Workplace Equity & Layoffs Part 1: How to Make Sure Layoffs Don’t Set Back Your Diversity Goals

| December 5, 2022 | 5 min read
01_Workplace Equity & Layoffs Blog Series_header

Rounds of layoffs have punctuated the final months of 2022, particularly large, high-profile layoffs in the technology sector. More may be on the horizon as many business leaders believe a recession is likely in the next calendar year. Planning a layoff — I’ll use the more precise term for when positions are eliminated, a “reduction in force” or RIF — is unpleasant, typically a last resort, but sometimes necessary. In this post, we’ll explore the sort of analyses organizations should perform to ensure that a RIF does not set back their diversity efforts.

Typically, I favor approaches for making workplace decisions (such as pay, promotions, performance ratings, etc.) that are based on neutral, job-related factors (e.g. experience, education, etc.) to mitigate the bias that comes with too much discretion. This holds true in a RIF, as well. However, in a RIF, even neutral factors can have disparate impacts on communities that have been historically excluded from management. In the words of the researcher Alexandra Kalev: “How you downsize is who you downsize.”


Neutral approaches can have non-neutral impacts

There are many advantages to using a rule-based approach to determine who goes and who stays in a RIF. Sometimes, specific business units or positions are impacted (e.g. Redfin recently shuttered its home flipping business, laying off a significant portion of its workforce) and those business decisions determine who goes and stays. Other times, organizations use criteria like tenure, taking a “last hired, first fired” approach with more senior folks staying.

Though these criteria-based approaches have their appeal in terms, following through on these approaches without considering potential differential impacts on specific communities can set back hard-earned gains in gender and racial diversity, particularly in management. Here are two illustrations, one anecdotal and one based on a statistical review of RIFs across many organizations.

  1. In 1974, GM conducted a round of layoffs and they had a last hired, first fired system. The problem is that they had just recently started hiring Black women in assembly-line positions — so every Black woman was laid off. Two steps forward, two steps backward.
  2. Alexandra Kalev and Frank Dobbin’s research of over 800 employers shows that either eliminating positions or taking a last hired, first fired approach sets diversity gains back. From Kalev’s 2016 HBR article

When organizations cut positions rather than evaluate individual workers, they end up with an immediate 9%–22% drop in the proportion of white and Hispanic women and Black, Hispanic, and Asian men on their management teams. When companies take a “last hired, first fired” approach to layoffs, they lose nearly 19% of their share of white women in management and 14% of their share of Asian men.

This is an example of how a firm can get results that disproportionately impact one race or sex even if racist or sexist bias did not impact individual discretionary decisions: you propose a rule that may make sense on paper, but the outcome could rewind the tape of diversity gains. Kalev again, from her related 2014 academic paper in the American Sociological Review:

Seemingly neutral rules that disregard deeply institutionalized biases, such as structural disadvantages, will deepen inequality rather than reduce it.


How to avoid moving backwards on diversity during a RIF

Kavel’s research shows that organizations who model the diversity impacts of a proposed approach to a RIF can avoid negative impacts. 

You will first want to analyze the people who are leaving the organization. Typically, these termination analyses are called an adverse impact analysis. There is a lot your organization can learn from an adverse impact analysis. Here are some key considerations to get the most out of these analyses:

Conduct the analysis at the direction of legal counsel. Kalev’s paper shows that the involvement of legal counsel in RIFs became more and more common after the Civil Rights Act of 1964. This is a good thing! Lawyers and leaders can test multiple scenarios under attorney-client privilege, allowing them to avoid approaches that have unequal impacts. I am not a lawyer, and I have always appreciated the lens they bring to these conversations — particularly when coupled with robust statistical analyses of potential impacts.

Measure statistical, practical, and overall impacts. Regularly in statistical analyses, we ask whether a result is statistically and practically significant. Statistical significance means the observed difference is unlikely to be due to random chance. Practical significance means that the difference is meaningfully large. A common standard in adverse impact analysis is the 4/5ths rule: a difference in rates is practically significant if the selection rate for one group is less than 4/5ths the selection rate for another. If 10% of Black workers are impacted by a RIF, this rule would flag any impact rate for other workers outside the range of 8%-12.5% as practically significant. Finally, there is the question of overall impact. It is possible for a difference to be statistically insignificant and fall within the 4/5ths rule range, and still have a negative impact on diversity. Adverse impact testing can also surface this net effect to be considered alongside statistical and practical considerations.

Run the analysis in aggregate and for specific communities, departments, and levels. If the impacted portion of the workforce is relatively small, it’s usually straightforward to conduct this analysis. Say, for example, we have 30 account executives and we want to downsize the team to 20. As a RIF gets larger in scope, it is helpful to zoom in and out to see if there are specific hotspots, high-level aggregate concerns, or some combination of the two. The three dimensions I consider are:

  • Community-based – using multiple and intersectional approaches
  • Organization-based – testing by department or region and testing in aggregate
  • Level-based – testing the net impact and impact among leadership, management, etc. 


Don’t stop once you have analyzed those who are leaving — also analyze the impact on those who remain

Many companies fall into the trap of looking at what is being cut, and miss the step of looking at the workplace that remains. 

Don’t forget to look at how your layoffs affect pay equity. Once you’ve taken a first pass at cuts, take a look at who is left. How have the cuts affected pay equity? If you are cutting staff blindly to reduce costs, you may also be creating pay disparities that need to be remediated, creating a whole new expense — or legal risk — for your company. Not to mention, pay disparities can be detrimental to workplace culture, causing further disruption to your business. Leveraging software enables you to compare scenarios. 

However you approach RIF planning, look at who you are thinking of cutting and examine the makeup of that group. Then look at the company after the layoffs — are there fewer people from underrepresented populations? Are there new pay disparities because of the change? If there are, plan for them now. It may seem incongruous to give someone a raise even as a layoff has just happened, but creating risk does not save in the long-run. 


Run the right analysis early, deep, and often — and invest in employees who are still with you

Planning for a RIF is not fun, but making sure you run the numbers from a diversity perspective can ensure you don’t make a difficult situation even worse by opening up your organization to claims of discriminatory impact or setting yourselves back on your progress towards your long-term goals. 

Research shows employees value transparency. Without communication from their leadership, they will build their own narrative. In uncertain economic times, that narrative is likely to be one filled with fear, doubt and mistrust. Leaders can immediately begin rebuilding trust with the employees that remain by sharing the drivers of the decision-making, and the considered impacts on the organization’s diversity and pay equity. This demonstrates your commitment to equity, even in difficult times.

Syndio designed our OppEQ® solution to run the right analyses at multiple levels so your team can test potential impacts quickly and repeatedly in your planning process. Explore how it works at the link below, and read Part 2 of our series on workplace equity and layoffs to learn questions to ask after a round of layoffs to assess the impact of your downsizing decisions on pay equity and opportunity equity.



The information provided herein does not, and is not intended to, constitute legal advice. All information, content, and materials are provided for general informational purposes only. The links to third-party or government websites are offered for the convenience of the reader; Syndio is not responsible for the contents on linked pages.

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