Laboratory Evidence of the Effects of Sponsorship on the Competitive Preferences of Men and Women

Many competitive career fields exhibit a gender gap in advancement. While entry cohorts may be balanced, there are far fewer women than men in senior positions. Sponsorship has become an increasingly popular solution to this gender gap. While mentors provide guidance and emotional support, sponsors go a step further. Sponsors take an active stake in their protégé’s success and advocate for their advancement. If their protégé is successful, both the protégé and sponsor benefit. Observational evidence indicates that sponsorship increases competitiveness, risk taking, and confidence among protégés. However, there is little direct evidence about the effects of sponsorship, and the evidence that does exist may suffer from a selection bias. It could be that the best employees are the ones that are most likely to be sponsored, but these individuals would have been successful without sponsorship. This selection problem is difficult to disentangle based on real-world data alone!

In this week’s WAPPP seminar, Professor Katherine Coffman discussed her research on a lab experiment designed to isolate the effectiveness of two key features of sponsorship. (Professor Coffman is being heavily recruited for a position at HKS, so we’re hoping that she enjoyed the talk as much as we did!) While working in a lab setting allows researchers to resolve the selection problem, it does narrow the focus to certain measurable aspects of sponsorship. In this case, Professor Coffman focused on what she calls the Belief Channel – that being sponsored is a vote of confidence in the protégé’s abilities that may increase their competitiveness – and the Payment-Tying Channel, which illustrates the link between the protégé’s success and the sponsor’s. These two features, competitiveness and earnings, are important for real-world outcomes, are easy to incentivize, and are easy to measure in a lab setting. It could be that the observed positive effects of sponsorship are due to something other than these two factors. However, if we see an effect of the Belief Channel and the Payment-Tying Channel, we may want to formalize these in sponsorship programs.

Professor Coffman’s experimental design parallels a famous study on competitiveness. Participants go through four rounds of a basic math task. In each round, they are given four minutes to solve as many addition problems as possible. The incentives for solving these problems vary across each of the rounds. In the first round, participants receive 50¢ for each problem they solve correctly. In the second round, researchers compare the performance of each participant. The top 25% earn $2 per problem solved, while the bottom 75% get nothing.

In round 3, participants get a series of choices about their incentives for the round. Over 9 binary-choice questions, participants are asked whether they’d rather get a guaranteed 50¢ per solved problem, or whether they’d rather get a certain dollar amount per problem for being in the top 25%. By varying the dollar amount, researchers are able to see what incentive level is enough to get participants to opt into the riskier competition-style incentive. Critically, participants have not gotten feedback on their performance in round 1 and round 2 and don’t know whether they placed in the top 25% in round 2.

Up to this point, all participants have had the same experience. After round 3, three participants are chosen at random to be sponsors, and all other participants become potential protégés. The key challenge here is balancing the selection effect (maybe only the best are chosen to be sponsored) with wanting sponsorship to be a meaningful vote of confidence. Professor Coffman strategically addressed this problem by creating a series of “matched pairs” for sponsors to choose from. This way, the sponsored and unsponsored groups had relatively equal ability, but the impact of being “chosen” was still meaningful.

All in all, there were four protégé treatments:

• Belief Signal participants were chosen by sponsors, but their sponsors did not earn money based on their performance.
• Payment-Tying participants were randomly assigned to a sponsor, but the sponsors earned 25¢ for every problem their protégé answered correctly if their protégé chose to compete and was in the top 25%.
• Belief Signal and Payment-Tying participants were chosen by sponsors, and their sponsors earned money based on the protégé’s performance. 
• Unsponsored participants were either never presented to a sponsor to be chosen or were not chosen by a sponsor. 

Participants were then asked to repeat the procedure from round 3—making a series of judgments about the necessary wage rate for them to opt into competition and then completing the task. The researchers examined differences in competitiveness and performance between rounds 3 and 4 to better understand the effects of sponsorship.

The three key metrics in the findings were the cutoff, the lowest wage at which a person was willing to compete, their performance, and their earnings.

In round 3, the average men's cutoff was $1.86, compared to $2.02 for women, which indicates a greater appetite for competition for men. Indeed, men were more willing to compete than women at every given wage rate. Men also outperformed women in round 3, solving about one more problem on average. As such, men outearned women in round 3 (average $11.25 versus $7.94). Conditional on performance, men outearned women by $1.03, which suggests that 1/3 of the gender gap is driven by choices about competition rather than performance.

In round 4, the unsponsored exhibited a smaller gender gap than in round 3. However, for each of the sponsored groups, the gender gap grew wider in round 4 than in round 3. While each group decreased their cutoff by about 20 cents, which shows that they became more willing to compete, cutoffs changed more for men than for women. Indeed, sponsorship increases the gender gap from 13¢ in round 3 to 37¢ in the Belief Signal group, 27¢ in the Belief Signal and Payment-Tying group, and 44¢ in the Payment-Tying group.

