February 2021
Inclusion in Netflix Original U.S.
Scripted Series & Films
Dr. Stacy L. Smith, Dr. Katherine Pieper, Marc Choueiti,
Kevin Yao, Ariana Case,
Karla Hernandez & Zoe Moore
FULL REPORT
Page 1
The purpose of this paper was to overview inclusion on screen (i.e., gender, race/ethnicity, LGBTQ, disability) and behind the
camera (i.e., directors, writers, producers) across live action Netflix U.S. original fictional films and scripted series.
1
The sample
included 126 movies and 180 scripted series released by the entertainment company during the 2018 and 2019 calendar years.
Below, the results are presented by inclusion indicator (i.e., gender, race/ethnicity, LGBTQ, disability).
2
On certain measures, the
inclusion profile of Netflix films and series was evaluated over time (2018 vs. 2019) and against industry metrics. Throughout the
paper, only differences of 5 percentage points or greater were deemed significant. This approach was consistent with our previ-
ous work and used to avoid making noise about meaningless deviation (1-2%).
3
The letter “n” appears throughout the document,
which illuminates sample size per row, column or cell analyzed.
GENDER
Within this section, we assess the distribution of gender on screen and behind the camera across two years of Netflix films and
series. For some indicators, we not only compare males to females but also look at gender differences yearly (2018 vs. 2019) and
within medium (film vs. scripted series). Industry comparisons were provided on certain measures by utilizing our own top-gross-
ing film reports as well as studies on scripted or episodic content by other scholars and groups.
On Screen
On screen characters were assessed for gender in multiple ways. First, we looked at who drove the storyline. In film, this was the
number of movies with a female-identified lead/co lead that centered the unfolding narrative. To create a comparable measure
for scripted series, we looked at the number of shows where girls and women were 50% or more of series regulars.
4
Overall, a total of 52% (n=157) of all Netflix films and scripted series were driven by girls/women which was just over U.S. Census
(50.8%).
5
Series (54.5%, n=96) significantly outperformed film (48.4%, n=61) in the number and percentage of shows with fe-
male-identified leads/co leads, however (see Table 1). Differences also emerged by year, as 2019 (55.2%, n=85) featured a higher
percentage of storylines with women and girls than did 2018 (48.6%, n=72).
Not surprisingly, a notable pattern was observed by crossing medium (film vs. series) and year (2018 vs. 2019). Just over half of all
films in 2019 (50.9%, n=29) depicted a female lead/co lead, which was up from 2018 (46.4%, n=32). In series, the uptick was more
pronounced and significant (7.1 percentage points). Fifty-eight percent of scripted shows starred female-identified protagonists
in 2019 (57.7%, n=56) and 50.6% (n=40) in 2018.
Clearly, Netflix storytelling was strongly grounded in gender equality during the time frame evaluated. When we look to see how
Netflix stacked up against the broader industry, the company outperformed top-grossing films. As shown in Table 2, the percent-
age of female leads/co leads in Netflix films (48.4%, n=61) was significantly higher than two years of the most popular movies at
the box office (41%, n=82).
6
This advantage was demonstrated in both years evaluated (see Table 2). For series, a comparable
industry statistic for leads was not available and thus that analysis was not conducted.
TABLE 1
PERCENTAGE OF FEMALE-IDENTIFIED LEADS/CO LEADS BY STORYTELLING MEDIUM AND YEAR
FILM
% of female-identified
leads/co leads
PERCENTAGE OF FEMALE-IDENTIFIED LEADS/CO LEADS BY STORYTELLING PLATFORM AND YEAR
TOTAL
2018
2019
2018
2019
TOTAL
46.4% 50.9% 50.6% 57.7%
52%
(n=32) (n=29) (n=40) (n=56) (n=157)
48.4%
(n=61)
SERIES
54.5%
(n=96)
TABLE 1
MEASURE
% of female-identified
leads/co leads
52%
57.7%
50.9%
2018
2019
TOTAL
(n=29)
(n=56)
(n=157)
48.4%
(n=61)
54.5%
(n=96)
INDICATOR
46.4%
(n=32)
2018
2019
50.6%
(n=40)
TOTAL
FILM SERIES
Note: Leads represent the central figure(s) across a story’s plot. In film, two equal protagonists sharing the
central journey of the story were counted as co leads. Scripted series with 50% or more female-identified
series regulars were included as having female leads/co leads.
INCLUSION IN NETFLIX ORIGINAL U.S. SCRIPTED FILMS & SERIES
Page 2
Second, we examined the main cast in films and scripted series. Main cast involved series regulars (i.e., recurring roles across a
season as defined by Variety Insight) in scripted shows or primary and secondary roles that drive the plot in movies. For movies,
the main cast was retrieved from the landing page of each film on the Netflix website. It is important to note that the main cast
analysis was different from leads/co leads as we used the character as the unit of analysis in the former analysis and the entire
story in the latter. By focusing on main cast characters, our sample size was much larger than the leads/co leads analysis.
TABLE 2
PERCENTAGE OF FEMALE-IDENTIFIED LEADS/CO LEADS IN NETFLIX AND TOP-GROSSING FILMS BY YEAR
Note: A total of 126 Netflix films were included in the analysis and 100 top-grossing fictional films in 2018 and 2019. Both
of these samples had movies driven by ensemble casts. While the ensemble films were counted in the sample total, they
could not qualify as female led or not. This latter distinction was reserved for films driven by only one or two (equal) pro-
tagonists.
% of Netflix films w/female-
identified leads/co leads
41%
48.4%
43%
50.9%
39%
46.4%
2018 2019 TOTAL
% of top-grossing films w/female-
identified leads/co leads
(n=32) (n=29) (n=61)
(n=39) (n=43) (n=82)
INDICATOR
PERCENTAGE OF FEMALE-IDENTIFIED LEADS/CO LEADS IN NETFLIX AND TOP-GROSSING FILMS BY YEAR
TABLE 2
TABLE 3
PERCENTAGE OF FEMALE-IDENTIFIED MAIN CAST BY STORYTELLING MEDIUM AND YEAR
FILM
% of female-identified
main cast
TOTAL
2018
2019
2018
2019
TOTAL
38.9% 41.1% 46.4% 48%
44.2%
(n=215) (n=195) (n=301) (n=357) (n=1,068)
39.9%
(n=410)
SERIES
47.3%
(n=658)
TABLE 3
MEASURE
Of the 2,420 main cast characters, 55.8% (n=1,351) were male and 44.1% (n=1,068) were female. Only 1 character within the
main cast analysis was non binary and thus was excluded from the remaining statistics. A higher percentage of female main cast
members were found in series (47.3%, n=658) than in film (39.9%, n=410). A non-meaningful difference was observed from 2018
(42.9%, n=516) to 2019 (45.4%, n=552). No significant patterns emerged when crossing medium by year (see Table 3). Given these
findings, Netflix approached proportional representation with U.S. Census in scripted series in 2018 and 2019 but in neither year
in film.
Lastly, we looked at the percentage of speaking and named characters by gender. A total of 12,168 speaking and named charac-
ters were evaluated across the 126 films (n=4,696) and 172 scripted series (n=7,472). A full 61.2% (n=7,441) were male characters,
38.8% (n=4,724) were female and <1% (n=3) were non binary. Due to the small sample size, the 3 non-binary characters (all in
scripted content) were excluded in the subsequent analyses.
TABLE 4
PERCENTAGE OF FEMALE-IDENTIFIED SPEAKING CHARACTERS BY STORYTELLING MEDIUM AND YEAR
FILM
% of female-identified
speaking characters
TOTAL
2018
2019
2018
2019
TOTAL
35.8% 36.5% 39.9% 41.1%
38.8%
(n=898) (n=799) (n=1,364) (n=1,663) (n=4,724)
36.1%
(n=1,697)
SERIES
40.5%
(n=3,027)
TABLE 4
MEASURE
Page 3
Speaking characters’ gender did not vary by medium (film vs. series) or year (2018 vs. 2019). As shown in Table 4, 36.1% (n=1,697)
of all speaking characters were girls and women in movies and 40.5% (n=3,027) in series. How did Netflix compare with the broad-
er entertainment ecosystem of female-identified speaking characters? As shown in Table 5, the percentage of girls and women on
screen in Netflix films (2018=35.8%, 2019=36.5%) was slightly but non meaningfully higher than the percentage in top-grossing
films in 2018 (33.1%) and 2019 (34%). Looking to series, Netflix was on par with the broader industry in 2018 (39.9% vs. 40%) but
still slightly below industry performance in 2019 (41.1% vs. 45%; see Table 5).
While the aforementioned paragraph illuminates the overall percentage of female-identified speaking and named characters, it
tells us little about how portrayals were distributed across the entire sample. To gauge the diffusion of girls and women on screen,
we asked two additional questions at the film and scripted series level: 1) how many stories featured a gender-balanced cast? and
2) how many narratives erased girls and women on screen? The former tapped proportional representation with U.S. Census
(50.8%) and the latter tapped invisibility or erasure.
8
A gender-balanced story was defined as featuring girls and women in 45.7% to 55.9% of all speaking or named roles.
9
As shown
in Table 6, 19.9% (n=61) of all stories depicted a gender-balanced narrative. Little deviation emerged by medium, as 16.7% (n=21)
of films and 20% (n=36) of scripted series portrayed proportional representation. Though shy of the significance criterion (e.g., 5
percentage point difference), films in 2019 (19.3%, n=11) tended to show more gender balance on screen than films in 2018 (14.5%,
n=10). There was no meaningful increase observed for gender-balanced casts in series. It is important to note that 15 movies and
23 scripted series across the entire sample exceeded proportional representation. No series or films excluded or rendered girls/
women invisible on screen. Put differently, every story featured at least one girl or woman speaking or named character on screen
across 2018 and 2019 content.
TABLE 5
PERCENTAGE OF FEMALE-IDENTIFIED SPEAKING CHARACTERS IN NETFLIX AND INDUSTRY
BY STORYTELLING MEDIUM AND YEAR
MEASURE
2019 2018 2019
% of female-identified speaking
characters Netflix
% of female-identified speaking
characters industry
TABLE 5
2018
35.8%
(n=898)
33.1%
FILM SERIES
36.5%
(n=799)
34%
39.9%
(n=1,364)
40%
41.1%
(n=1,663)
45%
Note: The film industry comparisons are from the Annenberg Inclusion Initiative report.
7
For scripted series, the percentages were
derived from San Diego State University (SDSU). Using the SDSU research, the 2019 cell is reflective of the 2018/19 season whereas the
2018 entry is reflective of the 2017/18 season.
TABLE 6
GENDER-BALANCE BY STORYTELLING MEDIUM AND YEAR
TOTAL
14.5%
OR
FILMS
19.3%
OR
FILMS
21%
OR
SERIES
18.8%
OR
SERIES
19.9%
OR
STORIES
8 Films
exceeded
proportional
representation
7 films
exceeded
proportional
representation
39 stories
exceeded
proportional
representation
11 series
exceeded
proportional
representation
12 series
exceeded
proportional
representation
TABLE 6
FILM SERIES
ACROSS YEARS, .% OR FILMS AND SCRIPTED SERIES MET OR EXCEEDED PROPORTIONAL REPRESENTATION
Note: A gender balanced cast featured girls and/or women in 45.7% to 55.9% of all speaking or named roles. This is a confidence
interval of 10% around the U.S. Census percentage of 50.8%.
Page 4
Behind the Camera
Given the findings above, we were curious about the frequency of women working behind the camera across Netflix films and
scripted series. For this section, the analytics were handled differently for film and series. Above-the-line personnel were assessed
at the episode level for scripted series but the story level for film. Using this approach, the sample sizes for specific roles behind
the camera in series (i.e., director, writer) were quite large and thus would skew or mask overall and film-based findings. As such,
we reported results for each medium separately.
Film. A total of 763 above-the-line personnel (i.e., directors, writers, producers) were evaluated across the 126 movies.
10
Looking
at specific positions, a total of 130 directors helmed the movie sample. A full 76.9% of directors were men (n=100) and 23.1%
(n=30) were women (see Table 7), which translated to a gender ratio of 3.3 male helmers to every 1 female. The percentage of
women directors non meaningfully decreased from 2018 (25%, n=18) to 2019 (20.7%, n=12). Interestingly, no women directors
were hired to helm horror or sci-fi/fantasy films across the 2-year movie sample.
How did Netflix employment statistics of women helmers compare to those found across the 100 top-grossing films of 2018 and
2019? As shown in Figure 1, Netflix films (2018=25%, n=18, 2019=20.7%, n=12) featured a substantially higher percentage of wom-
en directors than did top-grossing fictional films (2018=4.5%, n=5, 2019=10.7%, n=12) across the sample time frame.
Pivoting to screenwriters, a total of 202 were credited across the 126 movies. Seventy-five percent of writers were men (74.8%,
n=151) and 25% were women (25.2%, n=51). As shown in Table 8, the percentage and number of women writers was non-mean-
ingfully lower in 2019 (23%, n=23) than in 2018 (27.5%,
n=28). Netflix had a significantly higher percentage of
women screenwriters (25.2%, n=51) than did top-gross-
ing films overall (16.7%, n=73), however. Women writers
of Netflix films were more prevalent in 2018 (27.5%, n=28)
than were women writers of top-grossing movies (13.7%,
n=31). The gap reduced to non significance in 2019, howev-
er (Netflix women writers=23%, n=23, top-grossing wom-
en writers=19.8%, n=42).
In addition to directors and writers, producing credits were
assessed for gender. Only creatives credited as Producers
(e.g., Produced by) without any modifiers (e.g., Executive,
Co, Associate) were included in the analysis. This narrow
approach was taken purposely, to focus on those individu-
als responsible for films coming in on time and within bud-
get.
Summing up, Netflix films and series depicted female-identified leads/co leads consistent with or above U.S. population norms.
However, the percentage of females in the main cast and across all speaking or named characters decreased linearly from leads/
co leads. These latter findings represent a missed opportunity, ensuring that main roles as well as those characters with a few lines
or less reflect the broader representational ecosystem where girls and women take up half of the space of humanity.
TABLE 7
ABOVE-THE-LINE PERSONNEL IN FILM BY GENDER
MEASURES MEN WOMEN TOTAL
76.9% (n=100)
74.8% (n=151)
71% (n=306)
73% (n=557)
23.1% (n=30)
25.2% (n=51)
29% (n=125)
27% (n=206)
130
202
431
763
Directors
Writers
Producers
Total
TABLE 7
FIGURE 1
PERCENTAGE OF WOMEN DIRECTORS IN NETFLIX AND
TOP-GROSSING FILMS
25%
10.7%
20.7%
4.5%
Netflix Films
Top-Grossing Films
PERCENTAGE OF WOMEN DIRECTORS IN NETFLIX AND TOP-GROSSING FILMS BY YEAR
FIGURE 1
2018
2018
2019
2019
Page 5
A total of 431 producers were credited across the 126 movies, with 71% men (n=306) and 29% women (n=125). No differences
emerged by year (2018=28.6%, n=72, 2019=29.6%, n=53). See Table 8. Only 19% (n=130) of producers were women across 200
top-grossing films, with 21.5% (n=73) in 2019 and 16.6% (n=57) in 2018. As such, Netflix was substantially higher than the industry
norm on hiring women producers to carry film projects to completion.
Was having a woman behind the camera associated with gender prevalence on screen? To answer this query, we bifurcated the
sample into two groups. Starting with directors, every film was coded as having at least one woman director attached or not. Then,
we looked at the percentage of female-identified
film leads/co leads, main cast and speaking char-
acters across these two groups. The same process
was repeated for screenwriters and producers.
Not surprisingly, the results revealed that having
a woman behind the scenes in a leadership po-
sition matters. Figure 2 reveals that films with at
least one woman director attached had signifi-
cantly more girls and women on screen as leads/
co leads (75.9%, n=22, vs. 40.2%, n=39), main
cast (49%, n=122, vs. 37%, n=289) and speaking
characters (45.9%, n=436, vs. 33.7%, n=1,261)
than films without a woman director attached.
The same pattern held with stories penned by
women screenwriters in comparison to stories
without any women screenwriters on board: fe-
male-identified leads/co leads (70.7%, n=29, vs.
37.6%, n=32), main cast members (47%, n=163
vs. 36.3%, n=248), and speaking characters
(42%, n=577, vs. 33.7%, n=1,120).
TABLE 8
PERCENTAGE OF WOMEN ABOVE THE LINE IN FILM BY YEAR
MEASURES 2018 2019 TOTAL
25% (n=18)
27.5% (n=28)
28.6% (n=72)
20.7% (n=12)
23% (n=23)
29.6% (n=53)
Directors
Writers
Producers
TABLE 8
23.1% (n=30)
25.2% (n=51)
29% (n=125)
FIGURE 2
PERCENTAGE OF FEMALE-IDENTIFIED LEADS, MAIN CAST AND
SPEAKING CHARACTERS BY DIRECTOR GENDER
75.9%
45.9%
40.2%
37%
Women-Directed
Men-Directed
PERCENTAGE OF FEMALE-IDENTIFIED LEADS, MAIN CAST AND SPEAKING CHARACTERS
BY DIRECTOR GENDER
FIGURE 2
Female-Identified
Leads/Co Leads
49%
33.7%
Female-Identified
Main Cast
Female-Identified
Speaking Characters
TABLE 9
PERCENTAGE OF FEMALE-IDENTIFIED LEADS, MAIN CAST
AND SPEAKING CHARACTERS BY CONTENT CREATOR GENDER
FEMALE-IDENTIFIED
ROLE
No Woman
Writer
Attached
Woman
Producer
Attached
No Woman
Producer
Attached
37.6% (n=32)
36.3% (n=248)
33.7% (n=1,120)
56.4% (n=44)
43% (n=278)
37.7% (n=1,115)
35.4% (n=17)
34.7% (n=133)
33.4% (n=582)
Leads/Co Leads
Main Cast
Speaking Characters
TABLE 9
Woman
Writer
Attached
70.7% (n=29)
47% (n=163)
42% (n=577)
WRITERS PRODUCERS
Page 6
With film producers, two of the three comparisons were significant: female-identified leads/co leads (Women Produced=56.4%,
n=44 vs. Men Produced=35.4%, n=17) and main cast (Women Produced=43%, n=248, Men Produced=34.7%, n=133). A difference
that approached significance but fell short of the 5-percentage point criterion was observed on all speaking characters (37.7%,
n=1,115, vs. 33.4%, n=582), however. Taken together, across 8 out of 9 analyses, having a woman in a leadership position behind
the camera was associated with greater inclusion across three different types of film roles.
