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Less money in the pocket, and too much in the mind:

does financial constraint increase the anchoring effect

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Less money in the pocket, and too much in the mind:

does financial constraint increase the anchoring effect

Martin Miroslavov Proychev

University of Groningen

Faculty of Economics and Business

Master Thesis

Date: 22

nd

June 2017

Address: Bulgaria, Sofia, Deliyska vodenitsa 423, ap. 23, zip 1582

Phone number: +359882929400

E-mail: proychev_tsn@abv.bg

Student number: 3119173

Supervisors:

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Abstract

The anchoring effect is a well-known cognitive phenomenon and one of the most robust cognitive heuristics. However, not all individuals are equally influenced by anchoring cues. An experiment is conducted to study the anchoring bias between people that are induced to feel poor and a control group. Results indicate that poor people are slightly less influenced from the anchoring bias compared to regular people. Abnormally, mood was not different across conditions and it was not taken into account thereafter. When people grew up relatively wealthy, they were more strongly affected by the anchor, but only in the context of the control group. Moreover, when the anchor was high, the anchoring effect was also significantly higher, which is in line with previous research. However, gender and current income did not significantly influence the anchoring bias.

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TABLE OF CONTENTES

INTRODUCTION ... 1

THEORITICAL BACKGROUND ... 1

The anchoring effect ... 1

Poverty feelings and its effects on judgment and decision making ... 3

Poverty feelings and anchoring ... 4

Poverty decreases the anchoring ... 4

Poverty increases the anchoring ... 5

Stress and sad mood ... 5

Cognitive load, mental resources and self-control ... 6

METHODOLOGY ... 8

RESULTS ... 10

LIMITATIONS AND FUTURE IMPLICATIONS ... 15

CONCLUSION ... 17 BIBLIOGRAPHY ... 18 APPENDIX ... 27 Appendix 1 ... 27 Appendix 2 ... 30 Appendix 3 ... 31 Appendix 4 ... 34 Appendix 5 ... 35 Appendix 6 ... 37 LIST OF FIGURES Figure 1 - Anchoring between Poor and Control condition ... 10

Figure 2 - The anchoring effect for High and Low anchor ... 11

Figure 3 The relationship between mood and anchoring ... 12

Figure 4 Childhood and anchoring ... 13

Figure 5 Income and anchoring ... 14

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INTRODUCTION

Common to all human beings’ behaviour is the tendency to be influenced, more than we know or want, by different cognitive biases. One such influence is the anchoring phenomenon, whereby an initial hypothesis, value or piece of information significantly governs an individual’s decision-making process when making subsequent decisions. An unexplored field is the relationship between poverty and anchoring. This research focuses on the difference in the anchoring between a control group and a group that is induced to feel poor. The standard paradigm introduced by Tversky & Kahneman (1974) is applied in order to study the magnitude of anchoring. Two different perspectives are taken into account when determining the influence of poverty over the decision-making processes that participate and govern the anchoring effect. First, financial constraints could lead to anger, anxiety, depression, stress, sad mood, impatience, lower self-control, lack of willpower, unhappiness, higher cognitive load, all of which might increase the anchoring effect. Second, subjects experiencing negative affect are also more likely to rely on the information at hand, not ignoring information that is considered unimportant. On the other hand, poor people are powerless and dependent and have weakened attention and reduced effort, especially for information that is temporarily available, such as anchors. The two contradicting viewpoints are explored and an experiment is conducted to further investigate the relationship between financial deprivation and anchoring. Finally, some limitations and future implications are discussed.

THEORITICAL BACKGROUND

The anchoring effect

Research on anchoring as a phenomenon commenced after the psychological research and seminal work of Tversky & Kahneman (1974). They first proposed the explanation that a reference point (anchor) influences judgment because people adjust their judgment from the initial value, that might be irrelevant, and the adjustment is usually insufficient. Over the past forty years, anchoring

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knowledge/factual questions (Blankenship et al. 2008; Epley & Gilovich 2001; Epley & Gilovich 2005; Jacowitz & Kahneman 1995; McElroy & Dowd 2007; Mussweiler & Englich 2005, Study 1; Mussweiler 2003; Mussweiler & Strack 1999, 2001a; Strack & Mussweiler 1997; Mussweiler & Strack 2001b, Wegener et al. 2001), legal judgments (Chapman & Bornstein 1996; Englich & Mussweiler 2001; Englich & Soder 2009; Englich, Mussweiler & Strack 2005, 2006; Hastie, Schkade & Payne 1999; Marti & Wissler 2000), probability estimates (Chapman & Johnson 1999, Experiment 2; Plous 1989; Tversky & Kahneman, 1975), negotiations (Galinsky & Mussweiler, 2001), self-efficacy (Cervone & Peake, 1986), as well as preference reversals, the hindsight bias, subadditivity in likelihood judgment, social comparison, egocentric biases (Chapman & Johnson 2002; Epley 2004).