What makes this gender gap bigger? Sponsorship is intended to encourage talented but underconfident participants to make more competitive choices. It could be that this version of sponsorship isn’t having the intended effect for this target population. To test this, Professor Coffman examined participants’ beliefs about their abilities in rounds 3 and 4.

In the baseline data, both men and women are somewhat overconfident. More than half of men rate themselves in the top quartile! (In fairness, so do 34% of women). In general, the confidence gap between men and women is driven by women being underconfident, more likely to rate themselves below their true ability level.

Sponsorship treatments with the Belief Signal, the vote of confidence that a sponsor chose you, do increase participants’ confidence. Compared to the unsponsored groups, participants in the Belief Signal group rank themselves 0.2 quartiles higher, and participants in the Belief Signal and Payment-Tying Group rank themselves 0.17 quartiles higher. These changes in beliefs don’t vary with gender—everyone reacts to a vote of confidence in a similar way.

However, the group that was most likely to change their willingness to compete in round 4 was overconfident men. Women showed very little movement in their willingness to compete, whether they were overconfident, properly calibrated, or underconfident. This finding is a bit discouraging—improving confidence doesn’t impact those who need it most, but instead concentrates on those who already have plenty of confidence.

Professor Coffman also examined the effect of the sponsorship treatment on performance. In the real world, sponsorship is intended to have the biggest impact on the best performers. However, in this data, the strongest increase in willingness to compete came from men in the bottom three quartiles. There was very little change in willingness to compete for top-quartile men or for women at any performance rank. Participants in a Payment-Tying condition showed additional increases in their performance: having their sponsor’s compensation tied to their performance provides an additional incentive to do well. For women, the effect on performance was small, but for men it was significant. Not only was this group more willing to compete, but they were also improving their performance. This improvement in performance increased men’s earnings, while women’s earnings were not significantly impacted by any sponsorship treatment.

Sponsorship, therefore, is mainly reaching overconfident, low-ability men, and in this lab setting sponsorship fails to close the gender gap in competitiveness or earnings. Talk about unintended effects! The key takeaway, according to Professor Coffman, is that we’re not studying the parts of sponsorship that really matter for women. Other channels (like access to professional networks or sponsor advocacy in promotional meetings) may have a much greater impact. What features of sponsorship programs haven’t been examined that may be critical for closing gender gaps?

What Works: Gender Equality By Design (Part 2!)

Fresh from the World Economic Forum in Davos, Professor Iris Bohnet presented the second half of her forthcoming book “What Works: Gender Equality by Design” at the first HKS WAPPP Seminar of the spring semester.  Professor Bohnet had presented the first half of her book at the first WAPPP Seminar in the fall, and the group was eager to hear more about her focus on “de-biasing organizations” rather than “de-biasing mindsets.”

Professor Bohnet is the Director of the HKS Women and Public Policy Program and Co-chair of the Behavioral Insights Group at the Center for Public Leadership at HKS. Her research demonstrates a powerful truth: No matter how well-intentioned they are, people can still fall prey to bias. Instead of trying to break people of these often unintentional biases, we should be looking to organizational design to “make it easier for all of us to do the right thing.”

The limits of diversity training

Professor Bohnet began with a survey of existing interventions to overcome bias in the workplace, including diversity training, negotiation training, leadership training, mentorship and sponsorship, and networking. Despite being a billion-dollar industry, the evidence on diversity training is mixed. After hundreds of studies, it is hard to be optimistic about diversity training, if only because it is incredibly difficult to de-bias minds. Beyond stereotypical thinking, humans rely on a number of other cognitive shortcuts that are very difficult to unlearn.

The rest of these interventions are geared toward helping women navigate the workforce more effectively. One critique of this approach is that it places the onus of fixing gender discrimination on women. However, it is important to be pragmatic and to consider how these methods can be helpful. The evidence from mentorship and sponsorship networks is particularly encouraging. Female economists who participated in mentorship training workshops were more productive, more likely to publish in peer-reviewed journals, and more likely to get tenure. This is some of the best causal evidence to date about the impact of mentorship and sponsorship and demonstrates the positive impact of these interventions.

Using behavioral insights to move the needle

Behavioral insights and organizational design present an opportunity to develop novel interventions to decrease bias and improve diversity. For organizations looking to attract the best candidates, Professor Bohnet suggests they look at the language in their job advertisements. She presented a job advertisement for a teacher reading in part, “Looking for a warm and caring teacher with exceptional pedagogical and interpersonal skills to work in a supportive, collaborative work environment.” Gendered language like this resonates with women rather than men and can inhibit great male teachers from applying. And with boys falling about a year behind girls in reading and writing by age 15, partially because of the lack of male role models among their teachers, it makes a difference.

