Supplementary Information for

Inclusive content reduces racial and gender biases, yet non-inclusive content
dominates popular media outlets

Fashion house & Asian Black Indian Latinx M. Eastern White

AE77 0.0 0.0 0.0 0.0 0.0 100.0

AGL 0.0 0.0 0.0 0.0 100.0 0.0

ALIEN 0.0 100.0 0.0 0.0 0.0 0.0

AMAFFI 0.0 0.0 0.0 18.2 0.0 81.8

AVOCADO 0.0 0.0 0.0 50.0 0.0 50.0

Alberta Ferretti 0.0 0.0 0.0 22.2 0.0 77.8

Alexander Birman 0.0 0.0 0.0 0.0 0.0 100.0

Alexander McQueen 11.8 23.5 0.0 0.0 0.0 64.7

Ambush 0.0 100.0 0.0 0.0 0.0 0.0

Anne Klein & Co 1.0 3.7 0.7 13.4 1.8 79.3

Athena Cosmetics, Inc 0.0 0.0 0.0 0.0 0.0 100.0

BRIZO 0.0 100.0 0.0 0.0 0.0 0.0

BRUNO MD 0.0 0.0 0.0 66.7 0.0 33.3

Balenciaga 11.8 11.8 0.0 17.6 0.0 58.8

Bally 0.0 0.8 0.8 7.6 0.8 89.8

Balmain 16.7 33.3 0.0 0.0 0.0 50.0

Banana Republic 3.9 7.6 0.3 6.3 4.2 77.8

Bergdorf Goodman & Co 0.6 0.0 1.3 3.2 1.9 92.9

Bill Blass 0.4 3.5 1.1 3.9 2.8 88.4

Blackglama 0.0 7.2 0.0 7.2 0.0 85.5

Bloomingdales 1.2 1.4 1.7 3.8 1.7 90.2

Blumarine 0.0 0.0 0.0 0.0 0.0 100.0

Bottega Veneta 2.6 6.1 0.5 3.8 2.0 84.9

Bulgari 1.3 5.9 3.0 9.7 1.7 78.4

Burberry 1.1 3.7 0.9 3.2 0.7 90.4

Bvlgari 0.0 0.0 0.0 100.0 0.0 0.0

CASAMIGOS 0.0 0.0 0.0 0.0 0.0 100.0

CHANTECAILLE 16.7 16.7 0.0 33.3 0.0 33.3

COS 0.0 50.0 0.0 25.0 0.0 25.0

Calvin KIein 1.3 2.5 0.6 4.6 1.8 89.3

Canada Goose 0.0 100.0 0.0 0.0 0.0 0.0

Carolina Herrera 2.7 5.7 1.4 6.1 2.7 81.4

Cartier 2.1 6.3 2.1 3.7 4.2 81.7

Celebrity Cruises, Inc 0.0 50.0 25.0 0.0 25.0 0.0

Chanel 2.2 5.9 0.6 4.9 2.7 83.7

Chloé 1.0 3.3 0.8 2.9 1.2 90.8

Chopard 0.0 0.0 0.0 0.0 0.0 100.0

Christian Dior 1.8 5.5 0.6 3.7 1.8 86.7

Clairol 0.9 2.2 1.1 3.3 0.4 92.2

Clarins 2.2 3.0 0.0 3.0 0.7 91.1

Clinique Laboratories, Inc 0.0 5.0 0.0 5.0 0.0 90.0

Concord Watch Co 0.0 2.3 0.0 0.0 4.7 93.0

Covergirl 0.5 12.4 0.5 5.1 1.6 79.7

DEL CORE 0.0 0.0 0.0 0.0 0.0 100.0

DICK’S 0.0 42.9 0.0 28.6 0.0 28.6

Daisy Marc Jacobs 0.0 0.0 0.0 0.0 0.0 100.0

De Beers 0.0 66.7 0.0 33.3 0.0 0.0

Diesel 0.0 0.0 0.0 18.2 0.0 81.8

Dior 15.9 5.7 5.7 15.9 0.0 56.8

Dolce & Gabbana 1.8 4.6 0.8 5.9 6.7 80.3

Dolce & Gabbana 14.1 12.7 4.2 5.6 2.8 60.6

Donna Karan Company 2.5 2.9 0.5 5.2 3.4 85.5

Dyson 0.0 0.0 50.0 0.0 0.0 50.0

Elizabeth Arden Co 0.3 0.5 0.3 2.1 0.3 96.6

Escada 1.7 4.1 1.1 10.2 2.4 80.5

Esprit 0.0 0.0 0.0 33.3 0.0 66.7

Estée Lauder, Inc 1.6 7.4 0.8 4.5 2.9 82.8

Etro 8.3 16.7 0.0 16.7 0.0 58.3

FEAROFGOD 0.0 33.3 0.0 66.7 0.0 0.0

FREE PEOPLE 0.0 5.9 5.9 23.5 0.0 64.7

FTX 0.0 0.0 0.0 0.0 0.0 100.0

Fabiana Filippi 0.0 0.0 0.0 0.0 0.0 100.0

Fendi 5.3 11.4 1.2 4.7 0.9 76.5

Ferragamo 27.3 18.2 0.0 9.1 18.2 27.3

Ford Motor Company 2.0 12.8 1.4 5.4 6.1 72.3

Fp Movement 0.0 20.0 0.0 0.0 0.0 80.0

GEICO 0.0 100.0 0.0 0.0 0.0 0.0

GENTLE MONSTER 3.2 17.5 0.0 14.3 0.0 65.1

Gap 3.6 10.4 3.7 6.4 3.2 72.6

