Articles on Film industry

Displaying 1 - 20 of 71 articles.

articles about film industry

How movies use music to manipulate your memory

Libby Damjanovic , Lund University

articles about film industry

How Vivien Leigh survived Hollywood before #MeToo

Lisa Smithstead , Swansea University

articles about film industry

‘You have to be everybody’s best friend’: how dreams and desires leave TV and film crew vulnerable to workplace exploitation

Ewan Mackenzie , Newcastle University and Alan Mckinlay , Newcastle University

articles about film industry

Film camera departments operate on a system of who you know, so what happens when you’re not a member of the  in-group ?

Bronwyn Coate , RMIT University ; Ben Eltham , Monash University , and Deb Verhoeven , University of Technology Sydney

articles about film industry

Here’s how the Hollywood actors’ strike will impact the Canadian film industry

Ramona Pringle , Toronto Metropolitan University

articles about film industry

Making movies in video games: why the film world is finally ready to take ‘machinima’ seriously

Sam Crane , University of York

articles about film industry

Nollywood could see a major boost from Nigeria’s new copyright law - an expert explains why

Samuel Samiái Andrews. , University of Gondar

articles about film industry

Nepo babies: why nepotism is such a problem for British film and TV – and how to fix it

Bethan Jones , University of York

articles about film industry

‘The number one barrier has probably been stigma’: the challenges facing disabled workers in the Australian screen industry

Radha O'Meara , The University of Melbourne and Anna Debinski , The University of Melbourne

articles about film industry

Jordan Peele’s ‘Nope’ shines spotlight on animal work in entertainment

Kendra Coulter , Western University

articles about film industry

Precarious employment, hiring discrimination and a toxic workplace: what work looks like for Australian cinematographers

Amanda Coles , Deakin University ; Justine Ferrer , Deakin University , and Vejune Zemaityte , Tallinn University

articles about film industry

Michael Sheen is right – there is a class crisis in the arts

Dave O'Brien , University of Sheffield and Mark Taylor , University of Sheffield

articles about film industry

International franchises love filming in ‘Aussiewood’ — but the local industry is booming too

Mark David Ryan , Queensland University of Technology and Kelly McWilliam , University of Southern Queensland

articles about film industry

South Africa sets out to protect cast and crew involved in nudity and sex scenes

Fiona Ramsay , University of the Witwatersrand

articles about film industry

South Africa’s romcom revolution and how it reimagines Joburg

Pier Paolo Frassinelli , University of Johannesburg

articles about film industry

An Oscar for My Octopus Teacher is a boost for South African film. But …

Liani Maasdorp , University of Cape Town and Ian-Malcolm Rijsdijk , University of Cape Town

articles about film industry

The ‘Oscar Halo’ – how awards and nominations direct where money goes in the film industry

Ewa Mazierska , University of Central Lancashire

articles about film industry

Film and TV diversity behind the camera is getting much worse

Beth Johnson , University of Leeds

articles about film industry

Regal Cinemas’ decision to close its theaters is the latest blow to a film industry on life support

Matthew Jordan , Penn State

articles about film industry

Enter the micro-budget film: lockdown amplifies South African cinema trends

Liani Maasdorp , University of Cape Town

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Professor of Screen Media, King's College London

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Visiting Fellow, University of Technology Sydney

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Executive Director, Compton School

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Senior Lecturer in Film Studies, Keele University

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Professor of Creative and Cultural Industries, University of Nottingham

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Senior Lecturer in Film, University of Roehampton

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Visiting Professor of Film, Brown University

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Lecturer in Film Studies, Department of Liberal Arts, King's College London

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Evolutionary Culturologist, University of Newcastle

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Film industry in the United States and Canada - statistics & facts

Moviemaking in north america: quantity and qualities, diversity on and off screen, key insights.

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Box office revenue in the U.S. and Canada 1980-2023

Movie releases in the U.S. & Canada 2000-2022

Tickets sold at box offices in the U.S. & Canada1980-2023

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Filmed entertainment revenue in selected countries worldwide 2021

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Combined market share of major film distributors in the U.S. & Canada 2000-2022

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  • Box office in the U.S. and Canada
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  • Premium Statistic Leading box office markets worldwide 2021, by revenue
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  • Premium Statistic Costs of the most expensive film productions worldwide 2022
  • Basic Statistic Box office revenue in the U.S. and Canada 1980-2023
  • Premium Statistic Revenue of the motion picture and video industry in the U.S. 2021, by source
  • Premium Statistic Filmed entertainment revenue in selected countries worldwide 2021

Leading box office markets worldwide in 2021, by revenue (in billion U.S. dollars)

Leading film markets worldwide 2022, by number of tickets sold

Leading film markets worldwide in 2022, by number of tickets sold (in millions)

Costs of the most expensive film productions worldwide 2022

Costs of the most expensive film productions worldwide as of June 2022 (in million U.S. dollars)

Box office revenue in the United States and Canada from 1980 to 2023 (in billion U.S. dollars)

Revenue of the motion picture and video industry in the U.S. 2021, by source

Estimated revenue of the motion picture and video production and distribution industry in the United States in 2021, by source (in billion U.S. dollars)

Filmed entertainment revenue in selected countries worldwide in 2021 (in million U.S. dollars)

  • Basic Statistic Movie releases in the U.S. & Canada 2000-2022
  • Premium Statistic Number of 3D films released in the United States & Canada 2012-2021
  • Basic Statistic Number of movie releases in the U.S. & Canada 1995-2023, by genre
  • Basic Statistic Box office revenue in the U.S. & Canada 1995-2023, by movie rating
  • Premium Statistic Most viewed movie trailers in their first 24 hours as of April 2023
  • Basic Statistic Most popular movies on Facebook 2022

Movie releases in the U.S. & Canada 2000-2022

Number of movies released in the United States and Canada from 2000 to 2022

Number of 3D films released in the United States & Canada 2012-2021

Number of 3D films released in the United States and Canada from 2012 to 2021

Number of movie releases in the U.S. & Canada 1995-2023, by genre

Number of movies released in the United States and Canada between 1995 and 2023, by genre

Box office revenue in the U.S. & Canada 1995-2023, by movie rating

Box office revenue in the United States and Canada between 1995 and 2023, by movie rating (in billion U.S. dollars)

Most viewed movie trailers in their first 24 hours as of April 2023

Most watched movie trailers within 24 hours of release as of April 2023 (in millions)

Most popular movies on Facebook 2022

Movies with the most Facebook fans as of January 2022 (in millions)

Film studios

  • Basic Statistic Market share of film studios in the U.S. & Canada 2010-2021
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  • Basic Statistic Box office market share of Universal in the U.S. & Canada 2000-2021
  • Premium Statistic Box office market share of Warner Bros. in the U.S. & Canada 2000-2021
  • Basic Statistic Box office market share of Disney in the U.S. & Canada 2000-2022
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Market share of film studios in the U.S. & Canada 2010-2021

Market share of leading film studios in the United States and Canada from 2010 to 2021

Combined market share of major film distributors in the U.S. & Canada 2000-2022

Combined market share of the "Big Five" major film studios in the United States and Canada from 2000 to 2022

Box office market share of Universal in the U.S. & Canada 2000-2021

Box office market share of Universal in the United States and Canada from 2000 to 2021

Box office market share of Warner Bros. in the U.S. & Canada 2000-2021

Box office market share of Warner Bros. in the United States and Canada from 2000 to 2021

Box office market share of Disney in the U.S. & Canada 2000-2022

Box office market share of Disney in the United States and Canada from 2000 to 2022

Box office market share of Paramount in the U.S. & Canada 2000-2021

Box office market share of Paramount in the United States and Canada from 2000 to 2021

Movie theaters & tickets

  • Basic Statistic Leading cinema circuits in U.S. & Canada 2023, by number of screens
  • Premium Statistic Number of cinema sites in the U.S. 1995-2020
  • Basic Statistic Number of indoor cinema sites in the U.S. 1995-2020
  • Basic Statistic Number of drive-in cinema sites in the U.S. 1995-2020
  • Premium Statistic Number of digital 3D screens in the U.S. & Canada 2007-2021
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  • Basic Statistic Tickets sold at box offices in the U.S. & Canada1980-2023
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Leading cinema circuits in U.S. & Canada 2023, by number of screens

Leading cinema circuits in the United States and Canada as of March 2023, by number of screens

Number of cinema sites in the U.S. 1995-2020

Number of cinema sites in the United States from 1995 to 2020

Number of indoor cinema sites in the U.S. 1995-2020

Number of indoor cinema sites in the United States from 1995 to 2020

Number of drive-in cinema sites in the U.S. 1995-2020

Number of drive-in cinema sites in the United States from 1995 to 2020

Number of digital 3D screens in the U.S. & Canada 2007-2021

Number of digital 3D screens in the United States and Canada from 2007 to 2021

Number of cinema screens in the U.S. & Canada from 2008 to 2021, by format

Number of movie screens in the United States and Canada from 2008 to 2021, by format

Tickets sold at box offices in the U.S. & Canada1980-2023

Number of movie tickets sold in the United States and Canada from 1980 to 2023 (in millions)

Ticket price at U.S. movie theaters 2001-2021

Average ticket price at movie theaters in the United States from 2001 to 2021 (in U.S. dollars)

  • Basic Statistic Consumer spending on movie tickets in the U.S. 1999-2022
  • Premium Statistic Consumer spending growth rate on movie theaters in the U.S. 2021, by week
  • Premium Statistic Top films' audience distribution in the U.S. & Canada in 2021, by gender
  • Premium Statistic Movie theater audience in the U.S. & Canada 2018-2021, by ethnicity
  • Premium Statistic First-run movie consumption on VOD platforms during the pandemic U.S. 2020-2021
  • Premium Statistic Frequency of going to the movies before and after COVID-19 in the U.S. 2022
  • Premium Statistic Level of ease with going to see a movie in theaters among adults in the U.S. 2022
  • Premium Statistic Reasons why infrequent moviegoers do not regularly go to the movies in the U.S. 2022
  • Premium Statistic Top factors for Gen Z to decide on whether or not go to the movies in the U.S. 2021

Consumer spending on movie tickets in the U.S. 1999-2022

Consumer expenditure on admissions to motion picture theaters in the United States from 1999 to 2022 (in billion U.S. dollars)

Consumer spending growth rate on movie theaters in the U.S. 2021, by week

Year-over-year percentage change in weekly consumer spending on movie theaters in the United States from January to July 2021

Top films' audience distribution in the U.S. & Canada in 2021, by gender

Distribution of the audiences of the highest-grossing movies in the United States and Canada in 2021, by gender

Movie theater audience in the U.S. & Canada 2018-2021, by ethnicity

Distribution of the movie theater audience in the United States and Canada from 2018 to 2021, by ethnicity

First-run movie consumption on VOD platforms during the pandemic U.S. 2020-2021

Share of consumers paying to watch first-run movies that skipped theater on VOD services due to the coronavirus in the United States from July 2020 to June 2021

Frequency of going to the movies before and after COVID-19 in the U.S. 2022

Frequency of going to see a movie in theaters among adults in the United States from before the COVID-19 outbreak to April 2022

Level of ease with going to see a movie in theaters among adults in the U.S. 2022

Level of ease with going to see a movie in theaters among adults in the United States as of May 2022

Reasons why infrequent moviegoers do not regularly go to the movies in the U.S. 2022

Leading reasons why infrequent moviegoers do not regularly go to see the movies in theaters in the United States as of May 2022

Top factors for Gen Z to decide on whether or not go to the movies in the U.S. 2021

Leading factors for Gen Z to decide on whether or not see movies in theaters in the United States as of May 2022

  • Basic Statistic Employment in the motion picture & sound recording industries in the U.S. 2001-2023
  • Basic Statistic Hourly wages in the motion picture & recording industries in the U.S. 2007-2022
  • Basic Statistic Best-paid actors worldwide 2021, by income
  • Basic Statistic All-time top-grossing actors in the U.S. & Canada 2023, by total domestic box revenue
  • Premium Statistic Distribution of lead actors in movies in the U.S. 2011-2022, by gender
  • Premium Statistic Distribution lead actors in films in the U.S. 2011-2022, by ethnicity
  • Basic Statistic Highest-grossing film directors worldwide 2023, by cumulative global box office gross
  • Premium Statistic Distribution of movie directors in the U.S. 2011-2022, by gender
  • Premium Statistic Share of indie movies with female directors in Hollywood 2008-2022
  • Premium Statistic Distribution of movie directors in the U.S. 2011-2022, by ethnicity
  • Premium Statistic Film writers' gender distribution in the U.S. 2011-2022
  • Premium Statistic Distribution of movie writers in the U.S. 2011-2022, by ethnicity

Employment in the motion picture & sound recording industries in the U.S. 2001-2023

Total employment in the motion picture and sound recording industries in the United States from 2001 to 2023 (in 1,000s)

Hourly wages in the motion picture & recording industries in the U.S. 2007-2022

Average hourly earnings of all employees in the motion picture and sound recording industries in the United States from 2007 to 2022 (in U.S. dollars)

Best-paid actors worldwide 2021, by income

Best-paid actors worldwide as of August 2021, by income (in million U.S. dollars)

All-time top-grossing actors in the U.S. & Canada 2023, by total domestic box revenue

Highest-grossing leading actors of all time in the United States and Canada as of August 2023, by cumulative domestic box office revenue (in billion U.S. dollars)

Distribution of lead actors in movies in the U.S. 2011-2022, by gender

Distribution of lead actors in movies in the United States from 2011 to 2022, by gender

Distribution lead actors in films in the U.S. 2011-2022, by ethnicity

Distribution of lead actors in movies in the United States from 2011 to 2022, by ethnicity

Highest-grossing film directors worldwide 2023, by cumulative global box office gross

Highest-grossing film directors of all time worldwide as of February 2023, by cumulative global box office gross (in billion U.S. dollars)

Distribution of movie directors in the U.S. 2011-2022, by gender

Distribution of movie directors in the United States from 2011 to 2022, by gender

Share of indie movies with female directors in Hollywood 2008-2022

Share of independent films with female directors in Hollywood from 2008 to 2022

Distribution of movie directors in the U.S. 2011-2022, by ethnicity

Distribution of movie directors in the United States from 2011 to 2022, by ethnicity

Film writers' gender distribution in the U.S. 2011-2022

Distribution of movie writers in the United States from 2011 to 2022, by gender

Distribution of movie writers in the U.S. 2011-2022, by ethnicity

Distribution of movie writers in the United States from 2011 to 2022, by ethnicity

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Gender equity in film will only be reached in 2215 in canada, 2085 in u.k., 2041 in germany at current pace: study.

An interdisciplinary study looking at the impact of gender equity policies on the film business in the U.K., Canada and Germany finds "modest" numerical improvement but little change in power dynamics. "The film industries do not just need more women, but women in the right positions."

By Scott Roxborough

Scott Roxborough

Europe Bureau Chief

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A still from the film 'Women Talking' showing eight female characters gathered in a barn.

A new study on the impact of gender equality policies on the international film industry shows some improvement in the representation of women in the British, German and Canadian industries, but progress is slow.

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While the report found some small numerical improvement in women and gender minorities working in the countries’ film sectors, progress the report attributed in part to new GEP policies, the results were nothing to cheer about. The ranks of key creative positions, and that of the “network elite” were still dominated by men. In Germany, on average, 74 percent of all key creative positions and 86 percent of the network elite were men. The numbers in the U.K. were 78 percent and 81 percent, respectively. In Canada, they stood at 77 percent and 82 percent.

“At the current rate of progress, gender equity, where women occupy 50 percent of key creative positions, will only be achieved in the year 2215 in Canada (i.e. in nearly 200 years), in 2085 in the U.K. (in more than 60 years), and 2041 in Germany (in more than 15 years),” the report found.

(A similar study, published by UCLA in association with Lionsgate last year, found that, for directors, it will take the U.S. 175 years to reach gender parity.)

One of the report’s authors, network analysis expert Professor Deb Verhoeven of the University of Alberta, said the research underscored the need for GEP policies to address systemic issues, not just target numerical representation. “The film industries do not just need more women, but women in the right positions,” Verhoeven said.

Verhoeven pointed out that “the modest gains made by women and gender minorities have not come at the expense of men [but] have arisen as the result of an expansion of the industry rather than a displacement of men.”

“The task now is to mainstream policies that reach into industry practice and create accountability,” said policy analyst Professor Doris Ruth Eikhof of the University of Glasgow. “It is also clear that seeing women as ‘at fault,’ as lacking experience or confidence, is not going to bring the systemic change we need. Women need access to influential positions within the film industry, not just to the industry overall.”

You can download the full “Reframing the Picture” report here .

Updated on Feb. 22 : In response to the report’s publication, Canadian federal film funding body, Telefilm Canada, released the following statement:

Responding Statement from Julie Roy, Executive Director & CEO, Telefilm Canada

Over the past six years, Telefilm has undergone a significant transformation, embracing and implementing industry feedback, and best practices, on important matters relating to equity, diversity and inclusion – which encompasses our work on gender parity, environmental sustainability, supporting emerging filmmakers, and greater transparency through data collection reporting. The evolution of Telefilm, and the progress of the audiovisual sector in Canada, stem from collaborative efforts with industry partners from not only within our country but also extending well beyond our borders.

Contrary to recent claims published in the research study  Re-Framing the Picture , our available data presents factual statistics on the projects Telefilm has supported over the years, revealing a very different narrative. We have also adopted a rigorous and respectful data collection methodology, based on self-identification, and it is of utmost importance that we adhere to these standards.

