Diversity of artists in major U.S. museums

Abstract

The U.S. art museum sector is grappling with diversity. While previous work has investigated the demographic diversity of museum staffs and visitors, the diversity of artists in their collections has remained unreported. We conduct the first large-scale study of artist diversity in museums. By scraping the public online catalogs of 18 major U.S. museums, deploying a sample of 10,000 artist records comprising over 9,000 unique artists to crowdsourcing, and analyzing 45,000 responses, we infer artist genders, ethnicities, geographic origins, and birth decades. Our results are threefold. First, we provide estimates of gender and ethnic diversity at each museum, and overall, we find that 85% of artists are white and 87% are men. Second, we identify museums that are outliers, having significantly higher or lower representation of certain demographic groups than the rest of the pool. Third, we find that the relationship between museum collection mission and artist diversity is weak, suggesting that a museum wishing to increase diversity might do so without changing its emphases on specific time periods and regions. Our methodology can be used to broadly and efficiently assess diversity in other fields.

Comparing demographics of signatories to public letters on diversity in the mathematical sciences

“…We provide details about the information requested later in this section, and we provide the full text of our Amazon Mechanical Turk questionnaire in S1 Appendix. A number of peer-reviewed published studies adopt this crowdsourcing methodology to infer demographics; see, for example, work on diversity in art and diversity on mathematical sciences journal editorial boards [11,12]. We adopted several strategies to ensure data quality.…”

“…To omit them would make this research less verifiable. Third and last, the crowdsourcing approach to inferring demographics of public figures based on public information has been previously established in the literature [11,12]. We now describe in more detail the procedures we applied to each raw data record for each question on our survey instrument that is contained in our final data set.…”

“…In reality, an individual's gender is determined by that individual. However, we judged that surveying the signatories would be unlikely to produce sufficient data, and so similar to [11,12], we have used crowdsourced inferences, recognizing the limitations of this approach, and keeping in mind that self-identifications are always preferable. We proceed with our gender analysis because both actual gender (as self-identification) and inferred gender (as perceived by others) are salient to issues of representation in the academy.…”

Artistic careers in the cyclicality of art scenes and gentrification: symbolic capital accumulation through space in Bushwick, NYC

“…However, we know that the US art market is characterized by deep systemic issues of race (and gender) inequality. For instance, a recent study found that 85% of artists who have works exhibited in 18 US major museums are white and 87% are men (Topaz et al, 2019), and similar compositions go for art fairs and arts education. 4 Hence, there is scope to assume that the ethnic composition of the local art community in Bushwick is largely white.…”

A Community Mural Tour: Facilitating Experiential Learning About Social Determinants of Health

“… 8 , 9 Much of art-based education, however, is museum-based, where art is often not representative of the diversity of surrounding communities. 6 , 10 , 11 Conversely, murals are a form of social expression often designed by or in conjunction with community members. Directly embedded into built environments, they evolve as neighborhoods change.…”