Measuring the glass ceiling that keeps women down

Measuring the glass ceiling that keeps women down

A clear illustration of the glass ceiling can be found in a Yale study of a North American retail chain: women represent 56% of entry-level workers; 48% of department managers; 35% of store managers and; 14% of district managers.

The study by professor Kelly Shue, Yale and co-authored with Alan Benson, University of Minnesota and Danielle Li, MIT is based on assessment and promotion records for close to 30 000 workers. It shows that women are 14% less likely to be promoted at the company in each year.

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The promotion records show that a major factor preventing women from being promoted is that they are consistently judged as having lower leadership potential than men. Women’s performance is rated higher than men’s, on average. But their potential is rated lower—a pattern than continues even when women exceed those expectations, reports the Yale Insights.

For performance there are metrics but when it comes to performance the evaluation is more abstract and more subject to bias.

“What is commonly talked about in terms of management and potential are characteristics such as assertiveness, execution skills, charisma, leadership, ambition. These are, I believe, real traits. They’re also highly subjective and stereotypically associated with male leaders. And what we saw in the data is a pretty strong bias against women in assessments of potential”, Shue says.

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Women were 7.3% more likely than men to receive a “high” rating in performance but their potential ratings are 5.8% lower. The report estimates that lower potential ratings explain up to 50% of the gap in promotions.

The report estimates that also 70% of the gender pay gap at the company is attributable to gender differences in job levels.

As actions to prevent this bias the report recommends companies to pay close attention to internal data including use algorithms to look for systematic gaps between performance and potential ratings and to look at other available data and see what variables actually forecast being a good manager.”

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