Garnier 0.0 0.0 0.0 100.0 0.0 0.0

Genentech USA, Inc 0.0 0.0 0.0 0.0 0.0 100.0

Gentle Fluidity 0.0 0.0 0.0 0.0 0.0 100.0

Giorgio Armani 2.9 3.1 0.0 3.8 2.0 88.3

Givenchy 14.3 14.3 0.0 0.0 0.0 71.4

Givenchy Inc 0.6 1.7 2.2 6.1 3.3 86.2

Gottex 1.4 6.8 0.3 4.4 2.7 84.4

Gucci 1.8 5.4 0.8 2.6 1.4 87.9

Hermès Paris, Inc 14.3 0.0 5.7 0.0 2.9 77.1

Hublot 0.0 0.0 0.0 0.0 0.0 100.0

IL BISONTE 0.0 0.0 0.0 0.0 0.0 100.0

INNOVO 0.0 0.0 0.0 0.0 0.0 100.0

Jedora 0.0 0.0 0.0 0.0 0.0 100.0

Jimmy Choo 0.0 16.7 0.0 33.3 0.0 50.0

John Hardy 0.0 50.0 0.0 0.0 0.0 50.0

Karl Lagerfeld 0.0 25.0 0.0 0.0 0.0 75.0

Kimberly-Clark Worldwide, Inc 0.0 0.0 0.0 100.0 0.0 0.0

Kiton 16.7 16.7 0.0 16.7 0.0 50.0

L’Oréal 2.6 11.9 0.8 6.5 1.4 76.8

LUMENIS 0.0 0.0 0.0 0.0 0.0 100.0

Lancôme, Inc 0.5 9.8 0.5 2.7 1.4 85.1

Lanvin Parfums Inc 0.0 1.9 1.3 1.3 1.3 94.2

Lanvin Paris 0.0 100.0 0.0 0.0 0.0 0.0

Leica 0.0 100.0 0.0 0.0 0.0 0.0

Lexus 50.0 0.0 0.0 25.0 0.0 25.0

Loewe 0.0 16.7 0.0 16.7 0.0 66.7

Longines 0.0 0.0 0.0 0.0 0.0 100.0

Lord & Taylor Co 1.0 2.6 1.0 1.5 0.5 93.3

Loro Piana 0.0 40.0 0.0 0.0 0.0 60.0

Louis Vuitton 4.0 5.5 0.9 4.0 1.3 84.3

MCM 0.0 0.0 0.0 25.0 0.0 75.0

MESSIKA 0.0 0.0 0.0 0.0 0.0 100.0

Macy’s 2.0 6.2 0.8 6.4 1.2 83.4

Malone Souliers 0.0 0.0 0.0 0.0 0.0 100.0

Manolo Blahnik 2.0 6.1 0.0 4.1 6.1 81.6

Marc Jacobs 0.0 20.0 0.0 0.0 0.0 80.0

Marshall Field & Co 1.5 0.7 0.7 3.0 3.0 91.1

Max Factor & Co 0.0 0.5 0.9 3.3 1.9 93.5

MaxMara 14.3 36.7 0.0 2.0 0.0 46.9

Maybelline Co 3.7 8.1 1.0 6.6 2.1 78.6

Merck & Co., Inc 0.0 27.5 0.0 27.5 0.0 45.0

Merrell 0.0 0.0 0.0 0.0 0.0 100.0

Merz North America, Inc 0.0 100.0 0.0 0.0 0.0 0.0

Michael Kors 2.2 6.0 1.1 5.0 1.2 84.4

Missoni 0.9 3.9 0.4 4.4 2.6 87.8

Miu Miu 3.8 7.7 0.0 23.1 0.0 65.4

Moda &Company 0.0 42.9 0.0 0.0 0.0 57.1

Moschino 8.3 16.7 16.7 0.0 0.0 58.3

NEXPLANON 0.0 25.0 0.0 25.0 0.0 50.0

NOBLE PANACEA 0.0 0.0 0.0 0.0 0.0 100.0

NYDJ 0.0 0.0 0.0 0.0 0.0 100.0

Neiman-Marcus Company 5.8 3.9 0.4 3.4 1.1 85.6

Netflix 0.0 0.0 0.0 25.0 0.0 75.0

Neutrogena Corp 0.0 3.2 0.0 0.0 0.0 96.8

Nina Ricci 2.0 11.2 0.0 3.0 1.0 82.7

Nordstrom 4.3 10.1 1.5 5.5 2.7 76.0

Ogx 0.0 100.0 0.0 0.0 0.0 0.0

Olay 0.0 66.7 0.0 8.3 0.0 25.0

Omega 0.0 5.9 0.0 47.1 0.0 47.1

Organon group of companies 0.0 25.5 5.5 25.5 0.0 43.6

Oscar de la Renta 1.3 4.4 0.6 4.7 3.0 85.9

PETITE PLUME 0.0 25.0 0.0 25.0 25.0 25.0

Pantene Co 1.9 4.1 0.9 4.7 0.3 88.0

Peter Thomas Roth 0.0 0.0 0.0 0.0 66.7 33.3

Pfizer Inc 0.0 50.0 0.0 0.0 0.0 50.0

Piaget Watch Corporation 0.0 0.0 4.0 6.9 2.0 87.1

Polo Ralph Lauren 25.0 0.0 0.0 50.0 0.0 25.0

Porsche 33.3 33.3 0.0 0.0 0.0 33.3

Prada 1.8 6.1 0.8 3.2 2.3 85.8

Procter & Gamble Co 0.0 0.0 0.0 100.0 0.0 0.0

R. J. Reynolds Tobacco Co 2.4 3.1 0.0 3.9 3.1 87.4

Ralph Lauren 2.1 5.1 1.0 3.9 1.2 86.7

