etc. Unfavorable inequality and low social status can push people at the bottom toward riskier behavior. Studies by Wilkinson and Picket show that more inequality within communities (and not the absolute level of poverty) gives rise to crime. So having a relatively low status triggers more criminal behavior and this is a real-life example of excessive risk-taking caused by low status.

Similarly, higher inequality can push poor people to take part in demonstrations, revolutions and violence, which all are examples of excessive risk taking triggered by low social status. From an economic viewpoint this means that risk-attitudes can depend on social comparison.

This question has not been addressed in experimental economics until very recently, but it naturally follows from outcome-based social-preference models. As soon as the maximized utility function becomes dependent on social comparison, it follows that choices over risky prospects should also depend on social comparison. Such an impact logically follows from outcome-based social preference models which were initially developed to describe non-selfish choices in strategic interaction games (Bolton and Ockenfels, 2000; Fehr and Schmidt, 1999a). In these models the maximized utility depends not only on decision-maker’s money but also on money owned by others. These models often assume inequity-aversion which means that the decision-maker makes choices to decrease favorable or unfavorable inequality within the comparison group. Now suppose we have a group of people where each has a certain amount of money. Suppose also that people can compare themselves with each other. Imagine that one member of the group is facing a risky choice, such as to play a lottery where she can win or lose some money. In such case the lottery outcome can increase or decrease the overall inequality within the comparison group because it will change the decision-maker’s money and her relative place (or relative social status) within the comparison group. Inequity-aversion principle suggests that people must adjust their risk-attitudes to decrease the inequality. For example, assume that the decision-maker has a low rank position within the group and winning the lottery can help to increase her relative rank and decrease unfavorable inequality. This can serve as an additional incentive to play the lottery. In this example social comparison can push people to take more risk. A growing number of experiments are focused on understating precisely how peer effects affect risk-attitudes. Chapter

2 of this thesis gives a detailed review of existing literature on this topic. This literature is still small and there are many open questions. The main challenge is to find the best model describing social comparison and its impact on risk-attitudes. Inequity-aversion models are only one of the possible ways to describe risk-attitudes in social context. The general problem is to reveal those parameters of money distribution within the comparison group which form the subjective perception of status. Some models assume that we look at our relative rank position to decide whether we are “high” or “low” within the group. Other studies assume that our distance from group average plays a key role to assess our relative status. There are many possible ways to model risk in social context.

In our experiment we test a psychophysical model of contextual evaluation called range-frequency theory (RFT) to see if this theory is useful for modelling risk in social context (Parducci, 1995). We make a hypothesis that changing one’s social position between the top and bottom members of the comparison group (that is the range position) may have a separate impact on social risk-attitudes. Chapter 2 gives a detailed explanation of this psychophysical theory and describes how we can use it as a model for social comparison. We will also describe all the major economic models and experiments related to risk in social context and explain our contribution this literature.

Study 2: Local status and collective risky choices

Our second study is related to collective decision making. An interesting and newly developing research area in economics is focused on how small groups make collective economic decisions, how such collective decisions differ from identical individual decisions, and how the preference aggregation happens within small

33 groups. More specifically, our study is focused on risky choices made by small groups. Most of the economic research on risk and uncertainty is focused on individual decision-making. However, risky decisions in real life are often made by small groups, members of which have to make a collective choice despite having different risk- attitudes. We found around ten articles in experimental economics which are focused on collective risky decisions. In chapter 3 we give a detailed review of these studies. A central question addressed in these experiments is whether groups are more or less risk-averse than individuals. The topic is relatively new in economics and results of these experiments open more questions than provide answers. Some papers conclude that groups are more risky than individuals, others find an opposite result or no difference between groups and individuals. So there is no consensus on this issue whether groups are more or less risky than individuals. These studies often focus on decision theory (such as expected utility theory or prospect theory) and try to apply these economic theories to analyze collective risky choices. The main limitation of this approach is that the group is considered as a single entity, and then the final collective decisions are just compared with individual decisions. But they do not “dig-in” into the process of collective decision-making itself to understand the group dynamics.

In social psychology on the other hand, collective decision-making is a traditional research area and there are numerous empirical studies and experiments on group dynamics. Social psychologists have their own theories to analyze collective choices (discussed in detail in chapter 3). These theories are more general and non- mathematical. They are also not focused specifically on risk as it is formulated in economics. However this discipline looks at the same problem of collective choice from a very different angle, using very different core ideas to analyze group dynamics. More specifically, an interesting theoretical idea often used to describe small-group processes is the following: whenever a small group tries to make a collective decision,

34 an internal status hierarchy always emerges within the group and this hierarchy plays a coordinating role for conflict resolution (Anderson and Kilduff, 2009b; Van Vugt, 2006). This means that high status members informally have a bigger decision weight within the group and this asymmetry of influence plays a conflict-resolving role. This is an idea which is not used in economic studies. Mathematical theories of decision under risk do not include the concept of “social status”. Economic studies on collective risky choices don’t analyze the internal status hierarchy and its role as a conflict resolution mechanism, by default assuming an equal distribution of decision power. This assumption is generally not correct: on informal level people always have different degrees of influence on collective decisions, which is partly based on local status. Most importantly, this implies that groups should be not more or less risky than individuals but rather groups should follow their high status members.

In our study we try to connect these two separate research areas which are both centered around the same problem but use different theoretical ideas. We expand the economic studies and incorporate the idea of internal status hierarchy to understand collective risky decisions. As discussed above, status hierarchies are key to analyze human social relations, both on macro and micro levels. As sociologists and social psychologists put it, human relations are largely shaped by the relative social statuses of people. This idea is not well formulated in economics. So instead of testing whether groups are more or less risk-averse than individuals, we follow social psychologists and test whether internal high-status members are the ones who have the final word in collective risky choices.

In this lab experiment we have groups of four people collectively choosing between a risky and a safe lottery. This is a standard economic task, quite similar to Holt & Laury type of choices and also similar to previous economic experiments on collective risky choice. What is innovative, we impose high and low social statuses

35 on participants, based on their performance in a knowledge quiz. This methodology is again discussed in chapter 3. We use common status symbols to stress the status differences between participants (golden star stickers, applauding to high status members and reseating high status members at the front of classroom). People are naturally responding to such status symbols and adjust their behavior accordingly, as discussed above in literature about status signaling. So we try to test whether the information about the social status of group members has a conflict resolving role or any influence on final collective choice. In our main hypothesis we predict that groups with a vertical status hierarchy can be faster at reaching collective agreements compared to groups with flat status hierarchies.

Study 3: Bargaining with status-inconsistency

In our third study we investigate the potential conflict which can arise when people hold different statuses on different social hierarchies and overestimate those dimensions on which their status is high. It is well recognized in sociological literature that people hold ranks on many different social dimensions. People can have a high status on one important social dimension and a low status on another. When interacting with each other, this status positions are largely shaping human relations. However, people may overestimate the importance of the hierarchy on which they have a high status while forgetting about those dimensions on which they are low. Such attitudes can potentially result in conflicts between individuals or problems in individual-vs-society relations (Lenski, 1954). These potential conflicts have been investigated in a large body of sociological literature where this phenomenon is labelled as “status-inconsistency”. In chapter 4 we give a detailed overview of this literature. From an economics perspective, human relations are often analysed in the light of economic rationality, which often assumes maximising a utility function.

This approach is often not focused on the idea that relative social status shapes human relations. Few economists discuss these issues. In particular, Akerlof and Kranton discuss these issues in their book “Identity Economics”. According to this approach, people have expectations on how others should behave, depending on their social standing, gender, profession and other important societal dimensions. These “roles” put on people by the society largely teach people the appropriate standards of behavior and any departure from these standards can result in huge resistance by others (Akerlof and Kranton, 2010).

A classic example in economic studies is the ultimatum game where two players divide a valued resource: the first player proposes how to divide the valued resource and the second players agrees or disagrees. If there is no agreement then both players get nothing. So this game touches issues such as fairness, norms of dividing economic resources and economic rationality. This game has been extensively studied in experimental literature and is a good example of a simple “act of human relations”. From sociological perspective, relative social statuses of players must influence the decisions in this game because a high social status in real life can often justify acquisition of more resources (Weiss and Fershtman, 1998). Indeed, several economic experiments have demonstrated that imposing high and low social statuses in laboratory settings can shift the division point in the ultimatum game in favour of the high status player (Ball and Eckel, 1998).

In our experiment we build on the idea that having different statuses on different hierarchies may result in conflict because people overvalue the dimension on which they are high. Most economic articles focused on social status use only one status dimension. This is a limitation because in real life people have ranks on different important hierarchies. This study is the first economic experiment to fill this gap and test the impact of multiple status positions on economic decisions. In this experiment

37 people pass through two different types of tests: a general knowledge quiz and a visual reaction task. Then they are paired via computers and are shown each other’s statuses. We pair those participants who have orthogonal sets of statuses: the first player has a high score in one test and a low score in another while the opponent has the opposite sets of scores. Two players play an ultimatum game as described above. So we construct a “status-inconsistent interaction” which mimics real-life human conflicts where people have different statuses on different social dimensions. The main objective is to test the influence of having orthogonal status positions on economic bargaining. Based on sociological arguments we expect players to overestimate their high status. This should be reflected in ultimatum proposals and rejection rates. Our main hypothesis in this study predicts more conflict in status-inconsistent interactions.

The rest of this thesis is organised as follows: chapter 2 describes our first study on risk in social context. Chapter 3 is our second study where we investigate the role of internal status hierarchy during collective risky decisions. In chapter 4 we present our third experiment about status-inconsistency. In chapter 5 we give a general conclusion of this thesis, analyse the limitations of the three studies and point toward future research directions.

Chapter 2

2 Risk in social context: the impact of range position

2.1 Introduction

Decisions under risk and uncertainty have traditionally been investigated for individual choices. Applications of major theories on risky choice such as expected utility theory or prospect theory usually do not assume that subjects compare their income with peers while making a risky decision.

However, models of outcome-based social preferences assume that social comparison has an impact on economic choices. These models were initially created to describe “irrational” decisions in simple strategic games such as ultimatum and dictator games. In theory however such social preference models also predict an impact of social comparison on risky decisions. Indeed, Bolton and Ockenfels were among the first to mention about this possibility. Based on their own model of social preferences, they show in a simple experiment that comparing with others has an impact on risky decisions (Bolton and Ockenfels, 2008). In the last decade there have been more and more studies investigating many different aspects of peer effects on risky decisions. People may change their risk-attitudes when they merely observe the income of others, or observe risky choices of others, or when their risky choices also impact other people’s money (Trautmann and Vieider, 2012). Our study focuses on the most simple case when people just observe other’s income before making a risky decision, and when the decision doesn’t change other people’s money. Recent developments in decision theory try to model such a social impact on risky choices. The main approach is to assume the utility function consisting of two parts: traditional

39 individual part depending on own money, and a second social part depending on social context (Maccheroni et al., 2012). Following equation summarizes the main theoretical approach:

푈(푥) = 푢(푥) + 푦 ∗ 푛(푥, 푠) (1)

where first component 푢(푥) is the traditional individual utility depending only on own money 푥 . The second part 푛(푥, 푠) is the “social utility” which depends on “social context” or 푠, and describes individual’s social attitudes toward peers. Social utility is also multiplied by a coefficient 푦: the bigger this coefficient is the more people care about social comparison.

There are several different ways to model this second social aspect of utility. The most natural way is to assume that people compare their income with the average income of the comparison group. This means that social utility depends on the monetary distance between the decision-maker’s income and group average income. Average income of the group is considered as a “social reference point”, and social utility takes the form

푛(푥, 푠) = 푓(푥 − 푠) (2)

where 푠 is the group average income.

Thus social utility function 푓(푥 − 푠) describes choices depending on monetary gains and losses compared with the social reference point. The exact functional form of this social utility function defines people’s social attitudes toward risk. A number of studies have tried to identify the exact form of such social utility functions for social gains/losses with mixed results. Following prospect theory, group average income is considered as the social reference point, (in addition to individual reference point) and then separate functions for social gains and social losses are evaluated. These functions can be concave or convex for social gains or losses and their form will

40 define whether we are socially risk-seeking or risk-averse. Some studies try to find the form of such social gains/losses functions. For example, Linde and Sonnemans measure social risk-attitudes when another person’s income is considered as “social reference point”, trying to find the “reflection-effect” described by prospect theory. They find that people are more (less) risk taking when another subject’s fixed payoff is lower (higher), which contradicts the principle of loss aversion when the loss is defined as a “social loss” (winning less than the other person). So their estimate of the utility function for “social loss” domain is concave, not convex (people don’t become risk-seeking when facing “social loss”). They conclude that a straightforward application of prospect theory is not helpful for understanding risk in social context (Linde and Sonnemans, 2012). Vendrik and Woltjer measure the social utility function for social gains and losses in a panel data analysis. They define the social reference point as the average societal income. They find that the social utility function is concave for both social gains and losses, which potentially explains why there was no reflection effect in Linde & Sonneman’s study (Vendrik and Woltjer, 2007).

Another approach focuses on rank position within the comparison group, assuming that people care about their relative rank position compared to peers:

푖−1 푛(푥 , 푠) = 푓( ) (3) 푖 푁−1

where we have 푁 people in the comparison group, and decision maker has a rank position equal to 푖. This means that monetary distances between the decision- maker and her peers don’t matter, and only relative rank position matters. Some empirical studies have indeed found an impact of relative rank position on risky choices. For example, a recent lab experiment with investment professionals showed that a mere information about the rank position within a comparison group increases risk-taking by low-ranked investors (Kirchler et al., 2016). Dijk et al. show a similar

41 result with undergraduate students (Dijk et al., 2014). Kuziemko et al. find a “last- place aversion” effect: subjects with the smallest endowments within the group change their risk-attitudes to avoid the last place (Kuziemko et al., 2014). Dijk experimentally shows that a significant number of participants are socially risk- seeking, and prefer to take more risk if there is a chance to overcome the neighbour, even at a chance to end up much worse than her (Dijk, 2017). This result is in line with findings by Bault et al. where they show that social gains result in stronger physiological reactions than social losses (heartbeat rate, skin conductivity) (Bault et al., 2008).

So there can be different ways to define social utility function 푛(푥, 푠). Thus modelling the social impact on risk attitudes boils down to the problem of finding the correct social utility function.

Our study focuses on a psychophysical model of contextual evaluation- called Range-Frequency theory (RFT), developed by psychologist Alen Parducci (Parducci, 1965; Parducci, 1995). RFT was originally created to model relative perceptions of physical stimuli (for example, perceived loudness of a sound compared to background noise). This theory assumes that subjectively perceived value of an item depends on values of comparison items according to a simple rule described below. More precisely, Range-Frequency Theory predicts that subjectively perceived value of an item depends on:

a) item’s position between the smallest and biggest items of the comparison set (range position), and

b) relative rank position of the item within the comparison set (relative rank)

In other words, RFT defines the contextual value as a weighted sum of rank position and relative position between the top and bottom of reference group. This

42 theory was originally created to model our subjective perceptions of physical stimuli. For example, the ringtone of the mobile phone can seem very quiet in a noisy street but very loud in a quiet room. So the same sound is perceived differently when compared to different levels of background noise. In other words the same stimuli is perceived only in comparison to the context.

It is sometimes hypothesized that similar psychophysical mechanisms can also explain our social evaluations, such as our happiness with a salary compared to salaries of our peers. Some studies show that RFT is useful to understand our social attitudes. Brown et al. showed in their analysis of 16000 British employees that wage satisfaction is influenced by the ordinal rank position compared to other’s salaries and show that RFT can successfully explain this phenomenon (Brown et al., 2008). This means that the same salary is more satisfying when it’s the second highest salary than when it’s the fifth highest in the comparison group (Brown et al., 2008). Hagerty showed that the average levels of happiness in communities depend on wealth distribution within the communities and proved that the RFT can successfully predict this dependence (Hagerty, 2000). Highhouse et al. showed that salary expectations can also be predicted by RFT (Highhouse et al., 2003). So RFT seems to be useful to understand social impact on individual utility, and the impact of social comparison on risk.

In our experiment we focus separately on range position because the rank position is already analyzed in previous studies without any separate control for range. Range position may have a distinct and important role in human perception of social status because of several reasons. As psychology and neuroeconomic studies show, high status people attract more attention. So comparing to the top person in the group can have a separate bigger influence on social risk attitudes. On evolutionary perspective, being on top of the hierarchy results in reproductive benefits, so the

43 distance from the leader can have an impact on risky decision-making such as deciding to challenge the top member of the hierarchy. So all these different results from literature on status indicate that the range position can have a separate important role when assessing social status.

We use range-frequency theory as model of social utility. Suppose we have group of 푁 people in the comparison group. Each member 푖 has a monetary payoff equal to 푥푖, and people are rank-ordered by their payoffs so that 푥1 < 푥2 < 푥3 < ⋯ <

푥푁−1 < 푥푁. Then the subjective satisfaction or utility 푢푖 for member 푖 will be equal to:

푥푖 − 푥1 푖 − 1 푢푖 = 푤 + (1 − 푤) ; (4) 푥푁 − 푥1 푁 − 1

First component of the equation (4) is the range position or position between top and bottom members. Obviously this range position varies between 0 and 1. Second part of the equation is the normalized rank position within the group. Weighting parameter 푤 shows the relative importance of rank and range. In many psychophysical studies 푤 has been estimated to be around 0.5, which means that range position and rank position are about equally important. We focus on the range component of RFT and test if social risk attitudes depend on relative position between minimum and maximum of the reference group.

The few recent experiments about risk in social context (studies by Dijk et al. (2014) and Kirchler et al. (2016) have demonstrated that low rank position within the group results in higher risk-taking. But these studies don’t control for range position separately, and in their experimental design higher rank position also means higher range position. Our study is designed to separate these two components and to measure the impact of range position within the comparison group on risk-taking. We construct groups of 8 people where each member is endowed a sum of money. Only

44 one member is invited to make a risky investment with her endowment. By changing top member’s income in the group (and thus changing decision-maker’s range position), we measure the role of range position on social risk attitudes. Importantly, we have a full control for rank position so that we can measure the pure effect of range. We wish to test if range position itself plays a separate role when people evaluate their social standing.