Attaining gender parity, in the projects we fund, serves as a starting point in normalizing gender equity within our industry. As Telefilm establishes a new strategic plan for the next three years, the commitment to working through the lenses of equity, diversity, inclusion and sustainability remains.

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  • Published: 13 May 2020

Using data science to understand the film industry’s gender gap

  • Dima Kagan   ORCID: orcid.org/0000-0002-8216-8776 1 ,
  • Thomas Chesney 2 &
  • Michael Fire 1  

Palgrave Communications volume  6 , Article number:  92 ( 2020 ) Cite this article

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  • Complex networks
  • Cultural and media studies

Data science can offer answers to a wide range of social science questions. Here we turn attention to the portrayal of women in movies, an industry that has a significant influence on society, impacting such aspects of life as self-esteem and career choice. To this end, we fused data from the online movie database IMDb with a dataset of movie dialogue subtitles to create the largest available corpus of movie social networks (15,540 networks). Analyzing this data, we investigated gender bias in on-screen female characters over the past century. We find a trend of improvement in all aspects of women‘s roles in movies, including a constant rise in the centrality of female characters. There has also been an increase in the number of movies that pass the well-known Bechdel test, a popular—albeit flawed—measure of women in fiction. Here we propose a new and better alternative to this test for evaluating female roles in movies. Our study introduces fresh data, an open-code framework, and novel techniques that present new opportunities in the research and analysis of movies.

Introduction

The film industry is one of the strongest branches of the media, reaching billions of viewers worldwide (MPAA, 2018 ; UNIC, 2017 ). Now more than ever, the media has a major influence on our daily lives (Silverstone, 2003 ), significantly influencing how we think (Entman, 1989 ), what we wear (Wilson and MacGillivray, 1998 ), and our self-image (Polce-Lynch et al., 2001 ). In particular, the representation of women in media has an enormous influence on society. As just one example, a new study shows that “women who regularly watch The X-Files are more likely to express interest in STEM, major in a STEM field in college, and work in a STEM profession than other women in the sample” (Fox, 2018 ).

Movies are the fulfillment of the vision of the movie director, who controls all aspects of the filming. It is well known that movie directors are primarily white and male (Smith et al., 2017 ). With such a gender bias, it is not surprising that there is a male gender dominance in movies (Smith and Choueiti, 2010 ; Ramakrishna et al., 2017 ). Studies from the past two decades have confirmed that women in the film industry are both underrepresented (University, 2017 ; Lauzen, 2018b ) and portrayed stereotypically (Wood, 1994 ). A recent study found that the underrepresentation is so sizeable that there are twice as many male speaking characters as female in the average movie (Lauzen, 2018a ).

While the gender gap in the film industry is a well-known issue (Lauzen, 2018a ; Rose, 2018 ; Cohen, 2017 ; Lauzen, 2018b ; Wood, 1994 ), there is still much value in researching this topic. Most previous gender studies can be categorized into two types: the first type offers simple statistics from the data to emphasize the gender gap (Lauzen, 2018b ); and the second type introduces more advanced analytical methods, yet generally uses only a small amount of data (Agarwal et al., 2015 ; Garcia et al., 2014 ).

In this study, we present Subs2Network , a novel algorithm to construct a movie character’s social network. We demonstrate possible utilizations of Subs2Network by employing the latest data science tools to comprehensively analyze gender in movies (see Fig. 1 Footnote 1 ). This is the largest study to date that uses social network analysis (SNA) to investigate the gender gap problem in the film industry and how it evolved.

figure 1

The evolution of female representation in the Star Wars movies series.

The study’s primary goals are to answer the following four questions:

Question 1: Are there movie genres that do not exhibit a gender gap?

Question 2: What do characters’ relationships reveal about gender, and how has this changed over time?

Question 3: Are women receiving more central movie roles today than in the past?

Question 4: How has the fairness of female representation in movies changed over the years?

To answer these questions, we first analyzed movie subtitles using text-processing algorithms and a list of movie characters’ names (see Fig. 2 ). We then developed Subs2Network to construct a movie character’s social network. We created an open-source code framework to collect and analyze movie data, and we used this framework to construct the largest open movie social network dataset that exists today.

figure 2

Turning subtitles into a network, step by step: a perform named entity recognition on the subtitles; b match the entities to the movie characters; and c link the characters and increase the edge weight by one.

Using the constructed movie social networks, we extracted dozens of topological features that characterized each movie. By analyzing these features, we could observe the gender gap across movie genres and over the last 99 years. Moreover, by utilizing the dataset, we developed a machine-learning classifier, which is able to assess, how fairly women are represented in movies (i.e., if a movie passes the Bechdel test (Bechdel, 1985 )).

Our results demonstrate that in most movie genres there is a statistically significant difference between men and women in centrality features like betweenness and closeness . These differences indicate that men are getting more central roles in movies than women (see Fig. 2a, b , and section “Results”). Another sign of the underrepresentation of women in movies is found by analyzing interactions among three characters: only 3.57% of the interactions are among three women, while 40.74% are among three men. These results strengthen previous studies‘ results that women play fewer central roles (Agarwal et al., 2015 ; Lauzen, 2018b ), and indicates that on average women have more minor roles. Our results highlight how and where gender bias manifests in the film industry and provides an automatic way to evaluate it over time.

The key contributions presented in this paper are fivefold:

A novel algorithm (see section “Methods and experiments”) which utilizes movie subtitles and character lists to automatically construct a movie’s social network (see section “Constructing movie social networks” and Fig. 2 ).

The largest open movie social network dataset, 21 times larger than the previous dataset (Kaminski et al., 2018 ) (see section “Datasets”). Our dataset contains 15,540 dynamic networks of movies (937 of these networks are networks of biographic movies, which have information about real-world events).

An open-source framework for movie analysis. The code contains a framework to generate additional social networks of movies, facilitating research by creating and analyzing larger amounts of data than ever before.

A machine-learning classifier that can predict if a movie passes the Bechdel test (see section “Constructing the Bechdel test classifier”) and can evaluate the change in gender bias in thousands of movies over several decades (see section “Results”).

Our new and alternative automated Bechdel test to measure female representation in movies. This new test overcomes the weaknesses of the original Bechdel test.

Our study demonstrates that inequality is still widespread in the film industry. In movies of 2018, a median of 30% women and a mean of 33% were found in each movie’s top-10 most central roles. That being said, there is evidence that the gender gap is improving (see Fig. 3 ).

figure 3

The change in the percentage of women in top 1, 3, and 10 most central roles over time.

The remainder of this paper is organized as follows: In section “Related work”, we present an overview of relevant studies. In section “Methods and experiments”, we describe the datasets, methods, algorithms, and experiments used throughout this study. In section “Results”, we present our results. Then, in section “Discussion”, we discuss the obtained results. Lastly, in section “Conclusions”, we present our conclusions from this study and offer future research directions.

Related work

Movie social networks.

In the past decade, the study of social networks has gained massive popularity. Researchers have discovered that SNA techniques can be used in many domains that do not have explicit data with a network structure. One such domain is the film industry. Researchers have applied SNA to analyze movies, gaining not only new insights about specific movies but also about the film industry in general. For example, using social networks makes it possible to empirically analyze social ties between movie characters.

In 2009, Weng et al. ( 2009 ) presented RoleNet, a method to convert a movie into a social network. The RoleNet algorithm builds a network by connecting links between characters that appear in the same scene. RoleNet is based on using image processing for scene detection and face recognition to find character appearances. Weng et al. evaluated their method on 10 movies and three TV shows. The method was used to perform semantic analysis of movies, find communities, detect leading roles, and determinine story segmentation.

In 2012, Park et al. ( 2012 ) developed Character-net, another method to convert movies to networks. Character-net builds the social network based on dialog between characters, using script–subtitle alignment to extract who speaks to whom in the scene. Park et al. ( 2012 ) evaluated their method on 13 movies. Similar to RoleNet, Character-net was used to detect leading roles and to cluster communities.

In 2014, Agarwal et al. ( 2014 ) presented a method for parsing screenplays by utilizing machine-learning algorithms instead of using regular expressions. Their study showed that the parsed screenplay can be used to create a social network of character interactions. In 2015, Tran and Jung ( 2015 ) developed the CoCharNet, a method which adds weight to a link in the interaction network, where the weight is a function of the number of times two characters appear together. Tran and Jung used CoCharNet to evaluate the importance of characters in movies. They demonstrated that network centrality features such as closeness centrality, betweenness centrality, and weighted degree can be used to classify minor and main characters in a movie. For instance, they detected the main characters using closeness centrality with a precision of 74.16%.

In 2018, Lv et al. ( 2018 ) developed an algorithm to improve the accuracy of creating social networks of movies. They presented StoryRoleNet, which combines video and subtitle analysis to build a more accurate movie social network. The subtitles were used to add additional links that the video analysis might miss. Similar to RoleNet and Character-net, Lv et al. ( 2018 ) used the movie social networks to cluster communities and to detect important roles. They evaluated the StoryRoleNet method on three movies and one TV series, for which they manually created baseline networks (Lv et al., 2018 ).

Also in 2018, a dataset from Moviegalaxies (Kaminski et al., 2018 ) Footnote 2 was released. Moviegalaxies is a website that displays social networks of movie characters. The dataset contains 773 movie social networks that were constructed based on movie scripts. However, Moviegalaxies did not disclose the exact methods which were used for the construction of the networks.

Evaluating the gender gap

In recent years, there have been many studies that attempt to evaluate the gender gap between males and females across various domains (Jia et al., 2016 ; Larivière et al., 2013 ; Lauzen, 2018b ; Wagner et al., 2015 ). For example, in 2018 the World Bank evaluated that the costs of gender bias are vast; gender inequality results in an estimated $160.2 trillion loss in human capital wealth (Worldbank, 2018 ).

Over the years, researchers have discovered many manifestations of the gender gap in our society. Larivière et al. ( 2013 ) discovered that scientific articles with women in dominant author positions receive fewer citations. Wagner et al. ( 2015 ) observed that men and women are covered equally on Wikipedia, but they also discovered that women on Wikipedia are portrayed differently from men. Jia et al. ( 2016 ) found that in online newspapers, women are underrepresented both in text and images.

The state of women in the film industry is similar to other domains: women are underrepresented and badly portrayed (Lauzen, 2018b ; Wood, 1994 ). The Boxed In 2017–18 report (Lauzen, 2018b ) observed a 2% decline in female major characters across all platforms, compared to the previous year.

To tackle the underrepresentation of women in movies in 1985, the cartoonist Alison Bechdel published a test in her comic strip Dykes to Watch Out For to assess how fairly women are presented in filmed media. The Bechdel–Wallace test (Bechdel, 1985 ) (denoted as the Bechdel test ) has three rules that a movie has to pass to be considered “women friendly”:

It has to have at least two women in it.

The women have to talk to each other.

The women must talk about something besides a man.

To Bechdel’s surprise, the media adopted her joke, and today it is a standard for female representation in movies (Douglas, 2017 ; Morlan, 2014 ; Hickey, 2014 ; Shift7, 2018 ; O’Hare, 2017 ). Today the Bechdel test is considered to be the mainstream benchmark for assessing the fairness of female representation in movies and today only 57% of current movies pass this test. Additionally, it is currently the only test that has available labeled data for about 8000 (Fest, 2019 ) out of 516,726 movies available on IMDb (IMDb, 2019b ).

The Bechdel test is also used by researchers. In recent years, studies have utilized the test to evaluate gender bias in movies. In 2014, Garcia et al. ( 2014 ) quantified the Bechdel test and also applied it to social media. They joined YouTube trailers, movie scripts, and Twitter data, which resulted in 704 trailers for 493 movies and 2970 Twitter shares. Garcia et al. created a social network of dialogues for these movies. Additionally, they constructed a network of dialogues between Twitter users who discussed the trailers. They mapped dialogues between men who were referring to women and between women who were referring to men. This mapping was used to calculate the Bechdel score. They found that trailers of movies which are male biased are more popular. Also, they discovered that Twitter dialogues have a similar bias to movie dialogues (Garcia et al., 2014 ).

In 2015, Agarwal et al. ( 2015 ) studied the differences between movies that pass and fail the Bechdel test. Similar to Garcia et al., Agarwal et al. also constructed social networks using screenplays. They created a classifier to automate the Bechdel test, which was trained on 367 movies and evaluated on 90. In the evaluation, they discovered that network-based features perform better than linguistic features. Additionally, they discovered that movies that fail the Bechdel test tend to have women in less central roles (Agarwal et al., 2015 ). With this being said, the Bechdel test has several major flaws. The test does not take into account if women are represented stereotypically (Waletzko, 2017 ). Additionally, there are movies that are considered feminist but do not pass the test (Florio, 2019 ). Moreover, the test is considered to be a low threshold since a film can pass the test with a single line of dialogue between two women (Shift7, 2018 ).

In 2017, Ramakrishna et al. ( 2017 ) utilized screenplays to study the differences in the portrayal of characters in movies. For the analysis, they used 945 screenplays. Mainly they performed linguistical analysis to capture gender stereotypes. They discovered that movies with female directors have less gender-biased casts. Also, they found that female characters use more positive language than males. Additionally, they constructed social networks from the screenplays and performed centrality analysis. The networks in the study were constructed. For the construction of the networks they used a method that was originally developed for converting books into social networks. In the same year, Sap et al. ( 2017 ) used connotation frames to study gender bias in films. They performed their analysis on 7772 movie screenplays, discovering that men were portrayed to have more authority than women. Additionally, they studied the relationship between connotation frames and the Bechdel test. Surprisingly, they found that movies where female characters speak with high agency are less likely to pass the Bechdel test.

Graph features and named entity recognition

Data science tools and techniques have evolved rapidly in the past couple of years (Donoho, 2015 ). In this study, we primarily utilized data science algorithms from the domains of natural language processing (NLP) and SNA to computationally analyze movie content, movie social network structure, and how movie features change over time.

Namely, we used NLP to extract character names from the movie subtitles by utilizing named entity extraction (NER) algorithms (Nadeau and Sekine, 2007 ). We used both Stanford Named Entity Recognizer (Finkel et al., 2005 ) and spaCy Python Package (Honnibal and Montani, 2017 ) to find where characters appear in the text.

To match characters’ names in the subtitles with characters’ full names, we utilized FuzzyWuzzy (Fuzzywuzzy, 2019 ), a Python package for fuzzy string matching. Specifically, we used FuzzyWuzzy’s WRatio (Fuzzywuzzy, 2018 ), a method for measuring the similarity between strings. WRatio uses several different preprocessing methods that rebuild the strings and compare them using Levenshtein distance (Levenshtein, 1966 ). Also, WRatio takes into account the ratio between the string lengths.

After extracting the movie characters, we constructed the movie social networks and used various graph centrality algorithms, such as closeness, betweenness, degree centrality, and PageRank (Brandes and Erlebach, 2005 ) to identify the most central characters in each constructed movie network.

Methods and experiments

Constructing movie social networks.

One of this study’s primary goals was to develop a straightforward algorithm that would construct the social network of character interaction within a given movie. We achieved this goal by utilizing movie subtitles Footnote 3 and a list of movie character names. Namely, given a movie, we constructed the movie social network G  := 〈 V , E 〉, where V is the network’s vertices set, and E is the set of links among the network’s vertices. Each vertex v   ∈   V is defined to be a character in the movie. Each link e  := ( u , v , w )  ∈   E is defined as the interaction between two movie characters u and v , w times. For a movie with a given subtitle text and a given character list, we constructed the movie’s social network using the following steps (see Fig. 2 ):

First, we detected when each character appeared in the subtitles. To extract the characters from the subtitles we used NER, extracting all the entities which were labeled as a person or an organization. Additionally, for each entity, we stored the time the entity appeared in the movie.

Next, we matched the entities found in the subtitles with the character list. It worth mentioning that it is not possible to map one-to-one between the characters in the character list and the characters extracted from the subtitle. For example, in the movie The Dark Knight , Bruce Wayne was referred to as “Bruce Wayne” 3 times, as “Bruce” 16 times, and as “Wayne” 20 times.

To address the matching problem, we proposed the following mapping heuristic (see Algorithm 1). First, we split all the roles into first and last names and linked them to the actor and the character’s full name (line 2). Then, if there was only one character with a certain first or last name (one-to-one match), we linked to the character all its occurrences in the subtitles (lines 3–5). However, if we had several characters with the same first or last name, we did not always know who was referred to in the text. For example, in the movie Back to The Future there are three characters with the last name McFly; where only “McFly” was mentioned in the text, we could not determine which character was referenced. Another challenge we encountered was when only part of the character’s name was used. For instance, in the movie The Godfather , the main character is Don Vito Corleone, but he was never mentioned once by his full name because he usually was referred to as “Don Corleone.” Moreover, there are other Corleone family members in the movie. To overcome this challenge, we used WRatio to compare strings and match parts of a name to the full name. Using WRatio , we chose the highest matching character that received a score higher than Threshold (line 6).

In fact, we were able to overcome many of these problems by using hearing-impaired subtitles. In many hearing-impaired subtitles, the name of the speaking character is part of the text. This property allowed us to avoid most the problems we described earlier and gain additional information. For instance, the movie The Matrix has a scene in which Morpheus calls Neo, and we can know this only because of the tag [PHONE RINGS]. Afterward, there is an annotation “MORPHEUS:” which tells us that Morpheus is the one calling. Without this annotation, we could not know who is on the other end of the line (see Fig. 4 ).

figure 4

The textual format of subtitles in the SubRip format with additional data for hear-impaired. For example, the speaking charachter name, sounds in a textual fromat, etc.

