Workforce Science Project

The Workforce Science Project examines the emerging field that is transforming the long-neglected management of the workplace. The rapid expansion of workforce science is driven by the ever-increasing proportion of corporate value that consists of human capital and by the emergence of "Big Data," which is poised to increase dramatically the accuracy of human capital measurement.

Better human capital measurement will have four major consequences. First, human resource management (HRM) will become more data-driven, just as financial and operations management are today. The increased use of data will improve the quality of human capital metrics, helping companies develop better HRM practices. Second, as new metrics become available, firms will begin to disclose human capital metrics in financial reporting. Financial markets will then accelerate improvements in HRM by focusing managerial attention on preserving and increasing human capital. Third, better human capital measurement will reduce some of the social problems caused by current labor market imperfections.  Fourth, better workforce science will provide a foundation for improvements in the legal regime governing human resources. The regulation of the employment relationship today is often based on unsubstantiated assumptions.   In the future, the legal system will be able to incorporate more evidence about the effects of various HRM practices.  

The goal of the Project is to promote this four-pronged evolution by sponsoring and disseminating research through business-academic collaboration and by encouraging dialogue on policy issues through conferences that include regulatory groups and stakeholders.  Because the areas touched by the growth of workforce science are wide-ranging, the Project draws on experts in different disciplines including economics, psychology, management, and the law.

This Project is led by Nicola Persico, Academic Director of the Workforce Science Project and Professor of Managerial Economics & Decision Sciences, Kellogg School of Management, Northwestern University Director, CMS-EMS and Deborah M. Weiss, Searle Center Senior Research Affiliate and Director, Workforce Science Project.

Collaborative Research

A critical element of WSP is joint research, conducted on a non-profit basis, between academics and business partners using data supplied by partners. Our process is designed to produce useful analysis for business partners. Typically this research results in academic publication, with extensive safeguards for the confidentiality concerns of business partners.  Businesses and other organizations interested in research collaboration should contact Nicola Persico or Deborah M. Weiss.

Current Projects

The Workforce Science Project is currently sponsoring the following projects:

Toxic Employees

Dylan Minor (Northwestern) in partnership with Evolv: Everyone has worked with a “toxic employee” whose very presence in the workplace detracts from everyone else’s performance and may even pose a legal risk to their employer. We study termination reason codes (e.g., sexual harassment, drug/alcohol policy violations) to determine whether opaque questions at the point of application can offer up an accurate signal about an applicant's honesty and integrity.

Recessions and Productivity

David Berger (Northwestern) in partnership with  Evolv: In the last three U.S. recessions, productivity (output per hour) ceased to move with output but instead actually started to increase before the recessions officially ended.  Utilizing millions of data points on worker productivity from a variety of different industries, we test two explanations for this change: (1) Firms selectively fired poor performers or hired better performers; or (2) Employees worked harder and became more productive in order to retain their jobs.

Employee Engagement

Gad Allon (Northwestern) & Achal Bassamboo (Northwestern) in partnership with  Evolv: Research has shown that less engaged employees tend to perform worse and leave more quickly. We analyze millions of performance and attrition data points to develop models to predict when an employee is about to leave in order to allow the employer to re-engage that employee before he or she walks out the door.

Job Testing Software

Mitchell Hoffman (University of Toronto), Lisa Kahn (Yale) & Danielle Li (Northwestern) in partnership with Evolv: Over the past few years, many employers have introduced job testing software to their recruiting and hiring process. However, many recruiters are critical of this software. Using a very large sample of hundreds of thousands of employees, we are asking two questions: (1) Do employers that adopt this software hire employees who stay longer and perform better? and (2) What is the impact when recruiters adhere more or less closely to the recommendations made by the software?

Data Analysis of a Job Board

Diego Klabjan (Northwestern) and Baiyang Sidney Wang (Northwestern) in partnership with DirectEmployers.

Working Papers

2014-001 Training Contracts, Worker Overconfidence, and the Provision of Firm-Sponsored General Training
Mitchell Hoffman, University of Toronto Rotman School of Management
Stephen Burks, University of Minnesota

Searle Center Events

Research Roundtable on Workforce Science, November 2, 2013

Research Roundtable on Workforce Science II: Application (Fall 2014 TBD)