2.2 Testing range-frequency theory

In line with the general theoretical approach, we assume the utility consisting of two added components: personal and social:

푈(푥) = 푢(푥) + 푦 ∗ 푛(푥, 푠) (5) where the second component describes how people evaluate their social position within the comparison group. We want to test if range-frequency theory can describe our social risk-attitudes. So we hypothesize that the second social utility component depends of decision-maker’s range and rank positions within the comparison group:

푥푖−푥1 푖−1 푛(푥푖, 푠) = 푓 (푤 + (1 − 푤) ) (6) 푥푁−푥1 푁−1 where 푥푖 is decision-maker’s money, 푥푚푖푛 and 푥푚푎푥 are the biggest and smallest monetary amounts within the comparison group, 푁 is the number of people in the comparison group. This formula assumes that social utility depends on rank position and also separately on range position. In our experiment we have a total control for rank position: risky decision maker is always second to last in her group. But we vary the range position within the group. If social utility depends separately on range

45 position as well, then changing one’s range within a comparison group must have an impact on risk attitudes. Range position is a limited parameter which can vary from 0 to 1. Original theory by Parducci assumes a linear dependence of utility from range. However, this dependence can also be concave, convex, s-shaped or other. If monetary gains/losses change the range position, then a non-linear dependence on range shall predict a social impact on risky decisions. Such an impact depends on exact functional form of this range-dependence. We don’t have any previous empirical evidence on how social utility can depend on range-position within a comparison group. Following proposition summarizes our expectations:

Proposition 1: If social utility depends on range position within the comparison group, and if this dependence is other than linear, then changing one’s range position must also change one’s risk-attitudes in a social context.

2.2 Experiment design

Our experimental design allows to make testable hypotheses about Proposition 1. The experiment consists of Part 1 with a single treatment INDIVIDUAL and Part 2 with two treatments SOCIAL 1 LOW RANGE and SOCIAL 2 HIGH RANGE. In each treatment subjects make 15 different risky choices. They go through these same 15 choices three times: once in INDIVIDUAL and twice in two SOCIAL treatments. In INDIVIDUAL these are usual individual risky choices while in SOCIAL 1 and SOCIAL 2 subjects are part of an 8-member group and can observe the endowments of their peers before making the same risky choices.

Part 1 : INDIVIDUAL treatment.

Part 1 is the INDIVDUAL treatment, consisting of 15 rounds. In each round the subject is endowed a sum of money and is invited to invest some part of that money into a risky lottery. This investment may double or become zero with equal chances (Figure 2.1). The size of this investment is considered as the measure of risk. Similar decision problems for measuring risky investments have been previously implemented in other experiments (Huber et al., 2016; Gneezy and Potters, 1997)) as a tool to measure risk-attitudes. Table 2.1 shows the endowments in 15 rounds.

Figure 2.1: Screenshot of the experiment: treatment INDIVIDUAL

INDIVIDUAL

Rounds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 YOU $4.00 $4.25 $4.30 $4.40 $4.50 $4.75 $4.80 $4.90 $5.00 $5.25 $5.50 $5.60 $5.75 $5.90 $6.00

SOCIAL 1: LOW RANGE (Range = 15%) Rounds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Player 7 $26.8 $28.5 $28.9 $29.3 $30.1 $31.8 $32.2 $32.6 $33.0 $35.1 $36.7 $37.5 $38.4 $39.2 $40.0 Player 6 $9.9 $10.0 $10.7 $11.1 $11.1 $11.6 $12.0 $12.0 $12.0 $12.4 $13.2 $13.2 $13.6 $14.0 $14.0 Player 5 $9.5 $9.9 $10.3 $10.7 $10.7 $11.1 $11.6 $11.6 $11.6 $12.0 $12.8 $12.8 $13.2 $13.6 $13.6 Player 4 $9.1 $9.5 $9.9 $10.3 $10.3 $10.7 $11.1 $11.1 $11.1 $11.6 $12.4 $12.4 $12.8 $13.2 $13.2 Player 3 $8.7 $9.1 $9.5 $9.9 $9.9 $10.3 $10.7 $10.7 $10.7 $11.1 $12.0 $12.0 $12.4 $12.8 $12.8 Player 2 $8.3 $8.7 $9.1 $9.5 $9.5 $9.9 $10.3 $10.3 $10.3 $10.7 $11.6 $11.6 $12.0 $12.4 $12.4 YOU $4.00 $4.25 $4.30 $4.40 $4.50 $4.75 $4.80 $4.90 $5.00 $5.25 $5.50 $5.60 $5.75 $5.90 $6.00 Player 1 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

SOCIAL 2: HIGH RANGE (Range = 40%) Rounds 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 Player 7 $10.0 $10.7 $11.1 $11.1 $11.6 $12.0 $12.4 $12.4 $12.4 $12.8 $13.6 $13.6 $14.0 $14.4 $14.4 Player 6 $9.9 $10.0 $10.7 $10.7 $11.1 $11.6 $12.0 $12.0 $12.0 $12.4 $13.2 $13.2 $13.6 $14.0 $14.0 Player 5 $9.5 $9.9 $10.3 $10.3 $10.7 $11.1 $11.6 $11.6 $11.6 $12.0 $12.8 $12.8 $13.2 $13.6 $13.6 Player 4 $9.1 $9.5 $9.9 $9.9 $10.3 $10.7 $11.1 $11.1 $11.1 $11.6 $12.4 $12.4 $12.8 $13.2 $13.2 Player 3 $8.7 $9.1 $9.5 $9.5 $9.9 $10.3 $10.7 $10.7 $10.7 $11.1 $12.0 $12.0 $12.4 $12.8 $12.8 Player 2 $8.3 $8.7 $9.1 $9.1 $9.5 $9.9 $10.3 $10.3 $10.3 $10.7 $11.6 $11.6 $12.0 $12.4 $12.4 YOU $4.00 $4.25 $4.30 $4.40 $4.50 $4.75 $4.80 $4.90 $5.00 $5.25 $5.50 $5.60 $5.75 $5.90 $6.00 Player 1 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0 $0.0

Table 2.1: Endowments across 15 rounds in each of three treatments.

Part 2: SOCIAL treatments with different range positions

Part 2 has two treatments: SOCIAL 1 LOW RANGE and SOCIAL 2 HIGH RANGE. In both treatments subjects are anonymously connected into 8-member groups. In each round every subject is endowed a certain sum of money and every subject can also see the endowments of other 7 seven member of her group. Figure 2.2 shows the experiment screenshot for SOCIAL treatments.

Figure 2.2: Screenshot of the experiment: treatments SOCIAL 1 and SOCIAL 2

In each round only one member of the group is invited to invest part of her winnings into a risky lottery. The lottery is identical to part 1 (it doubles or nullifies the investment with equal chances). Other seven members of the group cannot do anything and just take their endowments. So in each round we have only one member making a risky investment while seeing the endowments of other peers. This means that in each of two social treatments each subject goes through 15 rounds where she makes an investment decision and 105 rounds where someone else in the group makes a risky decision. In SOCIAL 1 LOW RANGE the single decision maker in all groups always has a low range position equal to 0.15 (or 15%). In SOCIAL 2 HIGH RANGE treatment the single decision-maker in all groups has a higher range position equal to

0.4 (or 40%). Table 1 shows the distributions of initial endowments within groups. In SOCIAL 1 and SOCIAL 2 the decision-maker (mentioned as “YOU”) can observe the winnings of her peers in the group. By changing the income of the top member we change the range position of the decision-maker between two social treatments. This design has several important improvements over previous studies. First, in previous experiments all group members are making simultaneous investment decisions. This makes it impossible to predict your future social position. In our study the decision maker can clearly see what will happen with her social position if she wins or loses the lottery. This helps to reveal her social preferences more precisely. Second, in each of three treatments our subjects go through the same, financially identical 15 decisions, which allows for very precise within-subject analysis. Even a very small investment difference between treatments can be reliably measured for each subject. This is important because social impact on decision making is weak- only 5%-10% of investment in previous experiments.

We have 8 or 16 subjects in each session. First everyone goes through INDIVIDUAL treatment. In next part half of the subjects go through SOCIAL 2 HIGH RANGE then SOCIAL 1 LOW RANGE, while other 8 subjects go through SOCIAL 1 LOW RANGE then SOCIAL 2 HIGH RANGE. So we have a randomized order of social treatments.

The following table summarizes experiment design.

Treatment Number of observations per subject

INDIVIDUAL 15 investment decisions

SOCIAL 1 LOW RANGE 15 investment decisions

SOCIAL 2 HIGH RANGE 15 investment decisions

Table. 2.2: Summary of the experiment design

Controlling for rank position

In both social treatments the initial rank position of the decision-maker is always second to last. Importantly, decision maker cannot improve her rank when her investment doubles, even if she invests all her endowment in the lottery. This ensures a total control for rank. So we disentangle the two components of RFT- rank from range, to measure only the impact of range position.

2.3 Hypothesis development

In our experiment we have lottery choices with small monetary amounts. It has been shown that people are weakly risk-averse, almost risk-neutral for such small lotteries (Halt and Laury, 2002). The investment in our lottery task is either doubled either lost so the expected value of the lottery is always equal to the initial endowment. This means that risk-neutral people may invest any proportion of the endowment arbitrarily, while weakly risk-averse subjects may invest nothing. So it would be hard for us to differentiate between risk-neutral and risk-averse subjects. Our initial pilot sessions showed that in practice subjects invest about the half of their endowments, when the endowment is doubled or lost. However, the main goal

51 of our study is to find any systematic within-subject investment differences between Individual and Social treatments. So interpreting the decisions in Individual treatment can be hard but this lottery allows to have a good measure for Individual versus Social differences. Our main goal is to measure within-subject investment differences between Individual and Social sessions, so the details of individual risk-attitudes are not the main purpose of this study. On the other hand, if our multiplication coefficient was bigger than 2, too many subjects would invest all of their endowments and it would be hard to detect any differences between treatments. So our coefficient choice of 2 (when subjects invest about half of the endowment) is practically more convenient to detect any subtle between-treatment effects.

Remember our utility function with two components, with social component depending separately on rank and range positions:

푥푖−푥1 푖−1 푈(푥푖) = 푢(푥푖) + 푦 ∗ 푓 (푤 + (1 − 푤) ) (7) 푥푁−푥1 푁−1

This formula shows that if 푦 ≠ 0 then social comparison has an impact on risk- attitudes. This leads us to our first hypothesis:

HYPOTHESIS 1: If social utility is not equal to zero, then we must see difference in risky investments between treatment INDIVIDUAL and two social treatments SOCIAL 1 and SOCIAL 2.

We further proposed that social impact depends separately on one’s range position within the group. In both social treatments the decision-maker is always second to last, so her rank position is always equal to two. However her initial range position is always 15% in SOCIAL 1 and always 40% in SOCIAL 2. Also, the same monetary gains and losses will result in bigger change of range position for SOCIAL 2 than for SOCIAL 1. So any systematic difference in investments between SOCIAL 1

52 and SOCIAL 2 will mean that social utility depends on range position in a non- linear way. This leads us to our main hypothesis:

HYPOTHESIS 2: If social utility depends on range position within the comparison group and if that dependence is other than linear, then we should see systematic differences in risky investment size between SOCIAL 1 and SOCIAL 2.

It is hard to give a more precise prediction on how exactly range position will impact social risk-attitudes. This can be clarified only based on experimental results.

2.4 Participants and procedures

64 subjects took part in 6 sessions of the experiment, conducted in April to August 2017 in the experimental economics laboratory of a large Australian university. We had 8 or 16 subjects in each session, so in social parts we had 1 or 2 groups with 8 members in each group. Participants were recruited via online ORSEE system (Greiner, 2004). Participant age varied from 18 to 43, with a mean of 23.8. We had 32 male and 32 female subjects. Participants were given written instructions with experiment details. In addition, oral presentation was given before each part, explaining the experiment. Experiment was programmed and run on oTree platform (Chen et al., 2016). Participants were paid a fix show-up fee of $5 and also the payoff they won in one random round of the experiment.

2.5 Results and discussion

Table 5 shows total within-subject investment differences between three treatments, combined for all 15 rounds. Our measure of risk is the proportion of endowment invested in risky lottery, expressed in percentage. This allows us to pool the data from 15 different rounds to get total investment differences between treatments. In order to avoid violation of statistical independence, we cluster the data by subjects and use 2-sided t-tests.

Total differences in LOW RANGE- HIGH RANGE- HIGH RANGE – investments INDIVIDUAL INDIVIDUAL LOW RANGE

Mean difference (%) 5.25% 6% 0.8%

t-stat 1.69 1.98 0.42

p-value 0.095 0.052 0.67

Table 2.3: Within-subject risky investment differences between treatments, combined for all 15 rounds. Results for 2-sided t-tests clustered by subjects are shown.

Table 2.3 shows that there are small but weakly significant increases in risky investments between INDIVIDUAL and SOCIAL treatments. Compared to INDIVIDUAL treatment, on average subjects invested 5.25% more of their endowments in SOCAL 1 LOW RANGE (t=1.69, p=0.095) and 6% more in SOCIAL 2 HIGH RANGE treatments (t=1.98, p=0.052).

We also conducted post hoc power tests to understand the probabilities of type 2 errors in our tests. Such post-experimental power tests are generally not reliable,

54 because they assume that our effect magnitude is similar to that in the population, and so these tests are not very trustworthy. However, assuming that our effect magnitude (the difference of average investments between three treatments) is equal to that of the general population, post-experimental power-tests show following results: for LOW_RANGE vs IND difference we would need 180 subjects to achieve a power level of 0.8 (and a significance level of 0.05), assuming this effect size. And for the HIGH_RANGE vs IND difference we would need 134 subjects to achieve a power of 0.8 (and significance level of 0.05). While realizing all the limitations of such post- experimental power tests, they still indicate that our results do not look too unrealisitic.

These results are also in line with previous findings by Dijk et al. (2014), Kuziemko et al. (2015) and Kirchler et al. (2016). Indeed people take more risk when put in a low rank position within a comparison group. In these three mentioned experiments however subjects are making simultaneous investments and as a results their rank is constantly changing after each round. So they can increase their rank position if their excessive risk pays off. On contrary, subjects in our experiment do not have a chance to improve their rank position. Even if they invest all of the endowment and double it, they still remain second to last. So people still increase their risky investment even if they know its impossible to improve the rank position within the group. Such a result indicates that subjects increase risky investments even though they cannot improve their rank position.

However there is no significant difference in investments between SOCIAL 1 LOW RANGE and SOCIAL 2 HIGH RANGE treatments: only 0.8% (t=0.42, p=0.67). So these results do not suport our second hypothesis: changing range position within the group from 15% to 40% does not change social risk-attitudes.

Table 2.4 shows investment differences between treatments INDIVIDUAL and SOCIAL 1 LOW RANGE. Graphs on Table 2.3 show the distributions of within- subject differences in risky investments for 15 rounds. Decisions are financially identical in each corresponding rounds of two treatments so we take the absolute differences in investments for each subject. We use 1-sided t-tests to check weather these differences are bigger than zero.

Round 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 mean(%) 9.3 7.5 10.6 8 3.9 8.4 6.2 3.2 6.3 0.2 9.9 -3.2 3.5 5.8 -0.8 se 5.4 5 5.4 5.8 5.2 5 3.9 4.8 4.8 4.1 4.6 5.3 4.6 4.1 3.7 t-stat 1.7 1.49 1.953 1.382 0.738 1.669 1.559 0.66 1.308 0.052 2.108 -0.594 0.761 1.406 -0.218 p-value 0.046** 0.070* 0.027** 0.086* 0.232 0.05** 0.062* 0.255 0.098* 0.479 0.02** 0.723 0.225 0.08* 0.586 Table 2.4: Within-subject differences in investments (in percentage) between treatments SOCIAL 1 LOW RANGE and INDIVIDUAL. Average investments are bigger in treatment SOCIAL 1 LOW RANGE in all rounds except rounds 12 and 15. One-sided t-tests are weakly significant in 9 rounds.

Except rounds 12 and 15, in all arounds the average investment differences are bigger in favor of SOCIAL 1 LOW RANGE treatment. We use 1-sided t-tests and find that these differences are weakly significant only in 9 rounds. So having a low range position of 15% (as well as low rank position) results in slight increase of risky investments. This further proves our hypothesis 1.

Table 2.5 shows the difference between treatments INDIVIDUAL and SOCIAL 2: HIGH RANGE.

Round 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 mean(%) 10.8 6.2 5.3 5.6 0.9 6.7 10.4 3 7.1 5.6 9.9 4.7 8.5 6.9 -0.3 se 5.2 4.7 5.1 4.7 4.8 4.3 4.5 4.8 4.8 5 4.2 4.4 4.4 4.7 3.9 t-stat 2.085 1.291 1.024 1.174 -0.193 1.555 2.304 0.626 1.474 1.116 2.351 1.067 1.906 1.441 -0.082 p-value 0.02** 0.101 0.155 0.122 0.424 0.062* 0.01** 0.267 0.07* 0.134 0.01** 0.145 0.03** 0.077* 0.532 Table 2.5: Within-subject differences in investments (in percentage) between treatments SOCIAL 2 HIGH RANGE and INDIVIDUAL. Average investments are bigger in treatment SOCIAL 2 HIGH RANGE in all rounds except round 15. One-sided t-tests are weakly significant in 7 rounds

Within-subject differences in investments are positive in 14 rounds. This means that subjects invest more of their endowments in risky lotteries when they compare their money with bigger endowments of peers. In seven rounds these differences are significantly positive, in favor of social treatment (we use 1-sided t-tests for each round). Investment differences between INDIVIDUAL and SOCIAL treatments range from 0% to 10% of the endowment. This magnitude is comparable with results in previous similar experiments.

Round 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 mean(%) 1.5 -1.3 -5.3 -2.5 -2.9 -1.8 4.3 -0.2 0.8 5.4 0.1 7.9 5 1.1 0.5 se 3.6 4.1 4.3 5.3 3.8 3.9 4.4 4.7 3.8 3.8 3.5 4.2 4 4.3 3.3 t-stat 0.411 -0.323 -1.221 -0.462 -0.764 -0.444 0.967 -0.038 0.198 1.4 0.027 1.845 1.252 0.251 0.146 p-value 0.68 0.75 0.227 0.646 0.448 0.658 0.337 0.97 0.844 0.166 0.979 0.069 0.215 0.8 0.88 Table 2.6: Within-subject differences in investments (in percentage) between treatments SOCIAL 1 LOW RANGE and SOCIAL 2 HIGH RANGE. Average investments are not significantly different across two social treatments. Two-sided t-test coefficients are shown.

So we confirm the previous findings showing that subjects take bigger risk when placed in a low position within a group. These results generally confirm our first hypothesis.

