Many firms have DEI programs that fall short of stated goals, partly because they often don’t track the right metrics — which is even more relevant as firms explore flexible working models. Here’s how to establish programs that produce meaningful, lasting change by examining a new way to look at pay equity, along with our framework and recent case study.
Improving diversity, equity and inclusion (DEI) within an organization is not only a key competitive advantage; it is one of the most important drivers of sustained financial growth. Studies show that firms with diverse leadership teams can outperform their peers by as much as 20% to 30% on EBIT. Other researched benefits include higher earnings per employee, greater levels of innovation and an easier time attracting and retaining talent.
As the tangible value of a diverse and inclusive work culture becomes more evident, corporate DEI initiatives are gaining greater visibility and momentum — aided by today’s climate, where protests for social justice have swept across the United States, as well as many other parts of the world. This has been especially true in sectors like technology and insurance, which are experiencing rapid innovation and a shortage of skilled talent.
However, even while current events underscore the importance of DEI, companies run the risk of putting these programs on the backburner. Organizations are rapidly reconsidering their working models in response to the COVID-19 pandemic and run the risk of failing to invest as much into DEI programs or failing to assess the unintended consequences of workforce changes on progress toward DEI goals that has already been made. As organizations assess what proportion of their workforce is impacted by automation and what proportion is pivotal, or what proportion of workers can be contracted or work remotely and what proportion should continue working full-time in a physical company location, they should also consider the impact of these decisions on diversity and inclusion.
In making these determinations, organizations need to ensure they are also thinking about the impact on diverse talent pools or their current representation of minorities and women. Fortunately, data and analytics can improve DEI programs in an uncertain and rapidly evolving business environment; but first, companies must know which metrics to evaluate.
When executing on DEI initiatives, it’s important to keep in mind that what you don’t measure, you don’t improve. Organizations have so far focused on measuring DEI metrics like workforce representation, talent pipeline and attrition rates. While this has helped organizations understand where they stand, most companies fail to investigate the root cause of issues. What’s more, organizations are often focused on measuring the wrong metrics, relying too much on past statistics instead of focusing on the future.
The first step to ensuring your business is looking at the most meaningful metrics is to ask probing questions, such as:
- Why do we promote women at a slow rate and an even slower pace when they are women of color?
- What is the talent supply net deficit when we lose more people of color than we hire?
- If we continue with the same rate of hiring, attrition and promotions, what does progress toward our DEI targets look like? Will we reach our goals in two years or ten?
- Will spending money to close pay equity gaps get to the root cause of why we have pay gaps in the first place?
- How does our location strategy influence our ability to attract and retain diverse talent?
To answer these forward-looking questions, companies need to move beyond surface-level fixes and adopt a long-term strategy based on data. We recognize this isn’t an easy task. Organizations often lack reliable data on their talent pipeline, key workforce metrics and DEI practices. And while data is critical, knowing how to act on this data is just as important. Lack of a coherent data strategy means valuable data elements across the employee life cycle often fall through the cracks. Significant effort is expended in producing outputs that prioritize reporting symptoms of the DEI issues to management rather than providing an explanation of the issue itself.
Aon’s Data-Driven DEI Framework
Sustainable solutions to DEI challenges will require a coordinated data and analytics strategy that complements proactive leadership and cultural changes within the organization. Therefore, companies need a holistic approach that spans an employee’s tenure at the business and establishes strong connection points between pay gaps, workforce representation, location, talent availability and organizational culture.
The following are key elements of our recommended framework:
These four areas are interconnected and mutually reinforcing. Figure 1 illustrates how they work together with culture at the center — after all, the attitudes and willingness of employees to embrace DEI will ultimately drive the success of these programs.
- Pay Equity: Unexplained pay gaps are often the tip of the iceberg and probably the most reported metrics. However, if this information is viewed in isolation, it does not help resolve the DEI gaps.
- Representation: Pay inequities often result from lack of effective representation in the workforce, especially in some job families or among the higher job levels.
- Environment: Having the right location strategy helps influence the availability of diverse talent throughout job functions and levels.