Across all those domains four main paradigms are being observed. Anchoring bias can result from insufficient adjustment from a starting point, conversational inferences, numerical priming, and mechanisms of selective accessibility (Mussweiler, Englich & Strack, 2004). Anchoring effects are most typically examined in the classic paradigm introduced by Tversky & Kahneman (1974). The study in the current paper is also based on this standard numerical anchoring paradigm, using the traditional anchoring task. In this paradigm, anchors are explicitly provided by inducing judges to compare the target to the anchor value. After receiving an ostensibly random, presumably uninformative starting point for a judgment and indicating whether the target’s extension on the judgemental dimension is larger or smaller than the anchor value, participants are asked to give their best estimate. There are a few different ways to indicate the randomness of the anchor, among which spinning a wheel of fortune (Tversky & Kahneman, 1975), emphasizing the random selection in the instructions (Strack & Mussweiler, 1997), or throwing dice (Mussweiler & Strack, 2000b). Anchors can also be implicitly provided to the participants in cases in which it is clearly informative for the judgement at hand. For example, Northcraft & Neale (1987) demonstrated that real-estate pricing decisions depended on the listing price for the property. Anchors can also be self-generated. For example, participants who are asked to give their best estimate for the freezing point of vodka, for example, may generate 0˚C as the freezing point of water as an anchor, and then adjust

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Poverty feelings and its effects on judgment and decision making

A widely used definition for poverty is „the resources to obtain the types of diet, participate in the activities, and have the living conditions and amenities which are customary, or are at least widely encouraged and approved, in the societies in which they belong” (Professor Peter Townsend, cited by Child Poverty Action Group 2011). It can be a psychological state in which people feel financially worse off someone else. People can be persuaded to compare themselves to others that are

wealthier than them, making them feel poor. Poverty causes stress and negative affect which in turn may lead to short-sighted and risk-averse decision-making (Haushofer & Fehr, 2014).

The Chronic Poverty Report (2008) estimates that 320–443 million people live trapped in chronic poverty that lasts for many years, often for their entire lifetime and their children are likely to inherit their poverty as well. An influential literature on poverty traps argues that such persistent poverty is driven by constraints that are external to the individual. An alternative view highlights the role of internal constraints in perpetuating poverty traps. Behavioural biases or internal constraints such as myopia, lack of willpower and lack of aspirations are often cited as traits that the poor likely suffer from (Dalton, Ghosal & Mani, 2016).

According to large amount of research (Lawrance 1991; Banerjee &Mullainathan 2010; Tanaka, Camerer, & Nguyen 2010; Spears 2011; Gloede, Menkhoff & Waibel 2015; Bernheim, Ray & Yeltekin 2015; Carvalho 2013; Haushofer, Schunk & Fehr 2013), the poor are more impatient, more risk averse, and have lower self-control, all of which could trap them in a cycle of poverty. Shah, Mullainathan & Shafir (2012); Mullainathan & Shafir (2013) and Mani et al. (2013) argue that scarcity, defined as “having less than you feel you need” (Mullainathan & Shafir 2013, pg. 4), impedes cognitive functioning, which in turn may lead to decision-making errors and myopic behaviour.

The cognitive load from overthinking about their financial situation, affects poor people’s decision making (Vohs 2013; Mani et al 2013). “Lacking money or time can lead one to make poorer decisions, possibly because poverty imposes a cognitive load that saps attention and reduces effort.“ (Mani et al. 2013,p. 976; see the Perspective by Vohs). Poverty has psychological

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The multiple dimensions of poverty - including financial worries, time pressures, coping with stereotypes, and emotional distress – sap mental processing capacity, which in turn affects our judgement and decisions (Kaplan & Berman 2010; Mullainathan & Shafir 2013). Financial

dissatisfaction leads to stress, anger, and resentment (Wilkinson and Pickett, 2009). Haushofer and Fehr (2014) show that poverty correlates with unhappiness, depression, anxiety, and cortisol levels. According to (Englich & Soder, 2009) emotions are usually used explicitly as information in judgment situations, or they can indirectly influence decision making by changing how people process

information. Financially deprived people seek and consume scarce goods instead of abundant goods (Sharma & Alter, 2012), consume more high caloric food (Bries & Laporte, 2013), have increased preference for material goods instead of experiences (Tull, Hershfield & Meyvis, 2015), and are more likely to fall for unprofitable solutions, such as buying lottery tickets (Blalock, Just & Simon, 2007).

Poverty feelings and anchoring

As illustrated above, the poor often behave differently from the non-poor. The aim of this research is to investigate the relationship between feeling (being) poor and the anchoring effect. Different moderators that can strengthen or weaken the relationship are also taken into account.

Poverty decreases the anchoring

There are many similarities between people with financial constraints and people that are powerless or dependent. In many aspects poverty is connected to powerlessness. People who feel dependent are motivated to engage in controlled processing, whereby they try to be as accurate as possible in their judgment by looking for inconsistent information (Erber & Fiske 1984; Neuberg & Fiske 1987). Being in a dependent or subordinate position increases people’s tendency to process information that is inconsistent with expectations (Erber & Fiske 1984; Guinote & Philips 2010), suggesting that powerlessness may also hinder (interfere) both the activation and application of information. Another reason to believe that poor people might anchor less, is because their weakened attention and reduced effort (Mani et al., 2013) might significantly decrease the anchoring effect, since they might not be as aware of the anchor as others.