Table 2.6 shows the investment differences between two social treatments SOCIAL 1: LOW RANGE and SOCIAL 2: HIGH RANGE (1-sided t-tests for each round).

In 9 rounds these differences are positive showing a bigger investment in SOCIAL 2 HIGH RANGE and in 6 rounds the differences are negative showing bigger investments in SOCIAL 1 LOW RANGE. However these differences are not significant in 2-sided t-tests. So we do not find systematic investment differences between SOCIAL 1 LOW RANGE and SOCIAL 2 HIGH RANGE treatments. Changing the range position within the group from 15% to 40% does not result in different risk attitudes. So we do not find an impact of range position on social risk attitudes, at least when changing from 15% to 40%.

2.5.1 Secondary results: gender effect

If we look at male and female investment patterns, it seems that the difference between individual and social treatments is mainly driven by male subjects. Tables 2.7 and 2.8 show average investments of male and female subjects across all 15 rounds. We use 2-sided t-tests, clustered by subjcets, to identify any differences in investments across treatments. Table 2.6 shows that male subjects invested 8% more of their endowments in SOCIAL 1 LOW range (t=1.5, p=0.14) and 9.2% more of their endowments in SOCIAL 2 HIGH RANGE (t=1.744, p=0.091) treatments, compared to INDIVIDUAL treatment.

Total differences in LOW RANGE- HIGH RANGE- HIGH RANGE investments across INDIVIDUAL INDIVIDUAL – LOW MALE subjects RANGE

Mean difference (%) 8% 9.2% 1.1%

t-stat 1.5 1.744 0.49

p-value 0.141 0.091 0.624

Table 2.7: Male subjects: treatment differences in risky investments, combined for all 15 rounds. Results for 2-sided t-tests clustered by subjects are shown.

In contrast with males, female subjects almost do not change their risk-attitudes when facing social comparison. Small investment differences between INDIVIDUAL and SOCIAL treatments are not significant for females, as seen from Table 2.7. These results potentially indicates a gender bias towards social comparison: male subject’s risk-attitudes seem to be more sensitive to social comparison while females are basically not affected. Such a result is generally in line with gender-specific attitudes toward social status. Men are more competitive than women and more willing to

60 acquire higher status (Gneezy and Rustichini, 2004; Gneezy et al., 2003; Knight, 2002). Men also take more risk: in all three treatments men invest more in lotteries. Appendix A2 shows the regression analysis of risk-attitudes depending on demographic factors, and gender is the only significant factor.

Total differences in LOW RANGE- HIGH RANGE- HIGH RANGE investments across INDIVUDAL INDIVIDUAL – LOW FEMALE subjects RANGE

Mean difference (%) 2.4% 2.9% 0.5%

t-stat 0.77 0.94 0.15

p-value 0.44 0.35 0.87

Table 2.8: Female subjects: treatment differences in risky investments, combined for all 15 rounds. Results for 2-sided t-tests clustered by subjects are shown.

We also find that subjects are consistent in their risk-attitudes across all three treatments (Table 2.9). This means that more risk-loving subjects invest more of their endowments in lotteries in all three treatments, compared to less risk-loving ones. If we take the average risky investments across 15 rounds of each treatment, we can find significant correlations of these investments across all three treatments (for each subject). This means that people’s risk-attitudes are consistent during the experiment. We can see more noise when comparing INDIVIDUAL treatment with SOCIAL treatments which is the result of the social impact. On the other hand, risk-attitudes between two social treatments are more consistent.

INDIVIDUAL and INDIVIDUAL and LOW RANGE and LOW RANGE HIGH RANGE HGH RANGE p-value <0.01 <0.01 <0.01 Adj R- 0.37 0.41 0.73 squared Regression plot

2.5.2 Alternative models of social utility

Social utility depending on distance from group average

One of the most common and seemingly natural ways of modelling social impact is to assume that people compare their income with the average income of comparison group. In the context of our experiment this would mean that social utility depends on decision-makers monetary distance from the group average:

푛(푥, 푠) = 푓(푥 − 푠) (9)

where 푥 is the self-income and 푠 is the group average endowment. Our experiment allows to test such a model of social utility.

Group average $9.5 $10.0 $10.3 $10.7 $10.8 $11.3 $11.6 $11.6 $11.7 $12.3 $13.0 $13.1 $13.5 $13.9 $14.0

Distance from $5.5 $5.7 $6.0 $6.3 $6.3 $6.5 $6.8 $6.7 $6.7 $7.0 $7.5 $7.5 $7.8 $8.0 $8.0 group average

Table 2.10: Treatment SOCIAL 1 LOW RANGE: growing monetary distances between the decision-maker and group average, across 15 rounds

Indeed, Table 2.10 shows the endowment distributions in SOCIAL 1 LOW RANGE treatment. Across fifteen rounds, monetary distance between the decision-maker

and group average is growing from $5.5 to $8. However, this increase doesn’t result to any change in risky investments (Table 2.11). An ANOVA test shows that proportion of endowments invested in risky lotteries don’t change across 15 rounds (F=0.17, p>0.99)

Table 2.11: Distributions of risky investments across 15 rounds: treatment SOCIAL 1 LOW RANGE

Social utility depending on distance from the top member

We can also assume that people compare their income only with the top member of the comparison group. This means that social component of the utility depends on distance from the group member with highest endowment:

푛(푥, 푠) = 푓(푥 − 푥푀푎푥) where 푥푀푎푥 is the highest endowment in the group, and 푥 is the endowment of the decision maker.

We can test this hypothesis within the treatment SOCIAL 1: LOW RANGE. Table 2.12 shows that the distance from highest earner is growing from $22.8 to $34 across 15 rounds (while range position remains constant at 15%).

However the investments in risky lottery don’t grow in any significant way with this increase. This disproves the hypothesis that social impact depends on distance from the leader.

This analysis is harder to do for SOCIAL2 treatment because the distance from top player and from group average are changing much slower across the 15 rounds of this treatment.

Initial conclusion is that the increase of risky investments in social treatments (compared to individual) is due to low rank position within the group. Changing the initial range position did not affect risky investments. And changing the monetary distance from group average or top member also does not result in any change of social risk-attitudes, as we test these possibilities within the SOCIAL 1 LOW RANGE treatment. The interesting point is that subjects increase their risky investments when put in a low rank position, even though they cannot jump rank positions. In previous related experiments by Dijk et. al. (2014), Kirchler et. al. (2016) and Kuziemko et. al.

(2014) participants can improve their rank position if their risky investments payoff. So the natural conclusion is that low-rank participants increase risky investments to improve their rank. Our result shows that improving the rank is probably not the only motivation to take more risk when having a low social status. We can argue that people still want to decrease their distance from group average or from the top member. But our analysis of SOCIAL 1 LOW RANGE treatment shows that changing these parameters do not have an impact on social risk-attitudes. The main open question which arises from our results is about the motivation behind increasing risky investments when put in a low rank position, if there is no possibility to improve that rank. If we rule out the possibilities that people want to increase their range position or their distance from group average or distance from the leader, then increasing risky investments might be interpreted as an emotional reaction to low status. This idea can be linked to other economic studies. For example, a series of economic experiments about strong reciprocity concluded that rejection of small offers in ultimatum games should be explained as a rejection of imposed low status (Yamagishi et al., 2012). Other experiments on ultimatum games show that small unfair offers cause a negative emotional reaction (Sanfey et al., 2003; Pillutla and Murnighan, 1996). So being offered less money than peers triggers a negative emotional reaction leading to economically “irrational” choices in ultimatum games. Something similar might be happening in our experiment: when seeing other group members having much more money, participants may experience a negative emotional affect leading to bigger and economically unjustified risk. This line of thinking might be an alternative explanation of why the participants take more risk without having an opportunity to increase the rank position.

Another general issue is related to external validity of the experimental results. In the experiment we give a clear picture of who has how much endowment in the group, and present this information very clearly, both numerically and visually

(distances between players on the experimental screen are proportional to endowment sizes). In real life people have incomplete information about the incomes of their social comparison groups and thus biased beliefs about their social position on income hierarchy. In addition, it is not always clear who should be considered as a social reference group or with whom people compare themselves. For example, Boyce et. al. construct different social reference groups based on age, geography or education levels to show the relation between income rank and happiness. They find different impacts of social comparisons depending on with whom people compare- with people from their local neighborhood or people from their generation or people having similar educational status (Boyce et al., 2010b). So there is no one universal way to model the social comparison in real life, and our experiment gives only a general idea about its impact on risk-taking.

The following list summarizes key findings of this study:

1. In social treatments subjects invested about 5 to 6 percent more of their endowments in risky lotteries, compared to individual treatment. 2. It seems that this effect is largely due to male subjects, although more data will be needed for statistical power.

2.7 Conclusion

Growing body of literature about risk in social context shows that risk attitudes may depend on peer effects in different aspects. Our study focuses on how a mere passive observation of other people’s money can influence risk-attitudes. In our experiment we compare individual risky decisions with financially similar decisions

67 made after comparing with others. We have an individual treatment and two social treatments. In social treatments subjects become members of a group and can observe monetary endowments of others before making a risky investment. Previous studies have shown that such a passive observation of other people’s money has in impact on risk-attitudes. More importantly, each study tests different theoretical models to describe this social impact. Usually these models focus on relative rank within the group, or distance from a single social reference point. We test a psychophysical theory of contextual evaluation- Range-Frequency Theory, to test if subject’s range position within a comparison group has an impact on their social risk-attitudes. According to RFT, social impact on utility must depend on relative rank within the group and the range position between top and bottom members. Previous studies have indeed shown that relative rank position had a visible impact on risk: lower rank position results in higher risk-taking. However, in these studies there was no separate control for range position and in effect, the impacts of two factors- rank and range were measured together. So in our experimental design we separate the possible impacts of rank and range. We do this by completely controlling for initial rank position as well as for any possible gains or losses of rank position, and focus on the impact of range position only. We vary the initial range position within the group between two values: 15% and 40% to measure any difference in social risk-attitudes. We didn’t find any difference in risky investments when range position is changed within the comparison group. However we do find a social impact on risk taking: in both SOCIAL treatments subjects invested more of their money in risky lotteries, compared to INDIVIDUAL treatment.

Chapter 3

3 Collective risky decisions: the role of status hierarchy

3.1 Introduction

Many real-life decisions involving risk and uncertainty are often made by committees, boards or expert groups rather than individuals. However, economic theory and economic experiments on risk and uncertainty have been mostly focusing on individual choices and not so much on decisions made by groups. This limitation is changing in recent years, and more and more economic studies are investigating economic decisions jointly made by groups. For example, in his textbook “Behavioural Game Theory” Camerer announces this topic as one of 10 important issues in behavioral economics (Camerer et al., 2004). In a meta-analysis on this issue, Kugler et al. find that groups are often more rational than individuals, possibly due to the deliberative analysis and discussion made before the decision (Kugler et al., 2012). For example, Bornstein and Yaniv (1998) show that 3 person groups are more selfish than individuals when playing an ultimatum game: proposals made by groups are smaller than individual proposers (Bornstein and Yaniv, 1998). Studies on public dilemma games show that groups defect more often than individuals (Insko et al., 1993). Morone and Morone test several famous economic preference functions and finds differences in their performance for individuals and groups (Morone and Morone, 2014). So experimental investigation of how preference aggregation actually happens when groups make an economic decision is an emerging field in economics.

A number of economic experiments published in last decade focus specifically on risky choice made by groups. These experiments typically have groups of 3 to 5

69 people deciding on a lottery choice with financial risk and uncertainty. Formats of communication and decision rules vary and findings are somewhat mixed. The main question is to understand how the preference aggregation actually happens when a group faces a risky decision. Some studies find that groups are more risk-averse than individuals. For example, experiments by Baker et al (2007), Masclet et al (2009), Schupp and Williams (2008) have groups of 3 to 5 people making a collective choice between risky and safe lotteries. These experiments found that groups are more risk- averse than their members individually (Baker et al., 2007; Masclet et al., 2009; Shupp and Williams, 2008). However, similar experiments by Zhang and Casari or Keck et al. found the opposite: groups demonstrated less risk-aversion than individuals (Keck et al., 2011; Zhang and Casari, 2012). Other studies found no difference in risk- attitudes between groups and individuals (Brunette et al., 2011; Harrison et al., 2012). These regularities have been known from earlier psychology literature as “risky shift” and “cautious shift” effects (Stoner, 1968). Its important to note that these studies have slightly different rules of collective decision-making (unanimity versus majority rule, communication can be via messaging or face-to-face or merely anonymous information exchange). The reason for such shifts is not well understood but seems to be related to the actual rules of collective decision-making and how the responsibility is shared among the group members.

In social psychology on the other hand, group decision-making has long been a central topic. A huge body of literature exists investigating impacts of peer-effects, social comparison, status and power hierarchies on decisions made by groups. In particular, status and power hierarchies in groups and their impact on group decision- making is an interesting research direction. The so called functionalist theory of status argues that status hierarchies always emerge spontaneously in small groups and have regulatory or coordinating role in collective decisions (Anderson and Kilduff, 2009b; Van Vugt, 2006). Status is defined as the informal amount of respect

70 or prestige attributed to individuals by other team members. This is usually based on skills or expertise relevant for the problem or decision that group faces (Gould, 2003; Tiedens et al., 2007b). It is argued that such an internal status hierarchy facilitates coordination and conflict resolution when the group faces internal disagreements.

This idea goes back to sociology, claiming that internal hierarchical structure provides social order and prevents chaos in human societies (Hogg, 2001; Coser and Durkheim, 1997). Some studies show similar evidence for small groups with one-to- one interactions. For example, several studies have shown that individuals are better at coordinating on a common task when one is dominant and other is submissive (Tiedens and Fragale, 2003; Tiedens et al., 2007b; Tiedens et al., 2007a). Friesen et al (2014) show experimentally that hierarchical social structures were perceived more orderly and predictable than egalitarian organizations (Friesen et al., 2014). On the other hand, when too many dominant or too many status seeking members try to cooperate, they engage in internal conflicts with resulting decline of group performance (De Dreu and Weingart, 2003). This seems intuitively reasonable and a large amount of empirical studies tries to test this theory. The main argument here is that high status members in a group have bigger decision weight and this asymmetry of informal decision power has a crucial role in conflict resolution within the team. Such line of thinking is tested in a number of empirical studies on sports teams, management groups etc. Some studies show that vertical status hierarchies indeed result in better conflict resolution within groups. For example, Swaab et al. (2014) find that basketball teams with too many top “star” players often play worse than more balanced groups. They call this “too-much talent effect” (Swaab et al., 2014). Groysberg et al. find similar effects in investment banks, where groups of top- class financial analysts often fail to coordinate their efforts and as a result deliver bad results because of internal conflicts between high-status members (Groysberg et al., 2011). Another analysis of expedition history to Himalaya mountains shows that

71 expeditions from more hierarchical cultures are more likely to climb to the top, but also more likely to have fatalities (Anicich et al., 2015). In an experimental study by Ronay et. al. groups of three people work on a verbal puzzle task. They show that more balanced groups are more productive compared to groups with all-high or all- low status members (Ronay et al., 2012). In short, the functionalist interpretation of status tries to convince that groups with too many high-status members have more internal conflicts, groups with no high-status members lack internal coordination, while balanced groups are better at group decision making. Studies also indicate that such a theory works when teams face an highly interdependent task (Greer, 2014). However the opposite evidence also exists showing that unequal distribution of respect and status can lead to feelings of unfairness and dissatisfaction within groups and have negative impact. For example, studies about professional baseball teams find that higher inequality of player salaries (which is based on their skills) has a negative impact on team performance (Frick et al., 2003). So the impact of vertical status hierarchies within groups is highly context specific and depends on the particular case.

To sum up, this theoretical approach tries to use the concept of internal status hierarchy as a key driver of group dynamics. It argues that status hierarchies within groups emerge spontaneously and have a positive (or sometimes negative) impact on group dynamics, facilitating coordination or creating more conflict within the group. Economic experiments on group decisions under risk do not discuss the idea of internal status hierarchy and usually assume equal decision weights for each group member. Economic studies on groups don’t discuss the role of informal status hierarchies and the asymmetry of decision weights that they usually bring. In relation to question of whether groups are more or less risky than individuals, the hierarchy theory suggests that groups with vertical status hierarchies should be not more or less

72 risky than individuals, but rather the collective decision should be shifted towards the preferences of high status members. This idea is the main focus of our study.

We follow previous economic experiments on collective risky choice: anonymous groups of 4 people are invited to make a choice between risky and safe lotteries. In addition we impose an artificial status hierarchy within the group (based on results in a knowledge quiz and using status symbols). We construct groups with different types of hierarchy. We have flat groups with all high or all low status members and we have vertical groups with one high or one low status member. Such status configurations are supposed to mimic real life teams with different hierarchical organizations. We try to understand if such an artificial status hierarchy will bring more coordination into vertical groups or more conflict in all-high status groups and less coordination in all-low groups. In mixed groups we expect a shift of collective choices towards the preferences of high status members. Such a result would be in line with studies showing a positive role of status hierarchy. From an economics perspective, such a result would indicate that groups are not more or less risky than individuals, but rather groups just follow their informal leaders.

However, our result is more in line with an alternative line of research which argues and shows that internal inequalities of status are a source of perceived injustice and actually result in more conflicts. Our experiment showed that low status male participants were actually less eager to change their opinion during the deliberation process. As a result, collective decisions over the choice between safe and risky lotteries were slightly shifted towards the preferences of low status male subjects. Importantly, vertical hierarchies didn’t seem to solve the intra-group conflict over differing risk-attitudes.

3.2 The experiment

The experiment has two main stages: quiz with a following status-awarding ceremony and the group-decision stage.

Stage 1: Awarding status

In the first stage all participants pass a 15 question general knowledge quiz, with 4 possible answers for each question to choose from. They get 1 point for each correct answer. In each session we have strictly 16 participants. Based on quiz results we divided participants into two equal groups. Top eight subjects were invited to stand up and were given golden star stickers to stick on their t-shirts. Bottom eight participants were encouraged to applaud to their high-achieving peers. Then the top eight participants were reseated at front left part of the classroom while the bottom eight participants were reseated at the back–right seats in the classroom. The computer program was set to understand this reseating manipulation. This manipulation is considered as “status” giving and has been used in a number of previous experiments. Despite being arguable whether this is a real status, this manipulation often has an impact on behavior in different experimental settings. Because questions are very difficult, answers are mostly random and this ensures that participants are not divided according to their actual IQ.

