Key Takeaways
Many employers today rely on employee referrals as a key source of quality candidates, with some even offering cash bonuses for successful referrals. Glassdoor data shows that referral candidates receive offers at a higher rate (56 percent in 2021) than candidates who instead secured job interviews through online applications, colleges or universities, staffing agencies or recruiters. Employers not surprisingly have come to lean on referrals because they are from trusted sources already familiar with company standards and cultures. Plus, employee referrals reduce the need for recruiters to attract talent. However, as companies dedicate more effort than ever to enhancing workplace diversity, research supports that they must reevaluate the role of employee referrals in furthering their diversity goals.
A key concern around referrals is that employees might tend to refer candidates who share similar ethnic/racial or other demographic backgrounds. As a result, workforces that lack diversity risk perpetuating referral candidate pools that mirror the status quo. Looking at U.S. interview reviews submitted by Glassdoor users, we compared the reported gender, racial and ethnic makeup of candidates who secured job interviews through employee referrals versus through online applications. Our analysis reveals that while referral candidate pools have become increasingly diverse in recent years, they are still less diverse when compared to online applicant pools.
Since 2019, 45.7 percent of U.S. referral interviewees were BIPOC and 49.8 percent were women. Meanwhile, over half of interviewees who applied online were either BIPOC or women. While Asian candidates make up a larger share of referral pools (19 percent) than online pools (16.8 percent), there are lower shares of Black, Hispanic and Latinx candidates in referral pools compared to online pools.
Note: "Other races and ethnicities" included, but were not limited to, those who identify as Native Hawaiian or Other Pacific Islander, Indigenous American or Alaska Native, or Middle Eastern due to smaller sample sizes.
Referral pools have become more diverse since 2010.
In 2010, only 36.6 percent of referral candidates were women. This share significantly improved to 53.8 percent in 2021, reflective of the increasing gender parity in the labor force and the growing list of companies who have made commitments to workplace gender equity. Similar progress in the ethnic diversity of referral candidates can be seen, with the share of BIPOC candidates growing from 40.9 percent in 2010 to 45.8 percent in 2021.
However, progress toward more diverse referral candidate pools is less consistent in the Information Technology sector. While the share of women in the referral candidate pool has grown from 40 percent to 51.1 percent since 2010, the share of BIPOC candidates has decreased from 57.1 percent to 38.8 percent. This is consistent with overall diversity trends in the Information Technology sector: Between 2014 and 2020, the percentage of Latinx and Black technical employees at top technology firms rose less than 1 percent. On the other hand, some of the biggest tech firms made gains in hiring women, with an almost 10 percent increase in female representation at top technology firms between 2014 and 2020. While it is hard to say which came first, progress in referral pool diversity goes hand-in-hand with progress in workplace diversity.
Referral pools are less diverse across major sectors.
Despite the increasing share of women and BIPOC in referral candidate pools since 2010, women and BIPOC are still less represented in referral pools compared to online job applicant pools. This is especially true for the Health Care, Retail and Business Services sectors. The Finance sector is the only exception, where women make up a larger share (45.5 percent) of the referral pool than the online applicant pool (40.9 percent). In a nutshell: online applicant pools typically are better avenues for more diverse applicant pools.
The lower racial and ethnic diversity in referral applicant pools is even more consistent and pronounced across major sectors. The difference is most noticeable in the Retail sector, where BIPOC candidates make up 52.5 percent of the online applicant pool but only 34.6 percent of the referral applicant pool.
Recommendations for Employers
As companies strive to improve workplace DEI, attracting a more diverse pool of candidates is a critical step. While our analysis suggests that standard employee referrals may not be the best recruitment method for improving workplace DEI, employers can turn to online candidate pools or redesign their referral programs to ensure more diverse referrals. Some companies such as Accenture and Intel increased employee bonuses for diversity referrals in as early as 2016. Carefully-designed referral programs can urge employees to be more conscious of company DEI goals when referring candidates.
Employee referral programs can still be a valuable way to attract new talent and enhance company DEI. However, referral programs ideally should be one source of many for talent attraction, and should be carefully assessed to monitor and understand the makeup of referral recruits. Employers who promote employee referrals without including DEI initiatives in their hiring process risk having a significant blind spot that could impair or block the success of company DEI initiatives.
Methodology
Our analysis examined interview reviews submitted by Glassdoor users for interviews that occurred in the United States between January 1, 2010 and June 6, 2021. “BIPOC” included candidates who identify as Asian, Black or African American, Hispanic or Latinx, Native Hawaiian or Other Pacific Islander, Indigenous American or Alaska Native, Middle Eastern, or Multiracial.
Demographic data like race and gender was based solely on self-reported information from Glassdoor users. We take user privacy and anonymity very seriously and recognize that the demographic data used for this research is particularly sensitive. To ensure that the privacy and anonymity of our users was protected during our research, we relied only on personally de-identified data accessed within a secure research environment.