In total, the findings from this section illuminated that Netflix hired or green lit stories with women behind the camera above film
industry norms. This is particularly the case with women directors and writers, challenging much of the mythologizing around who
can carry a movie or the availability of the pipeline. These hiring practices matter, as content with women behind the camera was
associated with more girls and women on screen in leading roles as well as across the entire ecosystem of the story. The results
also revealed that men’s casting decisions were less likely to be gender inclusive than women’s casting decisions.
Scripted Series. Creators, producers, writers and directors were assessed across every episode of each Netflix series included in
the sample.
11
A total of 6,195 above-the-line personnel were evaluated. Overall, 66.2% of these jobs were held by men (n=4,098)
and 33.8% by women (n=2,097). Table 10 reveals the breakdown of gender by position and Table 11 illuminates the percentage of
women holding these posts by year.
12
In terms of leadership, the top position in scripted series was show creators. Just under a third of these prestigious roles were
held by women (29.8%, n=79). A significant uptick was observed in the percentage of women show creators from 2018 (26.9%,
n=32) to 2019 (32.2%, n=47). According to the SDSU report, women accounted for 22% of show creators across broadcast, cable,
and digital series in 2017/18 and 25% in 2018/19 season.
13
Hence, Netflix hired and showcased the work of women creators signifi-
cantly more than their industry counterparts during the years evaluated.
TABLE 10
ABOVE-THE-LINE PERSONNEL IN SCRIPTED SERIES BY GENDER
MEASURES MEN WOMEN TOTAL
70.2% (n=186)
63.3% (n=1,025)
63.6% (n=1,682)
72.3% (n=1,205)
66.2% (n=4,098)
29.8% (n=79)
36.7% (n=594)
36.4% (n=963)
27.7% (n=461)
33.8% (n=2,097)
265
1,619
2,645
1,666
6,195
Creators
Producers
Writers
Directors
Total
TABLE 10
TABLE 11
PERCENTAGE OF WOMEN ABOVE THE LINE IN SCRIPTED SERIES BY YEAR
MEASURES 2018 2019 TOTAL
26.9% (n=32)
33.3% (n=246)
30.6% (n=373)
25.1% (n=191)
32.2% (n=47)
39.5% (n=348)
41.3% (n=590)
29.9% (n=270)
Creators
Producers
Writers
Directors
TABLE 11
29.8% (n=79)
36.7% (n=594)
36.4% (n=963)
27.7% (n=461)
Page 7
Turning to producers, we examined credited individuals with the ‘Producer’ moniker in their title working across the majority of
the season. A full 63.3% of credited producers were men (n=1,025) and 36.7% were women (n=594). A higher percentage and
number of women were credited as producers in Netflix series in 2019 (39.5%, n=348) than in 2018 (33.3%, n=246). In comparison
to statistics on producers from SDSU for the 2017/18 season, Netflix employed fewer women in producing roles (33%) in 2018 than
the wider industry (40%) but kept pace with the industry norm in 2019 (40%).
To further understand women’s participation as producers across the sample, we evaluated specific credits within this moniker
by gender. As shown in Table 12, women’s participation as producers increased as the prestige of their credits decreased. Almost
one-third (31.6%, n=237) of executive producers and 39.3% (n=77) of co-executive producers were women, while women held
55.1% (n=27) of supervising producer credits.
Forty-three percent (43.3%, n=103) of all pro-
ducers and co-producers (n=58) were women
and 46.6% (n=27) of line producers were wom-
en.
Two positions were examined to understand if
and how women’s employment as Producers
on Netflix series changed over time. These roles
have been tracked by SDSU and reflect roles
with decision-making ability in series. The per-
centage of Executive Producers in Netflix series
in 2018 (28.9%, n=101) was not meaningfully
different than Executive Producers in the wider
industry (26%). This trend continued in 2019,
when 33.9% (n=136) of Executive Producers on
Netflix series were women compared to 30% in
the broader industry ecosystem. Among Producers, Netflix hired slightly more women in 2018 (44.8%, n=47) than its industry
peers (40%). This was also the case in 2019, though both differences were not significant. Forty-two percent of producers on
Netflix series (42.1%, n=56) were women compared to 40% of producers industry wide.
Looking to writers, the credited writer for each episode of a series was examined (n=2,645). In total, 63.6% (n=1,682) of writers
were men and 36.4% were women (n=963). As shown in Table 11, the percentage of women writers increased substantially from
2018 (30.6%, n=373) to 2019 (41.3%, n=590). Netflix (36.4%, n=963) significantly outpaced the industry (30%) in terms of hiring
women writers overall, as well as in 2018 (30.6% vs. 25%) and in 2019 (41.3% vs. 35%). Even as the industry increased hiring of
women writers, Netflix exceeded that rate.
The last behind-the-camera position evaluated was directors. Each series episode was examined, with a total of 1,666 directors
included in the analysis. Men (72.3%, n=1,205) were more likely to direct Netflix series than women (27.7%, n=461), which calcu-
lated to a gender ratio of 2.6 to 1 (see Table 10). The participation of women directors of Netflix series non meaningfully increased
over time, as 25.1% (n=191) of directors in 2018 were women compared to 29.9% (n=270) in 2019 (see Table 11). Overall, Netflix
(27.7%) was on par with the wider industry (28%) in its hiring patterns for women directors.
14
There was no difference between the
percentage of women directors working on Netflix series in 2018 (25.1%) and what the DGA reported for the industry in the 2017-18
season (25%). Netflix (29.9%) was slightly lower than the DGA figures for the 2018-19 season (31%).
The final analysis conducted in this section examined the relationship between gender of show creators and gender of on-screen
portrayals. Each series was placed into one of two categories: those with at least one-woman creator attached or those without
any attached. Then, we analyzed the percentage of female-identifying characters and series regulars in each of these categories.
We also conducted the same analysis with writers.
As shown in Figure 3, scripted series with at least one-woman creator attached featured more female-identified leads, series regu-
lars and speaking characters. Two-thirds (74.6%, n=53) of series with a woman creator featured girls and women in at least half of
the series regular roles, versus only 41% (n=43) of series with only men creators. In terms of series regulars, over half of recurring
cast (56.2%, n=304) were female-identified in shows that were created by women. Shows without a woman creator attached only
TABLE 12
PRODUCING CREDITS IN SCRIPTED SERIES BY GENDER
MEASURES MEN WOMEN
68.4% (n=514)
60.7% (n=119)
44.9% (n=22)
56.7% (n=135)
57% (n=77)
53.4% (n=31)
31.6% (n=237)
39.3% (n=77)
55.1% (n=27)
43.3% (n=103)
43% (n=58)
46.6% (n=27)
Executive Producer
Co-Executive Producer
Supervising Producer
Producer
Co-Producer
Line Producer
TABLE 12
Page 8
staffed 41.5% (n=354) of the recurring cast with female-identified series regulars. Finally, women creators filled 45.9% (n=1,332)
of speaking roles with girls/women. Male show creators, on the other hand, depicted girls/women on screen in 37.1% (n=1,697) of
all speaking roles. These results point to the clear influence that women in creative roles can have on achieving gender equality
on screen.
The same analysis was conducted for writers.
15
Here, series with at least one credited woman writer (n=113) across the first three
episodes were compared to those without any women writing on the first three episodes (n=63). In terms of the leads/co leads,
61.1% (n=69) of series with a woman writer featured female-identified characters in at least half of the cast, compared to 42.9%
(n=27) of series without women writers. This trend continued with series regulars. Half (50.8%, n=458) of series regulars were girls
or women when one or more women writers worked on the episodes evaluated. In comparison, 40.7% (n=200) of series regulars
were women when there were no women writers. Finally, for all speaking characters, 42.3% (n=2,011) of characters were girls/
women when a woman writer was present versus 37.4% (n=1,018) of characters when no woman writer was employed. As with
creators, women in this critical behind the scenes role impact the inclusion profile on screen in Netflix series.
Behind the camera in Netflix series, women represented roughly a third of the individuals hired to bring these shows to life. Impor-
tantly, Netflix saw gains over time in its hiring practices for women across all four positions evaluated (i.e., creator, writer, produc-
er, director). The company also outpaced the industry in its hiring practices for three of these four positions—only on directors
was Netflix on par with or slightly below DGA findings. The presence of women as series creators and episode writers was related
to seeing more girls and women on screen. As with film, the burden of responsibility to improve casting percentages for women
rests primarily with men in series creator and writer roles.
Overall, this section reveals the extent to which Netflix values storytelling by and about girls and women. Fictional films and
episodic series present girls and women in key roles on screen. Netflix can improve inclusion of female-identified main cast and
speaking characters, but as the behind-the-camera analysis shows, men in positions of influence must do more to include girls
and women on screen. Netflix outflanked the film industry in its inclusion of women in above-the-line roles, and often outpaced
the industry in its employment of women behind-the-scenes in series. While these findings demonstrate Netflix’s ongoing com-
mitment to stories centered on and by women, there were still pieces of the picture missing. Intersecting gender and race/ethnic-
ity can illuminate where there continues to be bias and hiring gaps in the industry. As a result, the next section addresses another
historically marginalized community with a particular emphasis on how women from underrepresented racial/ethnic groups fare
on screen and behind the camera.
FIGURE 3
PERCENTAGE OF FEMALE-IDENTIFIED LEADS/CO LEADS,
SERIES REGULARS AND SPEAKING CHARACTERS BY SERIES CREATOR GENDER
37.1%
41.5%
45.9%
At Least 1 Woman Creator
No Women Creators
FIGURE 3
Female-Identified
Main Cast
Female-Identified
Speaking Characters
56.2%
41%
74.6%
Female-Identified
Leads/Co Leads
Page 9
RACE/ETHNICITY
This section illuminates the prevalence of race/ethnicity on screen and behind the camera in Netflix films and scripted series. Be-
low, we map out how underrepresented status (i.e., white vs. underrepresented racial/ethnic group) was associated with specific
measures as well as medium (films vs. scripted series), year (2018 vs. 2019), and gender (males, females) differences. At the end
of the section, we provide a deeper dive into specific racial/ethnic groups.
On Screen
Leads/co leads, main cast and speaking characters were assessed for race/ethnicity. A modified version of the U.S. Census cate-
gorizations (i.e., White, Black, Hispanic/Latino, Asian, Native Hawaiian/Pacific Islander, Middle Eastern/North African, American
Indian/Alaskan Native, multiracial/multiethnic) was used to evaluate characters’ racial/ethnic grouping.
16
Then, this measure was
collapsed into two categories: white vs. underrepresented (UR).
In total, a full 31.9% of stories across Netflix films and scripted series featured underrepresented leads/co leads. Medium differ-
ences emerged, however. As shown in Table 13, film (35.7%, n=45) featured significantly more underrepresented leads/co leads
than did scripted series (29.1%, n=51). 2019 (37.3%, n=57) depicted more underrepresented leads/co leads than did 2018 (26.4%,
n=39). The uptick by year was observed for both film (8.5 percentage points) and scripted series (13.9 percentage points). 2019
was not meaningfully different from U.S. Census, which indicates that 39.9% of the population identifies as belonging to an under-
represented racial/ethnic group.
17
Table 14 depicts a breakdown of specific racial/ethnic leads/co leads by storytelling medium.
TABLE 13
PERCENTAGE OF UNDERREPRESENTED LEADS/CO LEADS BY STORYTELLING MEDIUM AND YEAR
FILM
% of underrepresented
leads/co leads
TOTAL
2018
2019
2018
2019
TOTAL
31.9% 40.4% 21.5% 35.4%
31.9%
(n=22) (n=23) (n=17) (n=34) (n=96)
35.7%
(n=45)
SERIES
29.1%
(n=51)
TABLE 13
MEASURE
% of underrepresented
leads/co leads
31.9%
35.4%
40.4%
2018
2019
TOTAL
(n=23)
(n=34)
(n=96)
35.7%
(n=45)
29.1%
(n=51)
INDICATOR
31.9%
(n=22)
2018
2019
21.5%
(n=17)
TOTAL
FILM SERIES
TABLE 14
PERCENTAGE OF LEADS/CO LEADS BY RACIAL/ETHNIC GROUP AND STORYTELLING MEDIUM
MEASURE FILM SERIES TOTAL
64.3% (n=81)
18.3% (n=23)
6.3% (n=8)
4% (n=5)
1.6% (n=2)
0
0
5.6% (n=7)
White
Black/African American
Hispanic/Latino
Asian
Middle Eastern/North African
American Indian/Alaskan Native
Native Hawaiian/Pacific Islander
Multiracial/Multiethnic
TABLE 14
77.1% (n=135)
9.1% (n=16)
3.4% (n=6)
1.1% (n=2)
1.1% (n=2)
0
<1% (n=1)
1.7% (n=3)
71.8% (n=216)
13% (n=39)
4.7% (n=14)
2.3% (n=7)
1.3% (n=4)
0
<1% (n=1)
3.3% (n=10)
U.S. CENSUS
60.1%
13.4%
18.5%
5.9%
1.1%
1.3%
<1%
2.8%
Page 10
As noted earlier, only comparable industry data was available for leads/co leads in film. Netflix featured significantly more under-
represented leads/co leads than did top-grossing films across the same time frame (see Table 15). The gap was particularly large
for 2019, where Netflix outpaced top-grossing fare by 11.4 percentage points (40.4% vs. 29%).
The above findings focus on underrepresented status alone,
not in combination with gender. Here, we cross these two
measures to gain insight on the similarities and differences
in access and opportunity for men and women of color on
Netflix fare.
As shown in Table 16, there was no difference in the num-
ber and percentage of male and female leads/co leads from
underrepresented racial/ethnic groups driving the sto-
rylines in film. The percentage of underrepresented leads/
co leads was lower in scripted series where men of color
(10.3%, n=18) had slightly more roles than women of color
(9.1%, n=16) as protagonists. U.S. Census reveals that 20%
of the population is composed of underrepresented males
and 20% underrepresented females.
18
As such, film is just
shy of proportional representation, but scripted series was
substantially below this benchmark. The breakdown of un-
derrepresented status by gender for Netflix and top-gross-
ing film leads/co leads can be found in Figure 4.
TABLE 15
PERCENTAGE OF UNDERREPRESENTED LEADS/CO LEADS IN NETFLIX AND TOP-GROSSING FILMS BY YEAR
% of Netflix films w/an
underrepresented leads/co lead
28%
35.7%
29%
40.4%
27%
31.9%
2018 2019 TOTAL
% of top-grossing films w/an
underrepresented leads/co lead
(n=22) (n=23) (n=4 5)
(n=27) (n=29) (n=56)
INDICATOR
TABLE 15
TABLE 16
PERCENTAGE OF UNDERREPRESENTED LEADS/CO LEADS BY STORYTELLING MEDIUM AND GENDER
% of underrepresented
male-identified leads/co leads
9%
10%
19%
19%
FILM SERIES
% of underrepresented
female-identified leads/co leads
(n=24) (n=18)
(n=24) (n=16)
INDICATOR
TABLE 16
42.1%
19%
35%
30.2%
Netflix Films
Top-Grossing Films
PERCENTAGE OF UNDERREPRESENTED LEADS/CO LEADS IN NETFLIX AND
TOP-GROSSING FILMS BY GENDER
FIGURE 4
White
Males
White
Females
UR
Males
UR
Females
12%
17%
19%
28.5%
FIGURE 4
PERCENTAGE OF UNDERREPRESENTED LEADS/CO-LEADS
IN NETFLIX AND TOP-GROSSING FILMS BY GENDER
Note: Underrepresented status reflects the central character in the story. Only human
or anthropomorphized characters with an ascertainable race/ethnicity were included.
Percentages do not sum to 100% as films with ensemble leads are not included and
heterogenous co leads were counted in each respective category.
Note: A total of 126 Netflix films were included in the analysis and 200 top-grossing fictional films in 2018 (n=100) and 2019 (n=100). Both
of these samples had movies driven by ensemble casts. While the ensemble films were counted in the sample total, they could not qualify
as underrepresented-led or not. This latter distinction was reserved for films driven by only one or two (equal) protagonists.
Page 11
Turning to the main cast across film and scripted series, a full 65.9% (n=1,583) were White, 15.7% (n=376) Black, 4.8% (n=116)
Hispanic/Latino, 4.7% (n=112) Asian, 1.7% (n=41) Middle Eastern/North African, <1% (n=11) American Indian/Alaskan Native, <1%
(n=8) Native Hawaiian/Pacific Islander, and 6.4% (n=154) multiracial/multiethnic. In total, 34.1% (n=818) of all main cast across
film and series were from an underrepresented racial/ethnic group.
