Bias is bad for productivity. Here’s an overview of a study that came out in July 2017. The findings are that perceptions of bias have a negative impact on idea-sharing and job commitment. “Of employees who experienced bias, 34% reported withholding ideas or solutions in the last six months and 48% said they looked for a new job while at their current job during the same time period.”
Perceptions of implicit bias are reduced by an inclusive environment. “Employees were 64% less likely to perceive bias at companies with diverse leaders, 87% less likely when they had inclusive leaders, and 90% less likely when they had sponsors.”
The methodology was to compare self-assessments of employee potential to those employees’ estimates of how they would be rated by their manager. Larger gaps were interpreted as an indicator of bias. There’s room for debate about the methodology, but the findings ring true. That is, that managers who favour people like themselves discourage the productivity of a diverse workforce. Bias is simply malfunctioning thinking. Leading an organization with malfunctioning thought would presumably be a hindrance to workplace effectiveness.
Human resources departments and those who handle their data are expected to guard the best secrets. But one of the biggest secrets is ironically an anti-secret. Did you know you’re allowed to talk openly about your own pay? Don’t tell HR. It’s embarrassing (for them).
This article in Atlantic.com by Jonathan Timm from July 2014 draws attention to the dubious practice of pay secrecy. I’m not talking about the employer’s obligation to keep your pay information confidential. Rather it’s an article about employees being obliged to keep their pay a secret from one another. These obligations are referred to as “gag rules.”
For the uninitiated, there is no meaningful moral obligation for employees to refrain to talking about their salary with each other. On the contrary, in the United States there are regulations that protect employees’ rights to discuss working conditions with one another. It’s on the edges of the legislation that allows employees to collectively discuss their lot in life, bargain for improvements, and possibly unionize.
In that context the moral judgement should be obvious. Those handling the file at human resources desks are not allowed to advance anti-union behavior, and as professionals they should always advise against such policies.
The article describes personal experiences of people struggling with these fake rules. What is notable is how people presume these gag rules are legitimate, employers and employees alike. Gag rules create a sense of guilt about whether we should put ourselves ahead of the employer. They make us self-consciousness about whether we’re being greedy. We’re embarrassed to talk about whether we’re losers for being the lowest paid person. Raising the topic with colleagues is “akin to asking about their sex life.”
These emotions are powerful stuff. But then, that’s how bullying is done, isn’t it?
Above and beyond beef-and-taters union issues, gag rules are also wrapped up in discriminatory pay practices. That is, it is easier to under-pay women and visible minorities or play favorites if employees don’t talk about their pay. A woman named Lilly Ledbetter complied with the gag rule at Goodyear for nearly three decades and ultimately found out she was under-paid. Ms. Ledbetter sued and lost because she did not complain about being under-paid within the first 180 days of her first paycheck.
Ironically, employers share pay information with each other all the time. They’re called compensation surveys. They happen on an annual basis (if not monthly), and they are delivered through specialized consulting services. The work is done under careful checks and balances that ensure data privacy and keep the whole process fair and legal. Those who have worked on such surveys are proud of their work. I used to do compensation surveys myself, and I was good at it.
One of the reasons why compensation professionals love doing this work is because it helps make pay fair and equitable. Looking down from the ivory tower, human resources people know that perceived unfairness in pay creates discord. So “good” employers put some work into getting it right, behind the scenes, in a kind of lab environment where social justice is organized by experts. But really we’re just trying to stay one step ahead of the riff-raff.
Let’s face it, employees and the social justice movements they created are the rightful owner of this dialogue. Gag rules and compensation surveys are just the cultural appropriation of working class politics.
Managers and human resource professionals are supposed to have non-discriminatory hiring practices. Yet we are only in the early days of seeing job applicants neutrally. There are several new (and not-so-new) methods for considering applicants fairly. There is also the possibility of using good math to prove and reduce bias.
Canada’s federal public service announced on April 20, 2017 that it is starting a pilot project to recruit job applicants on a name-blind basis. The minister responsible said “research has shown that English-speaking employers are 40 per cent more likely to pick candidates with an English or anglicized name…” At the end of the pilot they will analyze the two sets of candidate shortlists, both name-blind and traditional-method. The results of the experiment will be ready in October, for possible roll-out to the entire public service.
What is worth noting is that the Canadian government is running a formal experiment for a limited time. This raises hope that the eventual course of action will be determined by evidence, not speculation. They will measure the discrimination before attempting to remedy it, which could bolster support. The approach also implies the pilot has permission to fail. After all, they might find something totally different from what they expected. But that kind of thing that happens when you care about science.
Of course this pilot addresses only one part of the discrimination puzzle. I would speculate that résumés that still indicate the year and city in which a degree is attained will tip-off employers about age and ethnicity. An obvious next phase of analysis is to block-out the graduation date and the name of the University. After all, you only need to know if they finished their degree, plus the degree’s level and academic major, and a broad sense of the school ranking (i.e. top-100, top-400).