Using the matched characters, we created a link between characters u and v if they appeared in the movie in a time interval less than threshold t seconds ( t was defined as 60). For each such appearance, we increased the weight w between u and v by one. Since in subtitles we do not have an indication of when each scene begins and ends, we used a heuristic to model the interaction between characters. We assumed that two characters who appear one after another in a short period of time probably relate. For example, in Fig. 2 we have part of the subtitles from the movie The Matrix . Morpheus introduces himself to Neo, and we know that Morpheus and Neo are talking within an interval of 5 s. Since, 5 s was smaller than the threshold, we increased the link weight between Morpheus and Neo by one.

To reduce the number of false positive edges, we filtered all the edges with weight lower than w min ( w min was defined as 3). There were two main reasons for the formation of edges that did not exist in the movie. The first case was when we matched an entity to the wrong character. The second case happened when in the interval of t seconds there was more than one scene. These kinds of false positive links add noise to the graph. Most of these links have a very low weight; hence, filtering edges with weight lower than w min helps remove false positive links.

Evaluations of constructed networks

In addition to constructing movie social networks, we also empirically quantified the quality of these networks. Evaluating movie networks is a challenging task. Creating a perfect ground truth is a manual and unscalable process. It requires spending several hours for each movie to manually create ground truth networks. In previous studies (Weng et al., 2009 ; Park et al., 2012 ; Tran and Jung, 2015 ; Lv et al., 2018 ), manually labeling of movies has been done at a very small scale with only several movies (see section “Related work”). Another option is to use the IMDb or TMDB datasets character lists as a ground truth to evaluate only the network nodes. However, these lists contain mostly unnamed characters that are impossible to detect, for example, Guard #2. To solve this issue we could try using name datasets to filter these lists, but we will lose many characters that have foreign names or characters with unreal names like Batman, Superman, etc. To evaluate the quality of the constructed networks without the presented issues, we compared them to other publicly available movie network datasets. Since it is challenging to manually annotate movies, most of the studies only compared their networks to a handful of manually annotated ground truth networks (see section “Related work”).

In this study, to the best of our knowledge, we performed the first large-scale, fully automatic comparison between movie networks. For the comparison, we used a dataset published in 2018 by Kaminski et al. ( 2018 ) (denoted as ScriptNetwork ); this is the only other publicly available movie social network dataset. The ScriptNetwork dataset is based on screenplays and can be considered as much easier content to parse than subtitles. Screenplays have additional information such as the exact name of the character who speaks in the scene even if this character is unnamed. For example, freckled kid is a character in the X-Men (2000) screenplay; unnamed characters like freckled kid are almost impossible to detect in regular texts like books or subtitles. Screenplays can be considered very close to the ground truth. However, screenplays sometimes have big differences with the final movie. For instance, in many screenplays, there are missing and even additional characters (see section “Discussion”).

To evaluate Subs2Network -constructed networks, we performed two types of evaluations:

Central character analysis : We tested if the most central roles in Subs2Network are actually the most central roles in the movie. As a ground truth, we used the IMDb ranking list similarly to Tran and Jung ( 2015 ). The IMDb characters list is ordered the same way as movie credits, which are ordered alphabetically or by the order of appearance (IMDb, 2019a ). For the evolution, we filtered out all the movies where the credits were in alphabetical order, which was only 1%. The actor rank in the credits is considered to be a direct indication of the actor’s power and prestige (Rossman et al., 2010 ). Furthermore, it is very rare for an actor not in the top-10 credited roles to be nominated for an Academy Award (Rossman et al., 2010 ). In other words this indicates that in most movies the credit order has a significance, and the top-10 movie credits are likely to include most of the central characters.

We tested if the top-5 and top-10 ranked nodes (characters) at Subs2Network are the top-5 and top-10 ranked on IMDb. Additionally, we performed the same test on networks constructed from screenplays (Kaminski et al., 2018 ). Our motivation behind this experiment was to verify that Subs2Network’s networks contain the most significant characters in the movie.

Network coverage : We tested if the edges in Subs2Network are the same edges as in other movie networks. For each movie, we created two sub-graphs containing the characters that exist in both networks. Then we calculated the edge coverage in the created sub-graphs. Given two graphs G and H , we define the edge coverage as \({\mathrm {{Coverage}}}_H(G) = \frac{{|E_G\, \cap \,E_H|}}{{|E_H|}}\) . We calculated Coverage Subs2Network ( ScriptNetwork ) and Coverage ScriptNetwork ( Subs2Network ).

In addition to using the Kaminski et al. ( 2018 ) dataset for the network evaluation, we also constructed a small dataset of 15 character co-appearance networks utilizing Amazon X-Ray (Stiffler and Sampaco, 2018 ). The movies in the dataset were selected randomly from the Amazon Prime TV main page, Footnote 4 which includes the most popular movies in the platform. The dataset was constructed semi-automatically in the following way: given a movie, we define the movie ’ s social network graph G xray  := 〈 V xray , E xray 〉. Similar to Subs2Network , each character in the movie is represented as a vertex v   ∈   V xray . Edges are defined as two characters that appear in the same scene according to Amazon X-Ray data. Namely, the set of movie edges E xray is defined to be \(E_{\mathrm {{xray}}}: = \{ (u,v,w)|u,v \in V_{\mathrm {{xray}}}\}\) , where w is the number of scenes in which u and v appeared in the same scene. Additionally, as with Subs2Network , we filtered all the edges with weights lower than 3. Similarly to our comparison with the Kaminski et al. ( 2018 ) dataset, we also calculated Network Coverage. Additionally, we used the fact that Amazon X-Ray is based on the finished movie, which includes additional data such as the time the character appeared in the movie. By utilizing G xray , we analyzed how well Subs2Network contains characters by their screen time. To this end, we calculated the total screen time (denoted as screen( v )) of each character in the X-Ray dataset and divided the characters into deciles according to their screen time. Lastly, we calculated for each decile, d i , i  = 1..10, the percentage of characters that were detected by the Subs2Network algorithm, out of all the characters that were detected by Amazon X-Ray and had screen time in the d i decile. Namely, for each d i , we calculated \({CharCover}(d_i) = \frac{{|V_{Subs2Network}\, \cap\, \{ v \in V_{\mathrm {{xray}}}| {screen}(v)\, \in \,d_i\} |}}{{\{ v\, \in \,V_{\mathrm {{xray}}}| {screen}(v)\, \in \,d_i\} }}\) .

To evaluate and test our movie social network construction algorithm described above on real-world data, we assembled large-scale datasets of movie subtitles and movie character lists. In addition, we collected movie character lists from the IMDb (Internet Movie Database) website Footnote 5 and movie subtitles from 15,540 movies. Furthermore, we also used data from Bechdel test scores of 4658 movies. In the following subsections, we describe in detail the datasets we used.

IMDb dataset

To collect movie and actor data, we used IMDb, which is an online site that contains information related to movies, TV series, video games, etc. (IMDb, 2019b ). IMDb data is contributed by users worldwide. It contains 5,487,394 titles from which 505,380 are full-length movies (IMDb, n.d. ). In this study, we used the official IMDb dataset. Footnote 6 From the IMDb dataset, which contains only a subset of the IMDb database, we mainly used movies’ titles, crews, and ratings data.

Subtitle dataset

To inspect gender bias in movies, we decided to extract information out of subtitles. Subtitles are freely and widely available online on numerous sites. For instance, OpenSubtitles.org Footnote 7 alone hosts more than 500,000 English subtitles (opensubtitles.org, 2019 ) that were manually created by the community. We collected the subtitles using Subliminal Footnote 8 , a Python library for searching and downloading subtitles. Subliminal downloads subtitles from multiple sources, and using an internal scoring method, it decides which subtitles are the best for a specific movie. Using Subliminal, we downloaded subtitles for 15,540 movies.

Bechdel test dataset

Bechdel test data is available at Bechdel Test Movie List Footnote 9 , which is a community-operated website where people can label movies’ Bechdel scores. Using the Bechdel Test Movie List API, we downloaded a dataset that contains 7871 movies with labeled Bechdel scores, from which only 7322 are full-length movies.

Even for humans, it is a challenging task to determine if a movie actually passes the Bechdel test; Bechdeltest.com has a comments section where users discuss the scores and their disagreements (Agarwal et al., 2015 ). For example, according to Bechdeltest.com, the movie The Dark Knight Rises failed the test. However, by taking a closer look at the community comments, Footnote 10 we noticed users arguing regarding the test results, which are hard to determine.

Dataset preprocessing

The most critical part of building a social network of characters’ interaction is mapping correctly between the characters in subtitles and the characters in the character list. The IMDb character data includes data on even the most minor roles such as a nurse, guard, and thug #1. These nameless minor characters are almost impossible to map correctly to their subtitle appearances. Usually, they just add false positive edges and do not add additional information.

To clean the data from nameless characters, we created a blacklist of minor characters (for a detailed explanation of the blacklist construction process see Section S. 1 ). Additionally, to validate the characters’ names we used TMDb (The Movie Database) Footnote 11 , another community-built movie database. For each character, we matched the IMDb and TMDb data by the actor name. Then, we compared the lengths of the character names and kept the longer one. The usage of the longer names captures more variations of the name and helped us match more occurrences of the character in the subtitles. For example, in the film The Godfather (1972) James Caan portrays Sonny Corleone. Not surprisingly, on IMDb he is called Sonny Corleone, but on TMDB he is named Santino Sonny Corleone. In the film, he is addressed 12 times as Santino. By using the longer name, we can map these instances to the character.

Analyzing movie social networks to identify gender bias

Network features

To study gender bias in movies, we calculated five types of features: vertex features, network features, movie features, gender representation features, and actor features. Through the study, we analyzed how these features change over time. Additionally, we used these features to construct machine-learning classifiers. To create a ground truth for actors’ gender, we had to determine whether each actor was male or female. For most of the characters, we extracted the gender from IMDb similarly to Danescu et al. Danescu-Niculescu-Mizil and Lee ( 2011 ). IMDb has an attribute of “actor” or “actress,” which allowed us to identify gender. As we mentioned earlier, the IMDb dataset is only partial, so to overcome this issue we used a dataset that maps the first name to the gender. Footnote 12 In the rest of this section, we supply the definitions of these features.

Vertex features : For a given v   ∈   V , a neighborhood is defined as a set of v friends, Γ( v ). Following are the formal definitions of the vertex-based features:

Total Weight : The total weight of all the edges, which represents the number of character v appearances in the movie, \({\mathrm {{Total}}}_{\mathrm {w}}(v) = \mathop {\sum}\nolimits_{\{ (v,u,w)|\left( {(v,u,w) \in E} \right.\} } w\) .

Closeness Centrality : The inverse value of the total distance to all the nodes in the graph. It is based on the idea that a node closer to other nodes is more central, \(C_{\mathrm {c}}(v) = \frac{1}{{\mathop {\sum}\nolimits_{v \in V} d (v,u)}}\) Brandes and Erlebach ( 2005 ), where d ( v , u ) is the shortest distance between v and u .

Betweenness Centrality : Represents the number of times that a node is a part of the shortest path between two nodes Brandes and Erlebach ( 2005 ). A junction (node) that is part of more paths is more central, \(C_{\mathrm {b}}(v) = \mathop {\sum}\nolimits_{s,t \in V} {\frac{{\sigma (s,t|v)}}{{\sigma (s,t)}}}\) Brandes and Erlebach ( 2005 ), where v  ≠  s  ≠  t , σ ( s , t ) is the number of those paths passing through some node v .

Degree Centrality: A node that has a higher degree is considered more central, \(C_{\mathrm {d}}(v) = \frac{{|{\mathrm{\Gamma }}(v)|}}{{|V|\, - \,1}}\) Brandes and Erlebach ( 2005 ).

Clustering : Measures link formation between neighboring nodes, \(C(v) = \frac{{2T(v)}}{{|{\mathrm{\Gamma }}(v)|(|{\mathrm{\Gamma }}(v)|\, - \,1)}}\) (Saramäki et al., 2007 ), where T ( v ) is defined as the number of triangles through vertex v where a triangle is a closed triplet (three vertices that each connect to the other two).

Pagerank: A node centrality measure that takes into account the number and the centrality of the nodes pointing to the current node Brandes and Erlebach ( 2005 ).

Edge Number —the number of edges in the network | E |.

Vertex Number —the number of vertices in the network | V |.

Number of Cliques —the number of maximal cliques in the network Brandes and Erlebach ( 2005 ).

Statistical Network Features —set of features which are based on the vertex features. From these features, we calculate statistical features for the entire network. We calculate the mean, median, standard deviation, minimum, maximum, first quartile, and third quartile.

Gender representation features

Triangles with N women : The number of triangles that contain N females and 3- N males, where N   ∈  1, 2, 3.

Percent of triangles with N women : The percent of triangles that contain N females and 3- N males, where N   ∈  1, 2, 3.

Females in Top-10 roles : The number of females in top-10 roles ordered by PageRank.

Male count: The number of male actors in the movie.

Female count : The number of female actors in the movie.

Movie features:

Release Year —the year when the movie was first aired.

Movie Rating —the rating the movie has on IMDb.

Runtime —the movie total runtime in minutes.

Genres —the movie genre by IMDb.

Number of Votes —number of votes by which the rating was calculated on IMDb.

Actor features:

Actor Birth Year —the year the actor was born.

Actor Death Year —the year the actor died.

Actor Age Filming —the age of the actor when the movie was released ( \(Release\,Year - Actor\,Birth\,Year\) ).

Network feature analysis

To examine the state of the gender gap, in movies generally and by genre in particular, we analyzed only the most popular movies (movies which had more than n votes on IMDb). We analyzed only the most popular movies since they have better, more correct data, and more importantly, better represent the mainstream media. To decide on n , we observed the distribution of movies by year. We found a right-tailed distribution and decided that n  = 2000 should be a large enough number. To answer our first research question—if there are genres that do not show a gender gap (see section “Introduction”)—we calculated vertex and actor features (see section “Network features”) for all the roles. Next, we split the data by gender and movie genre. Finally, we utilized a Mann–Whitney U (Mann and Whitney, 1947 ) test on these features to check if there are statistical differences between the male and female roles in different genres.

To study relationships in movies, and to answer our second question regarding what relationships reveal about gender, we calculated all the relationship triangles in the network and grouped them by the number of women in each triangle. Afterward, we segmented the triangles by genres and how they changed over time.

To investigate the role of centrality by gender, our third research question regarding the centrality of female roles, we calculated PageRank for the nodes in all our movie networks. We analyzed the number of men and women in the top-10 characters in movies and examined how this number has changed over the years.

Constructing the Bechdel test classifier

As we described in section “Related work”, the Bechdel test is used to assess how fairly women are represented in a movie. The test has three criteria:

Are there at least two named women in the movie?

Do the women talk to each other?

Do the women talk about something other than men?

These criteria are hierarchical; hence, if a movie passes the last test, it has passed all of the tests.

To train the classifier, we extracted all the network, vertex, and gender representation features (see section “Network features”). For testing the trained model, we used the 1000 newest movies in the Bechdel test dataset. Footnote 13 The rest of the movies were used as the training set. As for the classifier, we used Random Forest with max depth 5 to avoid overfitting. For the classifier evaluation, we used AUC. This measure presents how many of the results the classifier is confident it classified correctly. Additionally, we compared our results to the results of Agarwal et al. ( 2015 ).

To answer the fourth research question regarding the fairness of female representation, we analyzed the change in the average probability of a movie passing the Bechdel test over time. Additionally, using the Random Forest feature importance, we inspected which feature was the most important for the Bechdel test classification. Finally, we analyzed the change over time by genre.

Alternative test

The Bechdel test has several major shortcomings; for instance, a movie passes the test if it consists of only one sentence between two women who do not speak about a man. For instance, American Pie 2 , which by no means can be considered to be a movie that fairly presents women, passes the Bechdel test in such a way. To offer solutions to the problems with the Bechdel test (see section “Discussion”), we propose a new gender equality test. We believe that a good test can be created by comparing the number of interactions according to each gender. Hence, we propose an interaction test that compares the total degree of male and female nodes. By utilizing over 15,000 movie social networks in our datasets, we observed that in only 16.7% of movies do female characters have an equal or higher total degree than male characters. Moreover, in 55.8% of analyzed movies, the total degree of male characters is at least twice as high as female characters. We think that a good rule of thumb for a movie should be \(0.8\, < \,\frac{ {{TotalDegree}_{\mathrm {F}}}}{{ {TotalDegree}_{\mathrm {M}}}}\, < \,1.2\) . The Gender Degree Ratio test is neither male nor female-biased; it is a gender equality test.

To evaluate the ability of the proposed test to distinguish between gender-biased and gender-equal movies, first we calculated the Gender Degree Ratio for all the movies in our dataset. Next, we performed significance tests between groups of movies with and without gender bias. Before performing the significance tests, we performed a Shapiro–Wilk test on the Gender Degree Ratio scores of our dataset to test if they distributed normally. To create the gender-biased and gender-equal movie lists, we utilized the three following movie lists:

The 100 best feminist films of all time (Rothkopf, 2018 ): From this list we had 67 movies in our dataset (see Section S. 2 ). We used this list to test if feminist movies get higher Gender Degree Ratio scores than the general population of movies.