Looking to content type, very little difference emerged in the percentage of main cast that were underrepresented by storytelling
medium (i.e., film= 35.4%, scripted series=33.1%). See Table 18 for a complete breakdown of each racial/ethnic group by medium.
Each showed a significant uptick in the percentage of underrepresented main cast from 2018 to 2019 (see Table 18), with scripted
series (+11.4 percentage points) gain larger than films (+6.6 percentage points). Most of the increase was due to the casting of
Black, Hispanic/Latino, and multiracial/multiethnic actors as main cast members.
We also crossed gender (males, females) and underrepresented status (white vs. underrepresented) for main cast members.
Across two years of content (see Table 19), a full 37.3% of main cast members were white males, 28.6% were white females, 18.5%
were underrepresented males and 15.6% were underrepresented females. Two other patterns were revealed in Table 19. In film
and scripted series, the percentage of underrepresented girls and women as main cast members significantly increased from
2018 to 2019. In series, men and boys of color across the same time frame increased whereas the casting of white boys and men
decreased.
TABLE 18
PERCENTAGE OF MAIN CAST AND SPEAKING CHARACTERS
BY RACIAL/ETHNIC GROUP AND STORYTELLING MEDIUM
MEASURE
Main
Cast
Speaking
Characters
64.6% (n=656)
18.2% (n=185)
3.3% (n=33)
5.9% (n=60)
2.4% (n=24)
<1% (n=6)
<1% (n=2)
4.8% (n=49)
White
Black/African American
Hispanic/Latino
Asian
Middle Eastern/North African
American Indian/Alaskan Native
Native Hawaiian/Pacific Islander
Multiracial/Multiethnic
TABLE 17
62.1% (n=2,805)
18.8% (n=849)
5.9% (n=266)
6.2% (n=282)
3.1% (n=138)
<1% (n=15)
<1% (n=18)
3.2% (n=144)
66.9% (n=927)
13.8% (n=191)
6% (n=83)
3.8% (n=52)
1.2% (n=17)
<1% (n=5)
<1% (n=6)
7.6% (n=105)
64.8% (n=4,640)
16% (n=1,144)
5.9% (n=419)
6.2% (n=441)
1.9% (n=137)
<1% (n=22)
<1% (n=20)
4.7% (n=338)
FILM SERIES
Main
Cast
Speaking
Characters
U.S.
CENSUS
60.1%
13.4%
18.5%
5.9%
1.1%
1.3%
<1%
2.8%
TABLE 17
PERCENTAGE OF UNDERREPRESENTED MAIN CAST BY STORYTELLING MEDIUM AND YEAR
FILM
% of underrepresented
main cast
TOTAL
2018
2019
2018
2019
TOTAL
32.3% 38.9% 27% 38.4%
34.1%
(n=175) (n=184) (n=175) (n=284) (n=818)
35.4%
(n=359)
SERIES
33.1%
(n=459)
TABLE 18
MEASURE
% of underrepresented
main cast
34.1%
38.4%
38.9%
2018
2019
TOTAL
(n=184)
(n=284)
(n=818)
35.4%
(n=359)
33.1%
(n=459)
INDICATOR
32.3%
(n=175)
2018
2019
27%
(n=175)
TOTAL
FILM SERIES
Page 12
Focusing on speaking characters (n=11,678), 63.8% (n=7,445) were White, 17.1% (n=1,993) Black, 5.9% (n=685) Hispanic/Lati-
no, 6.2% (n=723) Asian, <1% (n=37) American Indian/Alaskan Native, <1% (n=38) Native Hawaiian/Pacific Islander, 2.4% (n=275)
Middle Eastern/North African, and 4.1% (n=482) multiracial/multiethnic.
19
Across the two-year sample, film and scripted series
did not meaningfully differ across any of these specific racial/ethnic groupings. Combined, a full 36.2% (n=4,233) of main cast
members were from underrepresented racial/ethnic groups which was not meaningfully different from U.S. Census.
Table 20 features the percentage of underrepresented speaking characters by storytelling medium and year. While no meaningful
difference was observed over time for film, scripted series increased underrepresented speaking characters from 2018 (32%) to
2019 (38%). In terms of industry comparisons, Netflix films were no different than top-grossing films in the percentage of under-
represented speaking characters overall or in 2018 but 2019 was significantly higher (Netflix=40.2%, n=855, TG Films=34.3%,
n=1,336).
The intersection of underrepresented status and gender for all speaking or named characters is found in Table 21. Here, the find-
ings showed that over time no differences emerged by storytelling medium or year. Just under 40% of all speaking or named
characters were white males, 24.1% were white females, 21.1% were underrepresented males and 15.1% were underrepresented
females. White males over indexed relative to U.S. population estimates whereas white and underrepresented females under in-
dexed. Underrepresented males were featured in line with their percentage in the U.S. (see Table 21).
Two additional measures, proportional representation and invisibility, were assessed using race/ethnicity of speaking or named
characters. In terms of proportional representation, we captured whether stories came within 10% of U.S. Census (35.9%-
43.9%). As shown in Table 22, only 11.4% (n=35) of all Netflix fictional stories were at or near proportional representation with film
(10.3%, n=13) and series (12.2%, n=22) not meaningfully different. What is important, however, is that 27.1% (n=83) of all stories
were above proportional representation with film (32.5%, n=41) showcasing more of those stories with underrepresented racial/
ethnic casts than scripted series (23.3%, n=42). Further, the percentage of stories that featured above proportional representa-
tion increased from 2018 to 2019 across both film and series.
TABLE 19
PERCENTAGE OF MAIN CAST BY UNDERREPRESENTED STATUS, STORYTELLING MEDIUM AND YEAR
FILM
% of white males
TOTAL
2018
2019
2018
2019
40.2% 37.2% 40.2% 32.7%
37.3%
(n=218) (n=176) (n=260) (n=242) (n=896)
27.5%
(n=149)
SERIES
TABLE 19
MEASURE
% of underrepresented
main cast
34.1%
38.4%
38.9%
2018
2019
TOTAL
(n=184)
(n=284)
(n=818)
35.4%
(n=359)
33.1%
(n=459)
INDICATOR
32.%
(n=175)
2018
2019
27%
(n=175)
TOTAL
FILM SERIES
20.5%
(n=111)
11.8%
(n=64)
23.9%
(n=113)
21.8%
(n=103)
17.1%
(n=81)
13.3%
(n=86)
13.8%
(n=89)
19.4%
(n=143)
28.6%
(n=687)
28.8%
(n=213)
32.8%
(n=212)
19.1%
(n=141)
18.5%
(n=443)
15.6%
(n=375)
30%
30%
20%
20%
% of white females
% of underrepresented males
% of underrepresented females
U.S.
CENSUS
TABLE 20
PERCENTAGE OF UNDERREPRESENTED SPEAKING CHARACTERS BY STORYTELLING MEDIUM AND YEAR
FILM
% of underrepresented
speaking characters
TOTAL
2018
2019
2018
2019
TOTAL
35.8% 40.2% 32% 38%
36.2%
(n=857) (n=855) (n=1,053) (n=1,468) (n=4,233)
37.9%
(n=1,712)
SERIES
35.2%
(n=2,521)
TABLE 20
MEASURE
% of underrepresented
speaking characters
36.2%
38%
40.2%
2018
2019
TOTAL
(n=855)
(n=1,468)
(n=4,233)
37.9%
(n=1,712)
35.2%
(n=2,521)
INDICATOR
35.8%
(n=857)
2018
2019
32%
(n=1,053)
TOTAL
FILM SERIES
Page 13
In terms of visibility, the number and percentage of Netflix films and scripted series missing at least one or more speaking or
named characters from specific racial/ethnic groups was depicted in Table 23. Almost all Netflix stories portrayed White speaking
characters whereas almost no stories portrayed Native Hawaiian/Pacific Islanders and American Indian/Alaskan Natives. Roughly
three-quarters of the films and series erased Middle Eastern/North African (MENA) characters and roughly half rendered Hispan-
ic/Latinos invisible. In Table 24, we illuminated how many films and series erased female-identified characters by race/ethnicity.
Fifteen out of 16 cells indicate that when we only focus on women and girls, the numbers become more dire reflecting greater
erasure (see Table 24).
Taken together, this section revealed a few major trends. In terms of leads/co leads, Netflix was near or at proportional represen-
tation for race/ethnicity particularly in film. Similarly, main cast and all speaking characters in film and series were at or just below
proportional representation in 2019. One problematic area pertained to invisibility, with a significant number and percentage of
stories across film and scripted series erasing people of color – particularly women and girls from underrepresented racial/ethnic
groups.
TABLE 21
PERCENTAGE OF SPEAKING CHARACTERS BY UNDERREPRESENTED STATUS, GENDER,
STORYTELLING MEDIUM AND YEAR
FILM
% of white males
TOTAL
2018
2019
2018
2019
41.8% 38.8% 41% 37.8%
39.7%
(n=999) (n=824) (n=1,351) (n=1,461) (n=4,635)
22.4%
(n=536)
SERIES
TABLE 21
MEASURE
21.7%
(n=518)
14.2%
(n=339)
21%
(n=446)
24.7%
(n=524)
15.6%
(n=331)
18.8%
(n=619)
13.2%
(n=434)
20.8%
(n=805)
24.1%
(n=2,810)
24.3%
(n=938)
27%
(n=890)
17.1%
(n=663)
21.1%
(n=2,446)
15.1%
(n=1,767)
30%
30%
20%
20%
% of white females
% of underrepresented males
% of underrepresented females
U.S.
CENSUS
TABLE 22
RACIAL/ETHNIC PROPORTIONAL REPRESENTATION BY STORYTELLING MEDIUM AND YEAR
TOTAL
11.6%
OR
FILMS
8.8%
OR
FILMS
16%
OR
SERIES
7.5%
OR
SERIES
11.4%
OR
STORIES
18 Films
exceeded
proportional
representation
23 films
exceeded
proportional
representation
83 stories
exceeded
proportional
representation
13 series
exceeded
proportional
representation
29 series
exceeded
proportional
representation
TABLE 22
FILM SERIES
ACROSS YEARS, .% OR FILMS AND SCRIPTED SERIES MET OR EXCEEDED PROPORTIONAL REPRESENTATION
Page 14
Behind the Camera
As with gender, we scrutinized the distribution of race/ethnicity behind the camera on Netflix films and series. As noted earlier, film
and series metrics are reported separately.
Film. There were 762 personnel in above-the-line roles across the 126 Netflix films evaluated.
20
Focusing on directors, more than
80% of helmers were white (83.1%, n=108) and 16.9% (n=22) were from underrepresented racial/ethnic groups (See Table 25).
This is a ratio of 4.9 white directors for every 1 underrepresented director. There was no change from 2018 (16.7%, n=12) to 2019
(17.2%, n=10) in the percentage of underrepresented directors of Netflix films. The percentage of Netflix directors did not signifi-
cantly differ from top-grossing films in the same time frame (see Figure 5).
TABLE 23
PERCENTAGE AND NUMBER OF NETFLIX FILMS AND SCRIPTED SERIES MISSING RACIAL/ETHNIC GROUPS
H/L Black AI/AN MENA MultiAsian NHPIWhite
68 23 122 95 5451 1202
86 18 169 129 5549 1702
Measure
# of films
# of series
% of series w/out
speaking characters
% of films w/out
speaking characters
54% 18.3% 96.8% 75.4% 42.9%40.5% 95.2%1.6%
47.8% 10% 93.9% 71.7% 30.6%27.2% 94.4%1.1%
TABLE 23
Note: H/L refers to Hispanic/Latino; AI/AN refers to American Indian/Alaskan Native; NHPI refers to Native Hawaiian/Pacific Islander; MENA refers to Middle
Eastern/North African; and Multi refers to Multiracial/Multiethnic.
TABLE 24
PERCENTAGE AND NUMBER OF NETFLIX FILMS AND SCRIPTED SERIES
MISSING GIRLS AND WOMEN BY RACIAL/ETHNIC GROUPS
Latina Black AI/AN MENA MultiAsian NHPIWhite
91 40 122 112 7071 1236
118 49 174 160 6976 1738
Measure
# of films
# of series
% of series w/out
speaking characters
% of films w/out
speaking characters
72.2% 31.7% 96.8% 88.9% 55.6%56.3% 97.6%4.8%
65.6% 27.2% 96.7% 88.9% 38.3%42.2% 96.1%4.4%
TABLE 24
Note: AI/AN refers to American Indian/Alaskan Native; NHPI refers to Native Hawaiian/Pacific Islander; MENA refers to Middle Eastern/North African; and Multi
refers to Multiracial/Multiethnic.
TABLE 25
ABOVE-THE-LINE PERSONNEL IN FILM BY UNDERREPRESENTED STATUS
MEASURES WHITE UR TOTAL
83.1% (n=108)
83.6% (n=168)
87% (n=375)
85.4% (n=651)
16.9% (n=22)
16.4% (n=33)
13% (n=56)
14.6% (n=111)
130
201
431
762
Directors
Writers
Producers
Total
TABLE 25
Page 15
The specific racial/ethnic breakdown for directors
was: 83.1% (n=108) white, 8.5% (n=11) Black/Af-
rican American, 3.1% (n=4) Hispanic/Latino, <1%
(n=1) Asian, 1.5% (n=2) Middle Eastern/North Afri-
can, and 3.1% (n=4) Multiracial/Multiethnic. There
were no directors who were American Indian/Alas-
kan Native or Native Hawaiian/Pacific Islander who
directed a Netflix film in 2018 or 2019.
To further investigate opportunities for directors,
we explored the intersection of underrepresented
status and gender. White men were most likely to
helm Netflix films, while women of color were least
likely (see Table 26). Focusing on differences for
underrepresented men and women directors over
time reveals no significant changes. In both 2018
and 2019, Netflix (2018=11.1%, n=8, 2019=10.3%,
n=6) fell significantly below the percentage of
underrepresented men working as directors of
top-grossing movies (2018=20.5%, 2019=16.1%). In the same time frame, Netflix (2018=5.6%, n=4, 2019=6.9%, n=4) had a slight-
ly higher percentage of underrepresented women working as directors than did top-grossing movies (2018=<1%, 2019=3.6%).
Turning to screenwriters, there were 201 credited on Netflix films; 83.6% were white (n=168) and 16.4% were underrepresented
(n=33). Netflix increased the percentage of underrepresented writers hired between 2018 (13.9%, n=14) and 2019 (19%, n=19).
How does this compare to top-grossing films? A slightly higher—but not significant—percentage of underrepresented writers
penned Netflix movies in 2018 (13.9%, n=14) than top-grossing films that year (11.2%, n=25). The difference was significant in 2019,
as 19% (n=19) of Netflix writers were underrepresented compared to 13.2% (n=28) of top-grossing film scribes.
Of the 201 writers of Netflix films, 83.6% (n=168) were White, 7.5% (n=15) were Black/African American, 1% (n=2) were Hispanic/
Latino, 3.5% (n=7) were Asian, 1.5% (n=3) were Middle Eastern/North African, and 3% (n=6) were Multiracial/Multiethnic. There
were no writers who were American Indian/Alaskan Native or Native Hawaiian/Pacific Islander.
16.7%
19.6%
17.2%
21.4%
Netflix Films
Top-Grossing Films
PERCENTAGE OF UNDERREPRESENTED DIRECTORS IN NETFLIX AND TOP-GROSSING FILMS BY YEAR
FIGURE 5
2018
2018
2019
2019
FIGURE 5
PERCENTAGE OF UNDERREPRESENTED DIRECTORS IN NETFLIX
AND TOP-GROSSING FILMS BY YEAR
TABLE 26
NETFLIX FILM DIRECTORS BY UNDERREPRESENTED STATUS AND GENDER BY YEAR
UNDERREPRESENTED STATUS 2018 2019 TOTAL
63.9% (n=46)
19.4% (n=14)
11.1% (n=8)
5.6% (n=4)
69% (n=40)
13.8% (n=8)
10.3% (n=6)
6.9% (n=4)
White Men
White Women
UR Men
UR Women
TABLE 26
66.2% (n=86)
16.9% (n=22)
10.8% (n=14)
6.2% (n=8)
TABLE 27
PERCENTAGE OF UNDERREPRESENTED PERSONNEL ABOVE THE LINE IN FILM BY YEAR
MEASURES 2018 2019 TOTAL
16.7% (n=12)
13.9% (n=14)
14.7% (n=37)
17.2% (n=10)
19% (n=19)
10.6% (n=19)
Directors
Writers
Producers
TABLE 27
16.9% (n=22)
16.4% (n=33)
13% (n=56)
Page 16
We also examined writers with an intersectional lens (See Table 28). Men and women of color were least likely to work as writers
on Netflix films. There was no meaningful difference by year for underrepresented men (2018=10.9%, n=11, 2019=14%, n=14), nor
for underrepresented women (2018=3%, n=3, 2019=5%, n=5). There were also no significant differences between Netflix writers
and top-grossing films in terms of underrepresented men in 2018 (Netflix=10.9%, top-grossing=9.8%) or 2019 (Netflix=14%,
top-grossing=9.9%), or for underrepresented women in either year (2018=3% vs. 1.3%, 2019=5% vs. 3.3%).
Producing credits (e.g., Produced by) were also evaluated. Of the 431 producers of Netflix films, 87% (n=375) were white and 13%
(n=56) were underrepresented. From 2018 (14.7%, n=37) to 2019 (10.6%, n=19) the percentage of underrepresented producers of
Netflix content declined non-significantly. In comparison to top-grossing films, in 2018 there was no significant difference in the
percentage of underrepresented producers of Netflix content (14.7%) compared to top-grossing movies (11.3%). In 2019, how-
ever, significantly fewer underrepresented producers (10.6%) worked on Netflix films compared to top-grossing movies (19%).