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information about the target judgment that is consistent with the anchor. Also, people who feel powerful should be more likely (than those who feel powerless) to apply that information in their judgment. Various findings have shown that the powerful are more influenced by information that is temporarily more accessible, such as primed goals or situational cues (DeMarree et al. 2012;

Galinsky, Gruenfeld, & Magee 2003; Guinote 2008; Guinote, Weick, & Cai 2012). Anchors similarly function as temporarily accessible information that has the potential to automatically guide judgment (Mussweiler & Englich 2005). Lammers & Burgmer (2017) conclude that „therefore, people who experience power should not only be more likely to activate anchor consistent information about the target of their judgment, but they should also be more likely to rely on that accessible.” Ultimately, power significantly increases numeric anchoring effects on judgment, compared to powerlessness.

Poverty increases the anchoring

Stress and sad mood

Several authors have contended that poor people and feelings of being poor causes a great deal of stress in one’s life (Haushofer & Fehr 2014; Karen 2012; Mani et al. 2013; Wilkinson & Pickett 2009) Poverty accounts for many negative affective states. Income and socioeconomic status have well-known correlations with stress and anxiety (Chen et al. 2010; Fernald & Gunnar 2009; Evans and English 2002; Lupien et al. 2001), with levels of the stress hormone cortisol (Cohen, Doyle & Baum 2006; Li et al. 2007; Saridjana et al. 2010), and with depression (Lund et al. 2010; WHO 2001). When people are feeling more threatened, and many poor people are often feeling that way, they tend to anchor more (Kassam, 2009). Sad mood further increases the amount of numeric anchoring and sad people are universally more susceptible to anchoring (Bodenhausen, Gabriel & Lineberger, 2000). When people are feeling sad, they think more extensively. For example, sad mood induces judges to engage in more through information processing (Englich & Soder 2009; Schwarz & Clore 2007), thus induces them to be more through in the selective search for anchor-consistent information, resulting in higher anchoring. Bodenhausen, Gabriel & Lineberger (2000) and Englich and Soder (2009) found that participants in a sad mood were more susceptible to the heuristic bias of anchoring in

comparison to their counterparts in a neutral or happy mood. This is in line with the fact that conditions that lead to greater thought about the anchor produce greater bias. In addition,

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Cognitive load, mental resources and self-control

The ability to adjust from self-generated anchors requires controlled processing that can be diminished for a lot of reasons (Epley 2004; Gilbert 2002) such as alcohol consumption, time pressure, and cognitive load, all of which are common for the poor people (Kassam, 2009). A lot of poor people have their mental resources spent over thoughts about their situation and financial problems (Vohs 2013). This creates a high cognitive load that is found to increase numeric

anchoring. Anchoring based on a higher level of thinking involves greater use of judgement-relevant background knowledge, persists longer over time, is more resistant to subsequent attempts at social influence, and is less likely to result from direct numeric priming (Blankenship et al., 2008).

A growing body of psychological research divides our decision-making into two systems. System 1 produces the fast, intuitive reactions and instantaneous decisions that govern most of our lives. In System 1 thinking is fast, effortless, automatic, emotional, stereotypic, subconscious, impulsive, immediate, instinctive, associative and largely involuntary. It is autonomous and efficient, requiring little energy or attention, but is prone to biases and systematic errors. System 2 is more deliberative, slower, effortful, attentive, logical, calculating, conscious and voluntary. It requires energy and can’t work without attention but, once engaged, it has the ability to filter the instincts of System 1. We have limited capacity for System 2 thinking (Kahneman 2003; Kahneman 2012). System 1 is the default mode of thinking, especially for people that are financially deprived. System 1 is also closely related to different biases and heuristics including the anchoring effect. Therefore, people with financial constraints should be more prone to anchoring.

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resources are diminished (Furnham & Boo, 2011). Individuals are especially prone to the influence of heuristics when they are low in self-control (e.g., Fennis Janssen & Vohs, 2009). Therefore, poor people might be strongly influenced by anchors. In contrast, under high self-control conditions, people will react less impulsive, because their reflective system is more active, engaging in logical and effortful thinking, thus decreasing the magnitude of the anchoring. Anchoring is found to be greater when mental resources are diminished and when the cognitive ability is lower Bergman et al. (2010). Also, participants lacking the ability or motivation to think („low elaboration anchoring“) (Blankenship et al. 2008; Wegener 2010) are likely to treat the anchor as a „hint“ to a reasonable judgment (not considering its supposed to be randomly generated) (Schwarz, 1994).

High-elaborative anchoring involves thorough thinking that engages judges in more effortful information processing with existing knowledge and hence activate the anchor-consistent

information that bias judgments (Blankenship et al. (2008); Wegener et al. (2001, 2010). Poor people are also more dependent on financial resources than others. People who feel dependent are

motivated to engage in controlled processing, whereby they try to be as accurate as possible in their judgment by looking for inconsistent information (Erber & Fiske 1984; Neuberg & Fiske 1987). Accuracy motivation does increase anchor–estimate gaps when people are certain about the direction of adjustment, and that this is true regardless of whether the anchors are provided or self-generated (Simmons, 2010).

According to Isen & Means (1983), positive-affect subjects are less likely to review information they had already looked at, and were more likely to ignore information considered unimportant. Positive affect enhances problem solving and decision making, leading to cognitive processing that is not only flexible, innovative, and creative, but also thorough and efficient. People in whom positive affect had been induced were more efficient in the way they went about the complex task - they reached a decision sooner and showed less redundancy in the search process (Isen & Means, 1983). In contrast, negative affect increases the reliance on the information at hand (Bless & Schwarz 1999; Schwarz 1990).

