This paper explores whether teachers gender stereotypes affect students’ achievement. Carlana uses IPA test to show support for her research question. She makes use of four sources of data, the sources are: teacher and student survey data, administrative information from the Italian Ministry of Education (MIUR), and from the National Center for the Evaluation of the Italian Education System (INVALSI). She finds that female students assigned to female teachers or to teachers with a degree in Science, Technology, Engineering and Math (STEM) have lower math achievement test scores in grade 8 compared to their classmates, higher teacher implicit bias affects especially females from disadvantaged backgrounds, males are not influenced by teachers’ gender stereotypes, teachers foster negative self-stereotypes on girls only in male-typed domain, teacher bias has an impact in high school track choice, reducing the probability for females to enroll in scientific schools.
If the teachers who took the IAT test, were familiar with the test or took this test before, this would increase the noise in the IAT score and lead to an attenuation bias when estimating the impact of implicit stereotypes on student outcomes.
Some papers have argued that IAT has weak predictive validity. So, further investigation is needed in this regard. There is a school of thought who expressed doubt about the validity of IAT scores and thus the usage of IAT score for policymaking is debatable. It can also be affected by cultural experience of the respondent. So, it a noisy estimate of measuring stereotype.
She proposes a mechanism that is called “the interaction theory” that says that teachers who teach scientific subjects might spend less time with less performing girls.
But she couldn’t present sufficient data to support this theory. In line with recent studies suggesting that the females interest in STEM subjects dropped dramatically at the age of 15 because of gender stereotypes, lack of female role models, peer pressure and poor encouragement from parents and teachers, this paper provides an important contribution to the existing evidence for the Italian context since it can help policy makers to define adequate interventions aimed at removing gender stereotypes (i.e. policies to inform teachers about their bias or training programs to stimulate equal behavior towards boys and girls) and promoting equality of opportunities.
The paper addresses an important topic that is still a very relevant research question in current literature. The sets of results (see above) are very promising regarding their novelty. The analysis has the potential of yielding some interesting insights. However, this potential is not fully (but nearly) realized in the current version, and some additional work will be necessary before the paper can be published.I think this is a great job market paper because of its novelty in the data used and in the careful estimation strategy applied to identify the impact of teachers’ gender stereotypes in affecting student performance. Results, albeit based on a sample limited to five Italian provinces, provide important policy implications to prevent females from facing increasingly poor job opportunities, since most future employment opportunities will likely require STEM competencies.