The specific racial/ethnic breakdown for producers was: 87% (n=375) white, 4.2% (n=18) Black/African American, 2.6% (n=11)
Hispanic/Latino, 3.5% (n=15) Asian, <1% (n=4) Middle Eastern/North African, <1% (n=1) Native Hawaiian/Pacific Islander, and
1.6% (n=7) Multiracial/Multiethnic. There were no American Indian/Alaskan Native producers across both years.
Once again, we evaluated the producing ranks intersectionally (see Table 29). There was little difference over time in the percent-
age of underrepresented men and women credited as producers. Top-grossing (9.9%, n=34) and Netflix (8.3%, n=21) films were
roughly equal in 2018 while in 2019, 14.2% (n=48) of producers of top films were underrepresented men versus 6.1% (n=11) of Net-
flix producers. In terms of underrepresented women, Netflix movies in 2018 featured more underrepresented women producers
(6.3%, n=16) than top-grossing films (1.4%, n=5), though the difference was just shy of significance. In 2019, Netflix (4.5%, n=8)
had roughly the same percentage of underrepresented women producers as top films (4.7%, n=16).
We were once again curious about the relationship between underrepresented creatives behind the scenes and the on-screen
inclusion profile of Netflix stories. We split the sample into two groups. For directors, each film was determined to have an un-
derrepresented director attached or not. Then, we examined the percentage of underrepresented leads/co leads, main cast, and
speaking characters for films in each category. This process was repeated with writers and producers.
TABLE 28
NETFLIX FILM WRITERS BY UNDERREPRESENTED STATUS AND GENDER BY YEAR
UNDERREPRESENTED STATUS 2018 2019 TOTAL
61.4% (n=62)
24.8% (n=25)
10.9% (n=11)
3% (n=3)
63% (n=63)
18% (n=18)
14% (n=14)
5% (n=5)
White Men
White Women
UR Men
UR Women
TABLE 28
62.2% (n=125)
21.4% (n=43)
12.4% (n=25)
4% (n=8)
TABLE 29
NETFLIX FILM PRODUCERS BY UNDERREPRESENTED STATUS, GENDER AND YEAR
UNDERREPRESENTED STATUS 2018 2019 TOTAL
63.1% (n=159)
22.2% (n=56)
8.3% (n=21)
6.3% (n=16)
64.2% (n=115)
25.1% (n=45)
6.1% (n=11)
4.5% (n=8)
White Men
White Women
UR Men
UR Women
TABLE 29
63.6% (n=274)
23.4% (n=101)
7.4% (n=32)
5.6% (n=24)
Page 17
Figure 6 shows the results of this analysis. Films with
one or more underrepresented directors attached
were far more likely to feature underrepresented
leads/co leads (86.4%, n=19 vs. 25%, n=26), un-
derrepresented main cast (68.1%, n=109 vs. 29.2%,
n=250), and underrepresented speaking charac-
ters (67.1%, n=566 vs. 31.2%, n=1,146) than those
without an underrepresented director attached.
The screenwriting analysis revealed the same pat-
tern. Underrepresented writers were associated
with more underrepresented film leads/co leads
(76%, n=19 vs. 25.7%, n=26), main cast members
(61.3%, n=119 vs. 29.2%, n=240), and all speaking
characters (60.7%, n=541 vs. 32.3%, n=1,171) than
white scribes. For producers, the same differences
emerged for underrepresented vs. white producers
across the three groups (leads/co leads, 70.6%,
n=24 vs. 22.8%, n=21, main cast, 51.8%, n=144 vs.
29.2%, n=215, speaking characters 53.4%, n=770
vs. 30.6%, n=942).
Put differently, across 7 of 9 analyses, underrepresented creatives featured more than double the percentage of underrepre-
sented leads, main cast and speaking characters than white directors did. Clearly, underrepresented content creators were more
inclusive than their white counterparts when it comes to storytelling and casting. Although more white directors worked on Netflix
films, they contributed less to the on-screen inclusion profile of Netflix content. Hiring practices behind the camera should take
into account the need for inclusion on screen across all content.
The findings regarding film reveal that underrepresented content creators in above-the-line roles on Netflix films fell mostly below
industry norms, though this varied from year-to-year. Most importantly, there was a clear relationship between an underrepre-
sented creator behind the scenes and on screen casting choices. When underrepresented individuals worked behind the camera,
there were more underrepresented characters as leads/co leads, main cast, and speaking characters—in most cases, more than
twice as many. This suggests that underrepresented creatives were primarily responsible for the on-screen inclusion in Netflix
films.
Scripted Series. We assessed the creators, producers, writers, and directors of every episode of each Netflix series in the sample.
Of the 6,155 personnel evaluated, 82.7% (n=5,092) were white and 17.3% (n=1,063) were from an underrepresented racial/ethnic
group. This did not change over time; in 2018, 14.8% (n=415) of positions went to underrepresented individuals and in 2019, 19.4%
(n=648) of positions did. Table 31 provides the breakdown of each position by underrepresented status.
TABLE 30
PERCENTAGE OF UNDERREPRESENTED LEADS, MAIN CAST AND SPEAKING CHARACTERS IN FILM
BY UNDERREPRESENTED STATUS
UNDERREPRESENTED
ROLE
No UR
Writer
Attached
UR
Producer
Attached
No UR
Producer
Attached
25.7% (n=26)
29.2% (n=240)
32.3% (n=1,171)
70.6% (n=24)
51.8% (n=144)
53.4% (n=770)
22.8% (n=21)
29.2% (n=215)
30.6% (n=942)
Leads/Co Leads
Main Cast
Speaking Characters
TABLE 30
UR
Writer
Attached
76% (n=19)
61.3% (n=119)
60.7% (n=541)
WRITERS PRODUCERS
FIGURE 6
PERCENTAGE OF UNDERREPRESENTED LEADS, MAIN CAST
AND SPEAKING CHARACTERS
BY DIRECTOR UNDERREPRESENTED STATUS
86.4%
67.1%
25%
29.2%
UR-Directed
White-Directed
PERCENTAGE OF UNDERREPRESENTED LEADS, MAIN CAST AND SPEAKING CHARACTERS
BY DIRECTOR UNDERREPRESENTED STATUS
FIGURE 6
UR
Leads/Co Leads
68.1%
31.2%
UR
Main Cast
UR
Speaking Characters
Page 18
Beginning with creators, 87.8% (n=231) were white and 12.2% (n=32) were underrepresented overall. This varied significantly by
year, as 7.6% (n=9) of creators in 2018 were underrepresented but 15.9% (n=23) in 2019 were. Netflix series in 2018 were on par
with underrepresented series creators overall (9%), based on the UCLA Hollywood Diversity Report (2017-18).
21
In 2019, Netflix
(16%) was not meaningfully different from the industry (11.8%) in its work with underrepresented series creators.
The specific racial/ethnic breakdown for creators was: 87.8% (n=231) white, 6.1% (n=16) were Black/African American, 2.3% (n=6)
were Hispanic/Latino, <1% (n=1) were Asian, 1.1% (n=3) were Middle Eastern/North African, and 2.3% (n=6) were Multiracial/Mul-
tiethnic. None were American Indian/Alaskan Native or Native Hawaiian/Pacific Islander.
As with film, we explored the creator position with an intersectional lens over time (see Table 33). A total of 8.4% (n=22) of creators
were underrepresented men, while 3.8% (n=10) were underrepresented women. In 2018, 5.1% (n=6) of creators were underrep-
resented men, which increased significantly to 11% (n=16) in 2019. There was no corresponding increase for underrepresented
women over time.
TABLE 31
ABOVE-THE-LINE PERSONNEL IN SCRIPTED SERIES BY UNDERREPRESENTED STATUS
MEASURES WHITE UR TOTAL
87.8% (n=231)
85% (n=1,352)
82.6% (n=2,178)
79.9% (n=1,331)
12.2% (n=32)
15% (n=238)
17.4% (n=458)
20.1% (n=335)
263
1,590
2,636
1,666
Creators
Producers
Writers
Directors
TABLE 31
TABLE 32
PERCENTAGE OF UNDERREPRESENTED ABOVE-THE-LINE PERSONNEL IN SCRIPTED SERIES BY YEAR
MEASURES 2018 2019
Creators
Producers
Writers
Directors
Total
TABLE 32
15.9% (n=23)
16.7% (n=145)
20.3% (n=290)
21% (n=190)
19.4% (n=648)
7.6% (n=9)
12.9% (n=93)
13.9% (n=168)
19% (n=145)
14.8% (n=415)
TABLE 33
NETFLIX SERIES CREATORS BY UNDERREPRESENTED STATUS, GENDER AND YEAR
UNDERREPRESENTED STATUS 2018 2019 TOTAL
68.6% (n=81)
23.7% (n=28)
5.1% (n=6)
2.5% (n=3)
56.6% (n=82)
27.6% (n=40)
11% (n=16)
4.8% (n=7)
White Men
White Women
UR Men
UR Women
TABLE 33
62% (n=163)
25.9% (n=68)
8.4% (n=22)
3.8% (n=10)
Page 19
Among producers, 85% (n=1,352) were white and 15% (n=238) were underrepresented. This was consistent over time, as 12.9%
(n=93) of producers in 2018 were underrepresented compared to 16.7% (n=145) in 2019. Netflix (12.9%) fell significantly below the
industry average in 2018 (WGA=26.4%) and in 2019 (Netflix=16.7%, WGA=39.4%) for underrepresented producers.
22
The specific racial/ethnic breakdown for producers was: 85% (n=1,352) white, 5% (n=80) were Black/African American, 3.1%
(n=50) were Hispanic/Latino, 2.8% (n=45) were Asian, 1.3% (n=20) were Middle Eastern/North African, and 2.7% (n=43) were
Multiracial/Multiethnic. None of the producers were American Indian/Alaskan Native or Native Hawaiian/Pacific Islander.
Crossing the race/ethnicity and gender of producers revealed that 54.3% (n=864) of producing credits went to white men, 30.7%
(n=488) to white women, 8.7% (n=139) to underrepresented men, and 6.2% (n=99) to underrepresented women. Over time, there
was little difference in the prevalence of underrepresented men and women in producing roles. In 2018, 7.5% (n=54) of produc-
ers were underrepresented men which was similar to 2019 (9.8%, n=85). This was also true of underrepresented women in 2018
(5.4%, n=39) and 2019 (6.9%, n=60).
Table 34 reveals differences by race/
ethnicity in the seniority of producing
credits. Underrepresented producers
were most likely to receive Supervising
Producer credits (42.9%), followed by
Co-Producer (20.6%) and Co-Executive
Producer (20%) credits. As a counter-
point, underrepresented producers were
least likely to receive Executive Producer
(13%), Line Producer (12%), or Producer
(8.9%) credits. In comparison to the in-
dustry as a whole, Netflix had significant-
ly fewer underrepresented Co-Executive
Producers (WGA=28%, Netflix=20%),
Producers (WGA=39%, Netflix=8.9%)
and Co-Producers (WGA=42.5%, Net-
flix=20.6%). There was no difference in the percentage of Executive Producers (WGA=15.5%, Netflix=13%) or Supervising Produc-
ers (WGA=39.5%, Netflix=42.9%).
23
Episode writers were examined for race/ethnicity. Of the 2,636 writers credited, 82.6% (n=2,178) of writers were white and 17.4%
(n=458) were underrepresented. This increased significantly over time in Netflix series. In 2018, 13.9% (n=168) of writers were
underrepresented while in 2019, that figure was 20.3% (n=290). Compared to the wider industry (19.7%), in 2018 Netflix (13.9%,
n=168) had significantly fewer underrepresented writers. Netflix closed this gap (20.3%, n=290) and did not differ from the indus-
try at large (23.8%) in the percentage of underrepresented writers working in 2019.
24
The specific racial/ethnic breakdown of writers was assessed, with the results presented here: 82.6% (n=2,178) white, 7.4% (n=194)
were Black/African American, 2.2% (n=58) were Hispanic/Latino, 3.5% (n=91) were Asian, <1% (n=2) were American Indian/Alas-
kan Native, 1.1% (n=30) were Middle Eastern/North African, and 3.1% (n=83) were Multiracial/Multiethnic. Native Hawaiian/Pacific
Islander writers were missing entirely from Netflix series in 2018 and 2019.
When gender was considered, 8.7% (n=228) of writers were underrepresented men and 8.7% (n=230) of writers were underrepre-
sented women. There was a significant increase in the percentage of underrepresented men working as Netflix writers from 2018
(5.8%, n=70) to 2019 (11.1%, n=158). This was not the case for underrepresented women. In 2018, 8.1% (n=98) of writers were
women of color as were 9.3% (n=132) of writers in 2019.
Finally, the race/ethnicity of series directors was evaluated. The majority (79.9%, n=1,331) were white and 20.1% (n=335) were
underrepresented. This was consistent over time, as 19% (n=145) of directors in 2018 were underrepresented as were 21% (n=190)
of directors in 2019. DGA industry-wide statistics for directors of color in the 2017-18 season (24%) outpaced those for Netflix
episodes (19%). This was also the case in the 2018-19 season, when the DGA reported that 27% of episodic directors were under-
represented, versus 21% of Netflix directors.
25
TABLE 34
PRODUCING CREDITS IN SCRIPTED SERIES BY
UNDERREPRESENTED STATUS
MEASURES WHITE UR
87% (n=649)
80% (n=156)
57.1% (n=28)
91.1% (n=216)
79.4% (n=104)
88% (n=44)
13% (n=97)
20% (n=39)
42.9% (n=21)
8.9% (n=21)
20.6% (n=27)
12% (n=6)
Executive Producer
Co-Executive Producer
Supervising Producer
Producer
Co-Producer
Line Producer
TABLE 34
Page 20
The specific racial/ethnic breakdown of directors was evaluated. Across the series evaluated, 79.9% (n=1,331) were white, 7.3%
(n=121) were Black/African American,2.8% (n=47) were Hispanic/Latino, 2.3% (n=39) were Asian, <1% (n=2) were American Indi-
an/Alaskan Native, 1.9% (n=32) were Middle Eastern/North African, and 5.6% (n=94) were Multiracial/Multiethnic. There were no
Native Hawaiian/Pacific Islander directors who worked on episodes of a Netflix series released in 2018 or 2019.
The intersectional profile of Netflix series directors was evaluated (See Table 35). There was little change in the percentage of un-
derrepresented men or women working as directors from year to year. There was little difference between DGA-reported average
and Netflix in 2018 (DGA=17.8%, Netflix=13.4%) or
2019 (DGA=19%, Netflix=14.8%) in terms of un-
derrepresented men. Netflix (5.6%) matched the
DGA (6.2%) in its rate of hire for underrepresented
women directors in 2018 and in 2019 (DGA=8%,
Netflix=6.2%).
26
How does the identity of individuals working
behind the camera influence who appears on
screen? We assessed each series to determine
whether the creator was underrepresented or not.
We used this information to create two groups:
one for series with at least one underrepresent-
ed creator and the other for series without an un-
derrepresented creator. Then, we evaluated the
percentage of underrepresented main cast, series
regulars and speaking characters for each group.
The process was repeated for writers across the
first three episodes investigated.
The results for creators appear in Figure 7. Under-
represented series creators were responsible for
more underrepresented leads/co leads (53.8%,
n=14 vs. 24.8%, n=37), series regulars (57.3%, n=114 vs. 29.1%, n=345) and speaking characters (58.8%, n=612 vs. 31.2%, n=1,909)
than white creators. These results suggest that underrepresented creators were responsible for the on-screen racial/ethnic in-
clusion seen in Netflix series. These shows over-indexed against U.S. population proportions, while series from white creators
under-indexed. The same trends appeared for writers. When an underrepresented writer was credited across the first three ep-
isodes, a greater percentage of underrepresented characters were leads/co leads (51.7%, n=31 vs. 17.4%, n=20), series regulars
(49.9%, n=243 vs. 24%, n=216) and speaking characters (47.6%, n=1,200 vs. 28.5%, n=1,321).
The results for underrepresented racial/ethnic groups behind the camera in scripted series demonstrate that Netflix shows out-
paced the larger industry in some areas, such as for series creators, but fell behind in others (i.e., directors). Women of color
were least represented across all positions. The presence of underrepresented creators and writers was associated with more
underrepresented leads, series regulars and speaking characters on screen. Once again, underrepresented content creators are
responsible for driving inclusion on screen in Netflix content.
TABLE 35
NETFLIX SERIES DIRECTORS BY UNDERREPRESENTED STATUS, GENDER AND YEAR
UNDERREPRESENTED STATUS 2018 2019 TOTAL
61.4% (n=468)
19.6% (n=149)
13.4% (n=102)
5.6% (n=43)
55.2% (n=499)
23.8% (n=215)
14.8% (n=134)
6.2% (n=56)
White Men
White Women
UR Men
UR Women
TABLE 35
58% (n=967)
21.8% (n=364)
14.2% (n=236)
5.9% (n=99)
FIGURE 7
PERCENTAGE OF UNDERREPRESENTED LEAD/CO LEAD, SERIES
REGULARS AND SPEAKING CHARACTERS
BY UNDERREPRESENTED STATUS OF SERIES CREATORS
53.8%
58.8%
24.8%
29.1%
UR Creator
White Creator
PERCENTAGE OF UNDERREPRESENTED LEADS, MAIN CAST AND SPEAKING CHARACTERS
BY UNDERREPRESENTED STATUS OF SERIES CREATORS
FIGURE 7
UR
Leads/Co Leads
57.3%
31.2%
UR
Series Regulars
UR
Speaking Characters
Page 21
The previous section examined race/ethnicity on screen and behind the camera across Netflix fictional storytelling. Here, we
assess the three largest racial/ethnic groups (i.e., Black, Latinx, Asian, Middle Eastern/North African, American Indian/Alaskan
Native, Native Hawaiian/Pacific Islander). We focused on these three groups as they had large enough sample sizes to examine
medium, year, and gender differences. Additionally, we approached the analyses differently than we did in the previous section.