Based on the notion that poor people engage more in System 1 thinking and experience more stress, sadness, dependency, high cognitive load, lack of self-control and willpower, which are all factors increasing anchoring’s effect, it will be hypothesized that feelings of being poor increase the strength of numeric anchoring effects, compared to normal condition. The feeling of being poor alters

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Hypothesis 1: People with financial constraints will anchor more compared to those that do not have such limitations.

Further, it will also be investigated how does the relationship change while introducing different factors that might moderate it, such as mood; low vs high anchor; financial constraints during childhood and current income.

METHODOLOGY

Participants and Design: A total of 326 respondents (90% Bulgarians, 113 men, 213 women, mean age 32 years) participated in this research by completing an online survey. Twenty percent had a high school degree, 33% had a Bachelor degree and 46% had a Master degree. Respondents were randomly assigned to one of four experimental conditions of a 2 (condition: control vs. poverty) × 2 (anchor: high vs. low) between-subjects design. Participants in every condition were presented the same anchoring problem taken from Jacowitz & Kahneman (1995) and later used by Epley & Gilovich (2005).

The prediction that poverty increases anchoring is being tested by manipulating poverty feelings (poor versus control) and the value of an anchor (high versus low). An interaction effect is predicted, such that the difference in estimates between low- and high anchor conditions should be amplified for poor (compared to control) participants.

Procedure: Participants in the poverty condition were presented the following text: Students have

many financial constraints and the factors that contribute to these constraints tend to vary. What are the factors that require you to be careful with how you spend your money? What limits your monthly income? What makes you feel restricted in your expenditures? What are the factors that make you unsatisfied with your financial situation and make you uncertain about future? Include the aspects of your current situation that most contribute to your financial constraints (e.g., daily expenses, student loans, lack of income, limited savings, limited parental support, uncertainty of future income, . . .). Please be as detailed as possible, and write at least a couple of sentences about your financial limitations and how you feel about them.

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reason is that many people in the poor condition did not proceed further after reading the first question that might be perceived as slightly intimidating and demanding.

Then, half of the participants were assigned to receive the low anchor and half - the high anchor. They were asked whether the population of Chicago is more or less than 0.2 million (low anchor) or 5 million (high anchor), taken from Jacowitz & Kahneman (1995). Participants were instructed not to look up the correct number as we are interested only in their own opinion. In reality this value serves as an “anchor” for the next question. In the following question, participants were then asked give their best guess (to provide their own estimation) on the exact number of the population of Chicago.

Next, participants were asked to answer questions concerning demographics; their mood at the moment, on a 100-point scale, using a slider measure; monthly income; childhood socioeconomic status; honest judgement of how attentive they were, how quiet was their surrounding etc.

Deletion: Roughly 6 % of the data were deleted. The most extreme value was 20 million

(overestimating the actual population by factor of 7) so no participants were deleted as extreme outliers. One participant provided the correct answer and was deleted. It took him 3 second to answer the anchoring question but 37 second to provide his own estimation (2.72 million), which is the most accessible answer that Google and Wikipedia searches provide. Another participant confessed that look up the answer on-line so she was also deleted. Two other participants provided answers close to the correct value (2.7 and 2.8 million) but the response was retained, as it may have been simply a good guess and deletion did not affect results in any meaningful manner. Three participants in the poor condition stated that have no financial problems at all and their income was really high as well, so they could not be considered part of the poor condition and were deleted. Two people were just joking in their answers. Two people were filling words instead of numbers and had missing answers. One person was answering really fast all the questions, perhaps just to see all the questions. Four people admitted that were not attentive. One of them was also listening to music and was around colleagues, the other one was filling through smartphone while doing other things meanwhile. Five people said that they don’t know the answer. All of the above were deleted.

Methodological Notes: After deletion, the final sample size is 306 participants. This is in line with the

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RESULTS

In order to investigate whether the participants in the poor condition anchor more or less than those in the control condition, a 2 (condition: poor vs. control) x 2 (anchor: high vs. low) ANOVA was executed. Participant’s absolute deviation from the anchoring value acted as the dependent variable. This analysis revealed a significant effect of anchor, F(1, 302)=7.535, p=.006 and a non-significant effect of poverty condition, F (1, 302)= 1.705, p=.193. Furthermore, a non-non-significant interaction effect of condition and high versus low anchor was found, F (1, 302)=.547, p=.460. Compared to poor condition, participants in the control condition reported an anchoring effect that was on average 15,7% larger (CI mean-difference=.5 million, p=.176). These results disprove our hypothesis that, relative to control condition, people in the poor condition will anchor more. (Appendix 1)

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High vs. Low: The anchoring effect (operationalized as the difference in means between the high- and low-anchor conditions) was 37% stronger in the high-anchor conditions, CI

mean-difference=1.029 million, SE=.375, Sig.=.006, high anchor µ=3.774 million, low anchor µ =2.745 million. Also, 66% of the estimates made with a high anchor were higher than the anchor (pulled beyond the anchor), while 0.01% of the estimates made with a lower anchor were lower than it. There is a significant moderating effect of High/Low anchoring (p=.026) over the relationship between poverty and anchoring. The mean difference between the high and the low anchor is significant (CI mean-difference=1 million, p=.006). The least amount of anchoring is observed when the participants from the poor condition receive a low anchor (2.64 million). In contrast, when the participants in the control condition receive a high anchor – the most anchoring observed (4.16 million). (Appendix 1)

