The Impact of Social Information on the Proposers’ Strategic Concerns

Several studies investigated how proposers modulate their behavior depending on the responders’ social characteristics (e.g., Solnick and Schweitzer, 1999). These studies allowed us to evaluate the proposers’ internal representations of the world. In other words, how much is a specific responder “worth” to a proposer? Eckel and Ball (1996) investigated the effect of social status information on the proposers’ behavior. In their study, participants were attributed a star or not depending on their performance to a trivia quiz before playing the ultimatum game as either proposer or responder. The priming task (i.e., trivia quiz) enabled to allocate artificially a high (i.e., star) or a low status (i.e., no star) to the participants. Both high and low status proposers offered higher shares to high status responders than to low social status responders, confirming the impact of social status on economic decision-making (for an overview see Heffetz and Frank, 2008). According to status characteristic theory (Wagner and Berger, 1993), a status characteristic (i.e., gender, age, race, physical attractiveness, intelligence or occupation) affects people’s expectations of reward, and high status individuals expect to receive higher reward than low status individuals. To this extent, proposers may adapt their behavior, offering higher shares to high status responders compared with the low status responders in order to limit the risk of suffering a rejection (Güroǧlu et al., 2009). Proposers were also found to be influenced by the responders’ attractiveness offering higher shares to more attractive responders (Solnick and Schweitzer, 1999; Zaatari et al., 2009). Finally, some studies investigated the impact of the responders’ gender on the proposers’ decision. Overall, these studies demonstrated that proposers offer more to male responders than to female responders (e.g., Solnick and Schweitzer, 1999; Eckel and Grossman, 2001; Solnick, 2001). Saad and Gill (2001) found that male proposers were on average more generous with female than male responders, while the responders’ gender did not affect the behavior of female proposers, who offered equally fair shares to responders of both sexes. A greater variability in the behavior of male proposers was also found with altruistic male proposers or aggressive male proposers compared to female proposers, who showed less variability in their behavior (Castillo and Cross, 2008).

Because both gender and attractiveness are considered as status characteristics, the observation of the increased shares proposed to attractive responders (Solnick and Schweitzer, 1999; Zaatari et al., 2009) and to male responders in most gender studies (e.g., Solnick and Schweitzer, 1999; Eckel and Grossman, 2001; Solnick, 2001) may be interpreted in terms of social status differences.

Why Investigate the Impact of Gender Stereotypes on Economic Decision-Making?

Conflict theories postulate that because men have greater social status and power (Reskin, 1988), they allocate occupations that open access to resources (e.g., money, stocks, contacts, information) predominantly to men, thus favoring themselves over women, which creates an occupational segregation (Pratto et al., 1997). For this reason, stereotypically male occupations (e.g., engineer, electrician) are associated with higher social status and power compared with those stereotypically female (e.g., teacher, beautician; Eagly, 1987; Ridgeway, 2001). Nowadays, more and more women can enter the professions with a highly marked male stereotype (e.g., lawyer, banker, doctor), and more men access to occupations with a highly marked female stereotype (e.g., nurse, “mid-wife,” teacher; Eagly, 1987; Eagly and Karau, 2002; Phelan et al., 2008). Given the frequent interactions of men and women, it is critical to understand how gender stereotypical beliefs – i.e., a form of social knowledge linked to actions, attitudes, rules and other forms of knowledge attributed to individuals based on their biological gender (Greenwald and Farnham, 2000; Wheeler and Petty, 2001; Quadflieg and Macrae, 2011) – modulate economic decision-making.

A recent study investigated how proposers’ gender stereotyped descriptions (i.e., occupations marked with either a male or a female stereotype) influenced the responders’ decision-making (Fabre et al., 2015). When playing with female-stereotyped proposers (e.g., linguist), responders were longer to make their decision, reflecting a more deliberative reasoning (Sanfey and Chang, 2008) associated with an increase in acceptation rates. In contrast, participants were found to answer more quickly and to reject more frequently male-stereotyped proposers’ offers (e.g., economist) than those of female-stereotyped proposers. That study demonstrated that gender stereotype information of the proposer modulates the economic decision-making in the ultimatum game and the cognitive processes underpinning the decision-making. Therefore, we may reasonably expect gender stereotypical information of responders to modulate the behavior of proposers playing the ultimatum game. To our knowledge, this impact of the responders’ stereotypical identity on the proposers’ decision-making has never been investigated.