Rec. Boating & Fishing Foundation 0.0 0.0 0.0 50.0 0.0 50.0

Redken Laboratories, Inc 2.4 4.5 1.4 5.6 2.8 83.3

Reebok International Ltd 0.0 100.0 0.0 0.0 0.0 0.0

Revlon, Inc 1.9 4.6 0.4 11.6 0.7 80.9

Roberto Cavalli 0.0 0.0 0.0 0.0 0.0 100.0

Rolex Watch Company, Inc 7.8 2.6 1.7 4.3 3.5 80.0

SABYASACHI 0.0 0.0 0.0 50.0 0.0 50.0

SCHUTZ 0.0 0.0 0.0 0.0 0.0 100.0

Saint Laurent 0.0 16.7 0.0 16.7 0.0 66.7

Saks & Company 2.2 5.4 0.8 4.2 1.8 85.5

Saks Fifth Avenue 17.4 21.7 0.0 26.1 0.0 34.8

Salvatore Ferragamo 35.7 28.6 7.1 0.0 0.0 28.6

Sandals 0.0 0.0 0.0 100.0 0.0 0.0

Sandals Resorts 0.0 0.0 0.0 100.0 0.0 0.0

Secret 0.0 0.0 0.0 60.0 0.0 40.0

Sergio Rossi 0.0 0.0 0.0 0.0 0.0 100.0

Showtime Networks Inc 0.0 33.3 0.0 0.0 0.0 66.7

Skechers 12.5 25.0 0.0 12.5 0.0 50.0

Sonia Rykiel 0.8 0.0 3.9 4.7 2.3 88.3

Stokely-Van Camp, Inc 0.0 100.0 0.0 0.0 0.0 0.0

Swarovski 0.0 28.0 0.0 28.0 0.0 44.0

TUMI 0.0 0.0 0.0 0.0 0.0 100.0

Theory Project 0.0 33.3 0.0 0.0 0.0 66.7

Tiffany & Co 1.3 3.0 0.9 6.4 3.0 85.5

Tiffany & Co 7.1 28.6 7.1 14.3 0.0 42.9

Tom Ford 0.0 60.0 0.0 0.0 0.0 40.0

Tuckernuck 0.0 50.0 0.0 0.0 0.0 50.0

U.S. Bank 0.0 40.0 0.0 20.0 20.0 20.0

UBRELVY 0.0 100.0 0.0 0.0 0.0 0.0

Uncommon Beauty 0.0 0.0 0.0 0.0 0.0 100.0

Uncommon James 0.0 0.0 0.0 0.0 0.0 100.0

Unilever 0.0 100.0 0.0 0.0 0.0 0.0

VAGABOND 0.0 0.0 0.0 16.7 0.0 83.3

Valentino 2.4 6.8 0.8 5.9 2.0 82.1

Valentino Garavani 0.0 0.0 0.0 0.0 0.0 100.0

Van Cleef & Arpels Inc 0.0 0.0 0.0 5.6 1.9 92.6

Versace 1.3 5.6 1.6 5.7 2.6 83.3

Veuve Clicquot 0.0 66.7 0.0 33.3 0.0 0.0

Viktor & Rolf 0.0 0.0 0.0 0.0 100.0 0.0

Vogue Co 2.4 6.5 1.3 5.8 3.1 80.9

Walmart Apollo, LLC 11.1 33.3 0.0 22.2 0.0 33.3

Walmart Inc 0.0 100.0 0.0 0.0 0.0 0.0

Yves Saint Laurent 0.3 3.7 1.3 5.4 3.0 86.2

Yves Saint Laurent Parfums Corp 3.5 5.9 2.0 4.7 0.8 83.1

Zadig & Voltaire 0.0 0.0 0.0 0.0 0.0 100.0

Intimissimi 33.3 33.3 0.0 16.7 0.0 16.7

vVARDIS 0.0 0.0 0.0 100.0 0.0 0.0

Language of origin | Gender | Body only | Face | Full Body | Lower Body | Upper Body
---|---|---|---|---|---|---
Arabic | Female | 0 | 291 | 2291 | 1 | 1963
Arabic | Male | 1 | 532 | 2932 | 1 | 2792
English | Female | 6 | 676 | 4731 | 20 | 3215
English | Male | 9 | 1313 | 7433 | 18 | 5386
French | Female | 5 | 662 | 3713 | 20 | 3224
French | Male | 5 | 900 | 5079 | 8 | 4757
German | Female | 9 | 389 | 3153 | 10 | 2669
German | Male | 4 | 538 | 4011 | 3 | 3682
Hindi/Tamil | Female | 1 | 857 | 3063 | 3 | 3440
Hindi/Tamil | Male | 3 | 1454 | 4413 | 2 | 4898
Japanese | Female | 6 | 645 | 6154 | 6 | 3969
Japanese | Male | 2 | 1015 | 7795 | 9 | 5584
Mandarin/Cantonese | Female | 5 | 658 | 4258 | 5 | 3679
Mandarin/Cantonese | Male | 8 | 1167 | 7291 | 5 | 6025
Persian | Female | 0 | 194 | 790 | 0 | 945
Persian | Male | 3 | 288 | 1234 | 3 | 1355