100 Must see movies: The Essential Men’s Movie Library (McKay and McKay, 2019 )—from this list we had 79 movies in our dataset (see Section S. 2 ). This goal of using this list was to see if our test would give lower scores to male-centric movies than to the general population.

17 Blockbuster movies that surprisingly pass the Bechdel test (Allen, 2019 )—this list contains movies where women are not presented fairly but still pass the Bechdel test. From this list we had 15 in our dataset (see Section S. 2 ). The goal of testing these movies was to validate that they should fail the proposed test.

For the first two lists, we performed a significance test and compared their scores with the general population of movies. Additionally, the third list was used to test if the Gender Degree Ratio dealt with the shortcomings of the Bechdel test, specifically whether a movie with poor female representation yet passed the Bechdel test would fail our suggested ratio test.

To analyze the gender gap in the film industry, we analyzed subtitles of movies that had at least 1000 votes on IMDb. This resulted in a dataset containing 15,540 movies, which is a dataset 20 times bigger than the largest movie dataset currently available (Kaminski et al., 2018 ).

First, we analyzed the gender gap, in general, and by genres, in particular (see Tables S 1 and S 2 ). We found that the genres with the largest number of features that are distributed similarly between men and women are film-noir, history, horror, music, musical, mystery, and war. In these genres, 9 out of 10 features distribute similarly; only the clustering coefficient distributes differently between men and women. In terms of features, Total Weight and Weighted Betweenness are the features that distribute most similarities between the genders, with 15 out of 21 genres distributing the same. On the other side of the scale, Age Filming is the feature that distributes least similarly, with 0 out of 21 genres distributing similarly.

Second, to examine relationships among characters, we analyzed relationship triangles in the networks. We found that most triangles have three men, and triangles with three women are the least common (see Table 1 ). Out of 21 genres, in 8 genres the most common type of triangle is 3 men (without any women) and in all the others it is 2 men and a woman. According to the results, Romance is the genre with the most interaction among women and War is the genre where women have the least interaction. Inspecting the change in the number of triangles over time (see Fig. 5 ), we can observe that in many genres there is an equalizing improvement over the years, but there are genres like Sport without a big change.

figure 5

The change in the number of females in relationship triangles for each decaded for different genres.

Third, we analyzed how characters are ranked in terms of centrality (see Table 2 ). We found that among central roles, there are considerably more men than women. For example, men have about twice the roles that ranked in the top-10 most central roles than women. In all top-10 most central roles, the female percentage is the same except for the most central role.

Fourth, we analyzed the gender composition of the top-10 central roles in movies (see Fig. 6 ). We discovered that most of the movies have more men in central roles than women. Moreover, from the data, we can observe that there are almost no movies with no men and 10 women in the top-10 roles. Also, there are a considerable number of movies where the majority of the top-10 most central roles are men.

figure 6

The distribution of movies by gender of the top-10 most central characters where: a The percentage of movies where out of top-10 role N are of a specific gender. b The number of movies where out of top-10 role N are of a specific gender.

Fifth, we wanted to observe how the percentage of women in top 1, 3 and 10 most central roles has evolved over time. We analyzed the change in this metric over almost from 1965 up to today Footnote 14 (see Fig. 3 ).

It can be seen from the network that there is a constant rise in the number of women in top-10 most central roles.

Sixth, to create an automatic classifier that can assess the fairness of female representation in movies, we created the Bechdel test classifier. Our classifier achieved an AUC of 0.81. We also inspected which feature was more important (see Table 3 ). Seven of 10 features were triangle-based features. Moreover, all the features in the table are a subset of the Gender Representation Features (see section “Network features”).

Next, we trained our automated Bechdel test classifier on all the labeled data and calculated the average probability of the classifier by decade on all the unlabeled data (see Fig. 7 ). We can see that there is a trend of growth. Also, we examined how the probability changed by genres (see Fig. 8 ). Comparing our results to Agarwal et al. ( 2015 ) (see Table 4 ), we found that our classifier performs better than Agarwal’s in terms of F1 score.

figure 7

Trend line of the average probability of passing the Bechdel test in the past 60 years by decade.

figure 8

The average probability of a movie passing the Bechdel test by decade and genre.

Afterward, we analyzed the quality of the constructed social networks by comparing Subs2Network with the ScriptNetwork -released networks (Kaminski et al., 2018 ). We observed that the Subs2Network dataset contains 628 out of the 773 networks that appear in the ScriptNetwork dataset. On average, Subs2Network had more central characters than ScriptNetwork from the top-10 most central characters (see Table 5 ); for instance, in the top-10 characters Subs2Network matched 6.06 characters while ScriptNetwork matched 5.35 characters. In terms of edge coverage, we found that Subs2Network covered 65.4% of the edges in ScriptNetwork networks and ScriptNetwork covered 65.1% of the edges in Subs2Network networks. Additionally, we compared Subs2Network with networks we generated based on manually extracted Amazon X-Ray movie data. We observed that Subs2Network matched X-Ray nodes and edges at 79.6% and 54.5%, respectively. Additionally, when analyzing character matching by screen time, we found that we could detect main characters with a high accuracy of up to 96.4% (see Fig. 9 ).

figure 9

The percent of character that are overlapping between Amazon X-Ray and Subs2Network where the x axis is the screen time of the charcters.

Finally, we analyzed the Gender Degree Ratio test. We found that the average score of all the movies in the dataset was 0.6, meaning there were only 6 female interactions for every 10 male interactions. In fact, we found that today only 12% of all movies pass the gender degree ratio test by having scores between 0.8 and 1.2 (see Fig. 10 ). For instance, Resident Evil: Retribution and The Age of Innocence pass the test with scores of 1.06 and 0.94, respectively. On the other hand, Armageddon and Batman Begins fail the test with scores of 0.2 and 0.24, respectively. To check if the proposed test can distinguish between gender-biased and non-biased movies, we performed significance tests on two groups of movies. First, by performing the Shapiro–Wilk test, we observed that the movie scores were not from a normal distribution. Since the data was not normally distributed, we performed the Mann-Whitney- U test and found that list 1 (feminist movie list) distributed differently from the general population ( μ  = 1.26, p -value = 6.7 × 10 −15 ). Also, we discovered that list 2 (male-biased movie list) scores also distributed differently from the general population ( μ  = 0.34, p- value = 8.5 × 10 −07 ). Regarding the movies that surprisingly passed the Bechdel test, only the movie Grease passed the Gender Degree Ratio test.

figure 10

The number of movie and the ratio of between female and male characters.

In this study, we present a method that converts movie subtitles into social networks, and we analyze these networks to study gender disparities in the film industry. Using this method, we created the largest available corpus of movie character social networks. The method and the corpus are available for use by other researchers to study additional movies and even TV shows, and it has the potential to revolutionize the study of filmed media.

When looking at relationship triangles, we can see that in 77% of all triangles men are in the majority. In an equal society, we would expect to find that the number of triangles with three men, with three women, and with two men and two women would be the same. However, we discovered that, on average, there are 11.4 times more triangles with three men than with three women, and almost twice as many triangles with two men than two women. At a deeper level of granularity, we can see a difference in the number of triangles between different movie genres. The Romance genre has the highest number of triangles that have two and three women. On the other side of the scale, 90.6% of triangles in the War genre have a majority of men. This result makes sense intuitively. By looking at Fig. S. 1 , we can see that genres with a higher percentage of movies that pass the Bechdel test also have a higher percentage of triangles with a majority of women.

In terms of centrality (see Table 2 ), we can see that men have more central roles than women. We expected to find more females in less central roles, but the percentage of females distributes evenly in the top-10 most central roles. We believe that these results correspond to the total percentage of women in the dataset, which is 32.3% and is very similar to previous studies of Lauzen ( 2018a ) and Sap et al. ( 2017 ). This number is still lower than the total percentage of female roles in IMDb, which is 37.2%.

We also analyzed how many roles in a movie’s top-10 most central roles are those of women. Unsurprisingly, there is a dominance of movies with a majority of men. For instance, all Lord of the Rings movies have 10 men in the top-10 roles. We found only 5 films where all top-10 roles were female, and each of these featured only women (one of these films is called The Women , another movie Caged is about a women’s prison, and the movie The Trouble with Angels is about a girls’ school).

There is also the issue of what is considered fair. Mencarini ( 2014 ) states that fairness in gender context varies between cultures and historical periods. Sometimes women perceive their life as fair from a gender equality perspective while actually it is very low, and sometimes it is exactly the contrary. In a film context, some may argue that it is fair for war movies to have almost no women, while others will argue that it is not fair since women have taken part in all wars. Since fairness is subjective to measure, we used the Bechdel test, which is defined as “the basic measure to see if women are fairly represented in the film” (Fest, 2019 ). Centrality and fairness can sound very similar in the context of films, but they are two different notions. A character can be very central and very stereotypical at the same time. For example, Cinderella is the protagonist (most central character) in her story, but she is cooking and cleaning all day, and her life becomes better only when a rich and handsome prince arrives.

We also presented an automated Bechdel test classifier that can help assess the fairness of how women are presented in movies. We trained our model on data collected from bechdeltest.com, and we have indications that our model is even more accurate than the above presented results. We found that many movies on bechdeltest.com are misclassified. For example, The Young Offenders passes the test on bechdeltest.com (although the site does state this result is ‘dubious’), but our work classifies it as a fail. The reverse is true for the movie Never Let Go . Based on these observations, we believe that our classifier can automatically classify movies with high confidence in the classification. Moreover, while the Bechdel test is certainly a useful and important test, it fails to account for many parameters such as the centrality of the characters, repression, etc. Basically, if there is a movie with only two women who appear in one scene and talk about something other than men for 2 seconds, then the movie will pass the traditional Bechdel test. However, this is the only test that has data that can be used to train a classifier. Our classifier partially tackles this problem since it calculates a score of how strongly the movie passes the test.

To deal with the issues of the Bechdel test, we proposed a new test based on the ratio of the number of female interactions to the number of male interactions in a movie. We found that only 12% of all movies passed our Gender Ratio test (see Fig. 10 ), revealing how dominant gender disparities continue to be in the film industry. As anticipated, we found in our test that feminist movies received higher scores than the average movie. Additionally, we discovered that movies that passed the Bechdel test but did not have good female representation failed the Gender Ratio test, just as we had hoped. These results indicate that our proposed test dealt with some of the major problems of the Bechdel test and has the ability to differentiate between films with good and bad female representation. However, the test is not perfect and does not take into account context. For instance, we can see that Grease passed our test even though women in the film were presented stereotypically.

In future work, we are planning to perform statistical tests to compare the distributions of the degrees of male and female nodes and present a more accurate test. Creating a more accurate assessment of how women are truly represented in films requires manually watching thousands of movies and labeling data, which is impossible with the current research limitations. In the future, we plan to develop a more advanced method based on deep learning to create a better algorithm that will be able to create a much more accurate assessment of movie gender equality, taking into account additional parameters such as the context of the movie.

We also calculated the average probability of passing the Bechdel test for all the movies in our dataset that do not have a Bechdel test score. Afterward, we inspected the change in the average probability of movies passing the test over a long period of time and by different genres. In almost all genres there is a trend of improvement, and there is a correlation between relationship triangles and the Bechdel score. Looking at Fig. 8 , we see that historically war movies have the lowest probability of passing the Bechdel test.

There are many factors that affect our method’s accuracy. The most critical factor is the quality of both the subtitles and the cast information from IMDb. In movies where the name of the character in the subtitles does not correspond to IMDb data, the actor cannot be linked to a character. During our study, we stumbled upon subtitles with spelling mistakes and other inconsistencies. Also, in some movies like superhero movies, we did not know how to link the different identities of a character with names such as “Captain America,” that potentially could be filtered because it looks like a nameless character. In addition, nameless characters like “Street Pedestrian” sometimes eluded our cleaning process. There is a balance between cleaning the IMDb data too much and not enough. We observed that more accurate networks were in movies that had hearing-impaired subtitles since they have additional data and are less affected by the NER accuracy. Some of these limitations will be addressed in future research. Additionally, there are many different improvements that can done to increase the accuracy of the networks; for instance, it is possible to use co-reference resolution, train an NER for subtitles, etc.

One of the biggest challenges of this study was to evaluate the quality of the constructed movie networks. For the evaluation, we compared the networks created by our algorithm with the networks created by screenplay analysis and by Amazon X-Ray. Screenplays have easier content to analyze than subtitles, and they contain plenty of structured information, such as character names, scenes, etc. However, there are also some shortcomings in using screenplays. First, only a small fraction of movies have screenplays available online. Currently, the Internet Movie Script Database (IMSDb) Footnote 15 has only 1198 scripts, while there are hundreds of thousands of movies’ subtitles available online. Moreover, many publicly available screenplays are drafts and have major differences from the actual movies. For instance, the Minority Report Footnote 16 screenplay used by Kaminski et al. is completely different from the movie; almost all the characters’ names are different. Another example can be found in the X-Men (2000) movie where the character Beast appears in the screenplay. However, due to over-budget concerns, Beast was cut from the movie. From inspecting screenplays, we discovered many additional examples of extra, missing, and renamed characters. These problems show that comparing subtitles to screenplays is like comparing apples to oranges. The comparison indicates that there is a similarity between the networks, but it cannot be used as a precise measure of accuracy.

In addition to using screenplays to evaluate the constructed networks, we also used networks that were generated based on Amazon X-Ray. Unlike the screenplays, Amazon X-Ray is based on the finished movie and offers a more accurate representation of the movie’s social network. Using the X-Ray based networks, we found that even though sub2network is based on much less data than the X-Ray based networks, the networks are very similar. This similarity indicates that our graphs represent the essence of the movie. The biggest limitation in using X-Ray to generate movie social networks is that the full X-Ray dataset is not publicly available, and must be extracted manually.

There is no doubt that the presented method is not perfect. For instance, in the film Star Wars: Episode VI—Return of the Jedi (see Fig. 1 ), Princess Leia never meets Obi Wan Kenobi. Obi Wan Kenobi only talks with Luke about her, which created an edge in the graph. Nonetheless, from the network evaluation, we learn that the constructed networks represent the movie and have enough correct data to supply insights. Moreover, it is possible to perform many calibrations and parameter tunings to improve the method ’ s accuracy; for instance, we can manually select better subtitles to get more accurate networks. Such calibrations are out of the scope of this study, but in future studies we will explore such options.

Besides utilizing subtitles and screenplays, there are other possible ways to analyze movie content. The first option is to analyze movie videos as Weng et al. ( 2009 ) did. The problem with video analysis is that it is an expensive process which requires high computational power, especially when the plan is to analyze thousands of full-length movies. Moreover, most movies are copyrighted and not freely available online. The second option is to use speech recognition to extract information, which is what Park et al. ( 2012 ). However, this option has similar drawbacks.

Conclusions

Data science can provide great insights into many problems, including the gender gap in movies. In this work, we created a massive dataset of movie character interactions to present the largest-to-date SNA of gender disparities in the film industry. We constructed this dataset by fusing data from multiple sources, and then we analyzed the movie gender gap by examining multiple parameters over the past century.

Our results demonstrate that a gender gap remains in nearly all genres of the film industry. For instance, 3.5 times more relationship triangles in movies have a majority of men. In terms of top-10 most central movie roles, again there is a majority of men. However, we also saw an improvement in equality over the years. Today, women have more important movie roles than in the past, and our Bechdel test classifier quantifies this improvement over time by calculating a movie’s overall score. In a future study, we plan to analyze TV series, actors’ careers, and directors’ careers in a similar in-depth manner. We also plan to implement the tests that were proposed in (Walt et al., 2017 ) as well as develop new tests to gain further insight into how genders are represented in the film industry.

Data availability

The code and datasets generated during and analysed during the current study are available in the on the project’s website ( http://data4good.io/dataset.html#Movie-Dynamics ) and repository ( https://github.com/data4goodlab/subs2network ).

The Star Wars icons were created by Filipe de Carvalho and are licensed under CC BY-NC 4.0)

http://www.moviegalaxies.com

Many of the used movies’ subtitles were created by crowd-sourcing, i.e., by people who volunteered to create the subtitle.

American Beauty, Back to the Future, Back to the Future Part II, Funny People, Gladiator, Inglourious Basterds, Jurassic Park, Knight and Day, Marley & Me, Public Enemies, Serenity, Street Kings, Terminator 2 Judgment Day, The Godfather, The Godfather Part II.

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Similarly to Agarwal et al. ( 2015 ) this about 20%.

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The new era in filmmaking

(Credit: Getty Images)

Since the UK government greenlit the resumption of TV and film shoots under strict new safety measures in June, the cogs of production have been gradually grinding back into motion. But for those returning to sets, this has involved a whole new approach to working: in abiding by new safety guidelines, they have had to recondition themselves as to how they approach their roles.

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Here are a few accounts, from those on productions in the UK and beyond, about how film crews are successfully navigating through this new phase of filmmaking. 

Film and TV shoots have begun to start up across Europe again (such as, above, for Spanish horror La Casa del Caracol) with new measures in place (Credit: Miguel Valladares)

Film and TV shoots have begun to start up across Europe again (such as, above, for Spanish horror La Casa del Caracol) with new measures in place (Credit: Miguel Valladares)

Helen Jones – Producer

Jones is producing British psychological horror film Censor starring Niamh Algar and Michael Smiley, which is due to be released in cinemas in 2021.