27
Rather than using U.S. Census distinctions, we examined whether a character was identified as Black (yes, no), Latinx (yes, no),
Asian (yes, no), Middle Eastern (yes, no), American Indian/Alaskan Native (yes, no), Native Hawaiian/Pacific Islander (yes, no).
Characters that were multiracial or multiethnic were recategorized and could count in more than one racial and/or ethnic groups.
Below, we summarize the prevalence of characters (i.e., leads/co leads, main cast) and content creators (i.e., above-the-line
personnel) from these communities. Similar to the previous section, above the line roles were reported separately for film and
scripted series. At the end of this part of the paper, we also summarize briefly the remaining racial/ethnic groups (Middle Eastern/
North African, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander) by leads/co leads, main cast and personnel
behind the camera.
Black Cast & Crew
On Screen. A total of 15.2% (n=46) of all stories were led/co led with Black protagonists, with film (21.4%, n=27) significantly
outperforming series (10.8%, n=19). The percentage of all stories centered on Black leads/co leads (15.2%) was in line with the
proportion of the U.S. population that identifies as Black alone or in combination with another race (14.7%) with films notably
higher (+6.7 percentage points).
28
There was no change over time for Black leads/co leads overall, or for film. Series, however,
demonstrated a significant jump in Black leads/co leads from 2018 (6.3%, n=5) to 2019 (14.4%, n=14). Turning to individual char-
acters, Black leads/co leads were equally likely to be male-identified (51.5%, n=69) and female-identified (48.5%, n=65) across
both film and series.
Pivoting to main cast, 19.5% (n=485) were Black with film (20.8%, n=214) and series (18.5%, n=271) showing no meaningful
difference. In comparison to U.S. Census (14.7%), the overall Black main cast (19.5%) was higher in proportion, but just shy of
significant (4.8 percentage points) while film was significantly different (+6.1 percentage points).
29
Both film (17.9% to 24.2%) and
series (14.8% to 21.8%) significantly increased the percentage of Black main cast members from 2018 to 2019 (see Table 37). Black
main cast were more likely to be male-identified (61.2%, n=131) than female-identified (38.8%, n=83) in film, although there was
no corresponding difference in series (male-identified: 49.1%, n=133, female-identified: 50.9%, n=138).
SPECIFIC RACIAL/ETHNIC GROUPS
TABLE 36
PERCENTAGE OF BLACK LEADS/CO LEADS BY STORYTELLING MEDIUM AND YEAR
FILM
% of Black
leads/co leads
TOTAL
2018
2019
2018
2019
TOTAL
20.3% 22.8% 6.3% 14.4%
15.2%
(n=14) (n=13) (n=5) (n=14) (n=46)
21.4%
(n=27)
SERIES
10.8%
(n=19)
TABLE 36
MEASURE
% of Black
leads/co leads
15.2%
14.4%
22.8%
2018
2019
TOTAL
(n=13)
(n=14)
(n=46)
21.4%
(n=27)
10.8%
(n=19)
INDICATOR
20.3%
(n=14)
2018
2019
6.3%
(n=5)
TOTAL
FILM SERIES
TABLE 37
PERCENTAGE OF BLACK MAIN CAST BY STORYTELLING MEDIUM AND YEAR
FILM
% of Black
main cast
TOTAL
2018
2019
2018
2019
TOTAL
17.9% 24.2% 14.8% 21.8%
19.5%
(n=99) (n=115) (n=100) (n=171) (n=485)
20.8%
(n=214)
SERIES
18.5%
(n=271)
TABLE 37
MEASURE
% of Black
main cast
19.5%
21.8%
24.2%
2018
2019
TOTAL
(n=115)
(n=171)
(n=485)
20.8%
(n=214)
18.5%
(n=271)
INDICATOR
17.9%
(n=99)
2018
2019
14.8%
(n=100)
TOTAL
FILM SERIES
Page 22
Behind-the-camera. In film, 9.2% (n=12) of directors were Black with a significant increase observed from 2018 (6.9%, n=5) to
2019 (12.1%, n=7). This was in contrast to top-grossing films, where a significant decrease occurred from 2018 (13.4%, n=15) to
2019 (8%, n=9). For writers, 8% (n=16) were Black with 2019 significantly higher than 2018. Only 5.5% of top-grossing scribes
were Black with no changes observed by year. There were no differences in Black producers across the industry (i.e., Netflix=5.1%,
n=22, Top-Grossing=6.3%, n=43), year or the intersection of these two measures (see Table 38). Black women comprised 38%
(n=19) of the behind the scenes roles in film and were most likely to work as producers (50%, n=11), followed by writers (31.3%,
n=5), and directors (25%, n=3).
How inclusive were Black content creators when casting Netflix films? Black content creators were responsible for many of the
Black film leads/co leads as well as main cast members across the sample. In movies with a Black director attached, 83.3% (n=10)
of the leads/co leads were Black whereas only 14.9% (n=17) were in films with only non Black directors. A similar jump was ob-
served for main cast members. Films with Black directors accounted for 62.4% (n=58) of Black main cast actors. Movies without
a Black director attached only featured Black actors in 16.7% (n=156) of the main cast. For writers, the same trend emerged. Black
screenwriters were far more inclusive than non Black screenwriters for Black leads/co leads (81.8%, n=9, vs. 15.7%, n=18, respec-
tively) and main cast (65.8%, n=48, vs. 17.4%, n=166,
respectively). The same pattern appeared with Black
vs. non Black producers as well.
30
Above-the-line personnel for scripted series can be
found in Table 39. Only 6.5% (n=17) of series creators
across the two-year time frame were Black but a sig-
nificant increase emerged from 2018 (2.5%, n=3) to
2019 (9.7%, n=14). No differences were found over
time for producers, writers, or directors where Black
creatives held 5.6% (n=89), 8.6% (n=226) and 9.3%
(n=155) of all jobs, respectively. Black women held
43.1% (n=210) of these behind-the-scenes roles, in-
cluding 29.4% (n=5) of creators, 42.7% (n=38) of pro-
ducers, 51.3% (n=116) of writers, and 32.9% (n=51)
of directors. Black creators had a significant impact
on the prevalence of Black series regulars. When a
Black creator was behind a series, 72% (n=59) of se-
ries regulars were Black, while only 15.4% (n=212) of
series regulars were Black when a non-Black creator
developed a series.
TABLE 38
PERCENTAGE OF BLACK ABOVE-THE-LINE PERSONNEL BY INDUSTRY AND YEAR
NETFLIX FILMS
Directors
2018
2019
2018
2019
6.9% 12.1% 9.2% 13.4%
8%
(n=5) (n=7) (n=12) (n=15) (n=9)
5%
(n=5)
TOP-GROSSING FILMS
TABLE 38
MEASURE
5.2%
(n=13)
5.4%
(n=23)
11%
(n=11)
5%
(n=9)
8%
(n=27)
5.1%
(n=22)
6.6%
(n=50)
5.5%
(n=19)
5.2%
(n=11)
5.8%
(n=13)
8%
(n=16)
6.9%
(n=47)
7.1%
(n=24)
6.7%
(n=44)
10.7%
5.5%
6.3%
6.8%
Writers
Producers
OVERALL
OVERALL
(n=24)
(n=24)
(n=43)
(n=91)
Overall
TABLE 39
PERCENTAGE OF BLACK ABOVE-THE-LINE PERSONNEL IN
NETFLIX SCRIPTED SERIES BY YEAR
OVERALL
2018
2019
2.5% 9.7% 6.5%
5.5%
(n=3) (n=14) (n=17)
(n=154)
3.9%
(n=28)
NETFLIX SCRIPTED SERIES
TABLE 39
MEASURE
5.7%
(n=69)
7.1%
(n=54)
7%
(n=61)
11%
(n=157)
11.2%
(n=101)
8.6%
(n=226)
9.3%
(n=155)
7.9%
(n=487)
9.9%
(n=333)
5.6%
(n=89)
Creators
Producers
Writers
Directors
Overall
Page 23
Latinx Cast & Crew
Earlier in the report, we examined all Hispanic/Latino characters independently of their descent or geographical heritage. Here,
we took a more narrow approach. First, we examined Latinos across all racial groups (e.g., White, Black, Indigenous). Then, we
identified creatives on screen and behind the camera who were born in the U.S. or its territories. All those of Spanish origin with-
out other Latino identification were excluded prior to analysis. Given the small number of Latinx roles on screen and employment
patterns behind the camera, we do not focus on over time trends.
As shown in Table 40, only 2.6% of all stories featured Latinx leads/co leads and 4.5% of main cast members were Latinx. Ac-
cording to Pew Research Center and U.S. Census data, 67% of U.S. Hispanic/Latinos are U.S. born.
31
Thus, roughly 12% of the U.S.
population could be considered Latinx, a number substantially higher than the percentage of leads and main cast across film and
series. No differences emerged in leads/co leads or main cast members by storytelling medium (see Table 40). Slightly less than
half (42.9%, n=6) of Latinx leads/co leads were girls and women (film: 40%, n=2, series: 44.4%, n=4). Latinx main cast were also
equally likely to be male-identified (50.9%, n=56) as female-identified (49.1%, n=54). Series (53.4%, n=39), however, were more
likely than film (40.5%, n=15) to feature Latinx girls and women in main cast roles.
Focusing on content creators, less than 1% of above-the-line personnel were Latinx. Numerically, one director (<1%), one writer
(<1%) and 5 producers (1.2%) comprised the entire pool of Latinx storytellers working behind the camera in film. Three of the pro-
ducers were Latinx women. It is important to note that only 2 Afro-Latinos worked behind the camera across the Netflix sample.
Both were women and served as producers on 2 movies. How do Netflix films compare to top-grossing movies? The numbers and
percentages in Netflix films were lower than top-grossing films where 1.8% (n=4) of directors, <1% of writers (n=4) and 3.7% of
producers (n=25) were Latinx.
For series, only 2.7% (n=7) of creators, 2.6% of producers (n=41), 2.5% of writers (n=65) and 2.5% (n=42) of directors were
Latinx. Of the Latinx content creators in series, 40.6% (n=63) were women, including 28.6% (n=2) of creators, 34.1% (n=14) of
producers, 38.5% (n=25) of writers, and 52.4% (n=22) of directors. No industry metrics were available to compare Netflix series to
other episodic content. Because of the small sample sizes, the relationship between Latinx on screen roles and creative working
behind the camera could not be evaluated.
Asian Cast & Crew
Four percent of all stories were led/co led by Asian actors (n=20, 9=males, 11=females), with films (7.1%, n=9) featuring signifi-
cantly more than scripted series (1.7%, n=3). Of the main cast, 7% were Asian (n=175) with no differences between film (7.7%,
n=79) and scripted series (6.6%, n=96). More Asian main cast roles were held by girls and women (57.5%, n=100) than boys and
TABLE 41
PERCENTAGE OF ASIAN LEADS/CO LEADS AND MAIN CAST BY STORYTELLING MEDIUM
LEADS/CO LEADS
% of Asians
FILM
SERIES
FILM
SERIES
7.1% 1.7% 4% 7.7%
6.6%
(n=9) (n=3) (n=12) (n=79) (n=96)
MAIN CAST
TABLE 41
MEASURE
7%
OVERALL
OVERALL
(n=175)
TABLE 40
PERCENTAGE OF LATINX LEADS/CO LEADS AND MAIN CAST BY STORYTELLING MEDIUM
% of Latinx
4% 1.7% 2.6% 3.6%
5.1%
(n=5) (n=3) (n=8) (n=37) (n=74)
TABLE 40
MEASURE
4.5%
(n=111)
LEADS/CO LEADS
FILM
SERIES
FILM
SERIES
MAIN CAST
OVERALL
OVERALL
Page 24
men (42.5%, n=74). Series (63.2%, n=60) featured more female-identified Asian main cast than film (50.6%, n=40). The break-
down of series regulars’ ethnic background or descent can be found in Table 42, with actors of Indian, Chinese and Korean descent
or heritage (regardless of country of origin or nationality) working the most frequently across Netflix storylines.
Behind the camera, Asians comprised 3.1% of directors (n=4, 2=male, 2=female), 4% of writers (n=8, 7=male, 1=female) and
4.2% of producers (n=18, 10=male, 8=female). These percentages were not meaningfully different from the hiring patterns in
top-grossing films during the same 2-year time frame: 4.5% of directors (n=10), 4.4% of writers (n=19), and 3.2% of producers
(n=22). Only 4% (n=248) of all content creators across scripted series were Asian. The breakdown was as follows: 1.5% of creators
(n=4, 1=male, 3=female), 3.5% of producers (n=55, 30=male, 25=female), 4.4% of writers (n=115, 40=male, 75=female), and
4.4% of directors (n=74, 51=male, 23=female).
Middle Eastern/North African, American Indian/Alaskan Native and Native Hawaiian/Pacific Islander Cast & Crew
Three remaining racial/ethnic groups were explored. As shown in Table 43, very few leads/co leads were Middle Eastern/North
African (MENA), American Indian/Alaskan Native (AIAN) or Native Hawaiian/Pacific Islander (NHPI). This was also true for main
cast. Fewer than 1% of main cast roles were held by AIAN or NHPI actors, and just 1.4% by MENA talent. For MENA (n=25) and
NHPI (n=12) roles, the majority were filled by male-identified actors, while AIAN leads/co leads (n=2) and main cast (n=14) were
more likely to be female-identified.
TABLE 42
ASIAN MAIN CAST ACTORS BY STORYTELLING MEDIUM
TABLE 43
PERCENTAGE OF LEADS/CO LEADS AND MAIN CAST BY RACIAL/ETHNIC GROUP AND
STORYTELLING MEDIUM
COMMUNITY FILM SERIES
25.3% (n=20)
20.3% (n=16)
16.5% (n=13)
13.9% (n=11)
13.9% (n=11)
11.4% (n=9)
2.5% (n=2)
1.3% (n=1)
28.4% (n=27)
5.3% (n=5)
27.4% (n=26)
9.5% (n=9)
15.8% (n=15)
2.1% (n=2)
5.3% (n=5)
4.2% (n=4)
Indian
Japanese
Chinese
Filipino
Korean
Indonesian
Vietnamese
Pakistani
TABLE 42
FILM SERIES
1.3% (n=1)
1.3% (n=1)
0
0
0
0
7.7% (n=79)
2.1% (n=2)
1.1% (n=1)
2.1% (n=2)
1.1% (n=1)
1.1% (n=1)
1.1% (n=1)
6.6% (n=96)
Singaporean
Thai
Malaysian
Laotian
Nepalese
Tibetan
Total
COMMUNITY
LEADSCO LEADS
% of Middle Eastern/
North African
FILM
SERIES
FILM
SERIES
1.6% <1% 1% 2.2%
<1%
(n=2) (n=1) (n=3) (n=23) (n=11)
1.6%
(n=2)
MAIN CAST
TABLE 43
MEASURE
<1%
(n=1)
0
<1%
(n=2)
<1%
(n=6)
1%
(n=14 )
<1%
(n=9)
<1%
(n=2)
<1%
(n=12)
1.4%
<1%
<1%
% of American Indian/
Alaskan Native
% of Native Hawaiian/
Pacific Islander
OVERALL
OVERALL
(n=34)
(n=23)
(n=18)
<1%
(n=1)
Note: For film and series, the column totals do not add to 100%. Six film and six series actors identified as coming from multiple
Asian communities. Actors were classified by descent and could be of any nationality (e.g., American, etc.).
Page 25
In this section, we review the on-screen distribution of LGBTQ characters (i.e., leads/co leads, main cast, speaking characters)
in Netflix films and scripted series. We also provide comparisons to the wider industry statistics in two ways. First, we compare
Netflix movies to top-grossing films. Second, we compare Netflix series to statistics from GLAAD on primetime broadcast series.
Although GLAAD examines cable and streaming series, LGBTQ characters as a percentage of all characters are not reported for
film and series overall in their report. Thus only information on broadcast series regulars is included as a comparison.
Overall, 2.3% (n=7) of leads/co leads across film and series were LGBTQ. In comparison to the U.S. population (12%)
33
, Netflix
films and series under performed by 9.7 percentage points. Film (4%, n=5) and series (1.1%, n=2) did not differ significantly in the
presentation of LGBTQ leads/co leads (see Table 44). There was also no difference by year, as 2018 (1.4%, n=2) and 2019 (3.2%,
n=5) featured roughly equal percentages of LGBTQ leads/co leads. There was also no difference when year and medium were
crossed.