Anchoring (mean closer to null=more anchoring)

High Low

Condition N Mean Std. error N Mean Std. error

Control 101 2.64 .315 92 3.39 .330

Poor 56 2.85 .423 57 4.16 .419

Planned contrast showed that in the control condition the difference between the low and the high anchor was insignificant (F(1, 302)=2.72, p=.100, CI mean-difference=.75 million), while in the poverty condition it was significant (F(1, 302)=4.82, p=.029, CI mean-difference=1.3 million). Furthermore, when the anchor was high, there was no significant difference between poverty condition and control condition (F(1, 302)=.162, p=.687, CI mean-difference=.21 million). When the anchor was low, the difference was higher, but still not significant (F(1, 302)=2.07, p=.152, CI mean-difference=.77 million). (Appendix 2)

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Mood: Although mood was predicted to be disparate between poor people and average people, the current research found that the participants in the poverty condition had evaluated themselves to be only 4% unhappier that the people in the control condition. An ANOVA was executed in order to examine whether poverty predicts mood. The results showed no significant effect of poverty

condition: F(1, 304)=1.96, p=.162, CI mean-difference=3.8. Therefore, we ruled out mood as a factor in the research. Nonetheless, in order to determine how the mood of the participants influences their anchoring, this study made use of the PROCESS macro developed by Hayes (2013). The overall model was not significant: F(3, 302)=1.40, p=.242, R2=.014. There was no interaction between poverty condition and mood b=-.015, t(302)=-.97, p=.330. Although mood did not influence the interaction between poverty and anchoring, an interesting pattern is observed. Participants in the poor condition anchor 25% more, on average, when they are in a good mood (CI mean

difference=0.85). (Appendix 3)

Figure 3: The relationship between mood and anchoring (on the left side are participants that are in unhappy or sad mood and on the right side are those in happy or positive mood)

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validity between the three question was found (Cronbach’s Alpha=.788). Therefore, the three questions were combined, aggregated and averaged in a new variable. Again, PROCESS macro developed by Hayes (2013) is used to analyse the data. The childhood SES was examined in relation to both high and low anchor. The Overall Model was significant: F(7,298)=3.33, p=.002, R2=.24. Childhood SES was also significant b =-.27, t(298)=-2.05, p=.04. Condition was not significant b =.47, t(298)=1.14, p=.26. High versus low anchor was significant b=.93, t(298)=2.54, p=.011. The results show that there is a significant interaction between poverty/control conditions and childhood SES b=.57, t(298)=1.92, p=.056. On the other hand, the interaction between high/low anchor and childhood SES were not significant b=.057, t(298)=.214, p=.830. Participants from the poverty condition were not really influenced in their answers about the anchoring by their childhood SES. On the other hand, when participants from the control condition have a better childhood SES, they tend to anchor significantly more (CI mean-difference=1.323 million). (Appendix 4)

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Income: An ANOVA was executed in order to examine the relationship between poor/control condition and current income. The participants from the control condition had income that was, on average, 25% higher than those of the poverty condition: F(1,297)=5.09, Sig=.025, CI

mean-difference 356 BGN, 1622 bgn (830 EUR) for participants in the control condition versus 1266 BGN (648 EUR) for participants in the poverty condition. Again, a PROCESS macro developed by Hayes (2013) was used. The overall model was not significant F(7,291)=1.80, p=.085, R2=.047. The patterns for high and low anchor are contradictory: with increase of their income, participants in the control condition tend to anchor more when the anchor is high (MD=0.3 million), and anchor less when the anchor is low (MD=0.97 million). The opposite is true for participants in the poverty condition: with increase of the income, they tend to anchor more when the anchor is low (MD=0.33 million), and anchor less when the anchor is high (MD=0.77 million).

Figure 5: Income and anchoring ( The horizontal axis represents the mean centred income of the participants)

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average, male participants tend to anchor less than female participants (CI mean-difference .400 million), especially in the poverty condition. (Appendix 6)

Figure 6: Gender and anchoring

LIMITATIONS AND FUTURE IMPLICATIONS

One limitation to our research is that the magnitude of the different factors, influencing the

anchoring effect in the two conditions, is not measured. Therefore, it is highly probable that the two proposed speculations about the anchoring effect cancel each other, leaving no significant

difference between the two conditions.

Another limitation to the current research is that (due to the lack of calibration group) the anchoring effect itself was not measured. The calibration group’s median from Jacowitz & Kahneman (1995), is not so relevant to our research since the participants were from United States of Amerca, even though the population of Chicago decreased with only 80 thousand people since then. It was also possible that the individuals from the control condition experienced some of the attributes that are inherent for the participants in the poverty condition, e.g. the non-manipulated mood may be closer to the happy mood condition, sometimes closer to the sad mood condition (Schwarz & Clore, 1983)

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Mussweiler, 1997). Nonetheless, it would be interesting to test the interaction between poverty and extremity of anchors.

