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Show Me the Women: A Double Machine Learning approach to unravel the Bechdel test’s impact on box office performance

Hesslevik, Mathilda LU and Ramm-Ericson, Clara LU (2024) DABN01 20241
Department 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:
author
Hesslevik, Mathilda LU and Ramm-Ericson, Clara LU
supervisor
organization
course
DABN01 20241
year
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}},
}