Stage 2: Group decisions

After the status awarding ceremony subjects proceed to the second stage where they make collective choices over two lotteries. Second part has 30 rounds. In each round each participant is anonymously connected with 3 others and becomes part of a new group. The same subject becomes a member of different groups with different status configurations. As a result. there are no strategic considerations or reputational

74 issues because in each round we construct new groups. In each round we have 4 groups with different status configurations. We have two flat groups with 4 high status and 4 low status members, and two vertical groups with 1 high-3 low and 3 high-1 low members. Groups are anonymous. Each group is presented two lotteries to choose from (Table 3.1). Safe lotteries pay from $3.5 to $5 with high chances (65% to 70%) and $0 otherwise. Risky lotteries pay $8 to $10 with small chances (30% to 35%) and $0 otherwise. Differences of expected values for each pair of lotteries are small and can be positive or negative. Expected values are deliberately chosen to create disagreements among subjects. This was achieved by trial and error on pilot sessions where we found the conflict-creating lottery pairs. So our main goal is to create a disagreement within the group.

Safe lottery Risky lottery Difference: Round Payoff 1 Prob 1 Payoff 2 Prop 2 EV_Safe Payoff 1 Prob 1 Payoff 2 Prop 2 EV_Risky EV_Safe-EV_Risky 1 $5.0 65% $0 35% $3.25 $10.0 35% $0 65% $3.50 -$0.25 2 $5.0 66% $0 34% $3.30 $10.0 34% $0 66% $3.40 -$0.10 3 $5.0 67% $0 33% $3.35 $10.0 33% $0 67% $3.30 $0.05 4 $4.5 68% $0 32% $3.06 $10.0 32% $0 68% $3.20 -$0.14 5 $4.5 69% $0 31% $3.11 $10.0 31% $0 69% $3.10 $0.00 6 $4.5 70% $0 30% $3.15 $10.0 30% $0 70% $3.00 $0.15 7 $5.0 65% $0 35% $3.25 $9.5 35% $0 65% $3.33 -$0.07 8 $5.0 66% $0 34% $3.30 $9.5 34% $0 66% $3.23 $0.07 9 $4.5 67% $0 33% $3.02 $9.5 33% $0 67% $3.14 -$0.12 10 $4.5 68% $0 32% $3.06 $9.5 32% $0 68% $3.04 $0.02 11 $4.5 69% $0 31% $3.11 $9.5 31% $0 69% $2.95 $0.16 12 $4.5 70% $0 30% $3.15 $9.5 30% $0 70% $2.85 $0.30 13 $4.5 65% $0 35% $2.93 $9.0 35% $0 65% $3.15 -$0.23 14 $4.5 66% $0 34% $2.97 $9.0 34% $0 66% $3.06 -$0.09 15 $4.5 67% $0 33% $3.02 $9.0 33% $0 67% $2.97 $0.04 16 $4.5 68% $0 32% $3.06 $9.0 32% $0 68% $2.88 $0.18 17 $4.5 69% $0 31% $3.11 $9.0 31% $0 69% $2.79 $0.32 18 $4.0 70% $0 30% $2.80 $9.0 30% $0 70% $2.70 $0.10 19 $4.5 65% $0 35% $2.93 $8.5 35% $0 65% $2.98 -$0.05 20 $4.5 66% $0 34% $2.97 $8.5 34% $0 66% $2.89 $0.08 21 $4.5 67% $0 33% $3.02 $8.5 33% $0 67% $2.81 $0.21 22 $4.0 68% $0 32% $2.72 $8.5 32% $0 68% $2.72 $0.00 23 $3.5 69% $0 31% $2.42 $8.5 31% $0 69% $2.64 -$0.22 24 $3.5 70% $0 30% $2.45 $8.5 30% $0 70% $2.55 -$0.10 25 $4.0 65% $0 35% $2.60 $8.0 35% $0 65% $2.80 -$0.20 26 $4.0 66% $0 34% $2.64 $8.0 34% $0 66% $2.72 -$0.08 27 $3.5 67% $0 33% $2.35 $8.0 33% $0 67% $2.64 -$0.30 28 $3.5 68% $0 32% $2.38 $8.0 32% $0 68% $2.56 -$0.18 29 $3.0 69% $0 31% $2.07 $8.0 31% $0 69% $2.48 -$0.41 30 $3.0 70% $0 30% $2.10 $8.0 30% $0 70% $2.40 -$0.30 Table 1: Lotteries for each round: Safe lotteries pay $3.5 to $5 with high chances, Risky lotteries pay $8 to $10 with small chances. Table 3.1: Lotteries in each round: safe lotteries pay $3.5 to $5 with high chances, risky lotteries pay $8 to $10 with small chances

This lottery choices are similar to the middle-row choices in Holt & Laury type of lottery lists, when the choice is not obvious. Each member clicks on the lottery she prefers and then can see the preferences of other three group members. They can also see the statuses of all four group members (see Appendix B for experiment screenshots). Other than this information there is no other information exchange or communication between the subjects. This ensures that we measure the impact of

76 status only and other factors such as verbal communication skills don’t mediate the bargaining. So after the initial choices subjects see the choices of everyone in the group, and if there is a disagreement over which lottery to choose, then subjects are invited to make their choices once again. They have up to 5 opportunities to come to an agreement. We have an unanimity rule: the lottery is chosen only if all four members agree on the same lottery. If after 5 deliberation steps there is no unanimous choice then neither of the lotteries is chosen. Table 1 shows all the 30 pairs of lotteries used in 30 rounds. After this stage one of the 30 rounds is chosen randomly and the lottery chosen in that round by each group is played out.

Table 3.2 summarizes the experiment design.

Treatment Number of Maximum number of observations per group iterations in each group type decision

All HIGH 30 group decisions 5 iterations per agreement

1 HIGH- 3 LOW 30 group decisions 5 iterations per agreement

3 HIGH- 1 LOW 30 group decisions 5 iterations per agreement

All LOW 30 group decisions 5 iterations per agreement

Table 3.2: Summary of the experiment design

3.3 Hypothesis development

The theoretical framework we use argues that groups with both high and low status members will need less steps to decide on the lottery, because the status hierarchy will provide a coordinating role. Meanwhile, in all-high and 3 high-1 low

77 status groups there will be a conflict because high status members will engage in conflicts to decide who’s voice must be decisive. Such a conflict among high status members is generally hypothesized in hierarchy literature, in different contexts (Bendersky and Hays, 2012; De Dreu and Weingart, 2003). In very different settings, high status members within groups have been shown to engage in contests for domination, instead of focusing on the task itself (Greer et al., 2011; Overbeck et al., 2005; Porath et al., 2008). So in our group task, when four group members with different risk-attitudes must decide on a lottery, we expect a similar conflict between high-status members. In all-low status groups on the other hand we expect a lack of coordination within the group, and as a result a bigger number of steps needed for a collective decision. This expectation is based on experimental evidence showing that all-low status groups are performing worse in interdependent tasks because there is no coordination within the group (Ronay et al., 2012). Groups with only one high status member should find it easier to resolve disagreements because of unequal decision weight of the high status member: 3 low status members are expected to shift their choices towards the preferences of the high-status member. Such a result has been demonstrated in the same experiment by Ronay et al. (2012). More generally this is the predicted result of the hierarchy literature. However if this indeed happens then a question may arise: does the high status member really have a bigger decision weight or she only has a role of a coordination device just because of her salient difference from other three members? To control for this possibility we add a fourth type of group with 3 high and one low status members. The only low status member is salient within the group but shouldn’t have bigger decision weight if status hierarchy drives the collective decision.

Dependent and independent variables

We need to define a measure of coordination or conflict for the collective decision process. One obvious measure is the number of deliberation steps that a group needs to decide on a lottery. Less number of steps would indicate better coordination and less conflict within the group. This leads us to the first hypothesis:

Hypothesis 1: If group members accept the importance of status hierarchy and high status members have bigger decision weight, then:

a) 1High-3Low groups must require the smallest number of steps to make a collective decision, compared to other three types of groups- All-High, 3High- 1Low, All-Low.

b) All-High groups and 3 High-1 Low groups should need a relatively bigger numbers of steps for a collective decision (compared to 1 High-3Low groups) because of more conflict between the high status members.

c) All-Low groups should need bigger number of steps to agree (compared to 1 High- 3Low groups) because of lack of internal coordination.

Concerning the outcome of collective bargaining, our theory indicates a specific outcome for different types of groups. Besides the number of steps needed for a collective decision, we can also look at individual behavior. If high status members have a bigger decision weight within the group then in mixed groups the final choices should be shifted towards the initial preference of high status members:

Hypothesis 2: If high status members have higher decision weight then final group choices in 1 High-3 Low and 3Low-1 High groups should be shifted toward the initial choices of high status members.

Hypothesis 2 is particularly interesting in the context of economic studies on group decisions. A number of experiments on collective risky choice try to understand whether groups are more or less risky than its members individually. Theory of status hierarchy however predicts that groups just follow their internal leaders or high status members. Whether groups are more or less risky should depend on personal preferences of its high status members.

Another measurable description of bargaining attitudes is the comparison of initial and final choices of the subject during the collective deliberation. Each subject participates in 30 collective decisions, so we can look at the frequency of changing the initial decision for each participant, and see if this depends on her relative status within the group. This should lead us to our next hypothesis:

Hypothesis 3: In mixed groups we shall see more changes of initial choices made by low status members and less changes of initial choices made by high status members.

So final collective decisions in mixed groups- 1 high-3 low and 3 high-1 low, should be shifted towards the preferences of high status members. Alternatively, if the only differing member in mixed groups merely plays a role of coordinating device, then the final group decisions shall be shifted towards the preferences of three similar status holders.

3.4 Participants and procedures

48 subjects participated in 3 sessions of the experiment conducted in a large Australian university. Experimental sessions were conducted in August and September 2016. The experiment was programmed with oTree platform (Chen et al., 2016). ORSEE system was used to invite participants to the experiment. We have 23 female and 25 male subjects who are mostly undergraduate students. Participant age varies from 18 to 44, with an average age of 22.8. Written instructions were given to all participants with detailed explanations of the experiment. In addition, oral presentations with slides were given before each stage, explaining the experiment. Participants were isolated from each other by cubicles around each desktop. Data for 2 subjects (a male and a female) was removed from the analysis because they always did the same choice in all rounds. This indicates an inadequate understanding of the task (the male subject complained to the experimenter that he just doesn’t understand the task and simply clicks on the same option in all rounds. Both subjects were non- english speakers). Subjects were paid AUD5.0 for showing up and also could win from AUD0 to AUD10 as a result of lottery choices.

3.5 Results and discussion

3.5.1 Groups

In each session we had 30 rounds and in each round we had 4 groups, which means that each session had 120 unique groups making collective decisions. We have data from 3 sessions so we have 360 unique groups. In terms of status configuration we have 4 types of groups: All-High, 1 High-3 Low, 1 Low-3 high and All-Low, which means that we have 90 collective group decisions in each group category. 92 groups

81 didn’t have a disagreement because their members chose the same lottery on the initial step. So we further analyze only those 268 groups which initially had a disagreement. Table 3.2 shows numbers of groups at each stage of deliberation who had a disagreement. 31 groups bargained until the last sixth step, from which 9 agreed on this last step while 22 couldn’t reach an agreement.

ALL High 1 High 3 Low 3 High 1 Low ALL Low

Step 1 90 90 90 90

Step 2 66 70 68 64

Step 3 23 35 28 38

Step 4 15 25 15 24

Step 5 12 17 11 9

Step 6 6 (1 12 (3 7 (1 Agreement/ 6 (4 Agreement/ Agreement/ 5 Agreement/ 9 6 Disagreement) 2 Disagreement) Disagreement) Disagreement)

Table 3.2: Numbers of groups still bargaining at each step

3.5.2 Lottery choices

We have 46 participants each going through 30 collective decisions, which means we have 1380 individual choices on the first step. From these, 886 or 64.2% were the risky lotteries and 494 initial choices or 35.8% percent of the initial choices were the safe lotteries. So risky lotteries were initially slightly more attractive. 92 groups didn’t have disagreements after initial choices which means that 344 initial choices were also the last choices in these groups (remember that we removed data

82 from 2 participants but we still analyze the groups they were in). Final choices in those groups which had disagreements are shifted towards risky lotteries: 742 choices or 71.6% were the risky lottery and 294 choices or 28.4% were the safe lottery. So we have a slight risky shift which seems to be related to overall attractiveness of risky lotteries. This result however shouldn’t be interpreted as if groups are more risky than individuals because the initial choice is already made within the group and the initial choice has a consequence for other members. We don’t have a separate treatment with individual choices to compare with group choices, as previous experiments do. This is because we are interested in comparing different group types, not individual decisions with group decisions. Of those 92 groups who initially did not have a disagreement only 10 groups initially chose the safe lottery and the remaining 82 groups initially chose the risky lottery. So the risky lotteries seem to be initially more attractive.

3.5.3 Bargaining behavior on individual level: the impact of status

We first analyze the bargaining behavior on individual level. We do this by looking at the initial and final choices of subjects. We hypothesized that this should depend on status and on group type. We find however that status has an impact only on male subjects but does not change the bargaining behavior of female subjects. Table 3.3 shows how many times subjects changed their initial opinions in all group decisions they took part, depending on their gender and status (but not the group type in which they are in). Table 3.3 shows data for 22 females (12 high status and 10 low status) and 24 male subjects (10 high status and 14 low status). Collectively these 22 females participated in 506 group decisions, in which they changed their initial opinion 189 times (or 37.4%) and insisted on their initial opinion 317 times. Status

83 did not have an impact on female bargaining behavior in this respect: 37.3% versus 37.6%. We also have 24 male subjects, 10 high status males and 14 low status males. Table 3.3 shows that 10 high status males participated in 224 group decisions in which they changed their initial decision 79 times (35.2%). Low status males (14 subjects) participated in 305 group decisions in which they changed their initial choice only 78 times (or 25.6% of the times). So low status males change their initial opinion less often than high status males. To check for statistical significance, we need to make a binomial proportion test, corrected for repeated measures by each subject. We could not find an appropriate statistical test for this case, so we construct another statistical model to show the impact of status on male’s behavior.

Female Male

High status Low status High status Low status

100- Change 89- Change 79- Change 78- Change (37.3%), (37.6%), (35.2%), (25.6%), 268- Total 237 -Total 224-Total 305- Total

Table 3.3: Frequencies of changing the initial opinion (by comparing the first and last choices), for each gender and status group.

We calculate a parameter we call “Volatility of opinion” for each participant. We first look at the first and last choices of each participant i in group j : if choices are different then c(i,j)=1 and if choices are the same then c(i,j)=0. We then take the average c(i) for participant i across all groups where she was a member. This average c(i) for participant i is her “Volatility of opinion”. So if “Volatility of opinion” equals to one, it means that the subject’s initial and last choices were different in all group decisions where she took part. And if “Volatility of opinion” equals to zero, this means that subject’s initial and last choices always coincide in all group decisions where she

84 took part. So the bigger this number is for a subject, more often she changes her opinion during group deliberations. Table 3.4 shows the “Volatility of opinion” values for each subject, depending on her gender and status.

Table 3.4: “Volatility of opinion” for each subject, in each status and gender group. Group means and standard errors are shown

One-sided t-tests presented in Table 3.5 shows that volatility for low status males are on average smaller from high status males (t=1.58, p=0.065), and also smaller from high status females (t=1.99, p=0.029) and low status females (t=1.87, p=0.039).

Group mean SE t-stat p-value

Female_HIGH_STATUS 0.367 0.0404 1.9937 0.029

Female_LOW_STATUS 0.376 0.0504 1.8683 0.039

Male_HIGH_STATUS 0.35 0.045 1.5846 0.065

MALE_LOW_STATUS 0.26 0.035 - -

Table 3.5: Pairwise differences in "Volatility of opinion" between Male_LOW_STATUS group and three other groups (1-sided t-tests)

We conducted post-experimental power tests to find the number of subjects needed to achieve a power level of 0.8 and significance level of 0.05 (for a 2-sided t- test) for these pairwise comparisons. Assuming that these effect magnitudes are true for the general population, we would need 25 subjects for the Female_High_Status comparison, 24 subjects for Female_Low_Status test and 34 subjects for Men_High_Status test. So we could conclude that these results, while rather weak, have some chance to be true, if replicated for a bigger sample. So, on individual level low status males are less willing to change their choice compared to females or high status males. On average, our 14 low status males changed their opinion (when comparing the first and last choices) 26% of the times and did not change in 74% of the times. This is less than the 10 high status males and 22 female subjects, who changed their opinions in 35% and 37% of the cases correspondingly. Statistical significance is not very high, but this result can still be potentially interesting. Initially we did not expect a gender effect and the whole discussed literature about status hierarchies generally does not discuss the gender issue. So this finding actually complicated our statistical analysis.

3.5.4 Bargaining behavior depending on group type

However we should remember our main hypotheses: high status subjects should have bigger decision weight in mixed groups. This means that final collective decisions in mixed groups should be shifted toward the preferences of high status members. Table 3.6 shows the frequencies of changing the initial choice depending on status and group type. For all-high groups, we can see that subjects collectively participated in 236 group decisions in all-high groups, and changed their initial decisions 92 times (or 38.9%). This is higher than the 31.4% for all-low groups (83 changes from 264 total decisions). This difference between all-low and all-high groups is difficult to test statistically because each subject participated in different numbers of collective decisions. Binomial proportion tests needed here must be adjusted for this repeated measures, and we could not find an appropriate statistical test for this. However we can make two observations. First is that low status males are less flexible in bargaining. Second is that we have more low status males than females. The male/female-ratios in table 4 show the proportions of male and female subjects in each group category. We can see that all-low groups have more males than all-high groups. Together these two factors might be the cause for the small difference in group dynamics among all-high and all-low groups. This also means that final decisions in all-low groups are slightly shifted toward the initial preferences of male subjects.

Low status High status

Changed Total Frequency Male/Female Changed Total Frequency Male/Female Group type choices choices of change ratio choices choices of change ratio

All Low 83 264 31.44% 1.3

1 High 3 67 210 31.90% 1.33 29 63 46.03% 0.69 Low

3 High 1 20 68 29.41% 0.97 60 195 30.77% 0.82 Low

All High 92 236 38.98% 0.88

Table 3.6: Frequency of difference between initial and last choices, depending on group type and status

In hierarchical groups we can see an interesting picture. In 1 high-3-low groups we see a difference between high and low status subjects. Single high status subjects in this type of groups changed their initial choices 46% of the times, which is the highest rate among all categories. So the single high status members in 1-high 3 low groups clearly do not have a bigger decision weight in these groups, but rather the opposite. Other 3 low status members in 1-high 3-low groups change their opinion less often- only 31.9% of the times, and we have the highest proportion of males in this category. Bargaining behavior in 1-high 3-low groups may have different reasons (male reaction to status and the male/female ratio), but in the light of our initial theory and hypothesis this result is important. We clearly do not see any evidence supporting our initial theory: status hierarchy in 1-high 3-low groups does not play a coordinating and conflict-resolving role. Bargaining behavior in 3-high 1– low groups is not different for high and low status subjects.

