Applicant Tracking: Tips And Traps In Collecting, Compiling, And Reporting Applicant Data
2/15/2010
"Adverse impact." It's a phrase that strikes fear in the hearts of federal contractors around the country. Basically, you've just been accused of discriminating against women, people of color, or other protected groups in your recruiting and hiring procedures.
As a federal contractor, you're required to compile and report applicant tracking data for your workforce so that you can prove you're managing an appropriately diverse workforce. However, the OFCCP rules for gathering this information can be incredibly confusing - from deciding who actually qualifies as an "applicant" to keeping the proper records for jobseekers who contact you via the Internet. And even if you make an honest mistake, it can prove costly in both time and money if you are unlucky enough to be selected for an audit by the OFCCP.
Join us for an informative, interactive webinar , when our employment law experts will detail the pitfalls, traps, and requirements for tracking applicant data.
Learning Objectives:- Why you're required as a federal contractor to track and report data on your job applicants and hires
- Who actually constitutes an "applicant" under federal rules (and the different forms an applicant's "expression of interest" can take)
- The most effective ways to collect demographic data from applicants and encourage self-disclosure of this information
- How to track different types of applicants, from internal candidates and Internet applicants to jobseekers applying for more than one opening
- The steps you can take to avoid mixing affirmative action plan years as you track and report applicant data
- Best practices for storing expressions of interest, applications, resumes, and other applicant information (and how long you should retain these records)
- Why it's critical to reconcile race and gender data for new hires against their original applications
- How to spot and correct the most common applicant tracking mistakes, from low applicant-to-hire ratios to high percentages of unknown race and gender
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