Show Me the Women: A Double Machine Learning approach to unravel the Bechdel test’s impact on box office performance
(2024) DABN01 20241Department of Economics
Department of Statistics
- Abstract
- Despite societal discussions around feminist topics and increasing desire for stories centered on women, female characters in films still remain underrepresented. The Bechdel test, which evaluates female representation by requiring that two named female characters have a conversation about something other than a man, serves as a measure of this disparity. This study investigates the relationship between passing the Bechdel test and box office revenue for 3650 movies released between 1915 and 2020, utilizing Double Machine Learning techniques. The analysis reveals that, on average, passing the Bechdel test does not have a direct impact on overall box office revenue for films. However, for top-grossing movies and the most voted movies... (More)
- Despite societal discussions around feminist topics and increasing desire for stories centered on women, female characters in films still remain underrepresented. The Bechdel test, which evaluates female representation by requiring that two named female characters have a conversation about something other than a man, serves as a measure of this disparity. This study investigates the relationship between passing the Bechdel test and box office revenue for 3650 movies released between 1915 and 2020, utilizing Double Machine Learning techniques. The analysis reveals that, on average, passing the Bechdel test does not have a direct impact on overall box office revenue for films. However, for top-grossing movies and the most voted movies passing the Bechdel test leads to significantly increased box office revenue. These findings suggest that in large-scale Hollywood productions yielding significant attention, addressing the issue of female representation may play a crucial role in attracting audiences and boosting revenue. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9157463
- author
- Hesslevik, Mathilda LU and Ramm-Ericson, Clara LU
- supervisor
- organization
- course
- DABN01 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Bechdel test, Double Machine Learning, female representation, box office revenue, causal inference
- language
- English
- id
- 9157463
- date added to LUP
- 2024-09-24 08:32:48
- date last changed
- 2024-09-24 08:32:53
@misc{9157463, abstract = {{Despite societal discussions around feminist topics and increasing desire for stories centered on women, female characters in films still remain underrepresented. The Bechdel test, which evaluates female representation by requiring that two named female characters have a conversation about something other than a man, serves as a measure of this disparity. This study investigates the relationship between passing the Bechdel test and box office revenue for 3650 movies released between 1915 and 2020, utilizing Double Machine Learning techniques. The analysis reveals that, on average, passing the Bechdel test does not have a direct impact on overall box office revenue for films. However, for top-grossing movies and the most voted movies passing the Bechdel test leads to significantly increased box office revenue. These findings suggest that in large-scale Hollywood productions yielding significant attention, addressing the issue of female representation may play a crucial role in attracting audiences and boosting revenue.}}, author = {{Hesslevik, Mathilda and Ramm-Ericson, Clara}}, language = {{eng}}, note = {{Student Paper}}, title = {{Show Me the Women: A Double Machine Learning approach to unravel the Bechdel test’s impact on box office performance}}, year = {{2024}}, }