3.5.5 Status hierarchy and group dynamics Table 3.7 shows the numbers of steps needed for each group category to agree on a lottery. We show the data only for those 268 groups which initially had a disagreements. The data is normalized to 100% for each group category. So overall we do not see that vertical groups with a single leader need less steps to make a collective decision.

ALL High 1 High 3 Low 3 High 1 Low ALL Low

120.00%

100.00% 100.00% 100.00% 100.00% 100.00%

80.00% 59.38%

60.00%

50.00%

41.18%

37.50% 35.71%

40.00% 34.85%

24.29%

22.73%

22.06%

18.18%

17.14%

16.18% 14.06%

20.00% 12.86%

10.29%

9.38%

9.09%

8.82%

7.58% 3.13% 0.00% Step 2 Step 3 Step 4 Step 5 Step 6 Disagreement

ALL High 1 High 3 Low 3 High 1 Low ALL Low Step 2 100.00% 100.00% 100.00% 100.00% Step 3 34.85% 50.00% 41.18% 59.38% Step 4 22.73% 35.71% 22.06% 37.50% Step 5 18.18% 24.29% 16.18% 14.06% Step 6 9.09% 17.14% 10.29% 9.38% Disagreement 7.58% 12.86% 8.82% 3.13%

Table 3.7: Percentages of groups having disagreements at each step (of those groups which initially had a disagreement)

The result shows that 1High-3Low group had a the biggest share of disagreements after the last 6th step. This shows that the vertical hierarchy within these type of groups didn’t have any positive impact on conflict resolution. We can also see that flexibility during the bargaining doesn’t impact the number of steps needed for a collective decision. For example, subjects in all-high groups have been more flexible than those in all-low groups because the frequency of changing the initial decision is smaller in all-low groups: 31.4% vs 39.9%. But this difference is not reflected in group dynamics in any meaningful way. Overall this data shows that the number of steps needed for an agreement does not depend whether the status hierarchy is flat or vertical within the group. Importantly, this weak impact of status does not seem to affect the overall group dynamics. So our data does not support the theory that vertical hierarchies bring better coordination into groups. Rather the opposite is happening and low status males demonstrate less willingness to change their initial choices. Although this reaction to status does not impact the number of steps needed for a collective decision, it shows that high status members in our experiment clearly do not have a bigger decision weight. Following list summarizes the key findings in the experiment.

1. Status hierarchy within the groups doesn’t lead to more coordinated collective decision-making. 2. Status manipulation seems to have some effect on male subjects, but in a direction opposite to initial hypothesis: low status males seemed to be less willing to change their opinion during collective decisions.

So the main result is that the imposed status based on quiz results and status symbols (star stickers, applauding and reseating) has only a weak impact only on male behavior and not in the way we expected. This result points toward several issues.

Firstly, similar to previous experiment on risk in social context, only males reacted to status. This is again related to the findings that males are more sensitive to social status and its symbols (Gneezy and Rustichini, 2004; Gneezy et al., 2003; Knight, 2002). The fact that low status males were less willing to change their opinion may indicate that they find this division as not deserved because of the very hard quiz with random answers. It may also indicate some resistance to accept the inferior status, especially given the young age of participants. Similar arguments are sometimes made to explain the rejection of unfair offers in ultimatum games: subjects reject small offers to avoid being imposed an inferior status (Yamagishi et al., 2012; Sanfey et al., 2003). This is again in line with results in previous experiment: low status can lead to a negative emotional reactions.

A second issue is related to how the subjects perceive this status manipulation, given their diverse cultural and ethnic backgrounds. Status symbols we use such as stars, applauding or re-sitting in the front rows are more or less universal across cultures, but people’s reaction to these symbols in a classroom can be different, depending on which country they come from. For future research, we can have other methods of imposing status. For example, some studies from social psychology stress that groups informally “award” high status to those members who have a strong commitment to the group, high competence in the task of the group and a willingness to sacrifice for the collective (Lord et al., 1986; Hardy and Van Vugt, 2006). These ideas can be used to develop new methods of imposing status in the lab.

Based on our results we can also speculate about other possible preference- aggregation rules adopted by groups. People may feel as belonging to the high status or low status “cohort” and homogeneous groups might have less conflict than mixed groups. Or the “majority rule” may work and the only differing member in mixed

91 groups may just adapt to others. However the results on Table 3.6 do not seem to prove any these speculations.

3.6 Conclusion

Understanding how groups make collective decisions is an interesting and growing area in economics. Economic research on risky choice, including both theory and experiments, have been traditionally focused on individual decision making. This limitation is currently changing and a growing number of economic experiments published mostly in last decade focus on collective decisions involving risk and uncertainty. These studies try to understand how preference aggregation happens when members have different risk-attitudes. They also focus on issues central in economics such as risk-aversion, loss-aversion or violation of stochastic dominance, trying to understand whether economic theories of risk and uncertainty can be useful to understand group decisions.

Research in social psychology on the other hand has been focused on collective decision-making for many decades. This discipline has accumulated big empirical evidence on the issue and has developed its own theories and methodologies. In particular, social psychologists try to use the concepts of social status and internal status hierarchy to understand processes within groups. The main idea is that status hierarchies always emerge in small groups (based on respect awarded to each member by the team) and play a key role in group dynamics. In particular, inequality of decision weights in hierarchical or vertical groups (groups with high and low status members) are supposed to resolve the intra-group conflicts. On the other hand, having too many high status members is supposed to create more conflict within the group

92 and an absence of high status members is supposed to result in an absence of coordination within the group. These roles of social status and status hierarchy are not discussed in economic studies on group decision-making.

So we tried to use these insights from psychology in our economic experiment on group decision-making. In our lab experiment we impose an artificial status hierarchy based on results in a general knowledge quiz and also by using status symbols. We further constructed groups with flat and hierarchical status configurations, and ask them to choose between risky and safe lotteries. We found a limited impact of status on bargaining behavior: status changes only male bargaining behavior. Low status males are less willing to change their opinion during the group deliberation and as a result group decisions are slightly shifted towards the preferences of low status males. We found no impact of status on female bargaining behavior. Most importantly, we didn’t find that a vertical status hierarchy plays a coordinating role within vertical groups: vertical groups with one leader are not any better in resolving their internal disagreements. Instead the imposition of low status leads to less flexibility for male subjects. These results are more in line with studies which find a negative impact of vertical status hierarchies: our status manipulation resulted in more stubborn bargaining behavior for low status males. As a conclusion we can state that social status has an impact on collective risky decisions. Our study is limited to laboratory conditions and the artificial nature of the imposed status. Nevertheless we see that even within this limitations social status has some impact on collective decisions involving risk.

Chapter 4

4 Bargaining with status-inconsistency

4.1 Introduction

Models in neo-classical economics often assume agents who maximize a utility depending only on money or material resources. Economic agents are not assumed to analyze more subtle social factors when making economic decisions. In real life however, people have different skills, rights and access to resources or in other words hold different social statuses. Such asymmetries of bargaining power have a strong impact on social interactions. When people interact in a situation where some have higher and others have lower positions on an important social hierarchy, then this ranking can have an impact on their pure monetary or economic decisions. Trying to give a comprehensive definition of social hierarchy and social status, we use the definition by Magee and Galinsky , where they define the social hierarchy as an “implicit or explicit rank order of individuals or groups with respect to a valued social dimension”, in their words (Magee and Galinsky, 2008). Influence of such social rankings on socio-economic behavior has been extensively investigated in sociology, social psychology and in economics

In terms of research methodology, relative standing on important societal dimensions, such as levels of education or income, are often considered as proxies or indicators of social status. Most common is the “single dimensional approach” when the impact of only one hierarchical dimension is analyzed or an average of several dimensions is taken as one single indicator of social status. Such a simplification has been argued to have certain limitations. People simultaneously

94 hold ranks on different hierarchies, and in certain contexts more than one of these ranks can be equally important. A person can hold a high rank on one important hierarchy (such as education) but a low rank on another important dimension (income). Such a disbalanced state has been labeled “status-inconsistency”, and such a person called “status-inconsistent” (Lenski, 1954). For example, a person with high education and low income shouldn’t be equaled to the person with medium education and medium income, as the single-dimensional approach would suggest. The person with medium education and medium income might be happier than the person with high education and low income. Taking the average position on two hierarchies as a single proxy for status should be wrong here. More generally, a large body of sociological literature addresses this issue of multi-dimensionality of social status or “status inconsistency” (Zhang, 2008).

Multi-dimensional analysis of status has been an area of extensive and controversial research in sociology since 1950s. It started with the seminal paper by Gerhard Lenski where he tried to understand the impact of status-inconsistency on political preferences and voting. The idea suggested by Lenski and developed further by other sociologists argues that people try to present themselves in the society by stressing their strong sides (for example high education level) and hiding the weaknesses (small income or inferior social background). At the same time people also try to treat others in the opposite way, focusing on their weaknesses and disregarding their strength in other areas (Lenski, 1956; Lenski, 1966; Segal, 1969; Homans, 1988; Galtung, 1966). Such a bias can lead to conflicts in social interactions, as argued by these and other sociologists. People who are status-inconsistent may demand for more socio-political change, as hypothesized in Lenski’s seminal paper, and generally have problems with social integration. This research direction, which was popular in American sociology in 1950s to 1960s, tries to better capture the reality of life where people hold different ranks on multiple dimensions and where in certain contexts

95 disbalances between status positions might lead to additional behavioral effects- so called “status-inconsistency effects”. When constructing econometric models to analyze the impact of social status, the ranks on several hierarchical dimensions are not averaged as a single indicator of status but instead their impacts are analyzed independently. The main objective has been to understand whether it is legitimate or not to take the average of several dimensions as a single indicator of status. Most papers in this area try to find a “status-inconsistency effect”, which cannot be understood by merely adding the independent impacts of each status position. This is usually done by defining a new variable to describe the interaction effect between two status positions. The independent societal dimensions analyzed in these literature are usually the education, income, occupation and ethnic background. Studies try to link the discrepancies between these ranks to different aspects of political behavior, usually a demand for social change and general dissatisfaction with current social order, with mixed results. (Broom and Jones, 1970; Olsen and Tully, 1972).

Some studies show that such a status-inconsistency can lead to new behavioral effects which are not a mere addition of independent effects of each rank. For example, studies by Franck & Smith or Peter et al. demonstrate a negative impact of income-education gap on health (Smith and Frank, 2005; Peter et al., 2007). Lee et. al. demonstrate an impact of similar education-income gaps on migration attitudes (Lee et al., 2009). However most studies have run into trouble in finding appropriate econometric models to measure such status-inconsistency effects. The main challenge has been to differentiate separate impacts of each status position from additional “status-inconsistency effects”. A meta-analysis of previous sociological literature concludes that in many cases two orthogonal dimensions of status cancel out each other’s impact on behavior (Hendrickx et al., 1993). This alternative view suggests that when people have different status levels on several important dimensions, then during social interactions these differences should cancel out each other. This is the

96 so-called “canceling hypothesis”, which argues against the idea of status inconsistency (Freese, 1976; Sobieszek, 1972; Norman et al., 1988). So after 20 years and 200 published papers in 1950-60s, this research program did not come to comprehensive conclusions about usefulness of multi-dimensional analysis of status.

Henri Tajfel’s social identity theory is approaching this issue from a slightly different angle. Here it is argued that people strongly identify themselves and others with certain groups they belong to (Tajfel, 1982). As a result, their relations are determined not by their individual characteristics but rather by characteristics common for groups. The person can belong to different social groups and as a result have several “”selfs” associated with each group. This theory has been used to analyze discrimination, in the context of in-group out-group favoritism.

In the area of economics the idea of multi-dimensional status has been mostly neglected. Most studies on status just use a single dimension. In particular, most of the research on status in experimental economics has been using the single- dimensional approach. Economic experiments on status usually use the method of separating participants into advantaged and disadvantaged groups based on some criterion (such as high and low scores in a knowledge quiz or other tasks) and then testing if this division affects economic behavior. This experimental method links back to experiments on group behavior testing within-group and between-group attitudes (Jemmott and Gonzalez, 1989a). Experiments often find an impact of such a division on economic choices. First economic experiments on status demonstrated that even an artificially imposed status in the lab can impact economic choices. Experiments by Ball and Eckel or Hoffman et al. show how an artificially imposed status hierarchy in the lab (based on knowledge-quiz results) affects choices in an ultimatum bargaining (Ball and Eckel, 1998; Hoffman et al., 1996). Ultimatum- bargaining proposals in these two experiments were slightly in favor of high-status

97 subjects. Hu et. al. conducted an ultimatum game experiment where they induced a relative rank based on performance in a simple reaction-time task (Hu et al., 2014). Results were analogous to previous similar experiments where high status participants were less likely to accept unfair small offers in UG. The fMRI scanning of participant’s brain during the task showed that having a low status decreases the salience of neural reactions to unfair offers. Ball, Eckel and Grossman show a similar impact on market price. In these experiments subjects are divided into low and high status groups based on their performance in a knowledge quiz (Ball et al., 2001). Additional symbols of status such as golden star-stickers, applauds and preferential treatment are used to further strengthen the manipulation. Albrecht et. al. use a similar quiz-based status manipulation and show that high status justifies unequal allocation of money: low status subjects feel less unhappy with a relatively disadvantaged monetary outcome (Albrecht et al., 2013). von Essen and Ranehill use a dictator game with third-party punishment and show that low-status male subjects are punished more often for unfairly small proposals (von Essen and Ranehill, 2011). Huberman et al. find that subjects are willing to decrease their chances of monetary wins in exchange of being applauded by others (Huberman et al., 2004). Breton et al. use EEG brain scanning and find stronger activation of attentional resources in the brain when subjects see a face of high status player (Breton et al., 2014).

So these experiments show that even an artificial division of people into high and low status groups in the lab has an impact on their economic decisions. All such studies however use a single dimension to impose the status. Thus there is an open question (as far as we are aware) to understand the simultaneous impact of several hierarchical ranks on economic bargaining. Experimental techniques of analyzing status in the lab give an opportunity to look at the old problem with new methods. So in our study we try to analyze the impact of multi-dimensional status using the

98 experimental method of imposing statuses which are based on results of a certain task.

In our experiment we test the impact of status-inconsistency on bargaining, in an ultimatum game setting. We define a “status-inconsistent interaction” as one in which opponents have orthogonal positions on two status hierarchies: one is high on the first status dimension but low on the second while the other has an opposite set. We try to reveal if people overvalue the dimension on which they have high status. Such overvaluation on both sides can potentially result in conflict, which in ultimatum bargaining should be reflected in proposals and rejection thresholds. Alternatively a compromise scenario is possible, where the orthogonal statuses just cancel out each other. We impose two different statuses based on two independent criteria- a knowledge quiz and a visual reaction task. Each subject in our experiment has a high or low status in these two tasks. We also use real-life symbols of status: high-status players are awarded star-stickers while low-status players are asked to stand up and applaud the winners. We further have two types of interactions: a subject with double-high status bargaining with another with double-low status, and a second “status-inconsistent interaction” where subjects with orthogonal set of statuses play ultimatum game against each other. We call this a status-inconsistent interaction.

Our primary interest is to understand what happens in such a status- inconsistent interaction. Several scenarios can be imagined: subjects might overvalue the importance of the skill in which they have a high-status and this might lead to more conflict in the interaction. This conflict shall be reflected on proposals and rejection thresholds in the ultimatum game. Another possibility is the compromise scenario where the differences in two status dimensions just cancel out each other. This is the so-called “cancelling hypothesis”

Our experiment closely follows the procedures carried out in the original experiments of Ball and Eckel: we use a knowledge quiz to impose a status (we also use a visual reaction task to impose the second status dimension). We also use status symbols: star stickers for high-achieving subjects, low-scoring subjects applauding to high scoring ones. In the bargaining stage of the experiment we again follow the initial experiment where subjects try to divide ten candies in an ultimatum game. This isn’t an exact replication but procedures are generally similar. What is new in our study is that we have two status dimensions: we have two tasks and two awarding ceremonies: part of the subjects win in both, some win only in one, and some lose in both. Ultimatum bargaining is conducted anonymously via computers, and players know only the statuses of each other. Results of the experiment show a small but statistically non-significant impact of these status manipulations on bargaining outcomes. In high versus low and in double-high versus double-low interactions there is a small shift of proposals in favor of high or double-high players. However this impact of status is very small and statistically insignificant.

4.2 The experiment

Experiment has 4 stages. Table 4.1 gives a first idea of the experiment.

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Ultimatum game: DoubleHIGH vs DoubleLOW (3 rounds)

Ultimatum game: HIGH status Task 1 Task 2 vs LOW status (3 rounds)

Ultimatum game: HIGH_LOW vs LOW_HIGH (3 rounds)

Table 4.1: Four stages of the experiment

Stage 1: Knowledge quiz followed by a status awarding ceremony

At the first stage participants pass a knowledge quiz on Brisbane’s history (the city where the experiment was conducted) and are divided into “High” and “Low” status groups based on their performance. The quiz has 10 questions, each with 4 possible answers to choose from. So there is always a 25% chance to tick the correct answer. Questions are very difficult because of very specific details they ask about. Because of this difficulty, quiz results are mostly random. This ensures that we don’t divide people by their actual knowledge of city’s history and the division is almost random. Thanks to difficulty of the quiz, the impact of status cannot be attributed to good or bad knowledge at the quiz. Ball and Eckel stress this aspect in their study. Participant gets one point for each correct answer and 0 for each wrong answer. After the first quiz participants in the top half of the score list are assigned a “High” status. Other participants in the lower half of the score list are assigned a “Low” status. This status is awarded in a special ceremony. Top half of the subjects are invited to stand up by the experimenter and are given golden shiny star stickers which they are

101 encouraged to stick on their t-shirts until the end of the experiment. Lower half subjects are asked to applaud the high status members while they are standing.

