INVESTIGADORES
MESURADO Maria Belen
congresos y reuniones científicas
Título:
Inequity Aversion in a Vulnerable Sample of Argentine Children
Autor/es:
MARÍA PAULINA GUERRA; BELÉN MESURADO; MARÍA CRISTINA RICHAUD
Lugar:
Minneapolis
Reunión:
Conferencia; 2018 Society for Research on Adolescence Biennial Meeting; 2018
Institución organizadora:
Society for Research on Adolescence
Resumen:
Equity is the proportional distribution of resources between the members of a group, when it is assumed that everyone contributed equally (LoBue, Nishida, Chiong, DeLoache, & Haidt, 2009). When this distribution is not proportional, inequity aversion may emerge which is the resistance to this unequal distribution. We can distinguish two types of inequity aversion, when the person receives more than the others it is called advantage inequity aversion, and when the person receives less than the others it is a disadvantage inequity aversion. The objective of this presentation is to study the inequity aversion in advantage and disadvantage conditions in a vulnerable sample of Argentine children between 5 and 10 years old. The sample included 30 kids of both sexes of an urban-marginalized population of Argentina (Mean age = 7.23, SD = 1.16). We used the Inequity Game (Blake, & Mc Auliffe, 2011) to measure the inequity aversion. The changes across all conditions (equity, inequity in advantage and disadvantage) were assessed by Friedman's test for each group (5 to 7 and 8 to 10). These preliminary results showed no differences in the inequity aversion between kids of 5 and 7 years old (Friedman's test = 2, p = .37), but showed more disadvantage inequity aversion than advantage inequity aversion in kids between 8 and 10 years old (Friedman's test = 6.5, p < 0.05) See Figure 1. These preliminary results show that the disadvantageous inequity aversion emerges later in vulnerable children of Argentina than in children of other cultures (Blake, McAuliffe et al. 2015). These may be related to the specifics characteristic of this population. Nevertheless we need a bigger sample to make conclusions.