# An Agent-Based Simulation of How Promotion Biases Impact Corporate Gender Diversity
## 5 Results
### Experiment 4: The Impact of Promotion Biases on Individual Employees
Focusing now on each rank (each row in the figure), we see consistent patterns both in the passed-over data and in the time-in-rank data. First, as the bias increases, all simulations show that women are passed over more times per year than their male peers, as expected (charts on the left). Second, we see that the average tenure-in-rank increases for women, while staying flat or even decreasing for men (charts on the right). If we now compare the passed-over results across ranks (the charts in the left column of Figure 9), we notice another important trend. While at the entry level (bottom row) the average number of times passed-over for women seems to grow in a roughly linear proportion with the amount of promotion bias, for higher ranks this relationship seems to grow more geometrically: at the entry level (bottom row), raising the bias from 0.1 to 0.5 increases the number of times passed-over for women from 0.63 to 1.91--a ratio of 3.0; at the manager level (middle row), this ratio increases to 6.2 (from 0.5 to 3.1); at the VP level (top row), the ratio further increases to 15.1 (from 0.32 to 4.83). This, in retrospect, makes sense, because being passed over is compounded by the increasing proportion of men at higher ranks: the promotion bias gives each individual man an increasing chance of being promoted over a female peer, and there are a lot more men in each woman's peer group. Hence, even in the absence of biases, more men than women would be promoted. Figure 9: Average number of times passed-over (left column) and average number of years in rank (right column) for (**A**) VP level (top row), (**B**) manager level (middle row) and (**C**) entry level (bottom row) levels. In each chart, the dark bars represent simulation data for men, while the light bars represent simulation data for women. The number below each pair of bars represents the level of promotion bias. If we now focus on the tenure-in-rank data (the charts in the right column of Figure 9), we notice that the average time spent at each rank increases for women with increasing promotion bias, but this increase is fairly consistent across ranks. What is more interesting about the tenure-in-rank data is that, especially at the lower ranks, the average tenure-in-rank of men decreases while that of women increases. This result, while not entirely surprising, further quantifies the differences in the individual experiences of men and women that result when gender biases exist in the promotion process. There is one aspect of our simulations that could be made even more realistic. Namely, in our current simulation, employees only separate from the company at random times. In contrast, being passed over repeatedly in real life would likely cause a decrease in satisfaction, which would in turn lead to higher turnover rates for women than for men [58]. Hence, the idea that women will remain stuck at a level for a much longer time than men is somewhat unrealistic, because in real life, women in those situations would be more likely to quit. This observation does not change our results but suggests that gender biases may have an even more acute negative impact on women in the workplace than shown in the simulations. We are currently developing a version of this simulation in which each agent has an internal \"satisfaction\" variable that is impacted by being passed over for promotion, and which, in turn, increases the probability that an employee will voluntarily leave the company. In this case, we expect to see lower retention rates for women and immediate changes in the organization's turnover rate [59]--phenomena that are common across male-dominated industries. We leave this additional analysis to future work, and simply point out another advantage of agent-based simulation, viz, the ability to add details to the simulation without compromising or invalidating the results obtained with a simpler simulation.