Of the leads/co leads across film content in 2018 and 2019, 1 was lesbian, 1 was gay, and 3 were bisexual. The two series in 2019
with majority LGBTQ casts featured 2 lesbian, 2 gay, 6 bisexual, and 3 transgender characters. Moving from the number of stories
featuring LGBTQ leads to the 17 LGBTQ lead characters, audiences were equally likely to see female-identified (52.9%, n=9) and
male-identified (47.1%, n=8) leads/co leads in Netflix films and series. However, few underrepresented LGBTQ leads/co leads
(29.4%, n=5) appeared on either medium. Only 3 of the leads/co leads who were LGBTQ were women of color. Two other factors
LGBTQ
TABLE 44
PERCENTAGE OF LGBTQ LEADS/CO LEADS BY STORYTELLING MEDIUM AND YEAR
FILM
% of LGBTQ
leads/co leads
TOTAL
2018
2019
2018
2019
TOTAL
2.9% 5.3% 0 2.1%
2.3%
(n=2) (n=3) (n=1) (n=7)
4%
(n=5)
SERIES
1.1%
(n=2)
TABLE 44
MEASURE
% of LGBTQ
leads/co leads
2.3%
2.1%
5.3%
2018
2019
TOTAL
(n=3)
(n=1)
(n=7)
4%
(n=5)
1.1%
(n=2)
INDICATOR
2.9%
(n=2)
2018
2019
0
TOTAL
FILM SERIES
Behind the scenes, content creators from these communities were rare. In film, 1.5% of directors (n=2, both women), 1.5% (n=3,
all men) of writers, and <1% (n=4, 3 men, 1 woman) of producers were MENA. Only 2 male-identified film writers were AIAN, and 1
woman film producer was NHPI. Netflix films were not significantly different from the percentage of MENA, AIAN or NHPI content
creators working on top-grossing movies.
Turning to series, 1.9% (n=5) of creators, 1.5% (n=40) of writers, 1.5% (n=24) of producers, and 2.6% (n=43) of directors were
MENA. Of the MENA directors, writers, and producers in series, 13.4% (n=15) were women. Fewer than 1% of all series above-
the-line personnel were AIAN; of the 14 AIAN content creators, 9 were directors, 3 were writers and 2 were producers. Nine of
the 14 personnel positions were held by AIAN women. Only 4 creative positions were held by people from the NHPI community: 1
director and 2 producers were NHPI women and 1 male-identified NHPI producer worked on a Netflix series. No series creators
were MENA, AIAN or NHPI women.
The results in this section indicate that the percentage of Black leads/co leads and main cast in Netflix films exceeded proportional
representation with the U.S. population, while series were on par with this figure (14.7%).
32
However, for other groups, notably the
Latinx community, Netflix content fell short of reaching this level of representation. On screen, MENA, AIAN, and NHPI leads and
main cast were rare—there were no series that depicted a majority AIAN main cast in 2018 or 2019. And, while Asian cast were at
proportional representation, the full diversity of this community was not seen. Behind the camera, the presence of Black content
creators improved significantly over time. Yet, these same gains were not achieved for other groups, whose presence in key cre-
ative positions were minimized or even erased.
Page 26
captured the nature of LGBT portrayals: age and parental status. The majority of LGBTQ leads/co leads were young adults (41.2%,
n=7) and only two were depicted as a parent or caregiver across film and series content.
Netflix (4%, n=5) did not differ from top-grossing movies (2%, n=4) in the percentage of LGBTQ led films overall, in 2018 (Net-
flix=2.9%, n=2, Top-Grossing=2%, n=2) or 2019 (Netflix=5.3%, n=3, Top-Grossing=2%, n=2). Although Netflix was comparable to
industry, the lack of LGBTQ leads across the wider film ecosystem demonstrates that there is room for greater inclusion among
protagonists across both the Netflix medium and theatrical releases. There was not a comparable metric available for series, thus
the analysis is not reported.
Next, we examined the main cast of films and series. Of the 2,419 main cast in Netflix films and series, 5.3% (n=129) were LGBTQ.
Series (6.1%, n=85) and film (4.3%, n=44) had similar percentages of LGBTQ main cast. There was no significant difference over
time, as 4.2% (n=51) of main cast in 2018 and 6.4% (n=78) in 2019 were LGBTQ. Crossing year by medium did not result in any sig-
nificant deviations (see Table 45). Netflix and primetime broadcast series featured comparable percentages of LGBTQ main cast/
series regulars in 2018 (Netflix: 4.6%, n=30, primetime: 6.4%) and 2019 (Netflix, 7.4%, n=55, primetime: 8.8%).
34
Overall, main cast were most likely to be gay (2.7%, n=66) in Netflix films and series, followed by lesbian (1.5%, n=37), bisexual
(1%, n=23), and transgender cast (<1%, n=4). The percentage of transgender main cast was comparable to U.S. population figures
(0.6%) and all of the transgender main cast appeared in series released in 2019.
35
There were no significant differences per year
or by medium in the distribution of LGBTQ main cast.
36
LGBTQ main cast were more likely to be male-identified (61.2%, n=79) than female-identified (38.8%, n=50). Female-identified
LGBTQ main cast were more prevalent in series (41.2%, n=35) than film (34.1%, n=15). LGBTQ main cast achieved proportional rep-
resentation for underrepresented racial/ethnic groups (38.8%, n=50)—though few of the LGBTQ main cast were women of color
(14%, n=18). Film (43.2%, n=19) had more underrepresented LGBTQ main cast than series (36.5%, n=31). In terms of age, half of
LGBTQ main cast (46.5%, n=60) were young adults, while 26.4% (n=34) were teens, 22.5% (n=29) were middle aged, and 4.7%
(n=6) were elderly. Parental or caregiving responsibilities were shown on screen by a total of 93 main cast roles depicted parents,
but few (11.8%, n=11) of these roles were held by LGBTQ main cast.
Finally, we examined the percentage of LGBTQ speaking or named characters. Of the 12,018 speaking characters across film and
series, 2.8% (n=338) were LGBTQ. The percentage of LGBTQ speaking characters did not vary significantly by medium or by year.
In comparison to top-grossing films (1.4%, n=119), Netflix was not significantly different (2%, n=93) overall, in 2018 (Netflix: 1.9%,
n=47, Top-Grossing: 1.3%, n=58), or in 2019 (Netflix: 2.1%, n=46, Top-Grossing: 1.4%, n=61).
Gay characters (1.6%, n=192) were most likely to appear on screen in films and series, while fewer than 1% of all speaking charac-
ters were lesbian (n=101), bisexual (n=33), or transgender (n=15). Again, there was no change over time or variability by medium.
37
Of the LGBTQ speaking characters, 62.4% (n=211) were male-identified and 37.6% (n=127) were female-identified, which did not
differ by medium (film: 38.7%, n=36, series: 37.1%, n=91). In terms of race/ethnicity, 59.8% (n=201) of the LGBTQ speaking charac-
TABLE 45
PERCENTAGE OF LGBTQ MAIN CAST BY STORYTELLING MEDIUM AND YEAR
FILM
% of LGBTQ
main cast
TOTAL
2018
2019
2018
2019
TOTAL
3.8% 4.9% 4.6% 7.4%
5.3%
(n=21) (n=23) (n=55) (n=129)
4.3%
(n=44)
SERIES
6.1%
(n=85)
TABLE 45
MEASURE
% of LGBTQ
main cast
5.3%
7.4%
4.9%
2018
2019
TOTAL
(n=23)
(n=55)
(n=129)
4.3%
(n=44)
6.1%
(n=85)
INDICATOR
3.8%
(n=21)
2018
2019
4.6%
TOTAL
FILM SERIES
(n=30)
(n=30)
Page 27
ters were white and 40.2% (n=135) were underrepresented. Series (41.6%, n=101) were more inclusive of underrepresented LGBTQ
characters than films (36.6%, n=34). Women of color comprised 17% (n=57) of LGBTQ speaking characters across series and film.
Consistent with findings on leads/co leads and main cast, the majority of LGBTQ speaking characters were young adults (61.5%,
n=208) and few were parents (17.3%, n=28).
Overall, the findings in this section revealed that Netflix features few LGBTQ leads/co leads, main cast, or speaking characters in
film or series content. While not significantly different from the wider industry in terms of leads/co leads or main cast, Netflix falls
below population figures for this community. In the next section, we examine another historically underrepresented community
—characters with disabilities.
CHARACTERS WITH DISABILITIES
The prevalence of characters with disabilities in Netflix content was assessed. The definition of disability was based on the text
of the Americans with Disabilities Act (ADA) and included physical, communicative, and cognitive disabilities, as reported by the
U.S. Census.
38
In this section, we provide information on lead/co leads, main cast, and speaking characters across Netflix films
and series.
Beginning with leads/co leads, a total of 5.3% (n=16) stories were led by characters with a disability (see Table 47). Compared to
the 27.2% of the U.S. population that identifies as living with a disability, Netflix films and series show substantially fewer leads/co
leads.
39
Films (11.9%, n=15) were significantly more likely than series (<1%, n=1) to feature leads/co leads with a disability. There
was no difference over time for films and series combined, as 4.1% (n=6) of stories in 2018 and 6.5% (n=10) in 2019 were led by
characters with disabilities. When medium and year were crossed, however, a significant increase was observed for film. In 2018,
8.7% (n=6) of leads/co leads were shown with a disability, which rose to 15.8% (n=9) in 2019, though this was an increase of only
3 leading/co leading roles. No corresponding increase was observed for series.
TABLE 47
PERCENTAGE OF LEADS/CO LEADS WITH A DISABILITY BY STORYTELLING MEDIUM AND YEAR
FILM
% of leads/co leads
with a disability
TOTAL
2018
2019
2018
2019
TOTAL
8.7% 15.8% 0 1%
5.3%
(n=6) (n=9) (n=1) (n=16)
11.9%
(n=15)
SERIES
<1%
(n=1)
TABLE 47
MEASURE
% of leads/co leads
with a disability
5.3%
1%
15.8%
2018
2019
TOTAL
(n=9)
(n=1)
(n=16)
11.9%
(n=15)
<1%
(n=1)
INDICATOR
8.7%
(n=6)
2018
2019
0
TOTAL
FILM SERIES
TABLE 46
PERCENTAGE OF LGBTQ SPEAKING CHARACTERS BY STORYTELLING MEDIUM AND YEAR
FILM
% of LGBTQ
speaking characters
TOTAL
2018
2019
2018
2019
TOTAL
1.9% 2.1% 2.5% 4.1%
2.8%
(n=47) (n=46) (n=162) (n=338)
2%
(n=93)
SERIES
3.3%
(n=245)
TABLE 46
MEASURE
% of LGBTQ
speaking characters
2.8%
4.1%
2.1%
2018
2019
TOTAL
(n=46)
(n=162)
(n=338)
2%
(n=93)
3.3%
(n=245)
INDICATOR
1.9%
(n=47)
2018
2019
2.5%
TOTAL
FILM SERIES
(n=83)
(n=83)
Page 28
Of the leads/co leads with a disability, 66.7% (n=10) were male-identified and 33.3% (n=5) were female-identified in film. In series,
the single program with leading characters experiencing a disability had one male-identified and one female-identified co lead.
There were few leads/co leads with a disability from underrepresented racial/ethnic groups in film (20%, n=3), though both of the
leads/co leads in series were underrepresented. Three of the underrepresented leads/co leads with a disability were men of color
and 2 were women of color. There was a single film with a lead/co lead with a disability from the LGBTQ community.
Overall, Netflix (11.9%, n=15) featured slightly fewer leads/co leads with a disability than top-grossing films (14%, n=28). When
this was examined by year, Netflix (8.7%, n=6) and top-grossing films (9%, n=9) did not differ in the percentage of leads/co leads
with a disability in 2018, nor in 2019 (Netflix=15.8%, n=9, Top-Grossing=19%, n=19). There was no available industry comparison
for series, thus it is not reported.
The nature of the disabilities depicted in Netflix films and scripted series were also evaluated. Because leads/co leads could be
depicted with more than one disability, these categories do not total to 100%. Cognitive disabilities (56.3%, n=9) were most prev-
alent. Cognitive disabilities included dyscalculia, post-traumatic stress disorders, and depression.
40
Half of leads/co leads were
shown with physical (50%, n=8) disabilities, such as paralysis, missing limbs, mobility restrictions and severe heart conditions.
Slightly less than one-third of leads/co leads were depicted with a communicative (31.3%, n=5) disability, including leads/co leads
who were deaf, blind or mute.
Turning to main cast, 4.7% (n=113) of the main cast across film and series were depicted with a disability. There was no difference
in the percentage of main cast with a disability in Netflix films (4.1%, n=42) or series (5.1%, n=71), nor was there a difference over
time (2018=4.4%, n=53, 2019=4.9%, n=60). Table 48 reflects the results when medium and year were evaluated. No significant
differences emerged in the percentage of main cast with disabilities each year for either film or series content.
Slightly more than one-third of the main cast with a disability were female-identified (36.3%, n=41), with series (40.8%, n=29)
more likely to feature girls and women with a disability in the main cast than film (28.6%, n=12). Roughly one-quarter of main
cast with a disability were underrepresented (26.9%, n=29), no differences emerged by medium (film: 28.2%, n=11, series: 26.1%,
n=18). Crossing gender and race, 13.9% (n=15) of the main cast roles shown with a disability were held by women of color. These
women were more likely to appear in series (17.4%, n=12) than film (7.7%, n=3). Fewer than 10% (8.8%, n=10) of main cast with a
disability were LGBTQ, which was consistent across film (7.1%, n=3) and series (9.9%, n=10).
The percentage of main cast with a disability in Netflix series was compared to the percentage of series regulars in primetime
broadcast series with a disability, based on research by GLAAD.
41
In 2018, Netflix (5.2%, n=34) was not significantly different from
the 1.8% (n=16) of series regulars with a disability reported by GLAAD. This was also the case in 2019, Netflix (5%, n=37) and
primetime broadcast shows (2.1%, n=18) did not differ in their presentation of main cast with disabilities. Main cast were most
likely to experience physical disabilities (50.4%, n=57) across films and series, followed by cognitive (49.6%, n=56) and commu-
nicative (13.3%, n=15) disabilities.
Finally, the percentage of all speaking characters with a disability was evaluated. Overall, 2.1% (n=253) of all speaking characters
were shown with a disability. Netflix films and series fall far below U.S. Census (27.2%) statistics regarding disability in the general
TABLE 48
PERCENTAGE OF MAIN CAST WITH A DISABILITY BY STORYTELLING MEDIUM AND YEAR
FILM
% of main cast
with a disability
TOTAL
2018
2019
2018
2019
TOTAL
3.4% 4.9% 5.2% 5%
4.7%
(n=19) (n=23) (n=37) (n=113)
4.1%
(n=42)
SERIES
5.1%
(n=71)
TABLE 48
MEASURE
% of main cast
with a disability
4.7%
5%
4.9%
2018
2019
TOTAL
(n=23)
(n=37)
(n=113)
4.1%
(n=42)
5.1%
(n=71)
INDICATOR
3.4%
(n=19)
2018
2019
5.2%
TOTAL
FILM SERIES
(n=34)
(n=34)
Page 29
population. Table 49 reveals that there was no difference in the percentage of characters with a disability across film (1.5%, n=70)
or series (2.4%, n=183). There were also no differences per year (2018=1.9%, n=116, 2019=2.2%, n=137) in the depiction of char-
acters with disabilities. When year and medium were crossed, once again no significant differences emerged.
The demographic profile of speaking characters with a disability was evaluated. Characters with a disability were more likely to be
male-identified (63.1%, n=159) than female-identified (36.9%, n=93). Series (40.7%, n=74) featured more girls and women with
disabilities on screen than film (27.1%, n=19). Of the characters with disabilities, 71.3% (n=169) were white and 28.7% (n=68) were
from an underrepresented racial/ethnic group, with no difference by medium (film: 27.3%, n=18, series: 29.2%, n=50). There were
few girls and women of color depicted with a disability (14.3%, n=34), though series (17%, n=29) featured more underrepresented
girls and women with a disability than film (7.6%, n=5). Finally, few characters with a disability were LGBTQ (5.3%, n=13), which
did not vary by medium (film: 4.3%, n=3, series: 5.6%, n=10).
Compared to top-grossing movies (1.9%, n=171), Netflix films (1.5%, n=70) featured roughly the same percentage of characters
with disabilities. There were also no differences between Netflix and the wider industry in 2018 (Netflix: 1.3%, n=33, Top-Grossing
films: 1.6%, n=69) or 2019 (Netflix: 1.7%, n=37, Top-Grossing films: 2.3%, n=102). Speaking characters across films and series were
most likely to be presented with a physical disability (51.8%, n=131), followed by cognitive (43.1%, n=109) and communicative
(19.4%, n=49) disabilities.
The results in this section demonstrate that few characters with disabilities were shown in Netflix films and series across 2018
and 2019. When they did appear, these characters were more likely to be male-identified and were primarily white. As 27.2% of
the U.S. population reports living with a disability, there is room for Netflix to grow in order to depict the full range of how people
experience disability.
TABLE 49
PERCENTAGE OF SPEAKING CHARACTERS WITH A DISABILITY BY STORYTELLING MEDIUM AND YEAR
FILM
% of speaking characters
with a disability
TOTAL
2018
2019
2018
2019
TOTAL
1.3% 1.7% 2.4% 2.5%
2.1%
(n=33) (n=37) (n=100) (n=253)
1.5%
(n=70)
SERIES
2.4%
(n=183)
TABLE 49
MEASURE
% of speaking characters
with a disability
2.1%
2.5%
1.7%
2018
2019
TOTAL
(n=37)
(n=100)
(n=153)
1.5%
(n=70)
2.4%
(n=183)
INDICATOR
1.3%
(n=33)
2018
2019
2.4%
TOTAL
FILM SERIES
(n=83)
(n=83)
CONCLUSION
The purpose of this study was to examine inclusion across live action, U.S. original Netflix films (126) and series (180) from 2018
and 2019. On screen, gender, race/ethnicity, LGBTQ, and disability were evaluated for leads/co leads, main cast, and speaking
characters. Behind the camera, directors, writers, producers, and creators were assessed for gender and race/ethnicity. Below,
the major findings from the study are reviewed.
