Thursday, April 13, 2023

The gender gap in aspirations for tech jobs is considerably larger in high schools serving the Jewish majority than in those serving the Arab/Palestinian minority

The gendering of tech selves: Aspirations for computing jobs among Jewish and Arab/Palestinian adolescents in Israel. Jason Budge et al. Technology in Society, April 8 2023, 102245.

Abstract: This study uses original survey data to compare aspirations for computing jobs (“tech aspirations”) between students in Arabic- and Hebrew-language school sectors in Israel. Analogous to “paradoxical” patterns previously documented in cross-national studies, results show a smaller gender gap in tech aspirations in schools serving the Arab/Palestinian minority population. The strongest predictor of tech aspirations is students’ personal identification with computing workers, but this “tech identity” cannot account for sectoral differences in the aspirations gender gap because it is stronger for boys than girls in both sectors. Although mathematics affinity and academic instrumentalism are both greater in the Arabic-language school sector, these social-psychological variables also have limited power to explain sectoral differences in tech aspirations. The belief that computer science is for boys, by contrast, positively affects tech aspirations of Jewish but not Palestinian boys, suggesting that variability in the tech gender gap may partly reflect group-specific effects of gender stereotyping. Results underscore the importance of an intersectional approach for understanding the social-psychological drivers of STEM aspirations and how they vary across social groups.

Keywords: GenderComputingSTEMEducationIsrael

7. Conclusion

The main purpose of this study is to interrogate the social-psychological drivers of contextual variability in the gendering of tech fields. Building on cross-national analyses that have compared countries with different levels of gender liberalism (Stoet and Geary 2018) and differently gendered STEM orientations [19], we use original survey data to compare the gendering of STEM aspirations across ethno-religiously distinct school sectors within a single national educational system. We are thereby able to hold constant differences across countries and educational systems that often confound cross-national studies.

Results show that the gender gap in Israeli ninth-graders’ aspirations for jobs in computing and information technology (“tech”) is considerably larger in high schools serving the Jewish majority than in those serving the Arab/Palestinian minority. This finding evokes parallels with cross-national analyses showing smaller STEM gender gaps in less affluent societies [6,8] and within-country studies showing smaller STEM gender gaps in colleges with smaller percentages of white students in the United States [56].

Not surprisingly, the strongest overall predictor of Israeli adolescents’ aspirations for a career in computing is identification with tech workers (“tech identity”). Students who reported feeling similar to computer programmers were more than nine times more likely to report aspiring to a tech job than students who did not. Although causation undoubtedly runs in both directions, this strong association between supports arguments that a sense of belonging, or “fitting in,” is crucial to recruitment and retention of women and other underrepresented groups in STEM fields [20,38,39,57].

Variation in tech identity cannot explain contextual differences in the STEM gender gap, however. This is because girls in both Arab/Palestinian and Jewish school sectors are less likely than boys to identify with tech workers, and because effects of tech identity on aspirations do not vary by gender or sector. Affinity for mathematics and academic instrumentalism are also unable to account for contextual differences in the tech-aspirations gender gap, although both traits are stronger among Palestinian than Jewish students.

The only social-psychological indicator with some power to explain the observed cross-sectoral variability is tech gender stereotyping. Although the belief that computer science is “for boys” shows no significant association with students’ tech aspirations overall, it does have group-specific effects that emerge when the relationship is allowed to vary interactively with gender and school sector. Specifically, we find that believing in the masculine nature of computer science increases tech aspirations of Jewish but not Arab/Palestinian boys. This relationship requires further investigation. As suggested above, it may be attributable to the cultural association of computer science with Jewish military service and to the better tech career opportunities open to Jewish men. More generally, we would suggest that the male-labeling of Israeli tech fields reflects a hegemonic masculinity that is specifically Jewish and therefore less personally salient to Arab/Palestinian students. This underscores the importance of an intersectional approach for understanding the social-psychological drivers of STEM aspirations, and the multiple masculinities (and femininities) that may shape the gendering of these fields across contexts [58,59].

Results support arguments that exposure to different sociocultural environments during the formative adolescent years is likely to influence high school students’ career aspirations. The different gendering of student aspirations and course-taking that results from different school exposures constitutes the school environment that shapes attitudes, aspirations, and abilities of subsequent student cohorts. In other words, the social psychological variables considered here constitute both inputs and outcomes in school-to-student feedback loops that produce distinct institutional gender regimes. Of course, Jewish and Palestinian students bring to school many preexisting beliefs about gender and about tech. Future research, ideally in-depth interviews and participant observation, should explore the interplay of tech gender cultures in schools, families, and the broader Jewish and Arab/Palestinian communities. Contextual variability in the linkage between career aspirations and career outcomes warrants further research as well. While previous U.S.-based research indicates that aspirations are generally highly predictive of occupational outcomes [60], the strength of this relationship likely varies across social groups with different access to public child-care resources, higher education, and employment opportunities.

The uneven gendering of STEM fields revealed here and elsewhere suggests that gender segregation is more complex and multifaceted than is typically represented by unidimensional modernization accounts. While it is by now well established that women's representation in tech does not increase with economic development, more research is needed to isolate the macro-level forces driving contextual variability in this and other forms of gender inequality. Previous comparative research on the STEM gender gap suggests possible causal effects of socioeconomic precarity (versus material security), individualist (versus collectivist) cultural values, and institutional differences in school tracking policies (Mann et al., 2015; [23,47].

Understanding the sociocultural factors that reduce STEM access of women and other historically marginalized populations is not only important for advancing basic social justice and equity. Research shows that diversifying scientific and technical fields can promote national prosperity, productivity, innovation, and the development of more broadly accessible and democratic technology (Page 2017; [61,62].

The Unpredictability of Individual-level Longevity: Fitting 8 machine learning algorithms using 35 sociodemographic predictors to generate individual-level predictions of age of death

Breen, Casey, and Nathan Seltzer. 2023. “The Unpredictability of Individual-level Longevity.” SocArXiv. April 8. doi:10.31235/

Abstract: How accurately can age of death be predicted using basic sociodemographic characteristics? We test this question using a large-scale administrative dataset combining the complete count 1940 Census with Social Security death records. We fit eight machine learning algorithms using 35 sociodemographic predictors to generate individual-level predictions of age of death for birth cohorts born at the beginning of the 20th century. We find that none of these algorithms are able to explain more than 1.5% of the variation in age of death. Our results suggest mortality is inherently unpredictable and underscore the challenges of using algorithms to predict major life outcomes.