At the end of March, Jones was preparing to oversee shooting pickups (shots or scenes recorded after principal photography has concluded) for Censor when Covid-19 forced the production to pause. During her time in lockdown, the producer set to work compiling stringent filming guidelines, in line with the recommendations given by the British Film Commission , before returning to set on 27 July for four more days of filmmaking in London (though the original shoot took place in Northern England). “We knew that we had a certain window that we wanted to shoot in,” she says. “We needed to wait until it was safe to shoot, but we also didn’t want to lose any cast or key crew to other projects.”

The BFC guidelines – an evolving document that was first released on 1 June – provides a framework for those returning to set but advises that each production should tailor the measures to the specific type and size of their shoot. Jones referred to Censor’s guidelines daily, and their line producer (a film’s key manager of day-to-day operations) and first assistant director underwent additional training, while a Covid-19 health and safety supervisor was on set every day.

We had as few crew members present as possible on set. You had to ask yourself: is that person fully justified to be in that space?

The guidelines advise that productions adhere to social distancing wherever possible – and where the two-metre distancing rule isn’t possible, enforce mitigating actions such as limiting the time spent on a particular activity or scene and/or reducing the number of people that each person has contact with.

“On the actual set itself, we had as few crew members present as possible. You had to ask yourself: is that person fully justified to be in that space?” says Jones. “We had camera and sound crew there, but for the most part our costume, hair and makeup departments were away from the set. When that crew came in, there were designated areas that they and their equipment could be in.”

Safety measures didn’t stop with reducing the crew however: Jones and her colleagues had to introduce new ways for people to carry out their daily work safely.

“Something that worked really well for us was a remote monitoring system, which meant that everyone wasn’t huddling around one or two monitors,” she says. “Before lockdown we had one monitor which was used by Prano [Bailey-Bond, Censor’s director] and one that would be checked by the hair, makeup and costume departments, plus myself and other key crew that needed to see the action. With the new system, those who needed it had a secure link from the monitoring system to their iPads.”

During lockdown, Censor producer Helen Jones set to work compiling stringent filming guidelines, in accordance with the British Film Council’s guidance (Credit: Tim Walker)

During lockdown, Censor producer Helen Jones set to work compiling stringent filming guidelines, in accordance with the British Film Council’s guidance (Credit: Tim Walker)

With Censor being one of a very limited number of films to have resumed shooting in the UK so far, the crew found themselves presented with unique challenges to their work every day. However Jones believes the situation also drew them closer together, and after spending around three months without work, their collective enthusiasm about being back on set contributed to the shoot’s success.

“People had slightly different ways of dealing with things, and some cast and crew were more cautious than others,” she says. “But I think that going through this type of shared experience helped create a really good atmosphere and ultimately, a successful shoot.”

Prano Bailey-Bond – Director

Bailey-Bond is the director and co-writer of Censor, which is her debut feature film and currently in post-production.

As the director on a small-scale shoot, Bailey-Bond helped to devise the new safety guidelines for the film, but also had to come up with creative solutions to the limits that they imposed.

“Our Covid-19 safety officer would say for example, ‘you can only have six extras in this space,’ so as a director I had to work out how to make it seem like there were more people in that scene than there actually were.”

The director recalls filming a scene that took place in a restaurant. “On paper it’s just three people sitting having dinner, but because we were filming in a small space it meant that we had to stagger the different departments coming in,” she explains. “So the art department would come in to do their job, then you have to let the lighting crew do their bit. It ends up extending your whole schedule.”

I've been to doctors’ surgeries where I don't think they're putting as stringent guidelines in place as they are in our industry

The film is set in 1985, and stars Niamh Algar as a film censor who sets out to solve the past mystery of her sister’s disappearance after watching a strangely familiar ‘video nasty’. Bailey-Bond believes that the film’s nature worked to their advantage when it came to being able to start up again so quickly.

“I had to do an on-screen risk assessment for each scene, which is something that our financiers asked for. As the film is a psychological horror, we didn't need any hugging and kissing, so I was slightly less worried about the content we were shooting from that point of view.”

Censor director Prano Bailey-Bond was most concerned about the mental health and wellbeing of her colleagues coming back onto set (Credit: Darryl Foster)

Censor director Prano Bailey-Bond was most concerned about the mental health and wellbeing of her colleagues coming back onto set (Credit: Darryl Foster)

Instead, the director was most worried about the mental health and wellbeing of her colleagues. “For me, the biggest concern was the cast or crew bringing various levels of anxiety to the set and not feeling comfortable,” she says.

Advice on accommodating for this has been provided in the BFC guidelines, which points to resources like the 24-hour support line run by the Film and TV charity for those in the industry. Meanwhile Bailey-Bond and her team spent time leading up to the resumption of the shoot speaking with and catering for people who were nervous about returning to set.

With regards to the immediate future of filmmaking in the UK, the director wonders how the pandemic will alter the kind of films that people make:

“I’ve been sent a couple of scripts that are mainly focused on solo characters in a room, so it might be that filmmakers are looking to manage social distancing on screen through the content that’s being created.”

Now in the post-production phase of Censor, Bailey-Bond remains hopeful that she and her peers will be able to continue working, whatever the future holds. “I’ve been to doctors’ surgeries where I don't think they're putting as stringent guidelines in place as they are within our industry,” she jokes. “The film and TV industry is so adaptable and you have to be so organised and disciplined to work in it. I think if there’s any industry that can handle this kind of thing it’s this one.”

For satire Triangle of Sadness, production designer Josefin Åsberg helped create an interior of a luxury yacht, where some of the action takes place (Credit: Robin Aron)

For satire Triangle of Sadness, production designer Josefin Åsberg helped create an interior of a luxury yacht, where some of the action takes place (Credit: Robin Aron)

Josefin Åsberg – Production Designer 

Åsberg is working on Ruben Östlund’s first English-language feature Triangle of Sadness, which resumed filming in Sweden in late June. 

Production was paused for three months on Triangle of Sadness, a $11m (£8.3m) satire from Swedish director Östlund, best known for Force Majeure and Palme d’Or-winner The Square, and so far it has been one Europe’s biggest productions to resume filming. “My department came back to work before the main crew did, and the atmosphere was great,” says Åsberg, who is responsible for creating the visual world of the film. “Everyone was so happy to return after months without work.”

The film is set in part on a luxury yacht, with Åsberg working on a custom-build interior in a studio in the West Swedish city of Trollhättan that was assembled prior to lockdown. Östlund’s English-language debut stars Woody Harrelson and British actor Harris Dickinson, and was 25 days into production before Covid-19 forced it into hiatus. When it was confirmed that Harrelson would be allowed to fly over from his home in Hawaii to shoot his scenes, filming resumed under new safety measures.

“On the first day of the [resumed] shoot we were assigned different colours depending on what team we’re in – I’ve been given pink as I’m part of the art department, and also green, which is the main unit that’s allowed up on set at the same time as the director, director of photography, and the actors,” Åsberg tells BBC Culture.

I found it strange not being able to hug anybody, but this has become the new normal anyway

“We get breakfast, lunch and dinner in a box and the restaurant in our hotel is closed. We wear face masks and the costume, makeup and props departments plus anyone who works near the actors have to wear face shields. We’ve also been given thermometers to take our own temperatures, but I’m healthy and forget sometimes.”

On set, the production designer says that the new measures didn’t impose any real limitations on her ability to do her job, but at first she missed the social interactions with her colleagues: “I found it strange not being able to hug anybody,” she says, “but this has become the new normal anyway so soon it didn’t feel so different.”

The exception made for Harrelson to fly to Sweden for a week-long shoot was groundbreaking and the result of agreements made with the Swedish Border Police and the Ministry of Foreign Affairs amongst others. A travel ban on international travellers entering the country was enforced on 17 March and remains a strict guideline until 31 August. Exemption can only be made if the person is deemed a highly-skilled professional who cannot do their job remotely, but even then, there is no guarantee of entry as each case is assessed at the border control point by the Swedish Police Authority. When Harrelson was stopped in Los Angeles after the airline KLM believed him to have the wrong paperwork, a call had to be made by the film’s producer to convince them that everything was in order.

“Our producer Erik Hemmendorff worked really hard with officials to make sure that Woody was let in as an exception,” Åsberg says. “When he got here he was very relaxed about everything.”

In spite of the continuing pandemic, Åsberg remains optimistic about her work: “It seems like a lot of productions are starting up again so things look bright for the near future.”

Triangle of Sadness star Woody Harrelson was given a special exemption to enter Sweden for the shoot, despite a ban on international travellers at the time (Credit: Alamy)

Triangle of Sadness star Woody Harrelson was given a special exemption to enter Sweden for the shoot, despite a ban on international travellers at the time (Credit: Alamy)

Lizzy Talbot – Intimacy Co-ordinator

Talbot is currently working on two projects filming under Covid-19 measures. She founded the Intimacy for Stage and Screen network.

Intimacy coordinators are increasingly becoming a staple on film and TV sets, especially in light of the #MeToo and Time’s Up movements and their consequent call for more transparent conversations about women and consent across the industry.

“Our job is to bring the director’s vision to life in the safest way possible,” says Talbot. The role involves choreographing scenes that feature sexual acts, as well as other scenes involving non-sexual intimacy, such as close contact between families, from determining movements and boundaries between actors to talking with the cast and crew to ensure that the content isn’t triggering for anyone. They also help to reach an agreement between actors and directors on what they are willing to do within a scene.

Unsurprisingly, the new post-Covid-19 BFC guidelines pose a host of new challenges when it comes to filming scenes that involve an intense amount of physical contact. But they also stipulate that all sets must now have an intimacy coordinator present to guide the shooting of these sections – something that, pandemic aside, can only be a blessing in the longer term, says Talbot.

If people’s considerations of boundaries have increased because of Covid-19, this can only be a positive thing

“That obviously hasn’t been the case overall for lots of productions prior to this,” she says. “So the bar is being raised in terms of safety, and I think that consent is being highlighted in a way that it hasn’t been before because people have a heightened awareness of boundaries and personal space. If people’s considerations of boundaries have increased because of Covid-19, this can only be a positive thing.” 

Talbot says she cannot disclose specific details about the productions she is currently working on, as information about them is heavily guarded. However as a leading voice in her field, she has contributed to a Film and TV recovery plan compiled by the UK creative industries union Bectu, and helped to draw up detailed guidelines on filming intimate scenes while ensuring the safety of cast and crew. For example, they stipulate that camera tricks and insinuated action should be prioritised above scenes involving bodily contact and nudity.

The BBC romantic drama Normal People is among recent productions to have had an intimacy co-ordinator at the core of its crew (Credit: BBC)

The BBC romantic drama Normal People is among recent productions to have had an intimacy co-ordinator at the core of its crew (Credit: BBC)

The report also asks that modesty garments (guards or pouches that cover actors’ genitals when simulating sex on camera) are bought or made for each actor and never shared, and if the actors need to come within two metres of each other, that strict testing or isolation processes are adhered to. It is strongly advised that actors shooting intimate scenes self-isolate for 14 days before coming on set. 

If safeguards on set have improved, however, Talbot has also seen some worrying developments with the increasing move to online casting and auditioning. In one recent instance, a predator masquerading as a casting director was caught requesting sexually explicit material from actresses and reported . Casting agents have also tweeted to warn actors that they should never be required to go nude or simulate sex acts during an audition, either by self-tape or in person. “In some ways the casting world has opened up in terms of diversity as anyone can audition from anywhere, which is great, but we need to get this other problem stamped out,” says Talbot.

This new phase of post-lockdown filming has already seen production companies finding creative new ways to shoot scenes of an intimate nature. One of these has been bringing in actors’ real-life partners as ‘love scene doubles’, which has been the case with longstanding US soap opera The Bold and the Beautiful.

However “the blur between personal and professional lives makes this complicated,” says Talbot. “The emotion is real, and your body is experiencing all these hormones, but the situation is fake. You’re two people sitting under spotlights with 20 other people watching.” Another especially novel approach, also tried out by The Bold and the Beautiful, has been substituting actors with mannequins.

Thai and Bollywood film sets have banned love scenes altogether, while the Netflix show Riverdale will be ploughing its script full of innuendo in order to replace some of the action on screen.

Ultimately, a knock-on effect of this new, industry-wide emphasis on the personal safety of actors on set will be that the job of intimacy co-ordinators only becomes more fulfilling, says Talbot.  “Sometimes when we’re brought into productions it can feel a little bit like we’re health and safety officers standing in the corner,” she says. “[But] a lot of our training is as choreographers, and centred around physical technique and how to safely create dynamic intimacy. So there might be far more opportunity for that.”

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Margot Robbie in Barbie.

Study shows ‘catastrophic’ 10-year low for female representation in film

Despite Barbie’s success, study shows that out of 2023’s top 100 films, only 30 were led or co-led by women, down from 44 in 2022

A new study has shown that the number of female leads in Hollywood movies is at a 10-year low.

Despite the $1.4bn success of Barbie, last year’s top 100 films saw just 30 feature a female lead or co-lead, the worst result since 2014 according to a new study by the USC Annenberg Inclusion Initiative.

“This is a catastrophic step back for girls and women in film,” Dr Stacy L Smith, research head, said in a statement. “In the last 14 years, we have charted progress in the industry, so to see this reversal is both startling and in direct contrast to all of the talk of 2023 as the ‘year of the woman’.”

The results follow a record high in 2022 with a 44% result. Despite a number of major female-lead films moving to 2024, such as Luca Guadagnino’s romantic drama Challengers starring Zendaya, the study’s authors do not believe this to be the reason, writing that “we cannot explain the collapse” and calling it “an industry failure”.

The number of films led by women of colour also fell from 18 to 14 which still marks a major leap from 2007, when the study originated, with just one. Only three films in 2023 featured a woman over the age of 45 as a lead or co-lead compared with 32 for men in the same age category.

Those behind the study stressed that the success of Barbie, which became the year’s highest-grossing movie, is not reason enough to be optimistic. “One film does not represent progress across the industry and cannot bear the burden of lifting the industry to inclusion,” they wrote. “The results this year point to an industry grown apathetic about efforts surrounding diversity and inclusion.”

The study comes just weeks after another damning report from the same team which showed that female directors are also on the decrease, with 16% of the top 100 films coming from women compared with 18% the year before. “Over more than a decade and a half, the percentage of women in top directing jobs has not even grown by 10 percentage points,” Smith said at the time.

The biggest US box office hit so far of 2024 is Mean Girls, led by a female-heavy cast. In the next year, major box office hopes spearheaded by women include Godzilla x Kong: The New Empire starring Rebecca Hall, Furiosa: A Mad Max Saga starring Anya Taylor-Joy, A Quiet Place: Day One starring Lupita Nyong’o and Wicked starring Ariana Grande and Cynthia Erivo.

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Behind the Scenes: The Science of Moviemaking

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When you think of the business of movies, what comes to mind? The millions of dollars that big-name actors earn from blockbuster releases? Or, if you’re up on your media and entertainment industry trends , you might be curious about the lineup of NFT-sponsored events, parties and panels that are adding a whole new creative crypto vibe to Hollywood.

In reality, the business of movies is much broader in scope. Professors at the Wharton School have long been researching and measuring parts of the movie-making process to improve how Hollywood operates. For example: how can movie executives predict whether a film will be a hit or a flop? Or what is involved in marketing movies effectively?

Jehoshua Eliashberg , Wharton’s Sebastian S. Kresge Professor Emeritus of Marketing and Professor Emeritus of Operations , Information and Decisions, as well as Wharton marketing professor Jonah Berger , are among those business school researchers who have used analysis to understand the business of media and entertainment.

In a recent Knowledge@Wharton podcast , Professor Berger described his research-driven approach to the business of movies like this: “Often, we watch movies and think it’s just some magic, creative process where things gel together and there’s no way to understand whether it will succeed or fail. That’s not exactly right. It feels like magic…But there’s a science. [For example], we can understand the science of stories, of content more generally, by understanding the progression of ideas. By using tools that have recently become available…we can shed a light on some questions that might otherwise seem impossible to uncover.”

Here are 3 ways that Professor Berger and Professor Eliashberg have brought science to the movie industry through their academic research:

🎥 In a study titled “ How Quantifying the Shape of Stories Predicts Their Success ,” Professors Berger and Eliashberg (along with co-author from Columbia University, Olivier Toubia) figured out a way to measure language in movies, TV shows and academic papers to determine what makes some narratives more successful than others. They measure three things: speed (how quickly do you deliver the ideas in your story?), volume (how much total ground do you cover in your story?), and circuitousness (How direct are your ideas?), applying them to thousands of texts and examining if and how they are linked to success. Says Berger: “As marketers, as leaders, as others, these findings really help us think about how to better lay out the content — whether that content is a presentation, an argument, a speech — in a way that will impact the audience. Should we try to cover a lot of ground or relate the ideas more closely to one another? If we’re covering the same ground, should we use a very direct path or more of a spiral, where we go back to the same ideas again and again to deepen the understanding around those things?”

🎥 Professor Eliashberg has spent his academic career developing models and methodologies to solve business problems, with particular interest in media and entertainment. His most recent research, conducted with the help of Wharton marketing doctoral student Yi Liu, looks at the role of trailers (pre-launch campaigns that get you excited about watching a movie or some other media) and the economic value of the comments that they generate. Eliashberg studies a dataset of 363 movies released between 2014 and 2018. The authors write: “Trailers are commonly employed in the pre-launch campaigns of new products as an advertising tool to generate awareness and interest among the potential audience. In this paper, we argue that such trailers, whose costs range and are rising, should also be considered as a…tool having additional economic value. The incremental value is driven by the audience comments data that the trailer of a new product generates.” What is the value of that commenting buzz? Potentially hundreds of thousands of dollars. Stay tuned for more details as the paper hits publication.