Stage 2: Ultimatum bargaining between High and Low status players After this ceremony subjects proceed to first three rounds of the ultimatum bargaining. High status subjects play three ultimatum games with three different low status subjects, interchangeably as the proposer or the responder. Rules of the game are simple: proposer suggests a way to divide ten candies between her and the responder : if responder accepts then ten candies are divided between the two according to proposal. But if the responder rejects then both get nothing. Each subject plays three ultimatum games with three unknown players of opposite status. This round is a replication of the similar experiment by Ball and Eckel. We run this round to see if the procedures work and if we can get a similar difference in proposals in favor of high status members.

Stage 3: Visual reaction task followed by a second status awarding ceremony

After three ultimatum games subjects proceed to the second task: a visual reaction task. This task has three similar rounds. In each round the subject is shown a picture with a number of black dots, for five seconds.

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Figure 4.1: Visual reactions task: 3 pictures with different numbers of dots are shown for 5 seconds each

After five seconds the picture disappears and the subject must correctly count how many dots she saw on the picture. In three rounds the subjects see three different pictures with different numbers of dots. We calculate the distance of the answer from the correct number in each round and assign each subject a score which shows how good she was in this task. Then we divide subjects into equal numbers of high and low status players based on this score. This technique comes from previous experiments where subjects are divided into groups and then within-group and between-group relations are analyzed (Jemmott and Gonzalez, 1989b). Commins and Lockwood (1979) have first used this method to create status difference in a classroom. This method is also related to minimal group paradigm experiments, which show that dividing people into groups based on some simple unimportant parameter (t-shirt color or ability to run fast) can lead to in-group favoritism (Tajfel, 1982).

After assigning scores we conduct the second awarding ceremony, similar to the first one. High status subjects are invited to stand up and are given a slightly different orange star stickers which they stick to their t-shirts. Low status players are invited to applaud them. As a result we have four types of statuses: HH, HL, LH and LL.

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Stage 4: Ultimatum bargaining with status -inconsistency After the second ceremony we run another three rounds of ultimatum games. Here each subject plays three similar ultimatum games with three different subjects, randomly being the proposer or the responder, trying to divide ten candies. In terms of status, we have equal numbers of HL and LH players and they are always matched with each other in our status-inconsistent interactions. HH and LL players play with each other in double-high versus double-low interactions. Because the results of the first task are in principle independent from the results of the second task, we cannot directly control the distribution of players in four status category groups after the second quiz. However, when in both quizzes we independently divide N participants equally into “High” and “Low” groups, we end up with equal numbers of HH=LL and HL=LH participants, which technically enables us to match all HL and LH participants and also all HH and LL participants in the ultimatum game. This equalities are a natural result of the way we impose the status. A detailed explanation of how this happens is given in Appendix C.

After finishing these three ultimatum rounds subjects pass a short demographic questionnaire. We also run additional sessions with a reversed order of tasks: first the visual reaction task and then the knowledge quiz. This was done to control for the order effects of the two tasks.

Table 4.2 summarizes the experiment design.

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Treatment Number of ultimatum games per subject

HIGH vs LOW 3 ultimatum games played

DOUBLE HIGH vs DOUBLE LOW 3 ultimatum games played

HIGH-LOW vs LOW-HIGH (and the 3 ultimatum games played opposite)

Table 4.2: Summary of the experiment design

4.3 Hypothesis development

Previous experiments on ultimatum bargaining between high and low status subjects show that the division point is slightly shifted in favor of high status players. This directly leads us to our first hypothesis:

Hypothesis 1: If subjects implicitly accept the importance of status hierarchy and their place on it, then in High versus Low games (first three games) ultimatum proposals must be shifted in favor of high status members.

Experimentally proving hypothesis 1 will basically be a successful replication of previous similar experiments. In the third round we have HHvsLL type of interactions which are basically an even stronger versions of HvsL interactions. Based on previous studies we can expect even bigger shifts of proposals in favor of HH participants.

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Hypothesis 2: If subjects implicitly accept the importance of status hierarchy and their place on it, then in HighHigh versus LowLow games ultimatum proposals must be shifted in favor of high status members.

However we are first of all interested in HighLow versus LowHigh interactions. As mentioned before, many previous studies on status-inconsistency had an intuitive hypothesis that having a disbalanced status should lead to problems with socialization within the society, unhappiness or even health problems. This should happen if people overvalue the dimension on which they have a high status while stressing the dimension on which the opponents have a low status. In this line of thinking we can assume that a status-inconsistent bargaining should result in bigger conflict. In our case of HL-LH interactions we have an ultimatum bargaining between people with orthogonal set of statuses. This replicates a real-life status-inconsistent interaction in a simple way. In line with this general intuition on status- inconsistency, we might predict a conflict in such an interaction. This conflict may arise if each side overvalues the skill in which she has a high score. In our case the indicator of conflict or dissatisfaction in an ultimatum game shall be reflected in proposals and the rejection thresholds. This leads us to our main hypothesis:

Hypothesis 3: If subjects overvalue the skill in which they have high status then their proposals in HLvsLH interactions must be close to proposals of High or Double-High players made to Low or DoubleLow players.

Alternatively, as found in many empirical sociological studies, status- inconsistency can result in a compromise: high and low positions on important hierarchies cancel out each other. This line of thinking leads us to the alternative “cancelling” hypothesis:

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Hypothesis 4: If impact of the first status position merely adds up on the impact of the second status position, then the division point in HLvsLH interactions should be close to the average of HHvsLL and LLvsHH proposals.

Proving hypothesis 4 would mean that a status-inconsistent interaction results in a compromise but not conflict.

4.4 Participants and procedures

118 undergraduate students took part in seven sessions of the experiment (55 female, 63 male), aged 18 to 65, with an average of 23.7. Experiment was programmed in oTree and conducted in a large Australian university (Chen et al., 2016). Most subjects were undergraduate students. Subjects were recruited via ORSEE system. In each session we had even number of participants: 14, 16 or 18. Participants communicated anonymously via the computer program and knew only the statuses of the other player in the ultimatum game. They were given detailed oral explanations before each of the four stages, together with written instructions. There was a AUD10 payoff for participating in the experiment. In addition subjects were the given the number of candies they got in the ultimatum bargaining.

Order of the tasks

In the first five sessions participants did the knowledge quiz at the first stage and the visual reaction task at the third stage. In two following sessions the order of the tasks was reversed. This ensures a certain degree of control for the task order.

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4.5 Results and discussion

4.5.1 Status groups

Table 4.2 shows the distribution of subjects across status groups. Based on results of the first task (knowledge quiz or visual reaction test) participants are divided into equal groups, which means that in seven sessions we had 59 high and 59 low status subjects after first task. After the second task we got 33 participants in Double- High and Double-Low status groups each and 26 participants in High-Low and Low- High groups each. This symmetry of distribution is a natural consequence of independence between two tasks and is explained in Appendix 3.

High Low First task 59 59

Second Double High High-Low Double Low Low-High task 33 26 33 26

Table 4.2: Numbers of subjects across status groups.

Result 1: Proposals in High-Low interactions

Table 4.3 shows the ultimatum game proposals between High and Low status subjects in the stage 2 of the experiment. All subjects played interchangeably as

108 proposers and responders. Low-status proposers on average offered 4.22 candies to High-status responders, while High-status proposers offered on average 4.44 candies to Low-status responders. However this difference is not statistically significant in a two-sided t-test, clustered by subjects in order to avoid violation of statistical independence (t=0.69, p=0.49). So in single-status ultimatum games we don’t replicate the original results of Ball and Eckel, although the effect is in the same direction.

HIGH offering to LOW LOW offering to HIGH

mean 4.22 4.42

median 4 5

se 0.159 0.293

t-stat -0.56

p-value 0.57

Table 4.3: Difference in proposals: High-to-Low vs Low-to-High interactions. 2-sided t-test (clustered by subjects) doesn’t show a significant difference

Table 4.4 shows the frequencies of offers by high and low status proposers to responders of opposite status. There are two proposals of all ten candies and one proposal of nine candies to high status responders. We don’t see such behaviour in the opposite set of statuses. However these are single events and in general we don’t see big differences in proposals depending on status configuration. Most of the time subjects offered four or five candies to responders, and such behaviour is well in line with usually observed behaviour in ultimatum bargaining experiments. Based on our results we are unable to make firm conclusions about our first hypothesis: subjects

109 don’t seem to accept their status too seriously and it doesn’t have much impact on their ultimatum bargaining. In some of our sessions the subjects went through the quiz task in stage one and then through visual reaction task in stage three, while in other sessions this order was reversed. So we have randomized the order of the tasks. Here we show the pooled results of stage one across all sessions.

Table 4.4: Distributions of offered candies in single-dimensional status interactions: a) Left graph: Offers by LOW status proposers to HIGH status responders b) Right graph: Offers by HIGH status proposers to LOW status responders

We also conducted post hoc power tests to evaluate the chances of type 2 errors in our tests. Such post-experimental power tests are generally not reliable, because they assume that our effect magnitude is similar to that in the population, and such tests are not reliable. However, assuming that our effect magnitude (the difference of offers between HtoL and LtoH offers equal to 0.2) is equal to that of the general population, we would need 1100 subjects to get a power level equal to 0.8 and a significance level of 0.05. While realizing all the limitations of such post-experimental power tests,

110 they still indicate that our small and insignificant effects have a big chance to be an experimental fluke.

Result 2: Proposals in High-High vs Low-Low interactions

Table 4.5 shows the ultimatum game proposals between Double-High and Double-Low status subjects in fourth stage of the experiment. Double-Low proposers offered on average 4.53 candies to Double-High responders, and in opposite interactions Double-High proposers on average offered 4.18 candies to Double-Low subjects. So there is a slight shift of proposals in favor of Double-High players. However this difference is not statistically significant in a 2-sided t-test (t=1.18, p=0.24). This shift of proposals in favor of DoubleHigh status players is even bigger than the shift in previous single status interactions. So adding a second status dimension slightly increases the gap between average proposals. But because of being statistically non-significant, we cannot make firm claims about the impact of our status manipulation.

DoubleHIGH offering to DoubleLOW offering to DoubleLOW DoubleHIGH

mean 4.18 4.53

median 4 5

t-stat -1.18

p-value 0.24

Table 4.5: Difference in proposals: DoubleHigh-to-DoubleLow vs DoubleLow- to-DoubleHigh interactions. 2-sided t-test doesn’t show a significant difference

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Table 4.6 shows the frequencies of different offers in DoubleHIGH vs DoubleLOW interactions. The graphs show that DoubleLOW proposals are much more willing to offer five candies to DoubleHIGH responders. On the other hand, DoubleHIGH proposers more often propose one, two or three candies to DoubleLOW responders. So it seems that subjects think high status justifies bigger offers. This difference in proposals depending on status is in line with previous similar economic experiment with imposed status: higher status justifies bigger economic gains. However the effect is too small and statistically non-significant in our data, so we don’t make definitive conclusions about the impact of status. So we don’t make any firm conclusions about our second hypothesis. Subjects didn’t seem to take their status too seriously, and it doesn’t have a statistically significant impact on ultimatum bargaining.

Table 4.6: Distributions of offered candies in double- status interactions: a) Left graph: Offers by Double-LOW status proposers to Double-HIGH status responders

b) Right graph: Offers by Double-HIGH status proposers to Double-LOW status responders

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Result 3: Proposals in status-inconsistent interactions

Tables 4.7 and 4.8 show the proposals in mixed-status interactions between High- Low and Low-High players.

Table 4.7: Distributions of offered candies in status- inconsistent interactions

Table 4.7 shows that most offers are equal to 4 or 5 candies, which is in line with usual behaviour in ultimatum games. Table 4.8 compares the average proposals in mixed interactions with proposals made to DoubleHigh or DoubleLow players. Proposals in status-inconsistent interactions are on average equal to 4.35, with a median of 5. Statistically these proposals are not different from proposals made to

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DoubleHigh players (t=1.17, p=0.24), or DoubleLow players (t=-0.77, p=0.44). We use a 2-sided t-tests clustered by subjects.

DoubleHIGH Propsals in HighLow vs DoubleLOW offering to LowHigh or LowHigh offering to DoubleLOW vs HighLow interactions DoubleHIGH

mean 4.18 4.33 4.53

t-stat -0.48 0.77

p-value 0.63 0.44

Table 4.8: Proposals in status-inconsistent interactions compared to DoubleHigh vs DoubleLow interactions 2-sided t-tests don’t show a significant difference of mixed interactions from DoubleHigh vs DoubleLow interactions

Result 3 indicates that subjects in mixed interactions don’t seem to overvalue the importance of the task in which they are high. In case of such an overvaluation proposers should have been acting like DoubleHigh proposers playing with DoubleLow players. So we would expect the proposals in status-inconsistent interactions to be close (or statistically indifferent) from DoubleHigh to DoubleLow proposals. Such an outcome was expected in hypothesis 3. However, the outcome in status-inconsistent interactions seem to be closer to the predictions of hypothesis 4: effects of two status dimensions tend to cancel out each other. Because of the overall weakness of the status impact it’s hard to make a firm conclusion over our initial question: we cannot firmly prove or reject hypothesis 3 or hypothesis 4. Result 3

114 doesn’t exactly tell whether proposals in mixed interactions are similar or different from DoubleHigh vs DoubleLow proposals in any direction.

We conducted power tests similar to those for stage two (again realizing all of its serious limitations). The biggest difference across treatments is the one between HHtoLL and LLtoHH offers equal to 0.35 candies on average. In order to get a significance level of 0.05 for this test and a power level of 0.8 we would need 200 subjects. So our biggest difference in offers is still far from being trustworthy. The pairwise differences between mixed interactions offers (4.33 candies offered) and LLtoHH offers (4.53 candies offered) gave us an even bigger number of potential participants: we would need 550 subjects to achieve a significance level of 0.05 and power of 0.8, assuming that this magnitude equals to that in the general population. Similarly, for the difference between mixed interactions and HHtoLL interactions (4.18 candies offered) we would need 1320 participants to have a 0.05 significance level and a power of 0.8.

Many loosely-related older studies on status-inconsistency find that two orthogonal sets of statuses usually cancel out each other (Olsen and Tully, 1972). In our case the average proposals in status-inconsistent interactions are bigger than proposals by DoubleHIGH subjects but smaller than proposals by DoubleLOW subjects. However these differences are small and statistically non-significant. Tables C4 and C5 in Appendix C show that offers in ultimatum games are also not related to gender, age or country of origin,

As a result we are unable to give a firm answer to our main research question formulated in hypotheses three and four.

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4.5.2 Ultimatum game rejections in double-status interactions. Rejections of offers seem to be in line with usual ultimatum-game behavior: offers of 5 or more candies were always accepted (only one offer of 5 candies was rejected, proposed by a DoubleLow proposer to DoubleHigh responder). Table 4.9 shows the rejection rates for three types of interactions. Offers of 1 candy were always rejected and offers between 2 and 3 candies were partly rejected. Overall rejection rates don’t seem to be different across three types of interactions. It is hard to make a further statistical analysis of rejection rates because of very small data sample. Rejection thresholds might depend on different status configurations, but testing such a difference would require much bigger data samples.

DoubleHigh to Status-inconsistent DoubleLow to Proposals DoubleLow interactions DoubleHigh Candies Frequency Rejection Frequency Rejection Frequency Rejection Rejected Rejected Rejected offered of offers rate of offers rate of offers rate 1 1 1 100.00% 4 4 100.00% 1 1 100.00% 2 3 2 66.67% 3 0 0.00% 0 0 0.00% 3 11 3 27.27% 7 3 42.86% 7 3 42.86% 4 14 4 28.57% 23 6 26.09% 11 1 9.09% 5 17 0 0.00% 35 0 0.00% 27 1 3.70% 6 2 0 0.00% 4 0 0.00% 1 0 0.00% 7 1 0 0.00% 0 0 0.00% 2 0 0.00% 8 0 0 0.00% 1 0 0.00% 0 0 0.00% 9 0 0 0.00% 1 0 0.00% 0 0 0.00% 10 1 0 0.00% 0 0 0.00% 1 0 0.00%

Table 4.9: Frequencies of proposals and rejection rates in double-status interactions.

Described results show that our status manipulation did not have a statistically significant impact on ultimatum bargaining, even though there is a slight shift of proposals in favor of high status players. It is important to mention that in previous similar experiments by Ball & Eckel (1996) and Hoffman et. al. (1996) the shift is also

116 small and barely significant. For example, Ball & Eckel have different experimental treatments where players divide $10 or 10 candies. In their results the impact of status is not significant when players are dividing $10 and is significant only when players are dividing 10 candies. Candies are considered as low-value resource and this results is interpreted as if the importance of status is weaker when higher incentives are at stake. This was also the reason why we chose 10 candies as the divided resource. Another minor difference of design is that in our subjects were randomly assigned both roles in the six rounds of the ultimatum games. In original experiments each subject’s role was fixed. This can be another reason of non-replication. In addition, given the fact that experimental results are very often not replicated when the experiment is done again, our results are not too surprising. So the methodology we used to impose the status in the lab didn’t help us to reveal any status-inconsistency effects.

The following list summarizes the main findings in this study:

1. Our status manipulation doesn’t seem to impact on people choices in ultimatum games. 2. In mixed interactions we do not find any supporting evidence of more conflict.

In terms of future research, better methods would be needed to impose social status in laboratory settings. It seems that imposing status based on results in a quiz or using status symbols is not a very predictable method. In case of the quiz, the difficulty of the questions can make the results seem not deserved and based on luck. Thus the quiz results need to be more objective, at least in the eyes of the participants. In experiments by Ball & Eckel they use a quiz on economic topics with economics students, so the students really care about their results in the quiz. What students didn’t know was that the results of the quiz were completely random and

117 not based on their answers. So this was a direct deception of participants, which allowed to have a quiz with relevant questions and also not to divide students based on their actual knowledge of economics. However such a deception is not allowed today by ethical regulations in experimental economics. So the impact of a status based on quiz results may vary strongly depending on exact topic of the quiz and its importance for participants. Using this methodology may give highly volatile results and requires “fine tuning” of the quiz questions and good intuitive understanding of student mentality. This creates potential layers of uncertainty when using this method to impose a status in the lab.

Status based on the visual reaction task might also seem too arbitrary and not deserved. In addition, status symbols such as stars and applauding can also be highly culture specific. Given the cultural and ethnic diversity of the participants in our experiment, its possible that different students perceive these status symbols very differently. All together these possibilities are showing that the artificial status in the lab may be perceived very differently depending on cultural and other subjective factors. For future research we need to develop better methods to impose status in the lab, which need to be less culture-specific in terms of used status symbols, and less dependent on how the method is implemented in each case (such as the exact topic of the quiz questions which must be intuitively chosen correctly to be important for participants).