Portuguese | Female | 6 | 293 | 1560 | 5 | 1623
Portuguese | Male | 9 | 367 | 2040 | 9 | 2331
Spanish | Female | 9 | 845 | 4194 | 21 | 3856
Spanish | Male | 10 | 1177 | 5574 | 16 | 5763
Turkish | Female | 4 | 264 | 2274 | 2 | 1920
Turkish | Male | 6 | 371 | 3216 | 2 | 2844
Supplementary Table 1: Number of posters with a given language of origin which depict a given gender with a specific portion of their body. Decade | Gender | Body Only | Face | Full Body | Lower Body | Upper Body
---|---|---|---|---|---|---
1950s | Female | 2 | 610 | 3412 | 5 | 2965
1950s | Male | 4 | 989 | 4094 | 6 | 4216
1960s | Female | 13 | 788 | 3799 | 11 | 3453
1960s | Male | 7 | 1188 | 5449 | 9 | 5237
1970s | Female | 13 | 718 | 4482 | 15 | 3896
1970s | Male | 11 | 1162 | 6158 | 9 | 5683
1980s | Female | 6 | 736 | 4506 | 19 | 3762
1980s | Male | 9 | 1224 | 6535 | 7 | 5760
1990s | Female | 5 | 719 | 3689 | 10 | 3923
1990s | Male | 10 | 1315 | 5678 | 11 | 6063
2000s | Female | 6 | 900 | 5023 | 14 | 4669
2000s | Male | 9 | 1401 | 7213 | 13 | 6802
2010s | Female | 3 | 896 | 7482 | 16 | 5327
2010s | Male | 5 | 1276 | 10780 | 18 | 7987
2020s | Female | 3 | 407 | 3788 | 3 | 2508
2020s | Male | 5 | 567 | 5111 | 3 | 3669
Supplementary Table 2: Number of posters in a given decade which depict a given gender with a specific portion of their body. Gender | Body Only | Face Only | Full Body | Upper Body
---|---|---|---|---
Female | 1 | 238 | 1813 | 1269
Male | 3 | 167 | 1776 | 1192
Supplementary Table 3: Number of ads which depict a given gender with a specific portion of their body. Condition | Asian | Black | Latinx | White
---|---|---|---|---
Control | 38.4 | 40.6 | 41.2 | 64.1
Treatment | 48.8 | 43.5 | 45.3 | 63.9
p-value | 1.62e-10 | 0.03 | 0.006 | 0.42
Supplementary Table 4: Mean participant response when participants were asked “If you had to guess, what percentage of the US population associate the terms "fashionable" and "classy" with [insert race] people?” and “If you had to guess, what percentage of the US population associate the terms "beautiful" and "attractive" with [insert race] people?”. Values for each race participants were asked about are listed. One sided independent t-test p-values comparing control and treatment conditions are listed. Condition | Asian | Black | Latinx | White
---|---|---|---|---
Control | 19.5 | 20.3 | 15.8 | 66.4
Treatment | 28.5 | 29.1 | 23.0 | 59.7
p-value | 2.46e-06 | 8.72e-07 | 9.20e-06 | 0.0016
Supplementary Table 5: Mean participant response when participants were asked If you had to guess, what percentage of [insert race] employees play a leading role in their organization?. Values for each race participants were asked about are listed. One sided independent t-test p-values comparing control and treatment conditions are listed. Condition | Asian | Black | Latinx | White
---|---|---|---|---
