Aggregated Injustice

I ran across a colorfully titled Mother Jones article which documents “a brief history of men getting credit for women’s accomplishments.” As promised by the subtitle, the article is written to do just that, presenting a series of poignant vignettes from the Paleolithic era to the present.

The entries range from enraging:

1843: Mathematician Ada Lovelace shows how Charles Babbage’s Analytical Engine (a theoretical computer) could be induced to perform complex math. Her contribution, considered the first software, was dismissed by many male historians: “It is no exaggeration to say that she was a manic-­depressive with the most amazing delusions.”

To frustratingly understandable:

1840s: …Mary Ann Evans later writes Middlemarch as George Eliot, probably to avoid “being treated as ‘just’ a female writer,” one expert notes.

The piece also captures the uniquely terrible discrimination faced by African American women:

1888: Ellen Eglin sells the rights to the clothes wringer she invented to an agent. The invention brings “great financial success” to the buyer, who paid her $18. “If it was known that a negro woman patented the invention, white ladies would not buy the wringer,” she explains.

But while this article does an excellent job of encapsulating the gender discrimination which has gone on since nearly the dawn of time, it doesn’t quite capture the aggregated effects of such discrimination.

Consider Michael Spence’s economic model of gender-based pay disparity: imagine an employee pool in which people have two observable characteristics: sex and education. An employer assigns each employee to a higher or lower wage by inferring the unobserved characteristic of productivity. Assume also that gender and productivity are perfectly uncorrelated.

Intuitively, this should mean that gender and pay will also be uncorrelated, however Spence’s game-theoretic model reveals that after initial rounds of hiring, the employer will begin to associate higher levels of education with higher levels of productivity. More precisely, because an employer’s opinions are conditioned on gender as well as education, “if at some point in time men and women are not investing in education in the same ways, then the returns to education for men and women will be different in the next round.”

In other words, Spence finds that there are numerous system equilibria and, given differing initial investments in education, the pay schedules for men and women will settle into different equilibrium states.

While the correlation between education and productivity presents a simple toy model, the “signaling” generated by actual success would presumably create an even stronger effect.

That is, men taking credit for women’s inventions, insights, and effort is not just damaging to the person whose ideas are stolen – it is damaging more broadly to people who are identified as women. It weakens women’s equilibrium for signaling success – an effect, again, felt even more strongly by women of color.

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