4.6 Conclusion

Previous research on social status has demonstrated that relative ranks on socially important dimensions can affect economic decisions. Both single and multi-

118 dimensional approaches have been extensively used to analyze big real-life datasets and have revealed different kinds of impact that social status has on socio-economic behavior. Economic experiments have also demonstrated the impact of social ranking on pure economic decisions. These experimental studies have developed a methodology to test the impact of status in economic experiments. However most of these economic experiments have used only the single-dimensional approach. We didn’t find any studies which directly impose a multi-dimensional status in an experimental setting and measure its impact on economic decisions. Previous studies on multi-dimensional status have tried to find additional effects of status disbalances on behavior. Results of these studies are mixed. Our study tries to answer this old question with new methodologies developed to test the impact of social status in the lab. We conducted an ultimatum game where players each had a high or low status on two orthogonal dimensions- a visual reaction task and a knowledge quiz. Our experiment is a development of previous experiments by Ball et al. (1996) and others which show that status hierarchy changes ultimatum-game offers in favor of high- status players. We add a second dimension of status to create a status-inconsistent interaction. However our status manipulation doesn’t have a statistically significant impact on ultimatum proposals. Average offers are slightly in favor of high and double-high players, but this differences are not statistically significant. Average offers in status-inconsistent interactions are in between the offers by DoubleHigh and DoubleLow proposers, but these differences are very small and statistically non- significant. So we are unable to give a firm answer to our original research question based on our experimental results.

Absence of statistically strong results in this experiment can be attributed to the methodology of imposing status. These methods are rather vague. There are many factors which can differ in replication studies and lead to different effect magnitudes. It is almost impossible to keep all the factors in check (such as the exact questions of

119 the quiz, the native language and cultural background of the participants etc). Roughly speaking, it is not really clear why people react to these status manipulations in the experiments cited above, even though their behavior can always be “explained ” with the concept of social status. In addition, lab experiments are often non-replicable (we discuss these issues in the next chapter). All these “unknown unknowns” make it hard to speculate why in some experiments people react to these status manipulations while in other similar experiments the effect magnitude is negligible. However, the gradual accumulation of experimental findings (both with strong and weak results) can eventually help to reveal the main aspects of status that impact people’s economic behaviour.

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Chapter 5

5. General conclusion

5.1 Summary

Social comparisons that we make and resulting social status which we acquire as a result of such comparisons have a major impact on how we relate to each other as a society. Different disciplines such as economics, psychology, sociology and others have produced many interesting studies shedding light on these issues. Theories, empirical methods or interpretations of results in these different disciplines are often different, but they also look at the issue from very different perspectives. In this thesis we tried to learn from different scientific disciplines about the nature of human sensitivity to social comparisons and its impact on economic behavior. We tried to address several open questions suggested by the existing literature. We conducted three different studies all centered around one general issue: how social status affects economic decisions when people are given information about their relative rank compared to others. In all three studies we conducted economic lab experiments with student subjects to answer our research questions.

Our first study is related to a new and actively developing research area, namely of risk in social context. As soon as social preference models were created to address non-selfish behavior in strategic interaction games, they potentially predicted an impact of social comparison on risk-attitudes. This is because outcome-based social preference models include other people’s monetary income as part of the utility function. Authors of these models themselves have mentioned this potential impact on risk-attitudes. A growing number of lab experiments have been trying to better

121 understand how can people’s choices over risky prospects be affected by information about other people’s income. On theoretical level there are several ways to model such a social comparison, assuming that people care about the average income within the comparison group, their rank position within the comparison group etc. We tried to test a specific theory known from psychophysics, called range-frequency theory, to see if this psychophysical theory can be also useful to explain our social evaluations. This theory specifically predicts that our social evaluations should depend on our relative position between top and bottom members of the comparison group, or in other words range position within the group. We designed and conducted a computerized lab experiment to test whether social risk-attitudes are affected by people’s range position within the comparison group. Our experimental design is loosely related to the handful of recent experiments on social risk-attitudes, but also has a number of improvements. It’s also the first to test this particular theory of contextual evaluation. Our results didn’t show a specific impact of range position on social risk-attitudes. But we found some impact of social comparison on risk-taking. This result is generally in line with two or three previous similar experiments on social risk-attitudes. We found that subjects slightly increase their risky monetary investments when put in a low rank position within a comparison group. But social risk-attitudes didn’t change with range position within the group.

Our second study is related to another new and actively growing area in economics, namely about group decision making. Traditionally, all the research on risky decisions, both theory and experiments, is centered around individual choices. In real life however risky decisions are often made by small groups rather than individuals. So understanding how small groups make collective risky decisions is both interesting and relevant. In economic literature we found only about ten published experiments which directly address this question. Findings in these studies are mixed, suggesting that collective decisions can often be different from individual

122 choices. More specifically this studies are often focused to understand whether groups are making more risky or less risky decisions than its members individually. Some experiments find a “cautious shift” effect, meaning that groups are less risky than its members individually, while others find an opposite effect called “risky shift”. Such shifts have been earlier found in loosely related psychological studies. We looked in social psychology literature trying to find any insights relevant for this question. Social psychology has been concerned about collective decision-making more actively, and there is much more research done on collective choices in this discipline. While social psychology doesn’t use economic theory of risk and uncertainty, it has developed other theoretical approaches to understand and analyze collective decision-making. One currently popular research direction tries to show that collective decision-making is driven by internal status hierarchy within the group. According to this theoretical approach, small groups always develop an internal status hierarchy, which leads to unequal decision weights among group members. Members can have higher or lower social status within the group based on their skills relevant for the problem facing the collective. With such an approach it follows that groups should be not more or less risky than individuals, but rather groups should follow their informal leaders. So the collective risky decision should be shifted toward the preferences of high status members. Economic studies on collective risky decisions completely ignore such an aspect of groups and usually assume equal decision weights for each member. So we conducted a lab experiment where groups of four subjects had to choose collectively between a safe and a risky lotteries. This was designed quite similar to previous economic experiments on collective risky choice. What was new in our experiment is that we imposed a status hierarchy on group members, based on their performance in a general knowledge quiz, and also using status symbols (golden star stickers, applauding to peers, reseating within the classroom based on status). We constructed groups with different status

123 configurations, and tested the impact of internal status hierarchies on collective decision outcomes. We tested specific assumptions, following theories developed by social psychologists. In mixed groups we expected to see a bigger influence by high status members, more conflicts in groups where everyone has a high status, and a lack of coordination in groups with no high status members. What we found is that our status manipulation had some impact on male subjects. Those male subjects who underscored in the quiz and were imposed a low status were less willing to change their initial opinion during the collective decision-making process. Interestingly we don’t find such an effect for female subjects. As a result, collective decisions over risky lotteries we slightly shifted towards initial preferences of low-status males. This is not exactly what our initial theory predicted, but it still demonstrates that internal status hierarchy can have an impact on collective risky choices. This result is an example showing that looking for insights from other disciplines can often help to look at a traditional problem from a new angle. The role of internal status hierarchy is never considered in economic studies on groups, but in real life it can have an important role. Our experiment shows some evidence that this might be an interesting aspect to consider.

Our third study is related to multi-dimensional nature of social status. People hold high or low rank positions on multiple important dimensions. A person can have a relative standing on such important dimensions such as the level of education, level of income, ethnic background etc. This rank positions largely define her relation to society, and define norms of interactions between individuals. From economics perspective, relative status also defines the division norms of economic resources. For example, a person with high education level has a bigger contribution to public good and expects to have a higher compensation or income. In empirical research on status it is common to take the average rank positions on several important dimensions as a single measure of status, and then try to understand its impact on behavior. This

124 means that a person with high education and low salary is given a medium status, similar to a person with medium education and medium income. In real life however these two individuals can have very different social attitudes: the first one may be much more unhappy with her compensation than the second one, and their political or other type of behavior can be very different. Such discrepancies between important status dimensions have been labelled “status inconsistency” by sociologist Lenski (1954). More generally, it has been argued in later research that people tend to overvalue the skill or status dimension where they hold a high rank. Thus they tend to overvalue the importance of the role they have in society, and this might be a potential source of conflict. A large body of sociological research has tried to understand whether such status inconsistencies have any impact on behavior, with mixed results. In our study we have tried to design a simple lab experiment to reveal whether people overestimate the skill or status dimension on which they hold a high rank. We impose status on two different dimensions: one based on scores in a general knowledge quiz and a second based on scores in a visual reaction task. So we construct an interaction between two individuals who have high ranks on two very different dimensions. We conduct a simple ultimatum game between such participants to reveal if they overvalue the skill in which they are strong. Results in ultimatum game can be interpreted as norms of division of economic resources. If information about relative status of the other player has an impact on the division of symbolic resources in this game, then we can make conclusions whether orthogonal set of statuses creates a conflict. However, in our experiment we find a very small and statistically insignificant impact of status on game results. Division point seems to indicate that having a mixed set of statuses results in a compromise rather than conflict, but we cannot make firm claims because our experimental results are too weak statistically.

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Trying to generalize over our findings in these three experiments, we can conclude that any economic theory can largely benefit from focusing on different aspects of social comparison. This is an important point because neo-classical economic theories often model the economic reality as an interaction between self- interested agents, and such an approach has not been very successful in capturing social aspects of human behavior. On the other hand other human-centered disciplines such as sociology, psychology or evolutionary biology demonstrate that issues of social status and comparison are key to understand human behavior. As a result, whenever we introduce these ideas in traditional economic modeling, we can always have a fertile area for new findings. Many economic phenomena can be better described in formal economic language if we are able to better integrate social aspects of behavior into such modelling.

In our experiments we focus on how the information about relative social standing can be meaningfully integrated into traditional economic models, which are mostly centered around monetary incentives. The main goal of this thesis is to clarify the ideas about social status from other disciplines and try to “translate” them into the language of experimental economics. We see that this is often very hard as we are not very successful in getting statistically strong results in our experiments. Despite these problems, we think that further developments of experimental methods can enable to look at economic problems from a more human-centered perspective.

As a general conclusion, it seems that the experimental techniques we used still need improvement to answer the research questions we have proposed.

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5.2 General limitations

We designed and conducted laboratory experiments to answer our three research questions. While general ideas and theories we test are certainly very interesting, empirical methodologies we use are equally important. Lab experiments with student subjects are certainly a very useful empirical platform, but they have limitations. A major limitation in our experiments is the relatively small size of data obtained. We have data from 48 to 118 subjects for each experiment. While analysis of this data certainly gives very good insights to our questions, we still need to be cautious in making final conclusions. Recent large replication studies have shown that results of lab experiments are very often not replicated when the experiment is conducted again (Collaboration, 2015; Camerer et al., 2016). Indeed, any researcher conducting lab experiments is aware of this fact very well. Levels of statistical significance we obtain often don’t satisfy more strict robustness requirements. There are also other issues, such as the language factor: a big number of participants are not native English speakers and it is not always clear whether they properly understand the experiment’s instructions and tasks or how exactly they understand them. This adds another layer of uncertainty when we make conclusions. Behavioral effects that we find can be a result of misunderstanding the task. Informal discussions with participants often prove these concerns. All these issues of course do not mean that we shall stop doing lab experiments, but it indicates that conclusions we make based on our results should be regarded as indicative only.

Another important limitation is related specifically to the nature of social status. Our theoretical questions about social status and social comparison are certainly very interesting, but when we try to simulate such a status in the lab (based on quiz results), there is a certain degree of compromise in such methodology. In

127 terms of external validity of our results, we run into unavoidable risk of mixing apples with oranges. How our participants react to information about quiz results cannot be an ideal approximation of how people react to their social status in real life. There is a gap between what we actually measure in the lab and how we interpret these measurements. This is a general limitation of any lab experiments, which require a certain degree of simplification of general ideas. So we need to be careful in interpreting our results and avoid making overgeneralized conclusions. Table 5.1 below lists the main methods to impose status in the experimental lab. It shows the magnitudes of the observed impact of status on behavior.

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Method of Used in: Experimental Effect awarding status procedure magnitude and statistical significance Awarding status Albrecht et al Subjective self- Small effect, based on knowledge (2013) report satisfaction statistically quiz results only on a 10-level scale significant were measured for different divisions of money between 2 people with different statuses Applaud to “high (Huberman et al., Chances to win Small effect, status” participants 2004) money can be statistically exchanged for a significant chance to be applauded by others Award “Star” Ball and Eckel Ultimatum game Small effect, stickers, in addition (1998) played between statistically to applaud and quiz high and low status significant results players (with candies as incentives). Small effect statistically non-significant (with monetary incentives) Ball, Eckel et.al., Market-style Small effect, (2001) interaction (buying statistically and selling assets) significant between high and (with monetary low status groups incentives) Assign status based Zink et. al. (2008) fMRI brain Small effect on results in a visual scanning, measuring (statistically effort task (such as brain signals related significant count the number to attention toward of dots on the high status people screen in several Breton et. al (2014) EEG scanning Small effect seconds) performed, (statistically measuring brain significant)

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signals related to status and attention Commins and Experiment on in- Small effect Lockwood (1979) grup out-group (statistically discrimination significant) Status assigned Boksem et. al. EEG scanning Small effect based on estimating (2011) performed, (statistically a time interval as measuring brain significant) correctly as possible signals related to self-evaluation and status Hu et. al. (2014) EEG scanning Small effect performed, (statistically measuring brain significant) signals related to fairness and status in UG Status explicitly Jemmott and Simple word-puzzle Small effect randomly assigned Gonzales (1989) tests for children of (statistically (using a t-shirt fourth grade, significant) sticker) performed by groups children with different statuses Status based on von Essen and Measuring the Small effect surname Ranehill (2011) punishment of (statistically unfair offers by a significant) third party, in dictator games depending on status

Table 5.1: Literature review: different methods used to impose status in laboratory experiments

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5.3 Design limitations and directions for future research

When designing experiments to test certain hypotheses, we always face multiple technical obstacles which force us to go for certain compromises. This leads to unavoidable limitations of experimental designs, some of which become apparent only after analysis of the results. These limitations, together with results also point toward future research.

Study 1

In our first experiment on risk we try to focus separately on the impact of range position, while trying to control for other possible parameters as much as possible. We are able to control for rank but our control for group average is not perfect. In two social treatments we measure the difference in risky investments when range position changes from 15% to 40%. We achieve this by lifting the top player. This lift slightly increases the group average together with changing the range position so the control for group average is not total. However the main limitation of our design is the following: the difference in range position between two social treatments is from 15% to 40%. This is a relatively small change given the fact that social impact on risk is quite small. This might be the reason why we did not find an impact of range position on risk-taking. If we could vary the range position within bigger limits, say from 15% to 80% then we might register an effect. The impact of social context on risk is generally small- within the range of 10% of investment in our experiment and also in previous related experiments. So a bigger change of range position might be needed to find any small difference in risky investments. The reason we have only 15% to 40% limit is because we needed to control for other parameters such as rank and group average. This choice to control for other important factors creates big design limitations and we could not find a way to vary the range within bigger limits

131 while also keeping these controls. So for future research a completely new experimental design is needed which would allow to measure the social impact of range on risk-attitudes within much bigger limits while also controlling for other important factors. It is also noteworthy that in previous experiments by Dijk et. al (2014) and Kirchler et. al (2016) there is no any separate control for different parameters. In these experiments they vary the rank, range and group average all together within very big limits. Because of this it is not clear which parameters are important when modelling the social impact on risk.

We also need to remember that when making social comparisons, it is important with whom we are comparing. Comparing to the top-earning colleague in our workplace can trigger stronger emotions than comparing to richest man on the planet, simply because we know our colleague personally and have certain social ties with her/him, while the richest man in the world is a rather abstract person for us. So it’s a bit pointless to come up with a universal formula for social comparison or pro- sociality, because our social emotions are different towards our relatives, friends or people we have not even seen. In our experiment the comparison is with “other subjects” who are unknown to the decision-maker. There is no live face-to-face communication during the experiment. In such settings social emotions can be weaker than in real-life. So the fact that subjects compare their income with that of other unknown participants is also a limitation.

Another important issue is the framing. In our experimental instructions (see Appendix A) we explain what is range position and provide its mathematical formula. However on the computer screen we do not show the numerical value of range (15% or 40%). Instead we provide a visual distance approach where the distances between players on the table are proportional to their range position. This framing adds another layer of uncertainty. For example one of the examiners of this thesis thought that we

132 also need to show the numerical values of range position while the second examiner had an opposing concern, thinking that even the visual framing can result in an “experimenter demand” effect. Such framing issues are an inseparable part of lab experiments and our approach was to find the right balance between presenting the information in an intuitively clear manner while also not pushing the subject too much towards our hypothesis.

Study 2

The second study on group decision-making has its own design limitations related to somewhat artificial nature of the imposed status. As discussed in previous chapters, this method of imposing a status based on quiz and status symbols (stars, applaud, reseating) is debatable. Eckel and Grossman (2001) themselves go into lengthy discussions in their papers bringing arguments and justifying the methodology while also accepting its limitations. Because the concept of social status is itself rather abstract, any empirical method to impose or measure status is also questionable. This makes it hard to interpret our results. It is also hard to understand how the subjects perceive the overall status manipulation. Because we have a very hard quiz, the status based on it can be perceived as random and not deserved. Indeed, the fact that low status males are less willing to change their initial choices might indicate that our status awarding ceremony is perceived as unfair and even insulting.

Another major limitation of this experiment is the lack of any communication between participants, particularly face-to-face communication. This limitation again comes from our desire to control for every other factors which can influence collective decisions and to measure the impact of status only. This results in a rather unnatural collective decision-making environment without any face-to-face or

133 verbal communication. The impact of status in real life might be different when people communicate in normal ways. More precisely, in our experiment we just show a Star on the computer screen to mention the status of other group members. In real life status is signaled by many other ways (body language, eye-contact, others) which may result in a stronger unconscious impact (Anderson and Kilduff, 2009b).

One more technical limitation arises from our decision to put the same subject in different groups across 30 rounds. In each session we had 16 participants and 4 group types, so we have only one group of a certain type in each round. If we leave the subject in the same group for several rounds then the subject will start to think about creating a reputation in the group. But when we put the subject in different groups then we cannot use non-parametric tests to understand the between-group differences, because we have an issue with repeated measures: the same subject appears in different group types. This is a serious limitation for our statistical analysis, so we focused on individual behavior as much as possible to understand the impact of status.