🎥 Are you starting to see how business research can inform all dimensions of the Hollywood dazzle and help studios make smarter decisions? In the past 30 years, Professor Eliashberg has published numerous papers in such academic journals as Management Science, Marketing Science and the International Journal of Research in Marketing. Titles include: “From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts,” and “Of Video Games, Music, Movies, and Celebrities.” A few years back, Wharton Business Daily on SiriusXM invited him and a research colleague to talk about the economics of arguably the most important ceremony in the movie industry, the Oscars. The question posed to them: Can you measure the financial benefits of winning an Oscar at the Academy Awards? Eliashberg’s assessment? “In terms of the profitability of the movie, I think we have to distinguish here between two time periods: The time that the movie is nominated to the Oscar, all the way to the Oscar event, and the time that the winners are announced. From the data that I’ve seen, it is the nomination that gives rise to the box office, more than the actual winning of the movie.”

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Conversation Starters

What are a few ways that academic research can inform all dimensions of the Hollywood dazzle and help studios make smarter decisions?

If you could explore aspects of the movie-making business, what would you want to learn? What would be the thesis for your academic research?

Have you experienced a media and entertainment internship like Lauren M.? Tell your story in the comments section of this article.

3 comments on “ Behind the Scenes: The Science of Moviemaking ”

Trailers are gaining increasing importance in the marketing campaign of films. I can attest to that.

As an amateur filmmaker, I witnessed firsthand how the fast-growing trailer industry became highly commercialized. During my internship at the marketing department of CMC Pictures, a company that distributes China-produced films in overseas markets, I saw detailed divisions of labor and multiple commercial analytical tools used to achieve the greatest possible business outcome — that is, a successful trailer.

Previously, I saw trailer-making as a simple job — an uncreative editor merely taking clips from a film to tell a fragmented story and wow the audience.

I was wrong.

Here’s what I quickly learned: While entirely different from moviemaking, the trailer industry also requires absolute creativity and close collaboration.

I was on the trailer creation and market monitoring teams simultaneously. We started by trying to get inside the director’s head and examining how the film made us feel. By absorbing the music, studying the film’s pace and understanding the tone, we tried our best to replicate those elements in the trailer.

This is where creativity and collaboration kicked in and made all the difference.

To create an entire film, the creative directors and producers oversee the process; editors must figure out how to make each shot work; assistant editors will catalog and organize everything for editors to use; music supervisors will select the perfect piece of music or sound design.

Ideally, a film would appeal to various age and gender groups. But certain genres appeal to certain demographics more easily, and which group/groups to target is a tough marketing decision that involves grueling discussions among the whole crew.

But just like each film is unique, each trailer must be unique as well.

Whereas making a movie is like telling an epic, creating a trailer is like writing a composition on a given topic whose score is measured by box office figures.

Since the trailer is the audience’s first look at the film, filmmakers need to tell a compelling story in less than 2.5 minutes, a demanding job involving endless rounds of editing until one gets to the heart of the story.

This is where different types of trailers will do the trick.

Among the different types are “star trailers” and “story trailers.”

Star trailers feature the cast, director and producers, telling the audience to expect some big names in the film. Big names often imply bigger budgets that can move the film up the box office charts.

Meanwhile, story trailers are more fun to make but can be the most brain-racking as they demand getting to the film’s core and showcasing some of its best assets. What to reveal — and what to hold back — is more complicated than it looks.

In a story trailer, we wanted to unveil the film’s central narrative elements to the audience without spoiling the plot. Easier said than done! Selecting the best elements to illustrate the main narrative can sometimes be tricky in stories with lots of suspense.

Completing a trailer finally brings us to the core of the trailer business — promoting it and monitoring audience feedback.

Like Professor Jehoshua Eliashberg and Yi Liu concluded in their research paper “On the Role of the Trailer as a Marketing Research Tool: The Economic Value of the Comments It Generates,” trailers should be viewed as a tool to create additional economic value. The film industry, through the years, has reached a consensus that positive audience feedback and growing behavioral data generated from trailers often lead to incremental box office sales.

The first few trailers, or focus trailers, are pushed to the market as experiments in different stages of the marketing campaign. As the name suggests, we try to test how audiences react to different elements. Are they more interested in the superstars or intrigued by the story? Are they blown away by the sound and visual effects?

Then it’s time for a quantitative and qualitative analysis of the collected feedback. A list of elements will be generated according to their popularity rankings. These become the most critical criteria in selecting elements and materials for the most crucial trailer, namely the one that gets aired shortly before the film’s official release and has proven to be the most effective in stimulating ticket sales.

While I used to be solely focused on captivating audiences during filmmaking, there is more to this complex industry than meets the eye. I quickly learned that moviemaking is in constant flux, and trailers are pivotal in moving the business forward.

This is indeed something to keep in mind if you are interested in the film industry.

Zizhou, thank you for sharing your experience with movie and trailer making! I thoroughly enjoyed reading your thought process into creating a unique movie trailer during your internship. I’ve noticed many movies have different trailers but I didn’t realize that it was just a marketing ploy to gauge the audience’s reaction to each one. I especially liked the analogy you made of comparing a creation of a trailer to a writing assignment as it highlights that a trailer needs to be constantly refined and improved in order to have a “high score” which is determined by the audience. I’ve noticed that when going out for a movie with friends they usually check the Rottem Tomatoes ranking or IMDB rating before buying tickets which really shows how important a score is to viewers.

I strongly agree with your point on how films need to attract a certain demographic whether it be race or age. The fact is that some things will attract some people while others won’t. Everyone has their own preferences and that can’t be changed. This has happened numerous amount of times in my friend group where we’ve often changed the movie we’re going to watch based on the preferences of some. The decisions that the movie crew makes are usually favored towards making a profit and it can affect what kind of audience views it.

A comment that particularly stood out to me was, “But just like each film is unique, each trailer must be unique as well.” It may seem obvious, movies should be unique to succeed and make a profit. However, once you take a deeper look into the movie industry as a whole it becomes clear that we are headed in a direction where movies will no longer be unique but rather stale and generic. We see this play out in this article, where we see Professor Berger describe his approach to the business of movies as a “science”, like how there’s a formula to what makes a successful movie. If all movies follow the same model, how are they any different from each other? I’ve seen this happen with many of the movies that I’ve watched. Almost every single time, the main character goes through some sort of sacrifice or hardship, and in the end, they get a happy ending. It seems that the critics love this sort of trope since they all had decently high ratings. If companies just want to satisfy the critics so they give high reviews to gain a bigger audience, isn’t that only playing to the critics’ preferences? A movie “score” shouldn’t be affected by only the critics but should be debated by its audience.

On websites such as Rotten Tomatoes, we are able to see what movie critics and the general public rate a certain movie. Almost always, the score differs between the two. Yet what matters most is the amount of money the movie makes. In a research article written by Timothy King published the Journal of Cultural Economics, the data shows us that “critical ratings were indeed positively related to gross earnings.” The audience is affected by critic ratings but they may or may not have enjoyed the movie as much as the critics have. A solution to increase both profit and enjoyability from the public is to diversify the critics themselves. By showing the film to a larger demographic of critics (and assuming that they write positive reviews), then those demographics are more likely to enjoy that film.

If movie companies all follow this “science” for higher box office revenue and profit, then the magic of going to the movies is long gone. Critics only make up a small percentage of moviegoers but what they write greatly affects whether we watch something or not. Whether we enjoy a film they write positively about is what truly matters. In my opinion, movies are not something to be analyzed but rather something to pass the time with friends or family, something that you can be entertained by. Zizhou, I wish you the best of luck with your future in the movie industry and I hope you can put some of my words to good use!

As a huge movie critic, I think movie producers should focus on balancing the movie with the hype. Being humble about your movie is one way to go. Don’t overhype a movie that you think won’t be as big of a hit you think it would be. Rather, hype the movie in such a way it matches the reality and not create false expectations. I feel that it’s okay to overhype the movie a little bit in the trailer just to gain that attention, but if you drown your movie in hype, the expectations do not meet with those of the public. Movie producers tend to include some type of emotional scene for the audience to see, but it doesn’t exactly always hit the audience the way you want it to. To make the audience actually care about an emotional scene is to get them indulged in characters’ lives. Building up to one of the most important scenes(not the climax, just a huge scene) in a film is going to be a big hitter to the audience and will create buzz among the movie critics.

Something that most films do a great job on are trailers. The pre advertisement is super important to get the most attention to your film, and If you can craft a well filmed trailer you will generate a lot of money on release date. The box office isn’t scary; they’re simply just there to sell the tickets and to collect the revenue made from a movie. To generate the most money from the box office? There’s a lot that goes into it. Generally, sequels to very famous movies or trilogies make the most amount of money in the box office, but there have been a few single standing films that have pulled out on top. A method most producers use to create a high grossing film is to make the film based off a true story and recreate the timeperiod faithfully through detailed efforts. Research on favorite genres, favorite actors, and even favorite movies could all give the producer ideas on how to make a blockbuster film.

It is best to have a screen writer and a director who has past experience in decently high grossing films as they woud know what they’re doing and will also pique the interest of die hard movie fans who study and know many directers, music producers, and screenwriters. These experienced movie makers could give a movie a bit of a jump that some other movies might not have.

The current problem with the movie industry is that too many films are being introduced but almost none of them are popular and have not raised that much money. There has been a few here and there that raised quite the amount of money.

The way the movie industry is going right now, It seems to be doing okay. There have been a few mediocre movies here and there but then there have been some huge hits like “Top Gun: Maverick” which has raised almost as much as “Titanic” had. Producers nowadays have some good films in the making and I hope that the film industry continues to grow especially with many new phases being added to big hit studies like “Marvel” which should definitely create a stir within the film communities.

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The Indian film industry in a changing international market

  • Original Article
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  • Published: 03 May 2019
  • Volume 44 , pages 97–116, ( 2020 )

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  • Sayantan Ghosh Dastidar 1 &
  • Caroline Elliott   ORCID: orcid.org/0000-0002-1218-4164 2  

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India has a longstanding reputation for its acclaimed film industry and continues to be by far the world’s largest producer of films. Nevertheless, domestic demand for films appears to be waning as in a number of developed countries with mature film industries. Hence, the econometric analysis in this paper is particularly timely as with demand for films in Indian cinemas falling it is important to identify those factors that make films appealing for Indian audiences. An original dataset is utilised that includes data on all Bollywood films released in India between 2011 and 2015. Account is taken of the potential endogeneity between variables through the use of the generalised method of moments approach. Results are used to demonstrate how the Indian film market can continue to have a significant positive impact on the Indian economy. The discussion highlights appropriate film production company strategies and government policy responses that should be considered to ensure the continued success of the Indian film industry both domestically and in an increasingly competitive international market.

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Introduction

Avoid common mistakes on your manuscript.

1 Introduction

India has a longstanding reputation for its acclaimed film industry, with the term Bollywood synonymous with vividly coloured films featuring complex dance routines, singing and spectacular large cast scenes. India continues to be by far the world’s largest producer of films, producing 1724 films in 2013 compared to 738 films produced in the USA, and 638 films produced in China. Footnote 1 Nevertheless, domestic demand for films appears to be waning as in a number of developed countries including the USA and UK. For comparison purposes, film industry data for China, the USA and the UK as well as India are provided in Table  1 .

It is immediately clear that while India produces by far the greatest number of films, with the number of films produced continuing to rise, the number of consumers paying to see films at cinemas in India has declined dramatically in recent years, despite significant growth in GDP since 2000 and international investment in the Indian film industry, Fetscherin ( 2010 ). However, falling popularity of going to the cinema is not exclusive to India, with falls in cinema ticket sales also seen in the USA and UK. Each of these film markets can be considered mature markets, with long established, successful film production industries, and cinema visits a long-established social activity. Indeed, the first full-length Indian film, Raja Harischandra , was produced in 1913, and by the 1920s, large-scale Indian film studio companies existed, and Mumbai had established itself as an early hub for film making (to become known as Bollywood). See Jones et al. ( 2008 ) for a history of the Indian film industry.

The importance of the Indian film industry to the Indian economy cannot be overstated: in 2012, Indian cinema box office revenues were $1.6 billion (McCarthy, 2014 ), in a services sector which accounts for more than 50% of the Indian economy. Footnote 2 Fetscherin ( 2010 ) suggests that the film industry accounts for approximately 20% of all revenues in the Indian media and entertainment industries. Further, despite the high profile of ‘Bollywood’ which is based in Mumbai, film production also has positive spillover benefits to other local economies, particularly Chennai where film production has long been established, with films made in four key southern Indian languages. There are also notable film production activities in Hyderabad, Karnataka, Kolkata and Kerala that benefit the local economies. Local economy benefits are not restricted to the direct benefits and multiplier effects associated with film production and therefore employment in specific local economies. Bollywood, in particular, also has tourism benefits with Bollywood locations boosting tourist visitor numbers, i.e. an indirect channel through which the Indian film industry contributes to gross domestic product (GDP).

Yet, the Indian film industry currently faces a number of challenges. First, a major challenge remains film piracy which limits the revenues that can be reinvested by producers, distributors and exhibitors. A complicated system of regulations with responsibility shared by a number of national and state level government departments only contributes to the often ineffective nature of policies and laws that should guard against piracy, Jones et al. ( 2008 ). The problem of piracy results in lower revenues across the film industry, and the negative effect on investment in the industry is compounded by the high entertainment tax rates imposed in India.

Second, relative to many countries, domestic cinema ticket prices remain low, Jones et al. ( 2008 ) and see Table  1 . This again results in smaller box office revenues to be shared between film exhibitors, distributors and producers, reducing opportunities for reinvestment across the Indian film industry. This is particularly important at the moment as international film producers are increasingly investing in expensive technology associated with ‘enhanced format’ films, such as 3D and IMAX. Elliott et al. ( 2018 ) have found these films particularly popular with Chinese audiences, but these films require very large production budgets, as well as investment in cinemas by film exhibitors. Nevertheless, despite the costs of producing enhanced format films, they offer the advantage that the piracy of these films is less attractive as the special effects will be much less impressive to viewers when watching pirated films either on television screens or computer monitors. A further issue relating to low ticket prices is that regardless of this advantage, consumers are still purchasing fewer cinema tickets.

Meanwhile, despite difficulties until the early 1990s, the Chinese film market now continues to grow rapidly, both in terms of films produced and audiences’ desire to view films at cinemas. Despite initially slow growth of the film industry in China prior to the 1990s, its film box office revenues were expected to exceed $10 billion in 2016, coming close to overtaking the USA which enjoyed box office revenues of $11 billion in 2015, Shoard ( 2016 ). It is within this context that the performance of the Indian film industry has to be considered. The Indian and Chinese film industries share some similarities. Both countries have adopted economic liberalisation policies since the later years of the twentieth century, and as a result in both countries, the film industry has attracted greater foreign investment. Meanwhile, liberalisation has led to greater competition for domestic films from large budget, internationally produced films, often originating in Hollywood, with both Indian and Chinese audiences keen to watch these films.

This paper seeks to identify the factors that contribute to films’ success in Indian cinemas using an econometric analysis such that film production companies are in a better position to identify strategies to ensure their future success. These strategies relate to film characteristics as well as marketing strategy. Given the importance of the film industry to the Indian economy and the difficulties currently faced by the industry, our analysis is particularly important and timely. We believe this to be the first paper econometrically to estimate the determinants of domestic box office success for the Indian film industry. To do this, an original dataset has been collated and utilised, considering all Bollywood films released in Indian cinemas over the period 2011 to 2015. For each film, released data are collected on the size of production budget; Indian cinema box office revenues; film genre; the use of Bollywood star actors and directors; and the distributor of the film in India. Alternative measures of critical acclaim for each film are also collected. As well as identifying those factors associated with films’ Indian box office success, the results of the statistical analysis are used to develop government policy recommendations.

A literature review covering economic analyses of the film industry, both in India and more broadly, is provided in Sect.  2 . The data and econometric methodology are described in Sect.  3 . Results are reported in Sect.  4 , with discussion of these results, policy implications and conclusions provided in Sect.  5 .

2 Literature review

Despite the large and burgeoning film industry literature, spanning the Economics, Marketing and Management disciplines, there are very few analyses of the Indian film industry. The only econometric analysis of factors impacting on Indian film success is that of Fetscherin ( 2010 ), who considers the variables that affect the opening week and total cinema box office returns of Indian films exhibited in the UK and USA. Fetscherin ( 2010 ) concludes that neither star power nor the previous directing experience of the director impacted on opening week or total box office revenues, and that consumer online reviews, posted on the www.imdb.com website, also had no significant impact on either revenue variable. Our statistical analysis differs as we are interested in the factors determining the success of Indian films in the domestic, Indian market. Arguably, our analysis is also strengthened as we were able to obtain data on film production budgets and film critic review scores and could identify if a film was a sequel. Other analyses of the impact of the Indian film industry include Jones et al. ( 2008 ) and Balasubramanyam ( 2009 ), but a lack of Indian film industry data has resulted in a paucity of Indian analyses despite the high profile of Bollywood. Our analysis contributes directly to this limited literature.

A film represents an experience good, for which full details of the properties and quality of the good cannot be determined, prior to purchase and consumption. Consequently, it is important for consumers to receive quality signals in advance of cinema ticket (or DVD) purchase decisions, to help ensure that consumers select films that best match their preferences. Beyond the Indian market, there is a large literature that considers the factors that determine films’ box office success, and much of the literature highlights factors that can be considered quality signals. See Mckenzie ( 2012 ) for a recent literature survey.