Netflix Reflects Gender Equality in Key Roles
For more than a decade, advocates have pushed entertainment companies to include more girls and women on screen and to
hire more women behind the camera in film and television. Netflix has clearly heeded this call. On screen, girls and women filled
half of the leading and main cast roles in Netflix films and series from 2018 and 2019. One-third of the stories released over the
two-year sample time frame achieved or exceeded gender balance. These findings reflect the extent to which Netflix has focused
its storytelling on girls and women and ensured that its offerings represent half of the U.S. population—and the Netflix audience.
Behind the camera, key decision-making roles were also held by women. Netflix led its industry counterparts in the percentage
of women film directors and producers as well as those who were series creators and writers. It is clear that the company values
Page 30
the perspectives and leadership of women, an inference made not only from these results, but because half of Netflix’s workforce
and its senior leadership are women.
42
From its position as a leader in this area, Netflix can continue to address areas in which the prevalence of girls and women lags.
Girls and women represented slightly more than one-third of all speaking characters in Netflix content, leaving a gap between
fiction and reality that can be closed. In particular, films with only male directors, writers, or producers were less likely to feature
female-identified characters than those with women in these positions. Netflix can work with male content creators to ensure
that their stories feature a more gender-balanced cast. Another path Netflix could take to remedy this is to increase the number
of women working in senior creative roles. While the strong showing for the company is notable, women did not fill fully half of all
positions behind the scenes. This was especially true for women of color, which will be discussed below. Netflix can support tal-
ented women who are new or seasoned professionals by commissioning their work or hiring them for its content. This will bolster
the pipeline for women filmmakers and creatives and further increase the prevalence of girls/women on screen.
Netflix Features Racial/Ethnic Inclusion in Specific Storytelling Roles
Historically, underrepresented racial/ethnic groups have been marginalized and even erased in popular entertainment content—
both on screen and in creative roles that shape storytelling. While calls for increasing representation have grown increasingly
louder in the recent past, Netflix content demonstrates important lessons for the wider industry. First, the company demonstrat-
ed that significant growth is possible. On screen, the percentage of underrepresented leads and main cast across film and series,
as well as underrepresented speaking characters in series grew notably between 2018 and 2019. For leads/co leads and for main
cast in 2019, this growth ensured that Netflix films and series reached proportional representation. Behind the camera, increases
were limited to film and series writers and series creators. These changes were driven by greater inclusion of Black talent in on
screen roles as well as working in key creative positions. Not surprisingly, the percentage of Black employees at Netflix has also
increased over the past three years.
43
These results speak to what is clearly a company-wide commitment to creating greater in-
clusion in storytelling and in leadership roles.
However, there is still room for improvement in key areas. Not all racial/ethnic groups saw their representation increase over time.
In fact, invisibility is still a major concern in content for Hispanic/Latino, Middle Eastern/North African, American Indian/Alaskan
Native, and Native Hawaiian/Pacific Islander communities. Half or more of the stories in the Netflix sample did not feature a single
character from these groups in 2018 or 2019. This extended to behind the camera employment, few storytellers identified with
these communities or who were Latinx, Asian, Middle Eastern or Indigenous worked on Netflix’s film and series lineup. The lack
of inclusion for specific groups, particularly the Latinx community, contributes to the gap between on screen percentages and
proportional representation for underrepresented speaking characters. While it is important to celebrate the gains, Netflix must
continue to strive for representation that reflects all communities and presents authentic stories about the groups that comprise
its audience. That effort may begin behind the camera, with commissioning and hiring storytellers. It also requires working with
casting directors and—in particular—white directors, writers, producers, and creators—to ensure that their stories are inclusive
of underrepresented characters. By deepening its efforts to hire and cast inclusively, Netflix can continue to demonstrate how
companies can change the historical patterns of inequity that still persist in entertainment.
Women of Color are a Critical Component of Inclusion
When women and people of color are excluded, women of color face the greatest erasure on screen and behind the camera. Net-
flix content in 2018 and 2019 both confirmed this and demonstrated areas of progress. One bright spot for women of color was in
leading or co leading roles. Across films and series, girls and women of color were as likely as men of color to fill lead roles. Netflix
movies also surpassed top-grossing films in their inclusion of leads/co leads who were women of color. While centering the stories
of girls and women from underrepresented groups is a crucial part of expanding opportunities on screen, this inclusion must not
stop at prominent roles only.
There are areas for improvement, however. Women of color were least likely to fill main cast roles and to be shown as speaking
characters on screen across films and series. Moreover, the erasure that faced Hispanic/Latino, Middle Eastern/North African,
American Indian/Alaskan Native, and Native Hawaiian/Pacific Islander characters was compounded when the presence of women
from these groups was evaluated. Two-thirds or more of the fictional films and series in this 2018 and 2019 Netflix sample were
missing women from these communities entirely.
Page 31
Behind the camera, women of color were largely absent in creative roles on Netflix films and series—fewer than 10% of roles
across each position evaluated (directors, writers, producers, creators) went to underrepresented women. There was no signif-
icant change over time for women of color behind the camera, either. The lack of women of color in decision-making positions
is not unique to Netflix content, as these results mirror industry-wide trends. However, as Netflix continues to consider how it
can create more inclusive content, increasing the number of women of color working behind-the-scenes is an important place
to begin. By focusing on women of color, Netflix can take aim at where some of the biggest gaps between the population and its
content exists. Narrowing these divides can impact not only the stories seen on Netflix, but can have a ripple effect throughout
the wider industry ecosystem.
LGBTQ and Disability Communities are Rarely Seen and Heard in Storytelling
Representation of the LGBTQ community and people with disabilities in entertainment has received much attention in recent
years, with little evidence of substantive change. Thus, it was imperative to evaluate how Netflix represented these two commu-
nities in its content. Beginning with the LGBTQ community, while 12% of the U.S. population identifies as a member of the LGBTQ
community, Netflix content did not approach this benchmark in its representation of leads, main cast, and all speaking characters.
Additionally, in the two series with majority LGBTQ main cast there were only 3 transgender leading/co leading characters, while
few transgender main cast or speaking characters were shown in Netflix content. Beyond the lack of LGBTQ characters overall,
Netflix content also lacked intersectional inclusion in this community. For example, although half of leads/co leads were wom-
en, LGBTQ characters were primarily male. Representation of this community was also predominantly white. The lack of women
and people of color depicted as LGBTQ minimizes the authentic diversity of this community. Additionally, most LGBTQ-identified
characters were young adults, and few were shown as parents. These indicators speak to the nature of storytelling and the narrow
portrayal of LGBTQ characters in Netflix content.
A similar pattern unfolded for characters with disabilities. Netflix content did not approximate the 27.2% of the U.S. population
who identify as having a disability by depicting this group as leads/co leads, main cast, or in all speaking roles. Film provided more
opportunities than did series; 11.9% of leads/co leads in film across two years were shown with a disability versus fewer than 1%
of all series had a majority main cast with disabilities. Still, the depiction of characters with disabilities trended toward white and
male representation rather than showing women and girls or people of color in these roles. Very few characters with disabilities
were part of the LGBTQ community. Given the prevalence of disability in the U.S. population and thus among the Netflix audience,
this is an area where this entertainment company can seek to increase authentic representation—and can lead its industry peers
toward greater inclusion of this community.
Limitations
As with all studies, two brief limitations must be noted. First, this study focused on U.S. live-action, fictional, English-language
films and series released on Netflix in 2018 and 2019. Animated stories, non-fiction, or international offerings may differ in their
inclusion profile. Moreover, the subject of this study was Netflix original content; licensed offerings may also feature a different
portrait of inclusion. Subsequent investigations are necessary to examine how different forms of content present the groups as-
sessed in this study. Second, there are key methodological differences across this report and studies used to provide comparison
data on series. Given this, contrasts are presented to inform the reader but should be interpreted cautiously.
Final Takeaways
Overall, this report provides a deep look at the inclusion in Netflix films and series in 2018 and 2019. It is important to acknowl-
edge two things about this investigation. One, it was instigated by Netflix and demonstrates a commitment to self-reflection and a
desire for transparency. These are critical steps to take in a journey toward inclusion. Two, the size and scope of Netflix’s content
across these two years is an indication of its position in the industry. As such, each data point in this study is an echo of a decision
that the company and its partners made. Looking across a total of 22 inclusion indicators that were assessed in this report, Netflix
films and series showed small to significant improvement across 19 metrics between 2018 and 2019. The cumulative impact of
these decisions reverberates through Netflix content and to its audiences. Committing to inclusion requires approaching each
decision with intention. This report demonstrates that Netflix takes its decision-making role seriously, is moving toward inclusion,
and will continue to use data-driven metrics to ensure that its choices reflect the diversity of its audience and the talent that exists
throughout the entertainment industry.
Page 32
Aiyonna White
Aline Saruhashi
Alvin Makori
Amanda Lee
Amelia Fong
Ana Neffa
Ana Tessier
Anant Natt
Andrea D. Porras
Annaliese Schauer
Aram Mahserejian
Ashika Kumar
Ashna Paul
Audrey Kono
Aziza Wako
Bryan Davis
Bryan Guzman
Bryte Darden
Cami Robinson
Carmen Abuzid
Casey Fraser
Chanel Kaleo
Chris Posslenzny
Cody Uyeda
Dana Dinh
Diana Postolache
Eddie Jang
Eliana Rosenthal
Elizabeth Berkovich
Emily Aslan
Evelyn Luo
Izzy Brown
Jacqueline Martinez
Jasmin Ricki Serrano
Jenna Richter
Jennifer Lopez
Jocelyn Zeyang Yan
Julia Teymourian
Justin Marks
Kaavya Rajesh
Kaitlyn Francel
Kameron Brown
Katherine Kelly
Kayla Williams
Khanh Ngo
Kian Broder Wang
Kristijiana St.Clair
Maria Takigawa
Maya Penland
Mbinya Muthama
Meher Qazilbash
Minely Aghabegian
Noel Muluneh
Olivia Ellegard
Preston Long
Ronia Waltl
Samantha Stewart
Sanil Chawla
Sara Habeck
Shondiz Taghdis
Tahira Baig
Terrell Shaffer
Xinchi (Coco) Wang
Xinruihe (Serena) Wang
Xinyi (Wendy) Wang
Xinyi Zhang
Yamina Al-Asadi
Yasuko Yui
Yuri Yim
Zoily Mercado
ACKNOWLEDGEMENTS
A report of this nature requires incredible support to complete. We are indebted to Ted Sarandos, Bela Bajaria, Scott Stuber, Lisa
Nishimura, Tendo Nagenda, Cindy Holland, Vernā Myers, and Rachel Whetstone for championing this effort. Candela Montero
and Victoria Pavlics were our guides and collaborators along the way and this effort would not have reached completion without
them. We are also grateful for the Netflix staff members who provided information and insight for this report.
Our team at the Annenberg Inclusion Initiative—a group that is passionate about inclusion, and representative of the popula-
tions we study—worked tirelessly to complete this project. We are grateful for their time, perspective, and commitment to this
research.
ADDITIONAL ANNENBERG INCLUSION INITIATIVE STAFF TEAM
ANNENBERG INCLUSION INITIATIVE STUDENT RESEARCH ASSISTANTS
Emma Vranich
Artur Tofan
Sarah Voss
Hannah Clark
Danielle Otter
Page 33
Endnotes
1. U.S. fictional films and scripted series with release dates in 2018 and 2019
were included in the sample. Netflix provided a list of films and series, which
was reviewed by research team members for consistency with inclusion cri-
teria. The entirety of a film was analyzed in the study. For series content, the
first three episodes of a season were analyzed, and a composite of those three
episodes was created. This allowed for a character who appeared across all
episodes to be counted only once, and a series-level judgment made for vari-
ables that might change across the episodes. For series (n=29) that were re-
leased in both 2018 and 2019, both seasons were included in the final sample
to ensure comparisons could be made over time. Additionally Netflix provided
a list of four programs described as “TV movies” and four programs referred
to as “TV specials” that were included in the investigation of series content.
2. Across both film and scripted content, there were two units of analysis. The
first was the individual speaking or named character. When a living character
spoke one or more discernible words or was named on screen, that entity was
included in the subsequent analysis as a single line of data. Characters who
spoke simultaneously in heterogeneous groups (e.g., crowd scenes) or those
that spoke at different times but were identical were “grouped” as a line of
data. Only one group appeared across the entire sample (in a scripted series)
and thus was excluded prior to analyses.
In addition to unitizing the individual speaking character, a new line of data
was created whenever a character experienced a demographic (e.g., type,
age, sex, race/ethnicity) change. For example, a character might be present-
ed as an adult for the majority of a story, but a flashback to childhood occurs
during the story. In these cases, a new line of data was created when the char-
acter was presented at a different age. A total of 148 demographic changes
appeared across the sample of films, and 157 across scripted series. The gen-
der breakdown of these demographic changes was 59% male and 41% female
in film and 52% male and 48% female in series. Removing these lines affect-
ed overall percentages of gender minimally (64% Male, 36% Female for film;
60% Male, 40% Female for series). Lines of data representing demographic
changes were included in the final analysis. Data on leading characters was
determined based on the character’s demographic details over the longest
duration of the film or series.
Each film or scripted series was evaluated by three research assistants at the
Annenberg Inclusion Initiative. Research assistants were provided with rigor-
ous training regarding the methods, procedures, and definitions of the study.
Several training diagnostics were included to ensure proper identification of
speaking or named characters and deployment of variables and definitions.
Once sufficient reliability coefficients were achieved for each diagnostic, re-
search assistants began to evaluate content included in the sample. After
three research assistants had evaluated a film or scripted series, reliability
was assessed per film or per episode. Disagreements between coders were
adjudicated via discussion with one of the senior research team members
and resolved.
Reliability related to unitizing was computed per film. This form of reliability
measures the number of lines of data (i.e., characters) which at least 2 of 3
coders captured or agreed upon. Unitizing agreement across the sample for
movies was high. The ranges of coefficients are presented in four groups after
sorting all films by this measure from high to low and dividing the sample
into quartiles: The first quartile (Q1) of the sample spans the following range
of unitizing agreement: 100%-92.9% (n=31 films); Q2: 92.6%-87.6% (n=32
films); Q3: 87.5%-79.3% (n=32 films); Q4: 79.2%-45.2% (n=31 films). Four-
teen films had unitizing agreement below 70%. For scripted series (n=516
episodes, 4 “TV Movies”, and 4 “TV Specials”), a similar range of unitizing
agreement was observed. In quartiles: Q1: 100% (n=131); Q2: 100%-94.7%
(n=131); Q3: 94.4%-89.7% (n=131); Q4: 89.7%-56.2% (n=131). Seven episodes
had unitizing agreement below 70%.
A series of measures were included in the study and assessed at the charac-
ter and the film level. These measures were defined consistently with other
Annenberg Inclusion Initiative projects. For all characters, research assistants
utilized information presented in the story to render a judgment. That is, cues
related to demographics, sexual orientation/gender identity, and disabil-
ity were all pertinent indicators that allowed research assistants to decide
whether a character met the definition of a variable or its levels.
The first group of variables related to demographic indicators. Those includ-
ed, biological sex (i.e., male, female), age (i.e., 0-5; 6-12; 13-20; 21-39; 40-64;
65+), and apparent race/ethnicity (i.e., White, Hispanic/Latino, Black/African
American, American Indian/Alaskan Native, Native Hawaiian/Pacific Islander,
Asian, Middle Eastern/North African, Other/Multiracial).
Within the text, some analyses focus on Latinx individuals who are part of
the main cast (film) and series regulars (series). Here, research assistants
obtained information about the actor’s background, including their ethnicity
and birthplace. Latinx refers only to U.S.-born Latinos who are not of Spanish
descent (unless they were Spanish in addition to other Latino origin). In con-
trast, actors of Hispanic/Latino ethnicity include characters of Spanish origin
or descent, in line with the U.S. Census definition. Below, we break out the
number of main cast members and series regular cast who were Latinx versus
those who were born outside the U.S. or were of Spanish origin or descent.
In films, 4 main cast members were of Spanish origin or descent. In series, 9
series regulars were of Spanish origin or descent.
The sexual orientation of characters was ascertained. Characters were cate-
gorized as one of four values: gay or lesbian as a result of their identification
and enduring romantic proclivities to the same-sex, bisexual when attracted
individuals of the same- and opposite-sex, or not lesbian, gay, or bisexual in
the absence of any same-sex attraction. These decisions relied upon informa-
tion presented in the plot of the story about a characters’ enduring attraction
to others. Transgender identity (i.e., no, yes) was also evaluated. The pres-
ence of disability (i.e., no, yes) was evaluated using a definition based on the
Americans With Disabilities Act (1990). Once a character with a disability was
identified, they were categorized as having a disability in the communicative,
cognitive, and/or physical domain, in line with U.S. Census distinctions.
In addition to the values listed above for every measure, two additional op-
tions were available and used across the study: can’t tell and not applicable.
The former was utilized in situations where not enough information was avail-
able to render a decision amongst the possible values and the latter was used
when it was not possible to assign a value given the circumstances.
Variable reliability was also assessed across the sample, using the Potter &
Levine-Donnerstein formula for multiple coders. For each variable, we report
sample-wide medians, with mean and range included in the parentheses.