- Culture: Sitting at the heart of inclusion, measuring culture in quantifiable terms helps establish a robust action plan for improving DEI.
Next, we’ll explore what each of these pillars involves in greater detail.
Environment: Rethink Your Location Strategy for Hiring Talent
The physical location of a company plays a critical role in talent availability and selection. Locations also influence pay decisions based on cost of living and talent availability. This topic becomes even more nuanced as companies think about what roles are needed onsite and which can operate virtually.
These choices then shape talent sourcing strategies and typically constrain hiring efforts to these locations. While talent availability may be a valid concern, using location limitations as an excuse for failing to deliver on DEI goals is a missed opportunity.
Bolster representation with location analytics. Our work in this area finds that talent is mobile, the paradigm of talent availability across locations is ever changing and deep-rooted assumptions on talent pools may not hold true anymore. For instance, our study finds that traditionally untapped locations, such as Columbus, Ohio, have above average availability of female software developers.
Companies must tap into non-traditional locations and talent sourcing strategies. Adjustments can be made to enlarge the talent pool by tapping new locations. Additionally, remote working has been an underutilized way to hire in locations where a business may not have any physical presence.
Representation: Maintain a Diverse Workforce
Representation is critical to understanding the progress companies are making with their diversity, equity and inclusion plans — and is often touted as a key metric that organizations track.
Many companies tend to have disproportionate representation of females and minorities at lower job levels. When examining the full census results in our survey database, we find that women, African Americans and Hispanics have a steep decline in representation in middle management levels and even more so in leadership levels. These differences are especially stark when looking specifically at non-white females.
Organizations need to be deliberate in selecting the appropriate measurements for DEI programs by tracking and assessing specialized metrics that provide a fuller picture, such as:
- Management and Executive Diversity Index: The number of employees with diverse backgrounds in management roles divided by the total number of management employees
- Employer Brand Perception Index: The impact of brand perception on candidates with diverse backgrounds in comparison to overall candidate pool
- Availability and Diversity in Candidate Pool: Contrasting diverse employee availability for a role or skill in a location versus internal diverse employee staffing ratio
Analytics can unlock core representation challenges. Taking a data-based approach to tracking an employee’s time at an organization can often debunk long held institutional beliefs, bring to light overlooked and underrepresented areas for improvement and identify ripe areas for pilot programs and experimentation. But all of these are merely symptoms of the larger ecosystem of interconnect DEI problems. Look under the hood and you will find imbalanced representation can be either worsened or improved based on location strategy. Signs and benefits of these will also show-up in pay equity measurement. Enhancing proportional representation across all levels can lead to improvement in statistical pay equity analyses. This provides higher confidence to leadership and ensures that resources are being prioritized to remedy actual problem areas. When analyzed jointly, pay equity and career progression rates can help identify areas impacted by a culture of implicit or overt bias.
Equity: Implement Fair Pay Programs
Political and compliance pressures have encouraged many organizations to take a deeper look at pay practices and conduct a quantitative analysis of pay internally. Pay equity audits have proven effective in identifying areas for immediate remediation and ongoing monitoring. While many organizations have woken up to the tangible benefits of pay equity, several others have failed to comprehend how pay equity is deeply connected — and can be a byproduct of — imbalances of workforce representation and perpetuity of bias.
Pay equity analyses typically uncover unexplained pay gaps based on gender or ethnicity. Organizations rightly spend a few cents on the dollar to bridge the gap to stay compliant of pay equity regulations. But the discussion should not stop there: A larger analysis and discussion is needed around how workforce representation, talent availability and location play a factor in influencing the pay equity gaps.
As organizations change their talent strategy and consider remote hiring, there will be decisions made around updating pay practices. For instance, it might not make financial sense initially for a tech firm to hire software engineers in Michigan and pay them salaries comparable to those in the San Francisco Bay Area. But would this step create discrepancies in its pay strategies purely based on location? Such pay practices typically result in pay equity issues where similarly situated jobs are not compensated similarly. In combining location and pay equity analytics, organizations can build insight into downstream DEI implications of these decisions and develop more equitable pay practices.
