Behavioral Economics can enhance your Diversity & Inclusion strategy
Achieving diversity and inclusion goals remains a top strategic challenge for many organizations. However, despite billions of dollars invested each year in programs, there has been very little progress. This is even more apparent when we examine the movement of targeted populations up the leadership chain. A survey by Mckinsey and leanIN.org found that although women and men in financial services begin their careers at parity, making up roughly equal portions of entry-level staff, as you move higher up the ladder, women account for only 19 percent of positions in the C-suite (slightly lower than the 22 percent average for US women overall). As we’ve seen with solving many other complex behavioral challenges (such as texting while driving and obesity), traditional education and policy-focused solutions have limited long-term success. For example, mandatory diversity training programs have no long-term positive effects and can even increase bias. Other, more programmatic changes framed under the guise of “doing something,” such as standardized evaluations and grievance procedures are equally ineffective and can also backfire when oversight and outcome evaluations are lacking.
Positions in the C-suite
Behavioral Economics provides an important lens to help companies solve these challenges by taking a more human centric and evidence-based approach to these problems. We do this by applying research-driven insights in the design of new decision-making architectures by (i) finding ways to reduce human biases or heuristics so as to minimize their impact on our decisions and (ii) reformulating traditional processes to be fair, algorithmic, and measurably effective.
To see this in action, below are two examples of how we use insights from the behavioral sciences to inform our HireMetrics platform that helps companies select candidates fairer, faster, and better:
A study by Dunning and Ehrlinger looked at why women disproportionately avoid careers in Science found that although women performed equally to men on a science quiz, they were less likely to enter a science competition because they underestimated their performance. The research also found that the women thought less of their general scientific reasoning ability versus their male counterparts. A similar finding plays out in the McKinsey and LeanIN survey on the lack of women in upper management positions at financial services firms - women, especially women of color, were found to lack confidence in their ability to reach upper leadership positions (57% of entry-level women say they fear high profile failure would impact their day-to-day experience as a top executive versus 42% of men).
A solution to this misperception is feedback and guidance aimed at improving employees' perception of their ability or to help them have clarity of their performance and strengths relative to all others. To this end, our HireMetric platform (a) shows percentile scores to help candidates understand where they stand compared to others and (b) provides guidance on traits and thresholds that are required to move forward.
Not only are people generally poor evaluators of their own competence, individuals are also poor evaluators of others’ competence. Real-world studies on manager evaluations using 360 reviews by peers, bosses, and direct reports showed that more than 50% of the variance in manager’s rating could be explained by the unique rating patterns of the rater and not the performance of the individual or the source group doing the rating - this is referred to as the idiosyncratic rater effect.
To solve for this effect, we developed a work sample test that is not evaluated by managers or HR, but instead by a third party with no vested interest in the outcome. The work sample measures such traits as critical thinking and conscientiousness, but ensures that each trait is measured using Specific, Measurable, Achievable, Realistic, and Transparent metrics, reducing ambiguity for the applicant and subjectivity of the rater, and enabling individuals with even a low subject-matter knowledge to fairly and accurately evaluate performance and ability.
The two examples above illustrate a small snapshot of how behavioral science can help us reflect on the selection process through a psychological lens and use this information to build better systems and, consequently, experience for all parties - increasing confidence for all applicants and supporting employers objectivity across their evaluations. Below are additional biases that can hinder fairness during the promotion process. This list is not intended to be exhaustive, but to simply show that each step of the hiring process can be influenced by a myriad of biases that we may not be aware of. For the employer, we can solve for many of these biases by simply taking a more structured and algorithmic approach, similar to that of our HireMetrics platform.