For the film sample: sex 1.0 (M=1.0, range=1.0); race/ethnicity 1.0 (M=1.0,
range=1.0); age 1.0 (M=.98, range=.65-1.0); parental status 1.0 (M=.96,
range=.64-1.0); sexual orientation 1.0 (M=1.0, range=1.0); transgender iden-
tity 1.0 (M=1.0, range=1.0); disability 1.0 (M=1.0, range=1.0); communicative
disability 1.0 (M=1.0, range=.61-1.0); cognitive disability 1.0 (M=1.0, range=.61
-1.0); physical disability 1.0 (M=1.0, range=.61-1.0); occupation 1.0 (M=1.0,
range=.57-1.0); and occupation sector 1.0 (M=.96, range=0-1.0).
For the TV sample: sex 1.0 (M=1.0, range=1.0); race/ethnicity 1.0 (M=1.0,
range=1.0), age 1.0 (M=.98, range=.65-1.0); parental status 1.0 (M=.95,
FOOTNOTE TABLE
TYPE LATINX NOT LATINX TOTAL
37
74
18
49
55
123
Film Main Cast
Series Regulars
HISPANIC/LATINO CAST
Page 34
range=.64-1.0); sexual orientation 1.0 (M=1.0, range=.82-1.0); transgender
identity 1.0 (M=1.0, range=.61-1.0); disability 1.0 (M=1.0, range=1.0); commu-
nicative disability 1.0 (M=.99, range=.61-1.0); cognitive disability 1.0 (M=.99,
range=.61-1.0); physical disability 1.0 (M=.99, range=.61-1.0); occupation 1.0
(M=.97, range=.57-1.0); and occupation sector 1.0 (M=.96, range=.66-1.0).
In addition to evaluating characters on screen, information on main cast
members and series regulars was obtained. Main cast information was listed
on the Netflix details online for each film; when 3 or fewer individuals ap-
peared, Netflix provided the main cast information directly to the research
team. Series regular information was compiled from Variety Insight. When
this information was not available, information on IMDbPro was used to de-
termine the number of episodes in which a character appeared across the
season. Actors meeting a threshold of 85% (i.e., appearing in the majority of
episodes across a season) were included as series regulars.
Once main cast and series regular information was obtained, the gender and
race/ethnicity of actors filling those roles was investigated by the researchers.
This involved using online databases (e.g., Variety Insight, Studio System),
where possible. Other online information (e.g., interviews with actors, social
media posts) were also consulted for information on a cast members’ identi-
ty. When no information could be found, a photograph of the individual was
obtained, and two senior research team members rendered a judgment on
the individuals’ race/ethnicity and/or gender. This latter method was used
in 31 cases in film casts, 2 cases in TV casts. Our high quality of assurance in
inferring race/ethnicity is substantiated by a .90 correlation between our re-
search team’s independent judgments and the racial/ethnic identification of
2,175 series regulars across 305 broadcast, cable, and streaming series from
the 2014-15 season.
For films, the lead/co lead character was identified based on the story. The
lead character was the individual who drives the action in the plot. In the case
of co leads, two individual characters must equally share the story. Ensemble
films have three or more characters who drive the story arc.
3. Significance tests are not reported; in some cases due to small sample sizes
these tests could not be computed. Rather, once differences of 5 percentage
points or greater were observed, these were discussed in text as they repre-
sent meaningful deviations.
4. Series regulars were determined using Variety Insight and IMDbPro. Se-
ries regulars reflect recurring characters who appear throughout a season of
content. In cases where information was not available from online databases,
confirmation of series regulars was provided by Netflix.
5. U.S. Census Bureau (2020). Quick Facts. Retrieved August 17, 2020 from
https://www.census.gov/quickfacts/fact/table/US/LFE046218
6. Samples for top-grossing films were determined using cumulative domes-
tic box office for fictional 2018 and 2019 releases reported by Box Office Mojo
by IMDbPro. The range, high to low, of cumulative domestic box office for the
100 top-grossing films of 2018 was $700 million to $20.8 million and for 2019
the range was $858.4 million to $20.4 million.
7. Smith, S.L., Choueiti, M., Pieper, K., Yao, K., Case, A., & Choi, A. (2019).
Inequality in 1,200 Popular Films: Examining Portrayals of Gender, Race/
Ethnicity, LGBT & Disability from 2007 to 2018. Annenberg Inclusion Initia-
tive. http://assets.uscannenberg.org/docs/aii-inequality-report-2019-09-03.
pdf. Smith, S.L., Choueiti, M., & Pieper, K. (2020). Inequality in 1,300 Popu-
lar Films: Examining Portrayals of Gender, Race/Ethnicity, LGBT & Disability
from 2007 to 2019. Annenberg Inclusion Initiative. http://assets.uscannen-
berg.org/docs/aii-inequality_1300_popular_films_09-08-2020.pdf. Lauzen,
M.M., (2019). Boxed In 2018-19: Women On Screen and Behind the Scenes
in Television. Center for the Study of Women in Television & Film. San Di-
ego State University, CA. https://womenintvfilm.sdsu.edu/wp-content/up-
loads/2019/09/2018-19_Boxed_In_Report.pdf
8. U.S. Census Bureau (2020). Quick Facts. Retrieved August 17, 2020 from
https://www.census.gov/quickfacts/fact/table/US/LFE046218
9. The proportion of female-identifying characters within each program was
calculated to determine gender balance. A program was counted as gender
balanced if the percentage of female-identifying characters or main cast fell
within a range of 10% above or below the U.S. Census Bureau (2020) statistic
of females in the population.
10. Behind the scenes data for film was compiled based on the directors, writ-
ers, and producers listed on IMDbPro.com. The gender and race/ethnicity of
each individual was ascertained using the same procedure outlined for main
cast, described above, with a notable exception. Where possible, confirma-
tion on race/ethnicity was sought from individuals connected to the person
(e.g., agents, managers, etc.).
11. Every episode within a series was analyzed for behind the camera per-
sonnel. Using IMDbPro, each director, writer, and producer across every epi-
sode was identified. Then, gender and race/ethnicity were obtained using the
procedures outlined above. For analysis purposes, the list of producers was
reduced to individual members of the producing team who worked across
the season. This avoids double-counting individuals credited for their role as
a producer across every episode. The creator(s) of each series was obtained
using IMDbPro, Variety Insight, or additional sources.
12. In each series analysis behind-the-camera, “writer” refers to the writer of
the episode and not to writers credited in other roles.
13. Lauzen, M.M., (2019). Boxed In 2018-19: Women On Screen and Behind
the Scenes in Television. Center for the Study of Women in Television & Film.
San Diego State University, CA. https://womenintvfilm.sdsu.edu/wp-content/
uploads/2019/09/2018-19_Boxed_In_Report.pdf
14. Industry comparisons behind the camera come from the following sources:
Directors Guild of America (2019, November 19). DGA Reports New Inclusion
Records in the 2018-19 TV Season. Available: https://www.dga.org/News/
PressReleases/2019/191119-Episodic-Television-Director-Diversity-Report.
aspx. Writers Guild of America (2020). WGAW Inclusion Report. Available:
https://www.wga.org/uploadedfiles/the-guild/inclusion-and-equity/wgaw_
inclusion_report.pdf. Writers Guild of America (2019). WGAW Inclusion Re-
port Card: 2017-2018 TV Staffing Season. https://www.wga.org/uploaded-
files/the-guild/inclusion-and-equity/WGAW_Inclusion_Report_20.pdf.
15. For series, only the relationship between the presence of women as series
creators and writers was explored. These positions were chosen as those that
could have the most direct influence on the on-screen demographic profile
of a series. Directors and producers were not included in these analyses as it
was not possible to determine how much influence an individual in either role
would have on casting. For example, ambiguity in how producing credits were
allocated did not allow researchers to disentangle those with writing respon-
sibilities from those overseeing production.
16. U.S. Census Bureau (2020). Annual Estimates of the Resident Population
by Sex, Age, Race, and Hispanic Origin for the United States: April 1, 2010
to July 1, 2019. Retrieved August 17th, 2020 from: https://www.census.gov/
newsroom/press-kits/2020/ population-estimates-detailed.html
17. U.S. Census Bureau (2020). Annual Estimates of the Resident Population
by Sex, Age, Race, and Hispanic Origin for the United States: April 1, 2010
to July 1, 2019. Retrieved August 17th, 2020 from: https://www.census.
gov/newsroom/press-kits/2020/population-estimates-detailed.html. Arab
American Institute Foundation (2018). Arab American Demographics Fact-
sheet. Retrieved January 20th, 2020 from: https://censuscounts.org/whats-
Page 35
at-stake/arab-american-demographics-factsheet/
18. U.S. Census Bureau (2020). Annual Estimates of the Resident Population
by Sex, Age, Race, and Hispanic Origin for the United States: April 1, 2010
to July 1, 2019. Retrieved August 17th, 2020 from: https://www.census.gov/
newsroom/press-kits/2020/ population-estimates-detailed.html
19. Some characters are not capable of possessing a race and/or ethnicity.
That is, robots, supernatural creatures, or other non-human beings may be
part of fictional storytelling. For those characters who were not capable of
possessing a race and/or ethnicity, not applicable was used for this variable,
and these individuals were excluded from analysis.
20. The directors, writers, and producers for each film in the sample were col-
lected via IMDbPro.com. For writing, only screenplay and screen story credits
were included and writers of source material were not included. Across pro-
ducer titles only ‘Produced by’ credits were taken for Netflix films. Gender
and racial/ethnic information was obtained in the same method described
above, using data obtained for other research at the Initiative and confirming
with representatives and individuals when possible.
21. Hunt, D., & Ramon, A-C. (2020). Hollywood Diversity Report 2020: A Tale
of Two Hollywoods; Part 2: Television. UCLA College of Social Sciences. Avail-
able: https://socialsciences.ucla.edu/wp-content/uploads/2020/10/UC-
LA-Hollywood-Diversity-Report-2020-Television-10-22-2020.pdf
22. Writers Guild of America (2020). WGAW Inclusion Report. Available:
https://www.wga.org/uploadedfiles/the-guild/inclusion-and-equity/wgaw_
inclusion_report.pdf. Writers Guild of America (2019). WGAW Inclusion Re-
port Card: 2017-2018 TV Staffing Season. https://www.wga.org/uploaded-
files/the-guild/inclusion-and-equity/WGAW_Inclusion_Report_20.pdf.
23. Writers Guild of America (2020). WGAW Inclusion Report. Available:
https://www.wga.org/uploadedfiles/the-guild/inclusion-and-equity/wgaw_
inclusion_report.pdf. Writers Guild of America (2019). WGAW Inclusion Re-
port Card: 2017-2018 TV Staffing Season. https://www.wga.org/uploaded-
files/the-guild/inclusion-and-equity/WGAW_Inclusion_Report_20.pdf.
24. Hunt, D., & Ramon, A-C. (2020). Hollywood Diversity Report 2020: A Tale
of Two Hollywoods; Part 2: Television. UCLA College of Social Sciences. Avail-
able: https://socialsciences.ucla.edu/wp-content/uploads/2020/10/UC-
LA-Hollywood-Diversity-Report-2020-Television-10-22-2020.pdf
25. Directors Guild of America (2019, November 19). DGA Reports New In-
clusion Records in the 2018-19 TV Season. Available: https://www.dga.
org/News/PressReleases/2019/191119-Episodic-Television-Director-Div-
ersity-Report.aspx.
26. Directors Guild of America (2019, November 19). DGA Reports New In-
clusion Records in the 2018-19 TV Season. Available: https://www.dga.
org/News/PressReleases/2019/191119-Episodic-Television-Director-Div-
ersity-Report.aspx.
27. All individuals credited behind the scenes and main cast were researched
to determine how they identify their racial/ethnic grouping. Online databases
(Variety Insight, Studio System, IMDbPro) as well as press articles, interviews,
familial information reported online, and direct communication were used
to facilitate categorization. Measures were created counting the presence or
absence of identification for each possible race and ethnicity category (e.g.,
Black-Identifying, present or absent).
28. U.S. Census Bureau (2020). Annual Estimates of the Resident Population
by Sex, Age, Race, and Hispanic Origin for the United States: April 1, 2010
to July 1, 2019. Retrieved August 17th, 2020 from: https://www.census.gov/
newsroom/press-kits/2020/ population-estimates-detailed.html
29. U.S. Census Bureau (2020). Annual Estimates of the Resident Population
by Sex, Age, Race, and Hispanic Origin for the United States: April 1, 2010
to July 1, 2019. Retrieved August 17th, 2020 from: https://www.census.gov/
newsroom/press-kits/2020/ population-estimates-detailed.html
30. Films with Black producers were more likely to feature Black Leads or Co
Leads (86.7%, n=13) than films without a Black producer (12.6%, n=14). The
same pattern held for main cast where films with one or more Black produc-
ers included a larger proportion of main cast members that identified as Black
(69%, n=78, vs. 14.8%, n=136, respectively).
31. Noe-Bustamante, L. & Flores, A. (2019, September 16). Facts on Latinos
in the U.S. Pew Research Center. Retrieved January 20th, 2021 from: https://
www.pewresearch.org/hispanic/fact-sheet/latinos-in-the-u-s-fact-sheet/
32. U.S. Census Bureau (2020). Annual Estimates of the Resident Population
by Sex, Age, Race, and Hispanic Origin for the United States: April 1, 2010
to July 1, 2019. Retrieved August 17th, 2020 from: https://www.census.gov/
newsroom/press-kits/2020/ population-estimates-detailed.html
33. GLAAD (2017). Accelerating Acceptance. Retrieved January 20th, 2021
from: https://www.glaad.org/files/aa/2017_GLAAD_Accelerating_Accep-
tance.pdf.
34. GLAAD (2018). Where We Are on TV. Retrieved January 20th, 2021 from:
https://glaad.org/files/WWAT/WWAT_GLAAD_2018-2019.pdf
35. Flores, A.R., Herman, J.L., Gates, G.J., & Brown, T.N.T. (June 2016). How
Many Adults Identify as Transgender in the United States? The Williams Insti-
tute. Retrieved January 20th, 2020 from: https://williamsinstitute.law.ucla.
edu/wp-content/uploads/Trans-Adults-US-Aug-2016.pdf
36. While differences by medium were not significant, a few patterns can be
noted. In film, the number of lesbian cast rose (2018: <1%, n=2; 2019: 2.1%,
n=10), while the number of gay cast decreased (2018: 3.1%, n=17; 2019: 2.1%,
n=10). There was little change in the number of bisexual cast (2018: <1%, n=2;
2019: <1%, n=3), and no transgender cast appeared in film. Moving to series,
there were numerical increases for gay (2018: 2.2%, n=14; 2019: 3.4%, n=25)
and bisexual (2018: <1%, n=4; 2019: 1.9%, n=14) cast, but little change for
lesbian cast (2018: 1.8%, n=12; 2019: 1.7%, n=13).
37. In film, a numeric increase occurred for lesbian characters (2018: <1%,
n=12; 2019: <1%, n=18), while a decrease was witnessed for gay characters
(2018: 1.2%, n=29; 2019: 1%, n=22). The number of bisexual (2018: <1%, n=5;
2019: <1%, n=4) and transgender characters (2018: <1%, n=1; 2019: <1%, n=2)
varied little. In series, there were numeric increases across each group, as
follows: lesbian (2018: <1%, n=25; 2019: 1.2%, n=46), gay (2018: 1.5%, n=50;
2019: 2.3%, n=91), bisexual (2018: <1%, n=7; 2019: <1%, n=17), and transgen-
der (2018: <1%, n=2; 2019: <1%, n=10) characters.
38. U.S. Census Bureau (2018). Americans with Disabilities: 2014. Retrieved
January 20th, 2021 from: https://www.census.gov/library/publications/2018/
demo/p70-152.html. Americans with Disabilities Act (1990). https://www.
ada.gov/pubs/adastatute08.htm. Characters were counted as having a dis-
ability when the presence of a condition (rooted in the function, form, or
structure of a character’s mind and/or body) manifested a limitation, inter-
ference, and/or non-functioning related to any ‘major life activities’ or ‘ma-
jor bodily function’ for a period longer than six months. A disability could be
present in one or more of the following domains: Communicative (seeing,
hearing, speaking), Cognitive (learning, memory, thinking, emotions), and/or
Physical (mobility, breathing, internal and external corporeal components).
See Smith, S.L., Choueiti, M., & Pieper, K. (2016). Inequality in 800 Popular
Films: Examining Portrayals of Gender, Race/Ethnicity, LGBT, and Disability
from 2007-2015. Annenberg School for Communication & Journalism. Re-
trieved from: https://annenberg.usc.edu/sites/default/files/2017/04/10/MD-
SCI_Inequality_in_800_Films_FINAL.pdf
Page 36
39. U.S. Census Bureau (2018). Americans with Disabilities: 2014. Retrieved
January 20th, 2021 from: https://www.census.gov/library/publications/2018/
demo/p70-152.html.
40. U.S. Census Bureau (2018) reports include “any mental or emotional con-
dition that seriously interfered with everyday activities” (p. 3) in their defini-
tion of disability, noting that depression, anxiety, and other issues that result
in “trouble coping with day-to-day stress” are included in this spectrum of
conditions. Stories that featured characters coping with mental health issues
that severely restricted their day to day activities were scrutinized to ensure
that the restrictions experienced met the same threshold of restriction as
other (e.g., physical, communicative) disabilities.
41. GLAAD (2018). Where We Are on TV. Retrieved January 20th, 2021 from:
https://glaad.org/files/WWAT/WWAT_GLAAD_2018-2019.pdf
42. Myers, V. (2021, January 13). Inclusion Takes Root at Netflix: Our First
Report. Netflix. Available: https://about.netflix.com/en/news/netflix-inclu-
sion-report-2021
43. Myers, V. (2021, January 13). Inclusion Takes Root at Netflix: Our First
Report. Netflix. Available: https://about.netflix.com/en/news/netflix-inclu-
sion-report-2021