Psychology researchers have found that the more complex a task is the more likely people are to engage in System 2 decision making. For example, Alter et al. (2007), found that simply decreasing the legibility of the font used in a common cognitive test made people more likely to switch to System 2. By having the participants in the control condition write about three facts in the first question, we try to rule out the possibility that only participants in the poor condition might engage in System 2 thinking. Nonetheless, participants in the poor condition might immerse with their question and spend a lot of time thinking and writing about it. A quarter of the participants in the poor condition have written over 130 words, which might have really activated System 2 thinking. While System 2 is active, System 1 has greater influence over one’s behaviour, thus making one more prone to anchoring. Numeric anchors provide a subtle and implicit source of influence that affects judgments more automatically, “flying below the radar” (Mussweiler & Englich, 2005). Lammers & Burgmer (2017) found that paradoxically, the powerless are more influenced in their judgment by strong pressures, but less influenced by strong pressures, compared to the powerful. If we compare the powerless to the participants in our poverty condition, question 1 in the poverty condition is perceived as a strong pressure, and the anchoring questions are perceived as a weak pressure, then there is a significant reason to believe that the poor people will anchor less.

An elaborative and thoughtful process is a prerequisite for the anchoring effect to happens. Since poor people have diminished cognitive resources and capacity, their cognition is also reduced. On the hand, the rich had a calmer and trouble-free childhood that allow them to process the

information slowly and elaborately. They learn to dedicate more cognition to different tasks. Therefore, when faced with an anchor, they might be more influenced by it.

As the results from the current research show a pattern that poor people anchor less, a practical implication for the findings might be the area of public policy. For example, poor people should pay more attention to high-caloric food. If they had an increased cognitive ability, paid more attention and had an increased self-affirmation (Cohen & Sherman, 2014), they would anchor more and they would be more influenced by the anchor (calories), therefore paying more attention to their calories intake. By making the poor kids, the people from the minorities, the people from bad

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Future research can investigate, separately, the different factors, such as stress, unhappiness, weakened attention, reduced effort, etc., that account for the anchoring effect in financially deprived individuals.

CONCLUSION

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APPENDIX

Appendix 1

Univariate Analysis of Variance

Between-Subjects Factors Value Label N High_Low 1.00 High 157 2.00 Low 149 ConditionCoded .00 Control 193 1.00 Poor 113 Descriptive Statistics Dependent Variable: ABSOLUTE_ANCH_CHICAGO

High_Low ConditionCoded Mean Std. Deviation N

High Control 2.6386 2.24986 101 Poor 2.8509 2.76678 56 Total 2.7143 2.44010 157 Low Control 3.3904 3.15805 92 Poor 4.1570 4.60376 57 Total 3.6837 3.78041 149 Total Control 2.9970 2.73953 193 Poor 3.5097 3.84535 113 Total 3.1863 3.19621 306

Levene's Test of Equality of Error Variancesa Dependent Variable: ABSOLUTE_ANCH_CHICAGO

F df1 df2 Sig.

6.338 3 302 .000

Tests the null hypothesis that the error variance of the dependent variable is equal across groups.

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Tests of Between-Subjects Effects Dependent Variable: ABSOLUTE_ANCH_CHICAGO

Source

Type III Sum

of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powerb Corrected Model 94.138a 3 31.379 3.136 .026 .030 9.409 .726 Intercept 3025.781 1 3025.781 302.410 .000 .500 302.410 1.000 High_Low 75.395 1 75.395 7.535 .006 .024 7.535 .781 ConditionCoded 17.057 1 17.057 1.705 .193 .006 1.705 .256 High_Low * ConditionCoded 5.470 1 5.470 .547 .460 .002 .547 .114 Error 3021.678 302 10.006 Total 6222.574 306 Corrected Total 3115.816 305

a. R Squared = .030 (Adjusted R Squared = .021) b. Computed using alpha = .05

Estimated Marginal Means

1. Grand Mean

Dependent Variable: ABSOLUTE_ANCH_CHICAGO

Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

3.259 .187 2.890 3.628

2. High_Low

Estimates Dependent Variable: ABSOLUTE_ANCH_CHICAGO

High_Low Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High 2.745 .264 2.226 3.263

Low 3.774 .267 3.249 4.298

Pairwise Comparisons Dependent Variable: ABSOLUTE_ANCH_CHICAGO

(I) High_Low (J) High_Low

Mean Difference

(I-J) Std. Error Sig.b

95% Confidence Interval for Differenceb

Lower Bound Upper Bound

High Low -1.029* .375 .006 -1.767 -.291

Low High 1.029* .375 .006 .291 1.767

Based on estimated marginal means

*. The mean difference is significant at the .05 level.

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3. ConditionCoded

Estimates Dependent Variable: ABSOLUTE_ANCH_CHICAGO

ConditionCoded Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Control 3.015 .228 2.566 3.463

Poor 3.504 .298 2.918 4.090

Pairwise Comparisons Dependent Variable: ABSOLUTE_ANCH_CHICAGO

(I) ConditionCoded (J) ConditionCoded

Mean

Difference (I-J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

Control Poor -.489 .375 .193 -1.227 .248

Poor Control .489 .375 .193 -.248 1.227

Based on estimated marginal means

a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Univariate Tests Dependent Variable: ABSOLUTE_ANCH_CHICAGO

Sum of

Squares df Mean Square F Sig.

Partial Eta Squared Noncent. Parameter Observed Powera Contrast 17.057 1 17.057 1.705 .193 .006 1.705 .256 Error 3021.678 302 10.006

The F tests the effect of ConditionCoded. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.

a. Computed using alpha = .05

Univariate Tests Dependent Variable: ABSOLUTE_ANCH_CHICAGO

Sum of

Squares df Mean Square F Sig.

