Is beauty-based inequality gendered? A systematic review of gender differences in socioeconomic outcomes of physical attractiveness in labor markets

Figures & data

Table 1. Inclusion and exclusion criteria.

Included	Excluded
Written in English	Other languages
Peer-reviewed articles	Non-peer-reviewed articles, books
Quantitative studies	Qualitative studies
Attractiveness or beauty as a measure of appearance	Weight, BMI, height, dressing, femininity, or masculinity as a measure of appearance
Labor market-related socioeconomic outcomes (e.g. earnings, recruitment, obtaining a loan, electoral success, getting tip, and career success)	Other outcomes, such as success in relationships
Both men and women are acknowledged and compared in the analysis	Studies concerning solely either men or women, or making no comparisons

Figure 1. Overview of included studies by study type, year of publication, and continent. Number labels refer to article ID, see Appendix C.

Table 2. Measures of socioeconomic outcomes in the studies.

	Studies	%
Money	21	36,21
Hiring	20	34,48
Votes	9	15,52
Other	8	13,79
Total	58	100

Table 3. Occupations examined in the studies reviewed, categorized according to the International Standard Classification of Occupations (ISCO-08).

Categories	ISCO-08	Studies	%
High skilled white-collar	Managers	5	8,62
	Professionals	11	18,97
	Technicians and associate professionals	7	12,07
Low skilled white-collar	Clerical support workers	9	15,52
	Service and sales workers	13	22,41
High skilled blue-collar	Skilled agriculture, forestry and fishery workers	0	0
	Craft and related trades workers	0	0
Low skilled blue-collar	Plant and machine operators, and assemblers	2	3,45
	Elementary occupations	1	1,72
N/A	Unknown	33	56,9

Table 4. Distribution of gendered outcomes of articles (N = 58) reviewed.

Gendered outcome	Number of papers
Attractiveness more beneficial for women	19
Attractiveness more beneficial for men	20
Attractiveness beneficial for both	39
Attractiveness beneficial for neither	9
Attractiveness more detrimental for women	8
Attractiveness more detrimental for men	0
Attractiveness detrimental for both	2
Attractiveness detrimental for neither	1
Plainness more beneficial for women	2
Plainness more beneficial for men	1
Plainness beneficial for both	0
Plainness beneficial for neither	0
Plainness more detrimental for women	4
Plainness more detrimental for men	5
Plainness detrimental for both	6
Plainness detrimental for neither	2

Table

Search engine	EBSCO	Scopus	ProQuest	Google Scholar	Web of Science
Search string	(keywords) (DE ‘Facial Features’ OR DE ‘Physical Attractiveness’) OR ((DE ‘APPEARANCE discrimination’ OR DE ‘DISCRIMINATION in employment’) OR (DE ‘PHYSICAL characteristics (Human body)’)) OR (DE ‘FEMININE beauty (Aesthetics)’) OR lookism OR beauty OR ‘facial appearance’ OR attractiv* AND (abstrakteista) labo*r OR salar* OR financial OR benefit* OR recruit* OR employ* OR tipping OR earning* OR wage OR performance OR occupation* OR lookism OR penalt* OR outcome* OR discrimination OR inequal* OR success* OR wealth AND (mistä tahansa) appearance OR beauty OR attractiveness OR good-looking AND (mistä tahansa) gender NOT (abstract) mating	(keywords) beauty OR ‘facial appearance’ OR ‘physical appearance’ OR appearance OR lookism OR looks OR ‘physical attractiveness’ OR lookism OR attractiv* AND (article title, abstract, keywords) labo*r OR salar* OR financial OR benefit* OR recruit* OR employ* OR tipping OR earning* OR wage OR performance OR occupation* OR lookism OR penalt* OR outcome* OR discrimination OR inequal* OR success* OR wealth AND (all fields) appearance OR beauty OR attractiveness OR good-looking AND (all fields) gender	(All subjects & indexing) MESH.EXACT(‘Beauty’) OR MESH.EXACT(‘Physical Appearance, Body’) OR MAINSUBJECT.EXACT(‘Beauty’) OR MAINSUBJECT.EXACT(‘Personal appearance’) OR lookism OR attractiv* AND (anywhere except full texts) noft(labo*r OR salar* OR financial OR benefit* OR recruit* OR employ* OR tipping OR earning* OR wage OR performance OR occupation* OR penalt* OR outcome* OR discrimination OR inequal* OR success* OR wealth) AND (anywhere) appearance OR beauty OR attractiveness OR good-looking AND (anywhere except full texts) gender NOT (abstract) mating OR dating OR marriage	(beauty OR ‘facial appearance’ OR ‘physical appearance’ OR attractiveness OR lookism) AND (labor OR labor OR salar* OR financial OR benefit* OR recruit* OR employ* OR tipping OR earning* OR wage OR performance OR occupation* OR lookism OR penalt* OR outcome* OR discrimination OR inequal* OR success* OR wealth) NOT (mating OR dating OR mental)	(ALL = (beauty OR ‘facial appearance’ OR ‘physical appearance’ OR attractiveness OR lookism) AND ALL = (labor OR labor OR salar* OR financial OR benefit* OR recruit* OR employ* OR tipping OR earning* OR wage OR performance OR occupation* OR lookism OR penalt* OR outcome* OR discrimination OR inequal* OR success* OR wealth) NOT ALL = (mating OR dating OR mental)) AND LANGUAGE: (English) AND DOCUMENT TYPES: (Article)
Databases	PsycINFO, Academic Search Premier, SocINDEX with Full Text, Business Source Complete, Political Science Complete	Social Sciences, Psychology, Business, Management and Accounting, Economics, Econometrics and Finance ja Decision Sciences	ProQuest Central, British Periodicals, Periodicals Archive Online
Results	467	1376	2977	1100000 (1000 first chosen)	11922
Search date	October 2, 2019	October 1, 2019	October 2, 2019	October 9, 2019	December 18, 2019
Number of included articles	62	189	123	154	64