Control | 15.5 | 9.9 | 9.9 | 19.7
Treatment | 21.5 | 13.7 | 13.1 | 23.5
p-value | 0.0006 | 0.007 | 0.018 | 0.03
Supplementary Table 6: Mean participant response when participants were asked “If you had to guess, what proportion of [insert race] households make more than $500,000 a year?”. Values for each race participants were asked about are listed. One sided independent t-test p-values comparing control and treatment conditions are listed. Condition | Response
---|---
Control | 1.8
Treatment | 1.36
p-value | 0.00014
Supplementary Table 7: Mean participant response when participants were asked “To what degree do you agree with this statement: “In popular media, women are more likely to be hired for their looks than their acting skills”. Responses ranged from “Strongly Disagree (-3)” to “Strongly Agree (3)”. One sided independent t-test p-values comparing control and treatment conditions are listed. Condition | Response
---|---
Control | 68.8
Treatment | 61.1
p-value | 5.72e-08
Supplementary Table 8: Mean participant response when participants were asked “If you had to guess, in the Banking industry in the US, what percentage of employees are men?”. One sided independent t-test p-values comparing control and treatment conditions are listed. Demographic Variable | Control% | Treatment%
---|---|---
Gender |  |
Female | 40 | 52.9
Male | 57 | 45.2
Non-binary / third gender | 0 | 1.4
Prefer not to say | 0 | 0.5
Education |  |
Bachelor’s Degree | 38 | 36.2
Doctoral Degree | 4 | 2.4
High School Diploma or Equivalent | 14 | 13.8
Master’s Degree | 11 | 17.1
Other (specify) | 0 | 0.5
Some College or Associate’s Degree | 30 | 30.0
Income |  |
"$100,000 - $149,999" | 12 | 12.4
"$150,000 - $199,999" | 4 | 6.2
"$200,000 or more" | 0 | 1.9
"$25,000 - $49,999" | 27 | 28.1
"$50,000 - $74,999" | 20 | 17.6
"$75,000 - $99,999" | 12 | 13.3
"Less than $25,000" | 22 | 20.5
Race |  |
Asian | 13 | 10.0
Black or African American | 16 | 17.6
Indian | 0 | 0.5
LatinX | 7 | 4.3
Middle Eastern | 0 | 0.0
Other | 2 | 4.8
White | 58 | 62.9
Age | 40 | 44.1
Political Ideology |  |
Independent | 26 | 21.4
Lean Democrat | 20 | 16.7
Lean Republican | 9 | 12.4
Not very strong Democrat | 9 | 10.0
Not very strong Republican | 5 | 7.1
Strong Democrat | 24 | 23.8
Strong Republican | 5 | 8.6
Supplementary Table 9: Participant demographics for study analyzing effect of racial diversity in movie posters Demographic Variable | Control% | Treatment%
---|---|---
Gender |  |
Female | 46 | 56.5
Male | 50 | 42.6
Non-binary / third gender | 3 | 0.5
Prefer not to say | 0 | 0.5
Education |  |
Bachelor’s Degree | 34 | 38.3
Doctoral Degree | 2 | 2.4
High School Diploma or Equivalent | 18 | 13.4
Master’s Degree | 13 | 15.8
Other (specify) | 0 | 1.0
Some College or Associate’s Degree | 30 | 29.2
Income |  |
"$100,000 - $149,999" | 12 | 15.8
