Moneyball for Law Firms: Rethinking Associate Talent Acquisition

Transcription

1 Moneyball for Law Firms: Rethinking Associate Talent Acquisition Dr. Christopher Zorn, Professor, Pennsylvania State University and Principal, Lawyer Metrics Caren Ulrich Stacy, Lawyer Metrics

2 Speaker Info Dr. Christopher Zorn, Professor, Pennsylvania State University and Principal, Lawyer Metrics Dr. Christopher Zorn is an award-winning, national leader in quantitative methodology and statistical analysis. In addition to his world-class quantitative capabilities, Dr. Zorn is an immensely practical statistician with a remarkable aptitude for applied work. A professor of political science at Pennsylvania State University, Dr. Zorn is the author of three forthcoming books and nearly three dozen refereed articles. He is a recipient of the prestigious John M. Olin Foundation Faculty Fellowship, and the immediate past editor of the journal Political Analysis, and has given invited talks at more than 30 universities around the world. Dr. Zorn previously held academic positions at the University of South Carolina and Emory University, where he was the Winship Distinguished Research Professor of Political Science. He has also served as a resident scientist and Program Director for the Law and Social Science Program of the National Science Foundation (NSF). Among the four grants Professor has received from the NSF, he currently serves as principal investigator on two ongoing projects, including a study of decision-making within the legal/judicial hierarchy. Caren Ulrich Stacy, Lawyer Metrics Caren Ulrich Stacy has 20 years of experience in lawyer recruitment, development, and diversity with law firms across the country, including Arnold & Porter, Cooley, Weil Gotshal & Manges, McGuireWoods, and Jenkens & Gilchrist. As a noted expert in the field of lawyer selection and development, Caren was awarded the National Association of Legal Professionals (NALP) Mark of Distinction for Professional Development and Training in 2009 as a result of her vision, leadership and innovation. She was also elected as a Fellow of the College of Law Practice Management in 2010 an honor awarded to less than 200 individuals in the country. Caren has contributed to over 175 presentations and publications on legal talent management and authored two highly-regarded books, including Loyalty by Design: A Practical Guide for Developing an Effective Attorney Integration Program and her newest book, The Talent Trifecta: Effectively Selecting, Developing, and Managing Your Lawyers for the Benefit of Your Clients, which will be published in late In addition to her leadership roles as chair of NITA's Law Firm Advisory Committee and the Executive Council for the Virginia Bar Association Law Practice Management Division, Caren is an adjunct professor for the Denver University Sturm College of Law Masters of Science in Legal Administration Program. She has also served as chair and vice-chair of the NALP Attorney Development Committee, two terms on the board of directors for the Professional Development Consortium and as co-chair of The American Lawyer Chief Recruitment, Professional Development and Diversity Officers Conference. After working in law school career services and for many top law firms, Caren founded a successful consulting firm in 2009, which merged with Lawyer Metrics LLC in As the president and one of the founders of Lawyer Metrics, Caren works with a team of leading experts in quantitative analysis, organizational psychology, and legal labor markets to employ a metricsbased approach to lawyer selection, engagement, training, mentoring, and evaluations.

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4 Moneyball for Law Firms: Optimizing Associate Talent Acquisition Today s Speakers Dr. Christopher Zorn Principal, Lawyer Metrics LLC Professor of Political Science, Pennsylvania State University Caren Ulrich Stacy Principal & President, Lawyer Metrics LLC Adjunct Faculty, Denver University Sturm College of Law Masters of Science in Legal Administration Program 2 1

5 3 Moneyball Analysis Which pre-hire attributes (aka biographical factors) are positive or negative predictors of performance at the firm? Grades? Law Review? Team Sports? Clerkship? Work Experience? Pre-Hire Attributes On-the-Job Performance 4 2

6 Validity of Selection Methods PREDICTORS VALIDITY HIT RATE PERFECT PREDICTION SCHEME % COGNITIVE ABILITY TESTS.53 77% JOB TRYOUTS.44 72% BIOGRAPHICAL INVENTORIES (Moneyball).37 69% STRUCTURED BOARD INTERVIEWS.35 68% REFERENCE CHECKS.26 63% GRADE POINT AVERAGES.21 61% JOB-RELATED EXPERIENCE.18 59% ONE-ON-ONE INTERVIEWS.14 57% RATINGS OF TRAINING.13 57% YEARS OF EDUCATION.10 55% INTERESTS (PREFERENCES).10 55% RANDOM SELECTION SCHEME.00 50% Sources: Hunter and Schmidt, "Quantifying the Effects of Psychological Interventions on Employee Job Performance and Work-Force Productivity, Vol. 38, American Psychologist, 473, 474 (1982); Wiesner and Crowshaw, A Meta-Analytic Investigation of the Impact of Interview Format and Degree of Structure on the Validity of the Employment Interview, Journalof OccupationalPsychology,Vol. 61, 275, 282 table 1 (1988). 5 Moneyball Overview Biographical Data Performance Data Predictive Algorithm Firm-Specific Predictive Model 6 3

