Art and gender: market bias or selection bias?


We test for gender effects in the art market using auction prices for artists who graduated from the Yale School of Art. Yale’s female graduates have significantly fewer auction sales, controlling for their graduating year gender ratio. Conditioning upon sale, works by female artists obtained higher average prices. The results suggest that while institutions and career paths may condition on gender, the market may not.

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  5. 5.

  6. 6.

    Interestingly, all three of our working papers appeared at about the same time, using different data and methodologies.

  7. 7.

    Galenson (2005) notes that “…during the past 5 decades the Yale School of Art has produced a series of graduates who have achieved great success commercially as well as critically.”

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    Cf. Goetzmann et al. (2016).

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    Cf. Jacobs (2005).

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    Cf. Hengel (2017) and Tsugawa et al. (2017).

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    See Alper and Wassall (2006) for an excellent literature review.

  12. 12.

  13. 13.

    To further address the issue of marital surname changes, we used these 643 name changes to test a potential instrument for probability of name change: length and fraction of vowels in a surname. We found no correlation.

  14. 14.

    The auction data are from

  15. 15.

    This separation is motivated by Rengers (2002)’s “winner-take-all” finding.

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    p value of 0.0003. Not reported in the table.

  17. 17.

    Not reported in the table.

  18. 18.

    As a robustness check, we also perform an OLS with log of average auction price as the dependent variable: the results are qualitatively similar but only significant at the 10% level. Available upon request.

  19. 19.

    See Fig. 9 in “Appendix” for citation gender ratio by year of citation.


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We thank Lesley Baier, Eric Greenleaf, Lisa Kahn, Sharon Oster, Judith Schiff, and all the participants of the Symposium on Art and Gender at Yale School of Management for their comments and suggestions.

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Correspondence to William N. Goetzmann.

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See Tables 8, 9, 10, 11, 12, 13 and 14 and Figs. 8, 9, 10 and 11.

Table 9 Female artists in the Yale alumni directory
Table 10 Average auction prices at the artist level
Table 11 Distribution of artist fixed effects and test for equality of means
Table 12 Artists ranking by average pre-sale price estimate
Table 13 Equality of mean test for the auction house estimates
Table 14 Average price estimation error
Fig. 8

Distribution of art prices by gender. a The distributions of log(price) for different gender groups. bd The distribution of log(price) using the frequencies instead of density for all graduates, graduates before and after 1983, respectively. e The distributions of log(price) for all artists. The dashed line represents the normal distribution

Fig. 9

Yale School of Art graduates in the Google Books corpus (for 464 names). a The male/female ratio of citation counts by year of graduation, adjusting for gender ratio of each class. b The same ratio eliminating the observations with the ratio > 25 (3 observations). Dashed line represents the ratio equal to 1. (For many classes, we do not observe any citation, and therefore, this is a scatter plot and not connected.)

Fig. 10

Time to market = first sale graduation year

Fig. 11

Auction house estimates

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Cameron, L., Goetzmann, W.N. & Nozari, M. Art and gender: market bias or selection bias?. J Cult Econ 43, 279–307 (2019).

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  • Art market
  • Gender economics
  • Auction

JEL Classification

  • Z11
  • J16