Drive an Inclusive Culture
Culture is the beating heart of the DEI ecosystem. The values that employees hold and the way they interact can make or break DEI programs. These interactions are what translate vision into sustainable practices. However, measuring culture is difficult.
In recent years, advances in analytics capabilities, such as machine learning, have enabled organizations to hone in on the measurable aspects of culture like the strength and expanse of employee connectivity. Network analytics is a powerful predictive analytics tool that can uncover employee interaction and dispositions, in turn providing insight into target areas for further diversity, equity and inclusion training.
Network analytics can also measure organizational immersion (i.e., the immersion of different backgrounds of employees across an organization) to help understand if employees are building the right networks to keep them engaged within the organization. This is a key indicator to keep diverse candidates engaged and create an environment for them in which they can contribute and thrive. For instance, men and women network similarly at lower job levels, but at higher levels, networking activity by women drops significantly. Using such insights, organizations can create tailored coaching and mentoring processes for employees to enable network development for creating equal opportunities for career progression.
Case Study: How We Helped a Client Solve Pay Gaps in Their Engineering Department
We recently worked with a large U.S.-based insurance company that wanted to better understand whether, and to what degree, it may have pay inequities within its organization. Additionally, the client wanted to identify the root causes of any pay gaps and develop a holistic approach for managing pay equity going forward.
We began by conducting a pay equity analysis to statistically identify any gender or race-based pay gaps and model pay adjustments needed to close out identified gaps for current employees. We grouped employees into similarly situated peer groups to ensure that pay was compared among employees performing similar duties under similar conditions. We then used statistical regression modeling to measure the relationship between gender, race and pay within each peer group while controlling for legitimate factors expected to influence pay (e.g., tenure, location, pay band).
Our analysis identified a few parts of the organization where the pay gap between Caucasian and non-white employees (including Asian, Hispanic and African American employees) could not be fully explained by legitimate factors alone. This gap was found to be as high as 4% in the engineering peer group.
Our second step was to analyze internal data to assess representation issues. Aside from looking at statistical modeling results for the pay gap, we probed other internal data to understand the root cause of this gap. We found that Caucasian employees made up 83% of the engineering function with non-white employees only comprising 17%. Those numbers had remained fairly constant since 2018.
Next, we looked at hiring and turnover data trends over the last three years and discovered that non-white employees were being hired half as often as Caucasian employees. What’s more, the turnover rate of non-white employees was 20% higher, thereby exacerbating the lack of diversity within the engineering function.
We then modeled the impact of boosting overall representation of minority groups in the engineering function and found that this would ultimately help reduce the pay adjustments needed to close the pay gap. The pay gap falls from 4% to 3% when there is a higher proportion of non-white employees in the engineering department. Going a step further, we discovered that if minority representation is improved within each job level of the engineering function, the pay gap is reduced to 2%.
Knowing the importance of improving non-white representation, we examined market demographics to see if our client could easily meet its increased representation goals where it currently operates and recruits or if it would be better-served by expanding to other diverse, talent-rich areas. We found that within the Metropolitan Statistical Area (MSA) of the company’s location, the ratio of Caucasian to non-white engineering professionals was 80% to 20%. This further indicated that the firm was lagging on hiring eligible minority engineers.
In response, we helped prepare a competitive location hiring strategy within target MSAs for hiring diverse talent with engineering skills to help consistently improve the pay gap. For continuous monitoring, we also deployed quarterly refreshed, cloud-hosted dashboards with metrics for tracking hiring, turnover and workforce representation distributions.
Organizations need to take an integrated approach to managing diversity, equity and inclusion. Leveraging data and analytics to understand and resolve DEI challenges can produce long-term and sustainable benefits, empowering and engaging the workforce and, ultimately, influencing financial performance in the right direction.
To learn more about our people analytics solutions for DEI issues, please click here.
If you have any questions about the topics discussed in this article and want to speak to a member of our consulting team, please contact one of the authors or write to email@example.com.