Materials

The same two groups of 30 occupational role nouns each (one male, one female) with comparable stereotypicality, wealth and valence, lexical frequency and length used in the study of Fabre et al. (2015) were used in the present study (see Supplementary Material). In order to select the experimental materials, a written questionnaire listing 258 occupational role nouns, ending in –e,–ista or a consonant to avoid cues to the gender of the referent in the word form, was presented to 112 students not further involved in the experiment (56 females; age range 19–27 years; Mage = 23.6, SD = 2.92). Eighty of these students rated to what extent each role noun was stereotypically associated with male or female individuals on a 7-point Likert scale (i.e., stereotype strength; from 1 = only men to 7 = only women), 16 of them to what extent each role noun was associated with a positive or negative value (i.e., valence: from 1 = very negative to 7 = very positive) and 16 the wealth of a person described with each role noun (i.e., wealth: from 1 = very rich to 7 = very poor). The labels of the scale poles were reversed for half of the participants. The final rating assigned to each word was calculated by combining the ratings obtained with both directions of each rating scale. The 60 role nouns selected as experimental materials received comparably high ratings of stereotypicality (the experimental material and the associated ratings are available in Supplementary Material). In order to compare the stereotype strength of the two role noun groups, the ratings of the role nouns ranging from 4 to 7 (i.e., feminine stereotypes) were translated and ranged from 1 to 4 (i.e., X′ = 8-X, with X: initial rating and X′: translated rating). Stereotype strength (Female Stereotypes: M = 2.81, SD = 1.21; Male Stereotypes: M = 2.77, SD = 1.19), valence (Female Stereotypes: M = 4.42, SD = 0.69; Male Stereotypes: M = 4.36, SD = 0.71), wealth (Female Stereotypes: M = 3.81, SD = 0.94; Male Stereotypes M = 4.10, SD = 0.88), lexical frequency (Female Stereotypes: M = 5.66, SD = 0.87; Male Stereotypes M = 6.09, SD = 1.05) and length (i.e., number of characters; Female Stereotypes: M = 9.77, SD = 2.2; Male Stereotypes M = 8.83, SD = 1.82) of male and female occupational role nouns were comparable (ps > 0.05). The mean stereotypicality rating of feminine role nouns reported in the Supplementary Material Table S1 is the translated rating (i.e., X′).

Experimental materials also included 120 Italian familiar proper names (60 feminine) without any unisex names. With the final two groups of 30 occupational role nouns along with the 120 proper names, we created four experimental conditions, two stereotype-matching conditions: female stereotypical occupational role nouns followed by feminine proper names (e.g., linguista-Anna) and male stereotypical occupational role nouns followed by masculine proper names (e.g., economista-Davide); and two stereotype-mismatching conditions: female stereotypical occupational role nouns followed by masculine proper names (e.g., linguista-Leonardo) and male stereotypical role nouns followed by feminine proper names (e.g., economista-Cristina). Participants interacted once with 30 different responders of each kind (i.e., 120 different responders in total).

Procedure

Participants were seated comfortably in a darkened sound-attenuated room. They played a one-shot ultimatum game as proposers. An introduction explaining the rules of the ultimatum game was given to each of them. Stimuli were presented in light white upper case letters (Courier font, size 13) against a black background on a high-resolution computer that was positioned at eye level about 70 cm in front of each participant. A fixation cross appeared in the middle of a computer screen and remained until participants pressed a button to start a trial. Each occupational role noun was displayed for 700 ms followed by a blank screen for 300 ms. Then a proper name appeared and remained on the screen until the participants pressed the key on the keyboard corresponding to the numerical value of the offer they wanted to make (i.e., from 1€ to 9€ out of 10€). Each response was followed by a 1000 ms blank screen. No feedback on the responder’s answer was provided to the participants in order to avoid a modulation of their behavior along the experiment. Participants were asked to respond as fast as possible.

