Modeling Employee Choices

The Challenge

Every day, employees make choices. Many of these choices carry significant implications for organizations. In a highly competitive environment, knowledge of employee preferences and how these preferences influence their choices (e.g., to join or stay with an organization) is crucial to hiring and retaining top talent. Unfortunately, this knowledge often comes too late, after a decision has already been made. Further, understanding how these decisions may change in response to modifications in an organization's personnel policies can be challenging. An effective means by which organizations can address these issues is through modeling techniques such as discrete choice modeling that systematically relate employee preferences to actual choices.

What We Did

HumRRO, under contract to the U.S. Army Research Institute for the Behavioral and Social Sciences (ARI), recently employed such a modeling approach to investigate the implications of changes in the U.S. Army's recruiting policies. Faced with a difficult recruiting environment, the Army is moving to increase the cap on recruiting bonuses from its current maximum of $20K to $40K. Raising the bonus cap carries significant implications for the Army's ability to meet its personnel needs. To more fully understand these implications, the Army was interested in answering the following questions:

• How would the raised bonus cap impact applicants' job choices? Specifically, would the raised bonus cap channel Army applicants into higher priority jobs and longer terms of service (TOS) and away from lower priority job options?
• Would forecasted increases in the pool of Army applicants, resulting from the raised bonus cap, offset potentially adverse channeling effects on the Army's accession goals for lower priority jobs?

To answer these questions, HumRRO constructed a model that captured Army applicants' preferences and their relations to actual employment choices. Based on discrete choice modeling, the model enabled the simultaneous modeling of Army applicants' decisions to join or not join the Army, and their job and TOS choices. HumRRO first estimated and validated the model using actual applicant choice data from the first quarter of FY 2005 (n = 18,803). Once validated, we then used the model to simulate applicants' choices under two conditions: (a) the existing bonus cap of $20K and (b) the raised bonus cap of $40K.
From there, HumRRO extended these simulations under different scenarios, where the raised bonus cap was expected to expand the Army's pool of high quality applicants.

What We Found

Overall, the results of the simulations indicated that:
 

• Raising the current bonus cap to $40K is projected (a) to increase overall Army accessions, and (b) to uniformly channel applicants, particularly high quality applicants (e.g., those with some college), to higher priority jobs and to somewhat longer TOS.
• The forecasted increase in the number of high quality applicants resulting from the raised bonus cap has the potential (a) to further increase high quality accessions, and (b) to mitigate the potentially adverse channeling effects on lower priority jobs.

Impact

These results provide Army personnel managers and stakeholders insights into how raising the current bonus cap (from $20K to $40K) could impact the Army's recruitment mission. Further, Army personnel managers and policy makers can use the results to inform future bonus policy and personnel planning.

The Army's situation is not unique. Employees regularly make choices and decisions that carry implications for an organization's bottom line. Modeling techniques such as discrete choice modeling represent an effective means for understanding how employee choices could ultimately impact an organization.

For more information, contact:
Dr. Ted Diaz or Research Notes