• Katica Roy

Want To Close The Gender Pay Gap At Your Company? You’re Probably Doing It Wrong.


Welcome to my weekly Q&A roundup. (Scroll down to find the Q&A.)


If this is your first time here, welcome. I spend a fair amount of time speaking at events and conferences. At the end of my presentations, I leave space for audience members to ask questions—tough questions, brave questions, you name it. The level of candor and curiosity always inspires me, and I want to share that sentiment with you. So each week, I pick one question that I believe others would find most instructive and publish my response to it here.


The purpose of this weekly tradition is transparency and inclusivity.

  • Transparency: a behind-the-scenes look at my day-to-day.

  • Inclusivity: bringing others along in the journey.


Be Brave™



The Right Way To Achieve Pay Equity


Question:

What's the difference between your software, Pipeline, and other gender pay gap software?


Answer:

It helps to start with a brief overview of the four types of analytics:

  1. Descriptive analytics: what happened (past)

  2. Diagnostic analytics: why it happened (past)

  3. Predictive analytics: what will probably happen (future)

  4. Prescriptive analytics: recommendations you can take to affect outcomes (future)


No one type of analytics has a monopoly on 1st place. We need all four types to help us make sense of the world around us. That said, it’s important to understand what type(s) of analytics you need to achieve your stated goals. That way you can be sure you’re using the right solution for the right problem.


Pipeline focuses on prescriptive analytics.


Pipeline (the company I founded) is a SaaS platform that focuses on prescriptive analytics. We analyze vast amounts of human capital data, then we generate recommendations for our customers. The keyword here is recommendations.


The platform doesn’t make decisions for people. Rather, the platform helps people make informed decisions. We call this intelligent decision-making: users can choose to either accept or reject the data-driven recommendations at will.

Accompanying every recommendation is the predicted financial impact of either accepting or rejecting it. Let’s say you’re preparing to internally fill a newly-vacated role in your department. You have a handful of candidates that you believe could qualify for the role based on your informal workplace relationships with them. But could there be a more qualified employee to fill the open role—an employee you may have unknowingly overlooked?


Yes, there could be. And by failing to evaluate all candidates fairly (i.e. in an unbiased manner), one decision could cost the company revenue and further entrench workplace inequities.


Pipeline de-biases decisions before they are made.


For every 100 men promoted or hired to a managerial position, only 72 women are promoted or hired. The gap widens when intersected with race and ethnicity. Only 58 Black women are promoted to manager for every 100 men, and only 68 Latinas are promoted to manager for every 100 men.


We also know that only 46% of women (versus 51% of men) believe promotion criteria are fair and objective. In other words, the majority of women don’t trust the accuracy of promotion criteria. The Pipeline platform overcomes this hurdle by intercepting decisions before they are made, de-biasing them, and then turning out recommendations that are both:


a.) equitable, and

b.) in the company’s financial best interest.

We not only intercept promotion decisions, we also intercept decisions related to internal mobility, pay, performance, and potential.


The Pipeline platform accounts for the entire employee lifecycle because we learned that to close the gender pay gap, we cannot (cannot) start with pay.


Pipeline closes the gender pay gap—but it doesn’t focus on pay.


Saying this fact often triggers the Semmelweis reflex: the tendency to reject new evidence on the grounds that it goes against established norms. Many of the pay equity solutions on the market claim to close gender and racial pay gaps and they do so by focusing on pay.


Here’s why that doesn’t work: pay is the quantitative value companies place on their talent. How we evaluate an employee’s performance and potential represents their actual value to the organization. These evaluations of performance and potential are inputs to determining pay. Therefore, if we want to close the gender pay gap, we need to make sure the inputs to pay are equitable.


Pipeline is intersectional by nature.


Another point of distinction between Pipeline and other pay equity solutions is that Pipeline applies the intersectional gender lens (gender PLUS race/ethnicity PLUS age) to the data. For us, gender, race, ethnicity, and age are not additive components of DEI. Instead, we recognize that these categories often combine and in doing so, form distinct experiences.


Pipeline is performance-driven.


Finally, the Pipeline platform is not driven by compliance issues or affirmative action. Gender and racial equity gaps aren’t point-in-time problems with short-term solutions. For us, intersectional gender equity is a massive economic opportunity, and we have the data to prove it.


Our original research of 4,161 companies in 29 countries found for every 10% increase in intersectional gender equity, there is a 1 to 2% increase in revenue. We’ve collected one billion data points since the publication of our research to further confirm this relationship.


I built Pipeline for the 96% of CEOs who say DEI is a business imperative. I built it for the human capital leaders who want to transform the role of HR, taking it from a functional partner to a strategic driver of business performance. Ultimately, I built it so that we can close the intersectional gender equity gap in this lifetime.

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© 2021 Pipeline Equity™, Inc.