A number of potential quality signals under the control of film production companies have been considered, with research estimating their impact on film box office revenues. A consensus has emerged in the literature that there is a positive, significant relationship between a film’s budget and box office revenues with consumers perceiving higher film budgets as a signal of film quality, Elliott et al. ( 2018 ). Footnote 3 Related to film budget, one of the most expensive costs of producing a film may be the costs of employing high profile star actors and actresses. The use of stars is a very visible signal that production companies are willing to invest large amounts of money in a film, and also signals the actors’ belief in the quality of a film. A number of researchers have considered the impact of employing stars in films, including US analyses of Prag and Casavant ( 1994 ), Ravid 1999 ), Elberse ( 2007 ), Brewer et al. ( 2009 ), Akdeniz and Talay ( 2013 ), the Italian analysis of Bagella and Becchetti ( 1999 ), as well as Fetscherin’s ( 2010 ) Indian analysis. However, results remain mixed, with some but not all studies concluding that the use of stars positively impacts on film box office revenues. Similarly, the use of a high profile film director as a signal quality that may impact positively on box office revenues has been considered by Bagella and Becchetti ( 1999 ) as well as Fetscherin ( 2010 ).

If a film is successful in terms of box office revenues, this may then encourage film production companies to invest in a sequel, as considered by, Basuroy et al. ( 2003 ), Moon et al. ( 2010 ), Akdeniz and Talay ( 2013 ). Similarly, cinemagoers may be eager to pay to view a sequel if they have enjoyed an earlier film in a film franchise. Elliott et al. ( 2018 ) conclude that Chinese audiences are positively attracted to sequel films. The final quality signal under the control of film production and distribution companies is the date of release of a film, with firms keen for films that they think will particularly appeal to audiences to be screened around major holiday periods. This has previously been explored in a US context by Litman ( 1983 ), Sochay ( 1994 ), Einav ( 2007 ) and Brewer et al. ( 2009 ) and is considered in the Indian context in the analysis below.

A second set of potential quality signals that may influence consumers’ film ticket purchase decisions is not under the control of film production companies, namely expert critics’ review scores and online review scores given by members of the general public. The impact of critics’ review scores on box office revenues has been explored by Eliashberg and Shugan ( 1997 ), Basuroy et al. ( 2003 ), Reinstein and Snyder ( 2005 ) in the US context, and by Elliott and Simmons ( 2008 ) using UK data. Nevertheless, Moon et al. ( 2010 ) suggest that consumers may take more note of consumer reviews than those of expert critics. The increasing availability of online review scores posted by non-expert reviewers has led to Elliott et al. ( 2018 ) exploring the impact of these reviews on Chinese box office revenues, concluding that these reviews are positively and significantly related to box office revenues.

All of the quality signals highlighted above, including both those under the control of film production companies, and those associated with expert and non-expert critics are considered in the statistical analysis below.

A crucial issue addressed in the literature is how to take account of the potentially highly skewed distribution of film revenues which may also be characterised by unbounded variance, as identified by Collins et al. ( 2002 ); De Vany and Lee ( 2001 ); De Vany and Walls ( 1996 , 1999 , 2002 ). These characteristics result in film revenue variables violating the classical assumptions required for ordinary least squares (OLS) of variables having well-defined mean and constant and finite variance. De Vany and Walls ( 1999 , 2002 ) have suggested using the Pareto distribution to deal with the excessive kurtosis; Collins et al. ( 2002 ) used an ordered Probit model with threshold revenue values imposed, while Walls ( 2005 ) recommends using a t-skew distribution. These issues will be addressed in Sect.  3 .

3 Data and methodology

The dataset comprises all Bollywood films screened in Indian cinemas over five years, 2011–2015 for which data on all required variables were available. This gives rise to a dataset of 245 films as data were missing on key variables for a number of films released in India during this period. Nevertheless, we believe this to be the largest dataset collated to date on the Indian film industry. Variable definitions and data sources are detailed in Appendix 1, while descriptive statistics for the continuous variables are reported in Table  2 .

Both total Indian film box office revenues and opening week box office revenues are used as dependent variables ( REVENUE ). While much of the literature focuses on factors that determine total box office revenues, we also consider opening week revenues as these are particularly important in the Indian film market context given the rapidity with which films are pirated, with film revenues often falling rapidly within the first two weeks after they are released. Both variables are reported in 10 million Indian Rupees INR (crore) and converted into real values taking 2010 as the base year and using World Bank Consumer Prices Index (CPI) data from 2011 to 2015 to deflate the budget and box office revenue values during 2011–2015. Hence, the budget and revenue variables used are in 2010 constant prices. As is standard in the film industry literature, the revenue and budget variables are logged in the statistical analysis. In Sect.  2 , we highlighted that previously researchers have identified that box office revenues may not be normally distributed. We tested for skewness and kurtosis of our revenue variables. As anticipated, non-logged opening week and total revenues do suffer from kurtosis with values of 4.63 and 5.30, respectively. However, the skewness values are less concerning at 2.08 and 2.17 for opening week and total revenues, respectively. Reassuringly, the values for the logged variables indicate less of a problem with skewness and kurtosis, as the values for skewness are − 1.59 and − 1.08, and with kurtosis values of 2.61 and 1.23, again for opening week and total revenues, respectively.

As well as BUDGET , a number of further potential quality signals are used as explanatory variables. A dummy variable STARPOWER was created, indicating the reputation of the leading actor/actress. There are many rankings of Bollywood actors and actresses but the TIMES Celebex ranking is often considered the most well respected. The data of 2011 are not available as the ranking was only launched in 2012, Guptal ( 2015 ). Hence, we consider a film to have an actor/actress with star power if they have been in the TIMES Celebex ranking at any time during 2012–2015. There are approximately five actors and five actresses in the ranking in any one year, with persistence of actors and actresses in the top five ranking for at least one year and sometimes throughout the period 2012–2015. See Appendix 2 for a list of actors and actresses who are considered to have star power in our analysis. An alternative star power explanatory variable was also created, and rather than a dummy variable, this was a count variable of the number of films that the leading actor/actress had been in during their career, the data taken from www.imdb.com . This is in line with Chang and Ki ( 2005 ) and Fetscherin ( 2010 ) who similarly count the number of films an actor or actress has appeared in as a measure of star power. However, the coefficient on this variable was insignificantly different from zero; this also the case when the variable plus the squared variable were used reflecting a possible nonlinear relationship between star power and box office revenues. Hence, the statistical analysis continued using the dummy STARPOWER variable. As a further robustness check, the regression analysis was repeated using an interaction variable STARPOWER * ( logged ) BUDGET , but again, the coefficient on this explanatory variable was insignificantly different from zero so results with this interaction variable were excluded from the model. Again following Chang and Ki ( 2005 ) and Fetscherin ( 2010 ), a DIRECTORPOWER variable was created by counting the number of films a director had previously directed to reflect the star power and experience of a director.

Initially, regressions were run including a quality signal variable of Times of India critics’ film review scores, CRITIC . The reviews in this newspaper were selected as the Times of India is the largest selling English daily newspaper sold in India. However, this quality signal was not found to influence cinemagoers significantly and it was dropped from the analysis. Instead, mean online review scores for films posted on the internationally well-known website www.imdb.com were collected and used as an alternative quality signal, ONLINEREVIEW . Audiences will often submit online review scores very soon after films’ release that further potential audiences can access easily prior to deciding which films to watch at the cinema. Moon et al. ( 2010 ) conclude that for potential audiences, online reviews are a better quality signal than expert review scores.

A dummy variable, DISTRIBUTOR , was included to indicate a film distributed in India by one of the ten largest film distribution companies. However, given that the coefficient on this variable was consistently found to be insignificantly different from zero, alternative distributor dummy variables were also created. Given the traditional importance of family firms in the Indian economy, DISTRIBUTORFAM indicates that the distribution company is family owned, while DISTRIBUTORFAM10 indicates that a film was distributed by one of the ten largest distribution companies that are family owned. Only the coefficient on DISTRIBUTORFAM10 was ever found to be significantly different from zero so this is the variable used in the analysis below.

A SEASON dummy variable, taking the value unity when a film was released in India during key Muslim, Hindu and Christian festivals as well as around Independence Day, Republic Day and New Year, was also included to aid comparability with studies published that consider the impact on revenues of films released around major holiday periods. See Appendix 3 for details. Yet, again the coefficient on this explanatory variable was found to be insignificantly different from zero. Hence, alternative forms of this variable were considered. Successively, less important festivals were assigned a zero value, and the regressions rerun to test if the coefficient on the SEASON dummy variable became significantly different from zero. However, even when only the three largest festivals were assigned a value of unity, namely Christmas, Diwali and Eid, the coefficient on the SEASON dummy variable remains insignificantly different from zero. It is this final iteration of the SEASON dummy variable which is used in the results below. Both the DISTRIBUTOR and SEASON dummy variables can be considered potential quality signals as films expected to do well at the box office are likely to attract major distributors, with production and distribution companies keen to release films that they anticipate will do well around holiday periods.

Alternatively, potential film audiences may only have limited leisure time and have to select between alternative leisure activities. Izquierdo Sanchez et al. ( 2016 ) highlight that when key football tournaments take place, potential European cinema audiences may choose instead to watch football matches. Hence, to test whether potential Indian film audiences similarly select between cinema visits and the watching of major cricket matches, a dummy variable CRICKET is included that takes the value unity for films released during the Indian Premier League (IPL) season. Footnote 4

A set of genre dummy variables was included as these are commonly included in statistical analyses in the literature. For example, Elliott and Simmons ( 2008 ) conclude that, in the UK, films targeted at children do well, while Ravid and Basuroy ( 2004 ) consider US R-rated films, which are targeted at adult audiences. In our analysis, we categorised the films into the following genres COMEDY , DRAMA , ROMANCE and ACTION/THRILLER where COMEDY was treated as the control category of genre. Finally, a dummy SEQUEL variable was included, taking the value unity if a film was a sequel in a film franchise. Audiences may take a film’s sequel status as a signal of quality if they have enjoyed another film in a film franchise, while film production companies may be keen to invest in a sequel if a previous film in a franchise has had box office success.

Inspection of the correlation matrices indicated that multicollinearity is not expected to be a particular concern in the statistical analysis. Footnote 5 The lack of multicollinearity is confirmed in the results, Table  3 .

3.2 Methodology

The model first estimated was an ad hoc model with explanatory variables chosen following a thorough review of the film industry literature. Hence, we started by estimating the following equation:

for movie i, e denotes the error term.

Initially, the model was estimated using OLS, with robust standard errors to correct for potential heterscedasticity in the residuals. However, if there is endogeneity or reverse causality in the model, then the OLS results will be biased and unfit for drawing inferences. Potentially, there may be reverse causality from REVENUE towards BUDGET as production companies decide film budget levels partly on the expectation of film revenues that are likely to accrue. Further, as highlighted in Sect.  2 , it is generally accepted in the film industry literature that film budget may be a quality signal to potential audiences, with consumers believing that higher budget films are likely to be of higher quality. To test this hypothesis, we estimated the following model (Eq.  2 ) and employed the GMM C test to assess whether BUDGET is endogenous in our model.

where z denotes the residuals obtained from the estimation of Eq.  1 and u is the error term.

The quality signal CRITIC was dropped from the model because it is determined post-release of the movie and therefore cannot have any influence on the budget of a movie. We use STARPOWER and DIRECTORPOWER as the instruments of BUDGET as the estimation results for Eq.  1 indicated that neither of these variables affect REVENUE directly. Both variables may impact on film budget as it may be more expensive to employ film stars and/or a director with greater previous experience.

The results indicated that BUDGET is indeed endogenous, and so, to control for this issue, we continued our estimation of the model in Eq.  1 by employing the instrumental variable general method of moments (GMM) approach. Footnote 6 Both the OLS and GMM estimation results indicated that expert critic ratings have no effect on the box office revenues of films. Hence, in the results reported in Sect.  4 , the CRITIC variable is replaced with the ONLINEREVIEW variable as a quality signal outside the control of film production companies. Finally, note that the methods proposed by Collin et al. ( 2002 ); De Vany and Walls ( 1999 , 2002 ) and Walls ( 2005 ) for dealing with non-normally distributed film revenues have the cost attached that because of computational complexity, it is only possible to estimate a reduced form model rather than a structural model. As we are able to confirm that BUDGET is in fact endogenously determined, and because the skewness and kurtosis values for our logged REVENUE variables do not give cause for major concern, we are reassured that it is appropriate to use the GMM approach, the results of which are reported in Sect.  4 .

4 Econometric results

Regression results for the key film box office revenue dependent variables are reported in Table  3 , controlling for endogeneity as outlined in the section above. The first thing to note is the robustness of the results, regardless of whether total or opening week box office revenues are selected as the dependent variable. This result is in line with expectations given the short period of time that films remain popular on Indian cinema screens, and the rapidity with which films are pirated. The Hansen J statistic indicates that the instruments are valid as are the over-identification restrictions.

The magnitude of a film’s budget is found to be key to a film’s success: budget has a positive, significant effect on both opening week box office revenues and total revenues. A film’s budget is one potential quality signal to consumers of the film’s quality. Unlike for many goods, film companies are often happy to reveal large budgets attached to film production. A large budget indicates a production company’s faith in the quality of film being produced, and film budget data are routinely reported on websites such as www.imdb.com . Indian film budgets typically remain low compared to those of Hollywood films (Balasubramanyam 2009 ), but as in previous USA and UK studies, a higher budget is associated with film box office success.

The other signal of quality that is found to be a crucial influencer is the mean of online review scores posted by the general public on www.imdb.com . Again, the coefficient on this variable is always positive and significant, regardless of whether we consider its impact on opening week revenues or total revenues. This impact of online reviews is in line with that found in Elliott et al. ( 2018 ) in the Chinese film market context and highlights the importance for production and distribution companies of establishing and maintaining effective communication with potential audiences.

No consensus has been reached in the literature to date on whether films enjoy greater revenues if they are released around major holidays. For example, Litman ( 1983 ) suggests that Christmas is the optimum time to release a film in the USA, while Sochay ( 1994 ) concludes that the summer holiday period is preferred, with Brewer et al. ( 2009 ) supporting this result but also indicating that in the US films perform better if they are released around Thanksgiving. Our results indicate that release date has no significant impact on a film’s box office revenues in the Indian market despite testing various formulations of the SEASON dummy variable. This may reflect the diversity of holiday dates in the Indian calendar as highlighted in Appendix 3. Similarly, in the econometric analysis, we test whether films perform significantly worse if released in the IPL season but find no significant evidence of this. This result indicates that Indian audiences do not substitute between viewing films at the cinema and watching major cricket matches.

If a film is a sequel, our results suggest that this may have a limited, positive impact on a film’s success. Consequently, a film’s sequel status may be a weak indicator of film quality. Meanwhile, while films distributed by an Indian top ten distribution company that is family owned may not perform significantly better in terms of total box office revenues, they enjoy greater box office success in the opening week of general Indian release, at least at a ten per cent significant level. This suggests that until film quality information is spread through word-of-mouth, for example, through IMDB online reviews, the distributor of films can have some impact on opening week revenues.

Finally, the coefficients on the genre dummy variables indicate that films that can be categorised as romantic, action/thriller or drama all perform significantly worse in terms of box office revenues than the excluded film category – comedies.

Note that in initial regressions, the use of star actors and actresses and major distributors were not found to impact significantly on box office revenues, even though both may be considered as potential signals of a film’s quality. These variables have been found to have a positive, significant effect on revenues in a number of country contexts previously, see for example Elliott et al. ( 2018 ). Nevertheless, the result in the current analysis is in line with that of Fetscherin ( 2010 ) who also considers the Indian market. This result is encouraging as it indicates that two potential barriers to entry, namely the use of costly stars and distributors, are not important in the Indian film industry context.

The analysis above contributes directly to our understanding of the question posed in this research paper, namely what factors contribute to a film’s box office success in the Indian market. Results suggest that two factors are key and are signals of film quality, namely film budget and online review scores. The first, but not the second, is under the control of film production companies.

5 Discussion and conclusions

Higher film budgets and better online reviews result in higher Indian box office revenues for Bollywood films, a result in line with conclusions previously drawn for the Chinese film market, Elliott et al. ( 2018 ). Comparisons between the Indian and Chinese film markets are arguably appropriate as there are similarities between these two major Asian economies that similarly have liberalised in recent years, both subsequently enjoying substantial economic growth. However, while economic growth is associated with greater spending power at least for many consumers, demand for Indian films at Indian cinemas has stagnated, while in China, it has flourished. Other potential quality signals found to impact on Chinese film revenues, namely the production of sequels, the use of stars and major distribution companies, do not significantly affect Indian film revenues. These results may be indicative of a potentially competitive Indian film industry. While admittedly large film budget is important for box office success, production firms including new entrants do not need to rely on the use of stars or major distribution companies. This is reassuring as it is potentially more difficult for new production company market entrants to attract stars and major distribution companies to film projects. Note that Balasubramanyam ( 2009 ) also highlights the competitive nature of the Indian film industry, but rather indicates the lack of horizontal and vertical integration, the importance of family firms and the industry’s spread across the country. Yet, the competitive nature of the Indian film industry does not explain its recent difficulties, namely falling cinema attendance. As Elliott et al. ( 2018 ) note, the Chinese film industry is also increasingly becoming competitive.