There are numerous behavioral interventions that workplaces can develop to overcome bias. Two simple things organizations can start with: get rid of “potential” rankings in employee evaluation, and stop managers from seeing employees’ self-evaluations before giving their own rankings. There is an enormous amount of bias in potential rankings. In another iteration of “seeing is believing,” a lack of female senior partners at a firm may lead to biased thinking that women don’t have the potential or desire to ascend the career ladder. While it’s not clear how to get rid of this bias, it is simple enough to remove potential rankings altogether in evaluating employees. Similarly, there can be a significant difference in employee rankings. On average, women are inclined to give themselves lower self-evaluation scores than men. If managers can see employees’ self-evaluations before making their own judgments, they may be influenced by these scores and give women lower scores (and men higher) than they otherwise would.  Instead, the manager could have conversations with team members about their performance, but wait to see a numerical self-evaluation until after they have made their own evaluations. These two findings are low-hanging fruit for organizations looking to reduce workplace bias; research increasingly shows that mindsets change once behavior has changed, and these low-impact methods reduce the effect of bias in the workplace.

Gender diversity in teams

In recent years, the “business case for diversity”—evidence that more diverse organizations do better than those that are more homogeneous—has attracted a lot of attention. A meta-analysis of 120 studies finds a small diversity premium for diverse corporate boards. However, these studies are just observational: it could be that diversity really does pay, or that companies that are high-performing are also more inclusive. One study measuring collective intelligence found that gender diverse teams outperformed homogeneous teams on each of the tested tasks. Gender diverse teams demonstrated complementary skill sets and a greater tendency to listen to and build on others’ ideas. Gender diverse teams are much more likely to avoid groupthink, which increases their effectiveness.

However, Professor Bohnet emphasized, we should care about gender equality because it’s the right thing to do, not just because of the business case for diversity. In closing, she discussed several organizational improvements that could encourage equitable behavior, including a “comply or explain” system for corporate diversity. Goal setting, transparency, and accountability all play a crucial role in changing behavior and, by extension, changing mindsets. Professor Bohnet’s book is available on March 4—be on the lookout for more bias-reducing, diversity-maximizing insights!

What Works: Gender Equality by Design

Imagine the following situation: You are a young professional musician. And you are really good. Music is what makes your blood flow. Ever since you were a little girl, piano and violin lessons excited you rather than bored you. Today is a very important day for you: You're auditioning for a position in the National Symphony Orchestra. How would you feel if you knew that the minute you walked on stage, before even playing your instrument, your chances of being hired would decrease significantly?

Before research showed that having musicians audition behind a curtain, so the jury would not be able to tell their gender, increased the chance that a woman would be hired or promoted, and that these "blind" auditions alone could account for a third of the increase in the proportion of women musicians hired into top-tier American symphonies, female musicians would face just that scenario. The implicit biases of possibly well-meaning members of the jury would too often reduce women's chances to succeed in the audition.

Although we would all like to think we do not suffer from the same biases as the members of those juries, the opposite is likely true. During the first HKS Women and Public Policy Program seminar of the academic year, Professor Iris Bohnet explained that we are all biased in one way or another, "because seeing is believing". We observe patterns in the world, such as most kindergarten teachers being female, or most software engineers being male, so we come to expect people to fill those roles. Don't believe it? Take the test yourself.

Professor Bohnet is the Director of the HKS Women and Public Policy Program (WAPPP) and Co-chair of the Behavioral Insights Group at the Center for Public Leadership at HKS. During the seminar, she presented a preview of her forthcoming book “What Works: Gender Equality by Design”, in which she argues that we can use insights we learn from Behavioral Economics to close gender gaps caused by implicit biases.

Professor Iris Bohnet, Director of the HKS Women and Public Policy Program

These insights allow us to create “nudges", which are small actions designed to obtain the most desirable reactions from people, building on knowledge of how the -often irrational- human mind actually works. In the book, she talks about "nudges we can use to make the world a better place", because they can reframe the environments in which we work. Best of all, they are mostly cheap and can be introduced quickly.

Professor Bohnet described previous approaches to increasing diversity in the workforce, such as diversity, leadership, and negotiation training, and underscored that there is not enough evidence to prove that these interventions work. On the other hand, interventions like long-term capacity-building or mentoring have been found to be very promising. In a study that followed the career trajectories of women economics professors who were randomly assigned into a long term mentorship program, the professors in the program fared better than those in the control group.

She mentioned many other nudges to redesign the work environment, like putting up more images of female leaders -"what you see matters in what you think is possible for yourself"-, avoiding panel interviews, assessing job candidates on a pre-determined set of questions immediately after the interview, highlighting the increased presence of gender mixed corporate boards rather than their low proportion, and many more. Professor Bohnet is handing in the manuscript for the book next week, so look forward to reading more when it comes out!