Understanding Gender Bias: Differences in Tech Stereotypes According to the Socio-economic Background of Girls

Of all the countries that belong to OCDE, the Latin American countries have the highest levels of inequality. Chile is among them, with scores similar to Bolivia and Guatemala [10]. Also, the number of women living in poverty is higher than that of men [4]. Women’s economic context is essential for their families, as 90% of single-parent families are supported by women [12]. One way of achieving economic development may be choosing a career in technology, as tech jobs are among the highest-paid in the country [9]. Also, they are flexible, allowing women to balance work and family, and have been proven to promote social mobility and country economic growth [2, 7]. However, there is a well-known gender gap in technology; for example, only 24% of computer science students are women in Chile [9].

To inspire women to have a computer science career, interventions should be undertaken while they are girls, by addressing stereotypes that influence their attitude towards technology [6]. These stereotypes are influenced by the context in which girls grow [5]; in particular their socio-economic context [3]. Therefore, it may be essential to understand the context of girls, and their thoughts towards tech stereotypes, to create better computer science education interventions.

We conducted a preliminary interview study with sixth grade girls, since at this age, stereotypes can still be challenged [1, 11], while the opportunities to challenge stereotypes decrease from the eighth grade on [8]. The research question of this study is whether there are different stereotypes regarding technology among girls with different socio-economical levels. It has been hypothesized that there will be different stereotypes among these girls.

Twelve interviews were done: 6 with girls from low vulnerability contexts, and 6 with girls from high vulnerability contexts. The interview data were transcribed and analyzed using grounded theory under Charmaz’s perspective.

The results from this preliminary study were that girls from a high vulnerability context have a negative attitude towards technology mainly because of misconceptions regarding technology, e.g. what it does and how to work with it. On the other hand, girls from low vulnerability contexts have stereotypes in which tech careers were considered to be manly, and they also had concerns about family-work balance.

With this information, a semi-structured interview has been developed to apply to girls from low and high vulnerability contexts and analyzed with ground theory. Further, with this qualitative information, a quantitative tool will be developed. A national survey will be created to determine if these different stereotypes are also present in the larger population of girls. With this information, better computer science education interventions may be created, especially focused on high vulnerability contexts, considering the particular stereotypes that these girls have regarding tech stereotypes, that keep them away from computer science careers.