Before conducting the game, participants were told that they were playing against 120 real different responders of whom they would know their occupations and proper names. Each participant was presented with 30 trials in each of the four experimental conditions for a total of 120 trials. As a matter of fact, the responder was simulated by the computer. However, in order to make the participants believe they were playing against real responders, they were told that responders had been contacted prior to the game and that they had indicated the offers they were willing to accept if proposed by a student (i.e., a shifted in time ultimatum game). Moreover, participants were indicated that we were thanking the different partners involved in this experiment (e.g., the firefighters of Modena, the Oenology School of UNIMORE University, etc…). Participants were informed that at the end of the game, responders would receive the sum corresponding to a percentage of the accepted offers.

Response Times

The ANOVA on log-transformed response times showed a significant stereotype main effect [F(1,32) = 43.87, p < 0.001, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.58, see Figure ​Figure2A2A] with participants making their offers faster to male-stereotyped responders than to female-stereotyped responders (respectively, M = 847 ms, SD = 477; M = 1142 ms, SD = 706, see Table ​Table11). Participants were also faster when making their offers to male responders than to female responders (respectively, M = 893 ms, SD = 520; M = 1096 ms, SD = 693), as shown by a significant responder’s gender main effect [F(1,32) = 54.95, p < 0.001, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.63, see Figure ​Figure2B2B]. The participants’ gender [F(1,32) = 0.00, p = 0.96, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.00] main effect and Participants’ Gender × Responders’ Gender [F(1,32) = 0.04, p = 0.85, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.00], Stereotype × Participants’ Gender [F(1,32) = 0.02, p = 0.86, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.00], Stereotype × Responders’ Gender [F(1,32) = 2.66, p = 0.11, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.08], Stereotype × Responders’ Gender × Participants’ Gender [F(1,32) = 0.25, p = 0.62, ${\mathrm{\eta }}_{\mathrm{p}}^{\mathrm{2}}$ = 0.01] interactions were not significant.

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FIGURE 2

(A,B) Response times as a function of (A) responders’ gender stereotypes and of (B) responders’ genders. Error bars represent standard errors. p < 0.001.

Questionnaires

The correlations between the scores obtained by each participant in the BSRI and in the IRI and the resultants of the various differences in mean offer and in response time revealed only one significant result (see Supplementary Material). The difference in response times between responders characterized by a male vs. female stereotype [Stereotype (M – F)] was positively correlated to the score of the perceptive taking scale: the higher the score, the greater the Stereotype (M – F) difference [r = 0.393, p < 0.05, see Table ​Table22].

Table 2

Correlations between the questionnaires’ scores and the differences in mean offers and response times.

	Questionnaires					Mean offer		Response Times
	Interpersonal Reactivity Index				Bem sex	Stereotype (M – F)	Gender (M – F)	Stereotype (M – F)	Gender (M – F)
					Role Inventory
	PT	EC	DP	F
Questionnaires
IRI (pt)	_____
IRI (ec)	0.318	_____
IRI (dp)	-0.456	0.103	_____
IRI (f)	0.175	0.517	105	_____
BSRI	-0.140	-0.226	-0.314	-0.429	_____
Mean offer
Stereotype (M – F)	0.090	-0.092	-0.090	0.118	0.056	_____
Gender (M – F)	-0.033	0.061	0.192	-0.139	-0.135	-0.184	_____
Response times
Stereotype (M – F)	0.393	0.198	-0.090	0.000	-0.042	-0.174	-0.020	_____
Gender (M – F)	-0.205	0.033	0.227	-0.096	0.058	-0.194	-0.008	0.340	_____

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Bold values are statistically significant.

We would like to acknowledge Zarrin Chua, Laura Kerr and Vsevolod Peysakhovich for their help. We are also grateful to the Editor and the Reviewers for their comments and suggestions that helped improve the quality of the paper.