So in future experiments on collective decision-making it would be interesting to organize face-to-face collective decisions over risky choices and measure the impact of status while controlling for other important factors (extrovertedness, talking time, others). It is also important to better understand how subjects perceive the status manipulation and how this technique can be improved.

Study 3

Our third experiment also has design limitations related to status imposition. In addition to mentioned limitations of this status manipulation, there are several other issues. We impose two statuses based on two very different tasks. This seems to strengthen the impact for double-high and double-low subjects. However given the

134 absence of statistically significant impact of this manipulation, it seems that such a technique is not very reliable. Again we do not know how exactly participants perceive the status. Because both tasks are rather difficult, the status may seem undeserved. This might be the reason why we do not find an impact. In general, given the replication problems in economic experiments, the absence of results in this study is also a new piece of evidence which casts some doubts about previous similar experiments with statistically significant results.

As mentioned above, social status is an abstract concept, and measuring it in the lab is a challenging task. In our second and third studies we have very small or no impact of status on behavior. It seems that imposing a status based on a quiz is not yet a well-understood technique and does not guarantee a predictable impact on behavior. There are too many layers which we do not understand, such as how subjects perceive the imposed status and why they react to that status. In future studies it would be useful the play around with this status manipulation technique itself, with extensive after-experiment questionnaires and try to understand how the participants perceive such an artificial status.

5.4 Policy implications

Throughout this thesis we investigated how social comparison can affect human behavior in different settings. In terms of policy implications, our research is generally related to the current debate on rising socio-economic inequality. The main argument of this debate often warns that rising inequality of income can lead to social instability and can eventually become a threat for the democracy itself. Current rise of populist politics across Western world is often viewed by many leading economists as a direct result of rising inequality during last several decades. Proponents of neo-

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Keynesian approach argue for having more state intervention in the economy when free market fails to put the rising inequality under control. In particular, proponents of this approach often argue towards more progressive taxation for the “top 1 percent”, smaller bonuses for high level corporate executives, more transparency in economic offshore zones and elimination of tax heavens.

Our experiment on risk in social context can add some fuel to this argument. We show that low social standing leads to higher risk-taking. In this context, we might argue that concerns about rising inequality and its negative impact are based not only on intuition but also on empirical facts about human intrinsic attitude towards inequality and social comparison.

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Appendix A: Supplement to Chapter 2

A1: Experiment instructions

Thank you for participating in our experiment!

You can leave this experiment at any time if you no longer wish to participate.

You will be paid $5 fixed participation fee and also will have a chance to win up to $40.

Please raise your hand if you have any questions during the experiment.

This experiment has 2 stages: 1) Individual stage, 2) Group stage

Please read the next pages carefully for detailed instructions of each stage.

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Part 1: Individual stage

Part 1 has 15 rounds. In each round you will be endowed a certain sum of money. You will also have a chance to invest part of your endowment in a risky lottery.

Your investment in this lottery may double or become $0 with equal 50%-50% chances.

So in each round you are endowed a sum of money and then choose how much of your endowment you wish to invest in the risky lottery.

You will go through 2 unpaid training rounds before proceeding to Part 1.

Example:

YOU GET $5, YOU DECIDE TO INVEST $X IN LOTTERY, $5-X IS LEFT TO YOU:

• IF YOUR INVESTMENT $X DOUBLES: YOU GET ($5-X)+2X = $5+X • IF INVESTMENT IS LOST: YOU GET $5-X $5+X

$5-X

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Part 2: Group stage

Part 2 will have 240 rounds. In each round you will be ANONYMOUSLY connected with 7 other participants and form a group of 8 people. You will be an active group member in only 30 rounds, and a passive group member in 210 rounds.

In each round:

• ONLY 1 group- member is the active decision-maker. Other 7 members are just passive members. • Every group member is endowed a certain amount of money. • Only the active decision-maker (might be you or someone else in the group) is invited to invest part of his money in the risky lottery. This investment may DOUBLE or BECOME $0 with equal 50%-50% chances. (similar to part 1) • Other 7 passive members don’t play the lottery and just take their initial winnings. In those 30 rounds where you are the active member, your task is to decide how much you wish to invest in this risky lottery.

Your decisions will NOT AFFECT the winnings of other 7 members in your group.

Player 7 $10 Player 6 $9.25 Player 5 $9 Player 4 $8.75 Player 3 $8.50 Player 2 $8.25 Example:

YOU WIN $3. ONLY YOU ARE INVITED TO INVEST IN THE RISKY LOTTRY. YOU DECIDE TO INVEST $X IN LOTTERY, $3-X IS LEFT TO YOU:

• IF YOUR INVESTMENT $X DOUBLES: YOU GET ($3-X)+2X = $3+X $3+X • IF INVESTMENT IS LOST: YOU GET $3-X

YOU $3

139 $3-X

Player 1 $0 You will be given some information about Player 7 $11.50

your relative position in the group: Player 6 $10.25

Player 5 $9.50 Player 4 $9.25

1. RANGE POSITION: Player 3 $8.75 Range position shows your place between top and bottom Player 2 $8.50

players.

In the example picture:

difference between top and bottom players is 11.5 - 0 = 11.5, 11.5

your distance from bottom player is 3.5 - 0 = 3.5.

Your range position is 3.5/11.5 = 0.3 In percentage: 30% YOU $3.50

3.5 2. RANK POSITION

Rank position according to winnings. Your rank position is 7th from top (in this example).

Player 1 $0

3. GROUP AVERAGE WINNING

The average winning of all 8 members,. Equals $7.65 in this example .

In the end of experiment the computer will randomly choose one of the rounds, from part 1 or part 2. You (and other members of your group if its part 2) will be paid the money earned in that round.

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A2: Regression analysis of demographic and other factors: only gender has a significant impact on risk-attitudes.

Gender Age Country of Time to make Origin (dummy the decision variable for Australia (1) or other (0)

Estimate -0.137 0.0005 0.038 -0.028

SE 0.069 0.005 0.07 0.015

t-value -1.979 0.09 0.537 -1.716

p-value 0.052* 0.92 0.59 0.091

Table A2: Risky investments in INDIVIDUAL treatment (each subject’s average in 15 rounds) depending on age, gender, country of origin and time spent on the decision.

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Appendix B: Supplement to Chapter 3

B1: Experiment instructions

Thank you for participating in our experiment! This instruction contains everything you need to know in order to participate in this study.

You can leave this experiment at any time if you no longer wish to participate.

You will be paid $5 fixed participation fee and also will have a chance to win up to $10.

Please put your mobile phones on Silent mode.

It is not allowed to talk to each other or discuss the tasks during the experiment.

If you have any questions during the experiment, please raise your hand and you will be approached.

This is a computerized experiment. On the computer screen you will see the details for each task and directions what to do.

There will be two main types of tasks in this experiment:

1. Knowledge quiz

2. Collective decision task

You can see detailed instructions for each task on next pages.

142

1: General knowledge quiz

You will pass a general knowledge quiz and answer 15 multiple-choice questions.

You will get 1 point for each correct answer.

Your quiz results will not affect your final payment in this experiment.

Award ceremony.

Based on scores all participants will be divided into two groups:

TOP 8 group- Top 8 participants with highest scores

BOTTOM 8 group- remaining 8 participants with lowest scores.

After the quiz, we will hold a small awarding ceremony.

Students who scored well and reached the “TOP 8” group will be awarded special Gold Stars to mark their good score in the quiz. They will also be reseated on the left side of the classroom.

Students in the “BOTTOM 8” group will be reseated on the right side of the classroom. They will also be asked to welcome their peers and to applaud them.

After this ceremony you will proceed to the next stage.

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2: Group decision

During this task you will join several ANONYMOUS 4-member-groups, one by one.

The computer will ANONYMOUSLY connect you with 3 other participants and form a group.

The group will make a collective decision over 2 lotteries: which lottery to play?

(the picture below shows what you will see on the screen)

The group is offered two lotteries: Lottery A and Lottery B. Each lottery wins two different sums of money with different probabilities .

The winning of the lottery goes to each of the 4 members. So if the chosen lottery gives $X, then each of the 4 group members wins $X.

You must click on the lottery that you prefer to play. Other 3 members in your group will also make their choices.

(please look the next page)

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You will see each other’s choices on the next step (see the picture below).

If all 4 members do not agree to play the same lottery, then everyone must make their choice again: which lottery to play: A or B?

You will have up to 6 chances to make a collective decision: a lottery will be chosen only when everyone in the group agrees to play the same lottery.

If after 6 steps all 4 members still do not agree to play the same lottery, then neither of the lotteries will be chosen.

During this experiment you will become a member of several different groups, and try to make a collective decision as a member of each group.

In the end, the computer will randomly choose one of your groups. The lottery that was chosen by that group will be played for real. You will be paid the sum that this lottery gives.

If there was no choice made by this group, then you will not win any money.

145

B2: General knowledge quiz

1. When was the first iPhone introduced? a) June 2007, b) September 2007, c) June 2008, d) September 2008,

2. Which is the largest river in the world, by flowing water volume?', a) Nile b) Amazon c) Mississippi d) Yangtze

3. How long is the pregnancy of a blue whale?' a) 10 months, b) 11 months, c) 12 months, d) 13 months,

4. Height of the tallest building in the world, in meters?'

146 a) 830, b) 840, c) 850, d) 880

5. How old was Mozart when he wrote his first symphony?' a)7, b) 8, c) 9, d) 10

6. Which is the hottest desert on Earth?' a) Sahara, b) Gobi, c) Mojave, d) Dasht-i-Loot,

7. Which is the correct number Pi?, a) 3.14159, b) 3.14195,

147

c) 3.14199'

d) 3.14119

8. How fast can a Space Shuttle fly?' a) 26000 km/h, b) 27000 km/h, c) 28000 km/h, d) 29000 km/h,

9. Which of these chemical elements is NOT radioactive? a) Ruthenium, b) Thorium, c) Berkelium, d) Plutonium,

10. When was President Kennedy assassinated? a) September 1963, b) October 1963, c) November 1963, d) December 1963,

148

11. Who is the youngest world champion in chess? a) Garry Kasparov, b) Bobby Fisher, c) Magnus Carlsen, d) Viswanathan Anand,

12. How many symphonies has Beethoven written?', a) 7, b) 8, c) 9, d) 10,

13. Which is the highest mountain in Europe? a) Mont Blanc, b) Elbrus, c) Monte Rosa, d) Olympus

14. How long is a year on Venus, in Earth days?

149 a) 210, b) 215, c) 220, d) 225,

15. How deep is the deepest part in Pacific Ocean (in meters)? Give your best estimate

150

Appendix C: Supplement to Chapter 4

C1: Experiment instruction

Thank you for participating in our experiment! This instruction contains everything you need to know in order to participate in this study.

You can leave this experiment at any time if you no longer wish to participate.

You will be paid $10 fixed participation fee and also will have a chance to win up to 10 candies.

Please put your mobile phones on Silent mode.

It is not allowed to talk to each other or discuss the tasks during the experiment.

If you have any questions during the experiment, please raise your hand and you will be approached.

This is a computerized experiment. On the computer screen you will see the details for each task and directions what to do.

There will be three main types of tasks in this experiment:

1. Visual reaction task

2. A small anonymous game with another student.

3. Knowledge quiz

You can see detailed instructions for each task on next pages.

151

1. Visual reaction task

In visual reaction task you will see several pictures with a lot of circles on them. You will see each picture for only 5 seconds. You need to count how many circles you see on the picture, and give your best estimate.

Your score in this task depends on how close your answers are to the correct number of circles.

Your score in this task will not affect your final payment in this experiment

Award Ceremony.

Based on scores all students will be divided into two groups:

TOP HALF group- half of the participants with highest scores

LOWER HALF group- other half of the participants with lowest scores.

After the task, we will hold another small awarding ceremony. Students who scored well and reached the Top Half group will be awarded special Gold Stars to endorse their visual abilities. Students in the Lower Half group will be asked to welcome their high- scoring peers and to applaud them.

After this small ceremony you will proceed to the next stage.

152

2: Small game

During this experiment you will be ANONYMOUSLY paired with other participants in this room, and play a small game with each partner.

With each partner you will make a joint decision about dividing 10 candies between yourself and your partner.

In each decision you and your partner will be randomly assigned to roles: one of you will be the Proposer, and the other will be the Responder.

The picture below shows the rules of the decision:

Step 1: Proposer decides how to divide 10 candies between the two of you.

Step 2: Responder ACCEPTS or REJECTS this decision:

IF ACCEPTED: 10 candies are divided as Proposer decided. IF REJECTED: Both get 0 candies.

You will play this game several times with several partners.

At the end of experiment, after you play all the games, one of these games will be selected randomly . You will be given the candies you won in that game.

153

This is what you see on the screen when playing as the Proposer.

You must decide on division of the 10 points between your partner and yourself, select the amount and press Next. Here, 1 point = 1 candy.

You can also see your and your partner’s results in the knowledge quiz and visual task.

154

This is what you see on the screen when playing as the Responder.

Here you can see your partner’s offer to You: You should accept or reject his/her offer (press ACCEPT or REJECT). Here, 1 point = 1 candy.

You can also see your and your partner’ s results in the knowledge quiz and visual task..

You will play this game several times with several partners.

At the end of experiment, after you play all the games, one of these games will be selected randomly . You will be given the candies you won in that game.

155

3: Knowledge quiz about Brisbane.

You will pass a knowledge quiz testing your knowledge about Brisbane.

The quiz contains 10 questions. You will get 1 point for each correct answer. For each question there will be four answers to choose from. This means that your result in the quiz will depend both on your correct answers and also on your luck.

Your quiz results will not affect your final payment in this experiment.

Award ceremony.

After the quiz all participants will be divided into two groups:

TOP HALF group- half of the participants with highest scores

LOWER HALF group- other half of participants with lowest scores.

After the quiz, we will hold a small awarding ceremony. Students who scored well and reached the Top Half group will be awarded special Gold Stars to mark their good knowledge of Brisbane’s history. Students in the Lower Half group will be asked to welcome their peers and to applaud them.

After this small ceremony you will proceed to the next stage.

156

C2: Distribution of subjects across four status groups

LowHigh HighHigh

LowLow HighLow

Table C2: Distribution of subjects across four status groups

Suppose we have 푁 number of participants in a session. After first task we will have equal numbers of High and Low status subjects:

퐻 = 퐿 = 푁/2 .

After second task we again give a High status to half of the subjects and a Low to other half. This second status is independent from the first one. So we will have four status categories: HH and HL who were High after the first task, and LH and LL who were Low after the first task. Horizontal axis on Table C3 shows the first task. On the left we have the subjects who were LOW and on the right we have subjects who were HIGH after FIRST task. This mean that

퐻퐻 + 퐻퐿 = 푁/2 { (1) 퐿퐻 + 퐿퐿 = 푁/2

Vertical axis is the second task. So on the top we have subjects who were HIGH and on the bottom we have subjects were LOW after SECOND task. This means that

157

퐻퐻 + 퐿퐻 = 푁/2 { (2) 퐻퐿 + 퐿퐿 = 푁/2

From (1 ) and (2) it obviously follows that

퐻퐿 = 퐿퐻 { (3) 퐻퐻 = 퐿퐿

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C3: Knowledge quiz

1. The Brisbane City Hall clock is modelled on the design of: a) Big Ben in London, b) St Marks Campanile in Venice, Italy, c) The clock on the top floor of the Queen Victoria building, Sydney, d) The clock on Strasbourg Cathedral

2. Artist Donna Marcus had 7000 replica vegetable steamers made for her sculpture Steam in Reddacliffe Place that is made up of: a) 8 geodesic spheres, b) 10 geodesic spheres, c) 12 geodesic spheres, d) 15 geodesic spheres

3. Newstead House, Brisbane’s oldest surviving residence, is named after: a) The suburb Newstead, b) Newstead Abbey in Nottinghamshire, c) A grand home in Newstead, Victoria, d) The Creswick Newstead Road in Victoria

159

4. In 1901, the governor of Queensland, Baron Lamington, read the proclamation of the federation of the Australian Commonwealth from, a) The bar at the Breakfast Creek, b) The upper floors of Queensland’s Parliament House, c) A balcony on the William St side of the Treasury Building, d) A balcony on Land Administration Building

5. The University of Queensland opened its doors to students in 1911 for classes taught at Old Government House. Of the 83 students in the first year, a) None were women, b) Five were women, c) 23 were women, d) 55 were women

6. The steps leading up to Anzac Square Shrine are split into a first row of 19 steps and second row of 18 steps, signifying the end of World War I in 1918. The shrine itself, in another significant number, has: a) 20 columns, to mark the century, b) 14 columns, marking the start of the war, c) 18 columns, marking the end of the war,

160 d) Three columns, for the army, navy and air force

7. At the southern end of the Victoria Bridge is a memorial to 11-year-old Hector Vasyli. Hector tragically: a) Drowned while swimming in the Brisbane River, b) Died when climbing on the bridge pylons, c) Died when he was accidentally hit by a car while waving to servicemen returning from World War I, d) Died when he fell from Brisbane’s first tram in 1885

8. St Johns Cathedral is home to a coin collection dating back to: a) The first years of European settlement in Queensland, b) Captain Cooks voyage of discovery in 1770, c) 1000 years, d) 2000 years

9. Mary MacKillop, Australias only canonised saint, is honoured with a contemporary sculpture at St Stephens chapel. The chapel also has a relic set in stone that is:

161 a) A piece of the coffin in which she was buried, b) A piece of a crucifix she wore, c) A piece of her clothes, d) A piece of a plate she used when feeding the poor

10. How long is the Victoria bridge? Give your best estimate:

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Appendix C4: Regression analysis of demographic and other factors for ultimatum game offers in stage 2.

Gender Age Country of Origin (dummy variable for Australia (1) or other (0)

Estimate 0.38 0.005 -0.015

SE 0.26 0.02 0.26

t-value 1.48 0.27 -0.059

p-value 0.14 0.78 0.95

Table C4: Ultimatum game offers in stage 2 are not correlated with age, gender or country of origin

Appendix C5: Regression analysis of demographic and other factors for ultimatum game offers in stage 4.

Gender Age Country of Origin (dummy variable for Australia (1) or other (0)

Estimate 0.168 0.0011 -0.05

SE 0.14 0.146 0.15

t-value 1.2 0.081 -0.336

p-value 0.231 0.935 0.73

Table C5: Ultimatum game offers in stage 4 are not correlated with age, gender or country of origin

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