"$150,000 - $199,999" | 4 | 5.7
"$200,000 or more" | 3 | 1.9
"$25,000 - $49,999" | 19 | 24.9
"$50,000 - $74,999" | 21 | 20.1
"$75,000 - $99,999" | 16 | 16.7
"Less than $25,000" | 21 | 14.8
Race |  |
Asian | 12 | 8.1
Black or African American | 13 | 15.8
Indian | 0 | 1.9
LatinX | 7 | 8.1
Middle Eastern | 1 | 0.0
Other | 3 | 4.3
White | 62 | 61.7
Age | 42 | 43.7
Political Ideology |  |
Independent | 21 | 27.3
Lean Democrat | 15 | 15.3
Lean Republican | 7 | 8.1
Not very strong Democrat | 18 | 11.5
Not very strong Republican | 3 | 5.3
Strong Democrat | 24 | 23.9
Strong Republican | 8 | 8.6
Supplementary Table 10: Participant demographics for study analyzing effect of racial diversity in advertisements Demographic Variable | Control% | Treatment%
---|---|---
Gender |  |
Female | 43 | 47.2
Male | 55 | 51.9
Non-binary / third gender | 0 | 0.5
Prefer not to say | 0 | 0.5
Education |  |
Bachelor’s Degree | 37 | 40.1
Doctoral Degree | 2 | 4.2
High School Diploma or Equivalent | 13 | 17.0
Master’s Degree | 15 | 14.6
Other (specify) | 0 | 0.0
Some College or Associate’s Degree | 29 | 24.1
Income |  |
"$100,000 - $149,999" | 11 | 11.3
"$150,000 - $199,999" | 1 | 5.2
"$200,000 or more" | 2 | 4.7
"$25,000 - $49,999" | 28 | 20.3
"$50,000 - $74,999" | 16 | 24.5
"$75,000 - $99,999" | 13 | 14.2
"Less than $25,000" | 25 | 19.8
Race |  |
Asian | 9 | 10.8
Black or African American | 13 | 14.6
Indian | 2 | 1.9
LatinX | 4 | 5.2
Middle Eastern | 0 | 1.4
Other | 1 | 2.4
White | 67 | 63.7
Age | 42 | 42.9
Political Ideology |  |
Independent | 27 | 29.7
Lean Democrat | 13 | 11.8
Lean Republican | 8 | 8.0
Not very strong Democrat | 11 | 11.8
Not very strong Republican | 7 | 4.7
Strong Democrat | 25 | 28.3
Strong Republican | 7 | 5.7
Supplementary Table 11: Participant demographics for study analyzing effect of racial diversity in advertisements Demographic Variable | Control% | Treatment%
---|---|---
Gender |  |
Female | 62 | 54.0
Genderqueer | 0 | 0.5
Male | 34 | 42.5
Non-binary / third gender | 1 | 1.5
Other (specify) | 0 | 0.5
Education |  |
Bachelor’s Degree | 42 | 38.0
Doctoral Degree | 2 | 5.5
High School Diploma or Equivalent | 10 | 12.5
Master’s Degree | 14 | 14.5
Other (specify) | 0 | 0.0
Some College or Associate’s Degree | 30 | 29.5
Income |  |
"$100,000 - $149,999" | 15 | 9.5
"$150,000 - $199,999" | 3 | 2.0
"$200,000 or more" | 2 | 3.5
"$25,000 - $49,999" | 22 | 25.0
"$50,000 - $74,999" | 24 | 21.5
"$75,000 - $99,999" | 13 | 12.5
"Less than $25,000" | 17 | 26.0
Race |  |
Asian | 11 | 16.5
Black or African American | 21 | 13.0
Indian | 0 | 1.0
LatinX | 5 | 4.0
Other | 3 | 1.0
White | 57 | 64.5
Prefer not to say | 1 | 1.0
Age | 43 | 38.815
Political Ideology |  |
Independent | 18 | 22.5
Lean Democrat | 13 | 16.0
Lean Republican | 7 | 8.0
Not very strong Democrat | 15 | 13.5
Not very strong Republican | 9 | 3.5