Job applications also reveal writing style, which should be good. But there are differences between the sexes in the use of words. In the book The Secret Life of Pronouns by James W. Pennebaker the author reveals the findings of high-volume statistical analyses revealing (amongst other things) that men make bold pronouncements without referring to themselves in first-person. Women, by contrast, attribute their story to themselves, which is more clear, social, and modest. I personally think that confidence, and willingness to boast, are unreliable indicators of competence.
In classical music, blind auditions are now commonly used to select new hires onto symphony orchestras. They’ve been doing this for years. The musicians submit recordings of their auditions and provide live performances behind a physical screen. I have heard that judges gossip “you can tell” if the candidate is a man yet when the winner steps out from behind the screen it is often a woman. In this not-so-new paper from 2000, authors Claudio Goldin and Cecilia Rouse conducted an analysis of 7,065 individuals and 588 audition-rounds to see what impact blind auditions had. They identified that the blind auditions work.
When you’re fighting the man, words are important. When you’re putting change into effect, math is importanter.
It’s important in the modern workplace to know that there used to be a pervasive stereotype that women were bad at math. It’s relevant to all of us trying to advance math in human resources. We have the dual obstacle of getting good math across to clients, while also getting past unfair judgments directed at women who have perfectly good numbers in their hands.
This is a brief inter-generational memo which will be perceived differently depending on when you were born. In 1992, Mattel produced the toy Teen Talk Barbie. Amongst the 270 possible phrases the dolls would utter, 1.5% of dolls would say the phrase “Math class is tough.”
The doll was decried by the National Council of Teachers of Mathematics for discouraging women from studying math and science. It was also referenced when the American Association of University Women criticized the relatively poor education that women were getting in math. Mattel apologized for the mistake and announced that new dolls would not utter the phrase, and anyone who owned such a doll would be offered an exchange.
I don’t know the full history of women in math, but I do know enough to assert that Teen Talk Barbie was a critical incident. Mattel did us all a favor by screwing up in exactly the right way, obliging many people to snap out of it, encouraging more women to become great at math, and doubling our talent pool of qualified applicants for math-intensive positions.
What fascinates me the most about this incident, is that people born after 1980 show no outward assumptions that women are bad at math. For those of us who grew up with this assumption, we were repeatedly corrected that the stereotype was wrong, often by living-out an experience where women excelled. The younger half of the workforce appears to be advancing their careers in blissful ignorance of this archaic stereotype.
The historic stereotype is important within human resources. Human resources has historically been bad at math and is also a field with a large representation of women. Quantitative work is becoming increasingly important within human resources, and human resources is obliged to influence business peers who take math very seriously. As human resources becomes more sophisticated and makes its way to the big-kids table of decision makers, women who are good at math will speak their minds… as did Teen Talk Barbie. Shortly after the debacle, the Barbie Liberation Organization swapped voice boxes between the Barbies and talking G.I. Joe action figures. The liberated Barbies had access to the phrases “Eat lead, Cobra!” and “Vengeance is mine!”
It is my pleasure to draw your attention to a great paper produced by three students at the University of British Columbia. Grace Hsu, Geoff Roeder, and Andrew Lee produced a paper for their Statistics 450 course with Dr. Gabriela Cohen Freue which was put in for a student research contest. The paper, Analysis of Factors Affecting Resignations of University Employees won an honourable mention for the contest.
The paper identifies that “Millennials do not exhibit a practically significant different length of employment compared to other generational groups.” That is, that although those born after 1975 have a high quit rate right now, they are passing through a high-turnover age group. Prior generations that passed through the 25-34 year old age group in years past, themselves had high quit rates.
Getting more to the point… “This finding disrupts stereotyped representations of generational factors in the workforce and suggests that younger employees resigning sooner can be better explained as a feature of their age rather than their generational group.” My guess is that age 25-34 is when people figure out their career, partners, and housing, with some things changing a few times before getting stable.
Working with twenty years of data covering 7000 staff who quit, their data model chose “years of service” as the variable that would be explained by other data points. If we could predict the number of years a new hire would stay, this might be something an employer could improve. That is, assuming it was not illegal to pre-judge. Thankfully, their findings suggest we should not pre-judge.
Years of service prior to quitting averaged 1.2 to 1.9 years for 25-to-34 year olds, and 4.3 to 5.5 years amongst 35-to-44 year olds. There were small differences between generations, but not in a manner that strengthened a stereotype. For example Generation X quit more quickly when they were younger, but stuck around for longer once they were 35-44. Baby Boomers were not always big on job loyalty, being the quickest to quit in the 35-to-44 age bracket.
One more thing… men and women do not have a big difference in their length of service. When sizing-up job candidates for staying power, it is not just unfair and illegal to favour men; it is wrong on the facts. Keep that in your back pocket next time you help with hiring.