Partial Eta Squared Noncent. Parameter Observed Powera Contrast 75.395 1 75.395 7.535 .006 .024 7.535 .781 Error 3021.678 302 10.006

The F tests the effect of High_Low. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.

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4. High_Low * ConditionCoded Dependent Variable: ABSOLUTE_ANCH_CHICAGO

High_Low ConditionCoded Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

High Control 2.639 .315 2.019 3.258 Poor 2.851 .423 2.019 3.683 Low Control 3.390 .330 2.741 4.039 Poor 4.157 .419 3.333 4.981

Profile Plots

Appendix 2

Pairwise Comparisons Dependent Variable: ABSOLUTE_ANCH_CHICAGO

ConditionCoded (I) High_Low (J) High_Low

Mean

Difference (I-J) Std. Error Sig.b

95% Confidence Interval for Differenceb

Lower Bound Upper Bound

Control High Low -.752 .456 .100 -1.649 .145

Low High .752 .456 .100 -.145 1.649

Poor High Low -1.306* .595 .029 -2.477 -.135

Low High 1.306* .595 .029 .135 2.477

Based on estimated marginal means

*. The mean difference is significant at the .05 level.

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Univariate Tests Dependent Variable: ABSOLUTE_ANCH_CHICAGO

ConditionCoded Sum of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powera Control Contrast 27.212 1 27.212 2.720 .100 .009 2.720 .376 Error 3021.678 302 10.006 Poor Contrast 48.190 1 48.190 4.816 .029 .016 4.816 .590 Error 3021.678 302 10.006

Each F tests the simple effects of High_Low within each level combination of the other effects shown. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.

a. Computed using alpha = .05

Pairwise Comparisons Dependent Variable: ABSOLUTE_ANCH_CHICAGO

High_Low (I) ConditionCoded (J) ConditionCoded

Mean Difference

(I-J) Std. Error Sig.a

95% Confidence Interval for Differencea

Lower Bound Upper Bound

High Control Poor -.212 .527 .687 -1.249 .825

Poor Control .212 .527 .687 -.825 1.249

Low Control Poor -.767 .533 .152 -1.816 .283

Poor Control .767 .533 .152 -.283 1.816

Based on estimated marginal means

a. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Univariate Tests Dependent Variable: ABSOLUTE_ANCH_CHICAGO

High_Low Sum of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powera High Contrast 1.623 1 1.623 .162 .687 .001 .162 .069 Error 3021.678 302 10.006 Low Contrast 20.682 1 20.682 2.067 .152 .007 2.067 .300 Error 3021.678 302 10.006

Each F tests the simple effects of ConditionCoded within each level combination of the other effects shown. These tests are based on the linearly independent pairwise comparisons among the estimated marginal means.

a. Computed using alpha = .05 (µ=mean)

Appendix 3

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32 Dependent Variable: MOOD

ConditionCoded Mean Std. Deviation N

Control 63.9326 21.85083 193

Poor 60.1416 24.42294 113

Total 62.5327 22.86827 306

Levene's Test of Equality of Error Variancesa Dependent Variable: MOOD

F df1 df2 Sig.

1.976 1 304 .161

Tests the null hypothesis that the error variance of the dependent variable is equal across groups. a. Design: Intercept + ConditionCoded

Tests of Between-Subjects Effects Dependent Variable: MOOD

Source

Type III Sum

of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powerb Corrected Model 1024.314a 1 1024.314 1.965 .162 .006 1.965 .287 Intercept 1097179.138 1 1097179.138 2104.663 .000 .874 2104.663 1.000 ConditionCoded 1024.314 1 1024.314 1.965 .162 .006 1.965 .287 Error 158477.859 304 521.309 Total 1356065.000 306 Corrected Total 159502.173 305

a. R Squared = .006 (Adjusted R Squared = .003) b. Computed using alpha = .05

Estimates Dependent Variable: MOOD

ConditionCoded Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Control 63.933 1.643 60.699 67.167

Poor 60.142 2.148 55.915 64.368

Univariate Tests Dependent Variable: MOOD

Sum of Squares df Mean Square F Sig. Partial Eta Squared Noncent. Parameter Observed Powera Contrast 1024.314 1 1024.314 1.965 .162 .006 1.965 .287 Error 158477.859 304 521.309

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Macro by Hayes (2013

************* PROCESS Procedure for SPSS Release 2.16.3 ****************** Written by Andrew F. Hayes, Ph.D. www.afhayes.com

Documentation available in Hayes (2013). www.guilford.com/p/hayes3 ************************************************************************** Model = 1 Y = ABSOLUTE X = Conditio M = MOOD Sample size 306 ************************************************************************** Outcome: ABSOLUTE Model Summary R R-sq MSE F df1 df2 p .1172 .0137 10.1755 1.4022 3.0000 302.0000 .2422 Model

coeff se t p LLCI ULCI constant 3.1724 .1829 17.3432 .0000 2.8124 3.5323 MOOD -.0087 .0081 -1.0801 .2809 -.0246 .0072 Conditio .4641 .3793 1.2235 .2221 -.2823 1.2104 int_1 -.0158 .0162 -.9750 .3304 -.0478 .0161 Product terms key:

int_1 Conditio X MOOD

R-square increase due to interaction(s):

R2-chng F df1 df2 p int_1 .0031 .9506 1.0000 302.0000 .3304

************************************************************************* Conditional effect of X on Y at values of the moderator(s):

MOOD Effect se t p LLCI ULCI -22.8683 .8258 .5215 1.5834 .1144 -.2005 1.8522 .0000 .4641 .3793 1.2235 .2221 -.2823 1.2104 22.8683 .1023 .5395 .1895 .8498 -.9595 1.1640

Values for quantitative moderators are the mean and plus/minus one SD from mean.