Our results indicate that production firms should work to obtain financial backing for film projects, be that domestic funding or international funding, including from major Hollywood firms. Yet, while a competitive market is typically seen as advantageous as firms compete to satisfy consumer demands, can learn from each other, and have an incentive to produce efficiently, Indian film production companies should also consider whether too many films are currently being produced. This is particularly pertinent as consumer demand for watching films at the cinema is declining. Arguably fewer, larger budget, films should be produced with production companies focusing on producing higher quality films more likely to attract positive online reviews. Any marketing activities that may encourage positive online reviews should also be considered, and firms need to be aware that comedy films appear to be most popular with Indian audiences. Meanwhile, the timing of the release of a film in India appears irrelevant to its box office success. This may be explained by the diversity of audiences who speak different languages, and who celebrate festivals associated with different religions.

Producing films that can be exported successfully to increasingly sophisticated and better off diaspora is likely to remain a sensible strategy for film production companies, particularly as the Indian diaspora are believed to exceed 20 million, Jones et al. ( 2008 ). This strategy for Indian films has already been adopted, with the USA and UK being profitable export markets for Indian films, Eliashberg et al. ( 2006 ); Fetscherin ( 2010 ). However, exports of Indian films only make up approximately 10% of box office revenues and this figure has fallen slightly in recent years from 10.6% in 2010 to 9.2% in 2014. Footnote 7 Examples of particularly successful Bollywood films overseas include Monsoon Wedding (2001) and Slumdog Millionaire (2008). These are both relatively large budget films so reinforcing the importance of greater film budgets. The Industrial Development Bank of India (IDBI) set up India’s first major film fund in 2002, Jones et al. ( 2008 ). This initiative is important, but further large-scale investment is required, and the likelihood is that this will partly be funded by overseas, probably Hollywood investment, as well as domestic funds.

Nevertheless, attempts by Hollywood to produce films for domestic audiences have not always been successful, with locally produced films performing better at the box office. Even using Indian casts, crew and directors, it appears that US-backed films can struggle with possible ‘cultural discount’. The cultural discount hypothesis has already received support in other East Asian film markets, as discussed by, for example, Lee ( 2006 , 2009 ), Fu and Lee ( 2008 ), Moon et al. ( 2015 ). Our results above highlight the importance of film budgets, with higher budget films performing significantly better at the Indian box office. Then, the challenge will be to produce films that appeal to domestic audiences, and to do this, the notion of cultural discount will have to be at the forefront of producers’ concerns. The issue of cultural distance is complicated in the case of India by the number of languages in which films are produced, for example with films regularly made in the Bengali; Bhojpuri; Hindi; Kannada; Tamil; and Telugu languages, but the lack of a common language immediately limits the appeal of films to some Indian audiences, creating cultural distance even within country boundaries. Nevertheless, revenues are made from dubbing films into alternative local languages, or by remaking films with regional stars (Balasubramanyam 2009 ).

The challenge of producing big budget films that appeal to audiences is further complicated as evidence suggests that Indian film audiences are becoming increasingly sophisticated in their tastes, for example rejecting more formulaic plots. Meanwhile, middle-class film viewers are increasingly choosing to watch films in the comfort of their homes via cable television subscriptions or the Internet. Footnote 8

The statistical analysis above has a number of limitations, all reflecting data availability problems. Data are restricted to Bollywood films, although results are expected to be comparable for the film industry more broadly across India. This is an area for future research. Data could not be obtained on film advertising expenditures, the number of screens on which films are exhibited in their opening week, major Indian film award nominations both in India and internationally, and any enhanced format features of films released in India. Nevertheless, results for the Chinese film market reported by Elliott et al. ( 2018 ) indicate that enhanced format films are likely to become increasingly important in attempts to attract cinema audiences as well as to deter piracy. Consequently, yet again the importance of large film budgets is highlighted as the production of 3D or IMAX films is particularly costly.

Ultimately, the Indian film industry has faced increasing difficulties in recent years: even increased investment in the film industry by large Hollywood companies and the release in India of internationally produced films has not stemmed falls in audience numbers. The industry is competitive with the capacity to benefit local economies across the country, but the key theme to emerge from this analysis is that funding for large budget films is crucial, and in the future, this may include funding for more enhanced format films. Other mature film markets across the world including in the USA and UK are also struggling to attract cinema audiences, and as a result, film markets are increasingly competitive, with competition from internationally as well as domestically produced films. The Indian film industry cannot afford to delay investments. The continued importance of the Indian film industry cannot be overstated. Governments, both state level and national, must consider whether their policies are sufficient to encourage film funding, should question whether entertainment taxes stifle investment and if policies are sufficiently effective in combating film piracy.

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Acknowledgements

The authors are grateful to participants at the British Northern Universities India Forum workshop at the University of Bradford, March 2016, and the International Symposium on Innovation, Catch-Up and Internationalisation: Comparative Studies of China, India and other Emerging Economies, Southwestern University of Finance and Economics, Chengdu, July 2016, for very valuable comments and suggestions.

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Appendix 1: Variable information

Appendix 2: star actors and actresses.

  • Full year data on actresses in the ranking were not provided until 2013

Appendix 3: List of festivals and holidays

  • Where not specified the date of festivals varies from year to year

Appendix 4: OLS regression results

  • Multicollinearity is confirmed not to be an issue as we obtained mean VIF scores of 1.3 and 1.32 with total revenue and opening week revenue, respectively, as dependent variables (both VIF scores considerably lower than 10)
  • Robust standard errors in brackets; * p  < 0.10; ** p  < 0.05; *** p  < 0.01

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Dastidar, S.G., Elliott, C. The Indian film industry in a changing international market. J Cult Econ 44 , 97–116 (2020). https://doi.org/10.1007/s10824-019-09351-6

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Ukraine Industry Strikes Defiant Note in Berlin Two Years After Russian Invasion: ‘War Is Our Reality. It Is Not Our Identity’

By Christopher Vourlias

Christopher Vourlias

  • Abel Ferrara Talks Ukraine War Documentary ‘Turn in the Wound,’ the Nature of Evil and Why the Conflict Isn’t ‘Yesterday’s News’ 2 days ago
  • Omar Sy-Starring Berlin Premiere ‘The Strangers’ Case’ Depicts Many Faces of Refugee Crisis 4 days ago
  • Oscar-Nominated ‘Timbuktu’ Director Abderrahmane Sissako on Being ‘Free to Dream’ in Berlin Competition Romantic Drama ‘Black Tea’ 4 days ago

The Editorial Office

The Russian invasion of Ukraine will mark its second somber anniversary next week, though in recent months the conflict has been pushed from the headlines in the wake of the Israel-Hamas war. But with Ukrainian president Volodymyr Zelenskyy arriving in the German capital on Friday in an effort to shore up flagging European support for his country’s defense, Ukrainian film professionals at the Berlin Film Festival are determined not to quietly disappear from the global stage.

An air raid siren went off on Oksana Zabuzhko's phone during a press conference at Berlinale. The Ukrainian writer is a member of the main jury of the Berlin International Film Festival, which opened yesterday, 15th of February. Oksana Zabuzhko explained to those present that… https://t.co/VAEFycP6Y1 pic.twitter.com/Y1fvHhuc44 — Anton Gerashchenko (@Gerashchenko_en) February 16, 2024

As the international industry’s first major confab of 2024 kicks off this week, Ukrainian film professionals remain steadfast and defiant despite the physical, emotional and economic toll of the war with Russia. At the European Film Market , which takes place from Feb. 15-21, they are on a mission “to tell the world who we are and what we can do,” says Victoria Yarmoshcuk, CEO of production powerhouse Film.UA, insisting: “We never asked for help. We just want to be heard.”

Three Ukrainian feature films will be screening at this year’s Berlin Film Festival: Svitlana Lishchynska’s “A Bit of a Stranger,” premiering in the Panorama Dokumente section, as well as Oksana Karpovych’s “Intercepted” and Roman Bondarchuk’s “The Editorial Office,” both playing in Forum.

Meanwhile, one year after Sean Penn used Berlin as a launching pad for his Volodymyr Zelenskyy doc “Superpower,” another U.S. filmmaker, Abel Ferrara, will be unveiling his own Ukraine war documentary, “Turn in the Wound,” as part of the Berlinale Special lineup, in which the director attempts to answer urgent questions at the heart of the current conflict. “Where does this kind of evil come from? Where does this kind of violence come from?” Ferrara asks.

Lukich, whose last film, “Luxembourg, Luxembourg,” played at the Venice Film Festival, says he’s heartened by the ongoing solidarity shown to the Ukrainian industry by partners across the continent. “I’m proud that I’m a part of this European society of filmmakers who really stand for something, that unites more than divides,” he says.

A show of international support

International support has been crucial to shoring up the depleted Ukrainian biz. Public financing has been entirely diverted to the war effort, hampering domestic production and complicating co-production possibilities. Amid wavering morale, “the main challenge now is to keep the spirit of the team [high],” says Daria Leygonie-Fialko, founder of the TV production company SPACE and co-founder of the Organization of Ukrainian Producers (OUP).

Leygonie-Fialko’s Kyiv-based production outfit has nevertheless managed to keep cameras rolling throughout the war, producing 250 episodes of its top three shows in 2023. Its two latest series, the detective procedural “Bloodline” and the mystical drama “Sofia,” showcase how the company is looking to bolster its offerings with stories that can appeal not only to war-weary Ukrainians, but to foreign buyers for whom, however compassionate, a degree of fatigue may be setting in.

The market for non-fiction content from Ukraine plummeted following the Oct. 7 terrorist attacks in Israel, according to Igor Storchak, co-founder of the Organization of Ukrainian Producers (OUP). The producer was forced to shelve five documentary projects in advanced stages of development when his international partners pulled out, citing a shift in interest away from the two-year-old conflict.

Yet a string of recent deals offers an encouraging sign that the appetite for Ukrainian content is still robust. Dark Star Pictures acquired North American rights to the war thriller “Stay Online” and is planning a U.S. release later this year, while HBO CEE swooped in on the psychological thriller “Between Us.” Beta Film, meanwhile, closed a raft of territories on Starlight Media and Gaumont’s “In Her Car,” a drama series that will world premiere Feb. 19 during a special event in Berlin.

Meanwhile, Film.UA secured distribution across key territories for its animated feature “Mavka,” which grossed nearly $40 million at the global box office last year. The diverse slate the company is presenting to buyers at this year’s EFM, including its first horror film, “Witch of Konotop,” underscores Film.UA topper Yarmoshcuk’s conviction that the market is “just interested in good stories,” regardless of where they come from. “War is our reality,” she says, “but it is not our identity.”

Imagining life after the war

Once the “shock” of the Russian invasion wore off, Gornostai scrapped the project she had been developing and revisited “Antonivka,” a story about a young couple reckoning with an aging loved one’s mortality that is now set in an imagined future after the war’s conclusion.

“To simply write about the war, which has not yet ended, I had neither the strength nor the knowledge,” she says. “Therefore, I tried to imagine my idea in the conditions ‘after the war,’ thus bringing its sooner end into reality, as well as examining its already visible impact.”

Bondarchuk, whose Forum premiere “The Editorial Office” is set in Kherson on the eve of the Russian invasion, likewise added a coda to his movie — which was shot before the conflict began — that imagines the day when Ukrainian forces have emerged victorious. “We decided to move the film to the near future because I believe that films can influence reality, and this epilogue is a kind of contribution to bringing our victory closer,” he says.

The war’s impact nevertheless loomed large over the production: Editor Viktor Onysko and actor Vasyl Kukharskyi were both killed in action fighting Russian forces, while leading man Dmytro Bahnenko was recently called to the frontlines.

Bondarchuk is nevertheless hopeful that Bahnenko will be in attendance on the day of the film’s world premiere. “We hope to bring about 20 people from the Ukrainian team,” he says. “All of these people now live in different countries, some even in Berlin itself, and it will be a very heartwarming opportunity for all of us to see each other for the first time in two years.”

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A Film Festival in the Back of a Taxi

The TaxiFilmFest is partly a protest over the miserable state of Berlin’s taxi industry. But it’s also a celebration of the cab’s iconic place in the urban cultural landscape.

A group of men and women, wearing winter coats, hats and scarves, watch a movie on a TV in the back of a taxi van.

By Bryn Stole

Reporting from Berlin

Some of international cinema’s biggest names gathered on Tuesday night at the Berlin International Film Festival as the event honored Martin Scorsese with a lifetime achievement award. Before accepting his trophy, Scorsese listened as the German director Wim Wenders gave a laudatory speech to an audience including celebrities and local dignitaries.

Just around the corner, parked in the middle of a busy thoroughfare, a group of Berlin’s taxi drivers crammed into the back of a worn-out taxi van to watch a double-feature capped by Scorsese’s 1976 movie “Taxi Driver.”

Klaus Meier, who has been driving a cab in Berlin since 1985, handed out bottles of soda and beer, popping the caps with the blade of a pocketknife. Irene Jaxtheimer, who runs a taxi company, passed around homemade popcorn. A generator outside the cab powered a modest television, a DVD player and a small electric heater.

The unconventional screening, just outside a centerpiece event for one of Europe’s most prestigious film festivals, was part of the makeshift TaxiFilmFest . Running through Sunday, it is partly a protest over the miserable state of the taxi industry these days and partly a counterfestival to celebrate the taxi cab’s iconic place in the urban cultural landscape.

It’s also in objection to an exclusive partnership deal between the festival, known locally as the Berlinale, and the ride-hailing giant Uber to ferry filmmakers between the city’s movie theaters during the event. The deep-pocketed Silicon Valley company has drawn the ire of traditional cabdrivers the world over, and the protesters who packed in for the TaxiFilmFest screenings were railing against what they see as a too lightly regulated rival.

Beeping horns from the busy street outside — some of them coming from sleek black Uber vehicles emblazoned with the Berlinale logo — blended with the street scenes from “Taxi Driver” playing on the tinny television speakers. “Ah, I really miss those mechanical fare boxes!” Meier said as the fares ticked away in the onscreen cab of the movie’s unhinged antihero, Travis Bickle, who drives around mid-’70s New York with growing hatred and menace.

The back-seat festival is showing only taxi-themed flicks, and the potential repertoire is deep. Meier polled friends and fellow taxi drivers about which films to show, and said he had received dozens of suggestions about movies in which a cab plays a starring role.

The early feature on Tuesday was Barry Greenwald’s 1982 quirky slice-of-life documentary “Taxi!” about some odd characters driving cabs in Toronto. The previous evening, a small rotating crowd beat the rain to catch portions of the 1998 French action-comedy “Taxi,” a lighthearted flick from the director Gérard Pirès about sinister, Mercedes-driving German gangsters, hapless Marseilles cops and a lead-footed rookie cabdriver who turns out to be the only person fast enough to catch the criminals.

An early hit at the TaxiFilmFestival, which kicked off last Thursday, was “ Under the Bombs ,” a Lebanese drama set during the 2006 conflict between Hezbollah and Israel. In the movie, a Beirut taxi driver is hired to drive a woman into the war-torn south of Lebanon in hopes of finding her sister and son. Meier described it as “Shakespearean” and “a masterpiece,” and Berndt said it was clearly the “most moving taxi film” he’d ever seen.

But the clear favorite among attendees was Jim Jarmusch’s “Night on Earth,” a quirky, episodic 1991 film about taxi drivers and passengers in five cities around the world. The selection for TaxiFilmFest’s Sunday night finale had yet to be chosen, and Meier said he remained open to suggestions.

Between screenings, the taxi drivers lamented their industry’s many woes, which they blamed in large part on Uber and other multinational ride-hailing apps. Tightly regulated local taxis with fixed fares are struggling against upstart competitors that pay lower wages, they said.

Tobias Froehlich, an Uber spokesman, disputed the idea that Uber was responsible for the rough state of Germany’s taxi industry, and said that Uber drivers, too, had become part of street life in German cities. “Taxis are in a deep crisis almost everywhere, even in cities where Uber is not active at all,” he said.

The classic German taxi is as instantly recognizable and distinctive as its checkered-yellow counterpart in New York or London’s iconic black cabs. Traditionally a hefty Mercedes E-Class sedan, German taxis are painted a particular, subtle and yet somehow unmissable beige — officially “light ivory,” or number 1015 on the RAL color chart, a shade mandated in 1971 by West Germany’s transportation ministry.

The festival attendees, squeezed into the back of the van on Tuesday, also reminisced about better days for taxi driving, such as ferrying around American and British soldiers from the occupying Allies stationed in West Berlin. (The French troops, the small crowd agreed, had less cash and rarely hailed cabs.)

Another taxi driver who stopped by on Monday night, Michael Klewer, got his start in 1988 in East Berlin, driving a beat-up Trabant as a black-market cab. (Consensus: East Berliners tipped better.)

The days before the fall of the Berlin Wall were “blissful times, hard to even imagine anymore,” said Stephan Berndt, a Berlin taxi operator who now runs a company with about 50 drivers but started driving taxis in 1980s West Berlin to pay his way through university.

At the time, a student could make ends meet by driving just a couple of shifts per week, he said. Now, margins were tightly squeezed, he said, ramping up pressure on taxi drivers just to break even.

He said he also worried about the vanishing cultural importance of the iconic taxi, and the oddball cast of characters who have long made a living as drivers. If taxis were to disappear from Berlin’s streets, Berndt said, “a huge piece of a city’s culture would fall by the wayside. All that flair — which is why I love this job so much — would be completely lost.”

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