Strong Democrat | 24 | 29.0
Strong Republican | 10 | 7.5
Supplementary Table 12: Participant demographics for study analyzing effect of gender diversity in advertisements Demographic Variable | Control% | Treatment%
---|---|---
Gender |  |
Female | 57 | 58.0
Male | 40 | 39.5
Non-binary / third gender | 1 | 1.0
Other (specify) | 0 | 0.5
Prefer not to say | 1 | 1.0
Education |  |
Bachelor’s Degree | 38 | 32.5
Doctoral Degree | 2 | 1.5
High School Diploma or Equivalent | 15 | 14.5
Master’s Degree | 17 | 15.0
Other (specify) | 1 | 1.0
Some College or Associate’s Degree | 25 | 35.5
Income |  |
"$100,000 - $149,999" | 15 | 10.5
"$150,000 - $199,999" | 2 | 2.5
"$200,000 or more" | 2 | 3.0
"$25,000 - $49,999" | 23 | 28.5
"$50,000 - $74,999" | 20 | 20.0
"$75,000 - $99,999" | 14 | 15.5
"Less than $25,000" | 20 | 20.0
Race |  |
Asian | 8 | 11.0
Black or African American | 18 | 18.0
Indian | 0 | 0.0
LatinX | 6 | 8.5
Other | 4 | 4.0
White | 62 | 58.5
Age | 43 | 42.105
Political Ideology |  |
Independent | 22 | 22.0
Lean Democrat | 14 | 13.0
Lean Republican | 10 | 7.0
Not very strong Democrat | 13 | 15.5
Not very strong Republican | 3 | 7.5
Strong Democrat | 26 | 26.5
Strong Republican | 9 | 8.5
Supplementary Table 13: Participant demographics for study analyzing effect of
attire in movie posters

## Supplementary Figures

Supplementary Figure 1: Examples of movie posters where actors/actresses are
depicted in different postures. Supplementary Figure 2: Movie postures shown
to participants in the control condition that only depict White actors and
actresses. Supplementary Figure 3: Movie postures shown to participants in the
treatment condition that depict a racially diverse set of actors and
actresses. Supplementary Figure 4: Fashion magazine covers shown to
participants in the control condition that only depict White models.
Supplementary Figure 5: Fashion magazine covers shown to participants in the
treatment condition that depict a racially diverse set of models.
Supplementary Figure 6: Luxury watch advertisements shown to participants in
the control condition that only depict White models. Supplementary Figure 7:
Luxury watch advertisements shown to participants in the treatment condition
that depict a racially diverse set of models. Supplementary Figure 8: Banking
advertisements shown to participants in the control condition that only depict
male models. Supplementary Figure 9: Banking advertisements shown to
participants in the treatment condition that depict both male and female
models. Supplementary Figure 10: Movie posters shown to participants in the
control condition that only depict female actresses in revealing attire.
Supplementary Figure 11: Movie posters shown to participants in the treatment
condition that only depict female actresses in non-revealing attire.