Table B1. Measurements of attractiveness in the studies

	Studies	%
Basis of rating
Facial photograph of a real person	34	58,62
Digitally generated or edited photo	7	12,07
Real-life encounter	13	22,41
Other or multiple types	4	6,90
Number of ratings per picture
1	11	18,97
2–9	14	24,14
10–49	17	29,31
50–99	5	8,62
100–199	4	6,90
200+	2	3,45
Information unavailable	5	8,62
Pool of raters
Students	18	31,03
Interviewer	11	18,97
More diverse sample	19	32,76
Other or multiple	2	3,45
Information unavailable	8	13,79
Scale
5-point	18	31,03
7-point	9	15,52
9-point	10	17,24
Other	21	36,21
	58	100,00

Table

Article ID
1	Abramowitz et al. (1991).
2	Andreoni and Petrie (2008).
3	Anýžová and Matějů (2018).
4	Benzeval et al. (2013).
5	Berggren et al. (2010).
6	Biddle and Hamermesh (1998).
7	Bóo et al. (2013).
8	Cash and Kilcullen (1985).
9	Cash et al. (1977).
10	Chiang and Saw (2018).
11	Chiao et al. (2008).
12	Deng et al. (2020).
13	Deryugina and Shurchkov (2015).
14	Doorley and Sierminska (2015).
15	French (2002).
16	Frieze et al. (1991).
17	Fruhen et al. (2015).
18	Gehrsitz (2014).
19	Geiler et al. (2018).
20	Geys (2015).
21	Hamermesh and Biddle (1994).
22	Harper (2000).
23	Heilman and Saruwatari (1979).
24	Heilman and Stopeck (1985).
25	Jackson (1983).
26	Jäckle and Metz (2017).
27	Jawahar and Mattsson (2005).
28	Jenq et al. (2015).
29	Johnson et al. (2010).
30	Judge et al. (2009).
31	Kanazawa and Still (2018).
32	King and Leigh (2009).
33	Kraft (2012). (Study 1)
34	Kraft (2012). (Study 2)
35	Lee et al. (2018).
36	Lee et al. (2015).
37	Lev-On and Waismel-Manor (2016).
38	Lutz (2010).
39	Lynn and Simons (2000).
40	Marlowe et al. (1996).
41	Maurer-Fazio and Lei (2015).
42	Mavisakalyan (2018).
43	Nicklin and Roch (2008).
44	Oreffice and Quintana-Domeque (2016).
45	Parrett (2015).
46	Patacchini et al. (2015).
47	Patel and Wolfe (2021).
48	Paustian-Underdahl and Walker (2016).
49	Poutvaara et al. (2009).
50	Robins et al. (2011).
51	Rooth (2009).
52	Roszell et al. (1989).
53	Ruffle and Shtudiner (2015).
54	Ryabov (2019).
55	Sala et al. (2013).
56	Shahani-Denning et al. (2010).
57	Sigelman et al. (1986).
58	Wong and Penner (2016).

Table D1. Inconsistent gendered outcomes by country, year, study type, occupational categories, outcomes, basis of rating and pool of raters.

	Attractiveness more beneficial for women		Attractiveness more beneficial for men		Attractiveness more detrimental for women
	N	%	N	%	N	%
Country
North America	11	57,89	13	65	5	62,5
Europe	6	31,58	3	15	1	12,5
Asia	0	0	2	10	1	12,5
Others	2	10,53	2	10	1	12,5
Year
Median	2012		2007		2010
Mean	2009		2003		2003
Study type
Cross-sectional	6	31,58	6	30,00	1	12,50
Longitudinal	3	15,79	4	20,00	0	0,00
Lab experiment	7	36,84	8	40,00	5	62,50
Field experiment	3	15,79	2	10,00	2	25,00
Occupational categories
High-skilled white collar	5	26,32	7	35,00	6	75,00
Low-skilled white collar	8	42,11	6	30,00	6	75,00
High-skilled blue collar	0	0,00	0	0,00	0	0,00
Low-skilled blue collar	2	10,53	0	0,00	1	12,50
Unknown	11	57,89	10	50,00	0	0,00
Outcomes
Monetary	8	42,11	8	40,00	1	12,50
Hiring	6	31,58	9	45,00	6	75,00
Votes	2	10,53	2	10,00	0	0,00
Others	3	15,79	1	5,00	1	12,50
Basis of rating
Facial photograph of a real person	12	63,16	12	60,00	7	87,50
Digitally generated or edited photo	1	5,26	0	0,00	1	12,50
Real-life encounter	4	21,05	6	30,00	0	0,00
Other or multiple types	2	10,53	2	10,00	0	0,00
Pool of raters
Students	7	36,84	5	25,00	4	50,00
Interviewer	4	21,05	6	30,00	0	0,00
More diverse sample	4	21,05	6	30,00	4	50,00
Other or multiple	1	5,26	1	5,00	0	0,00
Information unavailable	3	15,79	2	10,00	0	0,00

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Data availability

The data extracted by the researchers is publicly and permanently available at: https://doi.org/10.5281/zenodo.7852214.