Values for dichotomous moderators are the two values of the moderator.

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Appendix 4

Model = 3 Y = ABSOLUTE X = Conditio M = LIFE_ALL W = High_Low Sample size 306 ************************************************************************** Outcome: ABSOLUTE Model Summary R R-sq MSE F df1 df2 p .2414 .0583 9.8466 3.3301 7.0000 298.0000 .0020 Model

coeff se t p LLCI ULCI constant 3.1906 .1819 17.5439 .0000 2.8327 3.5486 LIFE_ALL -.2702 .1316 -2.0526 .0410 -.5293 -.0111 Conditio .4667 .4110 1.1354 .2571 -.3422 1.2756 int_1 .5716 .2975 1.9215 .0556 -.0138 1.1571 High_Low .9348 .3676 2.5430 .0115 .2114 1.6582 int_2 .5511 .8312 .6630 .5078 -1.0847 2.1870 int_3 .0569 .2653 .2144 .8304 -.4653 .5790 int_4 .0480 .6002 .0800 .9363 -1.1331 1.2291 Product terms key:

int_1 Conditio X LIFE_ALL int_2 Conditio X High_Low int_3 LIFE_ALL X High_Low

int_4 Conditio X LIFE_ALL X High_Low R-square increase due to three-way interaction:

R2-chng F(1,df2) df2 p int_4 .0000 .0064 298.0000 .9363

************************************************************************* Conditional effect of X on Y at values of the moderator(s):

(39)

35

Values for quantitative moderators are the mean and plus/minus one SD from mean.

Values for dichotomous moderators are the two values of the moderator. Conditional effect of X*M interaction at values of W:

High_Low Effect se t p LLCI ULCI -.4869 .5482 .3439 1.5942 .1120 -.1286 1.2250 .5131 .5963 .4919 1.2123 .2264 -.3717 1.5642

Appendix 5

ANOVA

Tests of Between-Subjects Effects Dependent Variable: CURRENT_INCOME

Source

Type III Sum

of Squares df Mean Square F Sig.

Partial Eta Squared Noncent. Parameter Observed Powerb Corrected Model 8754817.565a 1 8754817.565 5.090 .025 .017 5.090 .614 Intercept 575726957.25 7 1 575726957.25 7 334.717 .000 .530 334.717 1.000 ConditionCoded 8754817.565 1 8754817.565 5.090 .025 .017 5.090 .614 Error 510851959.40 5 297 1720040.267 Total 1186839137.0 00 299 Corrected Total 519606776.97 0 298

a. R Squared = .017 (Adjusted R Squared = .014) b. Computed using alpha = .05

Estimates Dependent Variable: CURRENT_INCOME

ConditionCoded Mean Std. Error

95% Confidence Interval

Lower Bound Upper Bound

Control 1622.508 94.897 1435.752 1809.264

Poor 1266.278 126.199 1017.919 1514.636

Pairwise Comparisons Dependent Variable: CURRENT_INCOME

(I) ConditionCoded (J) ConditionCoded

Mean

Difference (I-J) Std. Error Sig.b

95% Confidence Interval for Differenceb

(40)

36

Control Poor 356.230* 157.898 .025 45.490 666.971

Poor Control -356.230* 157.898 .025 -666.971 -45.490

Based on estimated marginal means

*. The mean difference is significant at the .05 level.

b. Adjustment for multiple comparisons: Least Significant Difference (equivalent to no adjustments).

Univariate Tests Dependent Variable: CURRENT_INCOME

Sum of

Squares df Mean Square F Sig.

Partial Eta Squared Noncent. Parameter Observed Powera Contrast 8754817.565 1 8754817.565 5.090 .025 .017 5.090 .614 Error 510851959.40 5 297 1720040.267

The F tests the effect of ConditionCoded. This test is based on the linearly independent pairwise comparisons among the estimated marginal means.

a. Computed using alpha = .05

MACRO BY HAYES (2013) Model = 3 Y = ABSOLUTE X = Conditio M = CURRENT_ W = High_Low Sample size 299 ************************************************************************** Outcome: ABSOLUTE Model Summary R R-sq MSE F df1 df2 p .2164 .0468 10.0842 1.8087 7.0000 291.0000 .0853 Model

coeff se t p LLCI ULCI constant 3.2027 .1919 16.6858 .0000 2.8249 3.5804 CURRENT_ .0001 .0002 .6555 .5126 -.0002 .0004 Conditio .5428 .4432 1.2247 .2217 -.3295 1.4150 int_1 .0000 .0004 -.0752 .9401 -.0008 .0008 High_Low .9999 .3888 2.5719 .0106 .2347 1.7650 int_2 .7490 .8982 .8339 .4050 -1.0187 2.5167 int_3 .0002 .0003 .4481 .6544 -.0005 .0008 int_4 -.0009 .0008 -1.1079 .2688 -.0025 .0007 Product terms key:

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