7 Biographical Data Law School Law School Transfer Joint Degree Advanced Degree Law School Honors Law Journal Law Review Clerkship Undergraduate-Law Lapse Pre-Law Business Experience Pre-Law Legal Experience Blue Collar Work Experience Foreign Language Skills Legal Publication Non-Legal Publication Moot Court Legal Clinic Business Orgs. Team Sports Foreign-Born... 7 Performance Data Bonuses Evaluations Competencies Billable Hours Performance Other Measures? 8 4

8 Law Schools: Where Should We Focus Our Efforts? Law School Findings Longitudinal View of the Law Schools Yielding Highest/Lowest Number of High/Low Performing Lawyers Best Schools Historically for Recruiting: Entry-Level vs. Lateral Associates Partners Within Practice Groups Within Offices Diverse and Lawyers Practical Use: Focused OCI and Recruitment Strategy for 1Ls, 2Ls, 3Ls, Clerks, and Lateral Associates 10 5

9 Number of Attorneys By Law School Attended NYU Columbia Rutgers George Washington Cornell Georgetown Harvard Yale UVA Northwestern Texas Chicago Fordham UPenn Brooklyn Stanford Berkeley Duke Number of Attorneys 11 Percentage of High Performing Attorneys By Law School NYU Columbia Rutgers George Washington Cornell Georgetown Harvard Yale UVA Northwestern Texas Chicago Fordham UPenn Brooklyn Stanford Berkeley Duke Percent 12 6

10 Percentage of High Performing Attorneys By Law School Berkeley Chicago Rutgers NYU Georgetown Northwestern Cornell George Washington Brooklyn Yale UPenn Columbia Fordham Harvard UVA Duke Stanford Texas Percent 13 Value Score By Law School NYU Rutgers George Washington Georgetown Berkeley Chicago Cornell Northwestern Columbia Yale Brooklyn UPenn Harvard Fordham UVA Duke Stanford Texas Percent 14 7

11 Percentage of High Performers By School and Gender Berkeley Chicago Rutgers NYU Georgetown Northwestern Cornell George Washington Brooklyn Yale UPenn Columbia Harvard Fordham UVA Duke Stanford Texas Percent 15 Percentage of High Performers By School and Practice Area Corporate Columbia Cornell George Washington Yale NYU Rutgers Litigation Georgetown NYU Rutgers George Washington Yale Harvard Cornell Columbia Other Cornell NYU Rutgers Georgetown George Washington Yale Columbia Harvard Percent 16 8

12 Sample Law School Strategy Recommendations Using Data Continue to recruit aggressively at X, Y, and Z schools for entry-level associates. Consider expanding entry-level associate recruiting efforts at certain high value schools including J and K. Think about eliminating, scaling back, and/or increasing selectivity at specific schools, including C, D, E, and F. Increase recruiting efforts at H and X for corporate and Z for litigation associates. Consider increasing pre-oci efforts at L and Z to increase pipeline of historically underrepresented groups. Focus more on entry-level hiring versus laterals. 17 Law Students: How Do We Select Among Them? 9

13 Example Findings: Firm A Positive Factors No Influence Negative Factors Law School Grades Advanced Degree Foreign Languages Elite Law School Law Review Moot Court/Mock Trial Undergraduate Honors Lateral Hire Blue Collar Work Exp. 19 Example Findings: Firm B Positive Factors Non-Factors Negative Factors Elite Law School Law School Grades Pre-Law Work Experience Law Review Blue Collar Exp. Team Sports Leadership Advanced Degree Legal Experience 20 10

14 Example Findings: Firm C Positive Factors Non-Factors Negative Factors Law School Grades Law School Publication Blue Collar Work Exp. Team Sports Clerkships Elite Law School Undergrad Honors Law Review Moot Court/Mock Trial 21 Practical Application: OCI Prediction Summer Associates: Ranked Meryl Streep Robert Redford Judy Dench Olympia Dukakis Jack Nicholson Tom Hanks Brad Pitt Cathy Bates Hillary Swank Denzel Washington Mia Farrow Bill Murray Sandra Bullock Matthew McConnnaghey Sandra Bernhard David Spade Rosie O'Donnell Tom Arnold Arnold Schwarzenegger Predictive Index Score 22 11

15 Predictive Validity Percent No Predictive Model Percent Predictive Index > 6,C=1 2=B, 3 =A C Players B Players A Players C=1, 2=B, 3=A C Players B Players A Players 23 What is the monetary value to the firm of selecting a mid-level A-player associate instead of a C-player? 300 Billable Hours/ $135,000-$250,000 Per Associate Annually 24 12

16 25 Moneyball for Law Firms: Optimizing Associate Talent Acquisition May 1,