The U.S. Army recognizes that the recruiting environment has a significant impact on its ability to recruit, especially when the unemployment rate is lower, casualty rates increase, or operational difficulties mount. This report presents a forecasting model that provides a measure of the recruiting difficulty with up to a 24-month horizon. The resulting model forecasts whether the Army is likely to face a difficult or easy recruiting environment.
Developing a National Recruiting Difficulty Index
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- What types of environments contribute to recruiting ease or difficulty in the Army?
- What is the relationship between recruiting difficulty and the resources for recruiting?
- How can the Army understand the primary factors in recruiting difficulty?
- How much does each factor account for variations in recruiting difficulty?
- How do the relationships among the factors and conditions influence the recruiting environment?
- How can the Army predict the level of difficulty in future years and communicate future periods of potential difficulty to provide planners time to sufficiently resource the recruiting mission?
The U.S. Army has long recognized that the recruiting environment has a significant impact on its ability to recruit. Successfully achieving a mission goal is tremendously more difficult when the national unemployment rate is lower rather than higher. Additionally, when casualty rates increase or operational difficulties mount, recruiting difficulty worsens. The RAND Arroyo Center has built a forecasting model that provides a measure of the recruiting difficulty with up to a 24-month horizon.
The recruiting difficulty index model consists of seven equations. Three of the equations are for outcomes reflecting recruiting difficulty, and four equations are related to the recruiting process and reflect decisions made by the Army in an ongoing effort to meet recruiting targets. The model's structure is as follows. First, the exogenous variables can affect all seven outcome variables. Second, the policy response variables — quick-ship bonuses, Military Occupational Specialty bonuses, duty recruiters, and conduct waivers — can be entered as explanatory variables in the equations indicating recruiting difficulty (in terms of the percentage difference between graduate-alpha contracts and mission, average months in the Delayed Entry Program [DEP], and training seat fill rate). Third, the criterion of mean-squared prediction error is used when estimating the model in deciding which variables to include as explanatory variables in each equation and whether lagged values of the dependent variables should be included in the explanatory variables (and, if so, how many lags). The resulting seven-equation model forecasts whether the Army is likely to face a difficult or easy recruiting environment.
Recruiting resources are determined by several factors
- The Army decides how many soldiers it would like to enlist.
- Traditionally, resources have been allocated without regard to the recruiting environment.
- Recruiting resources may be insufficient when the recruiting environment is difficult and overly abundant during periods of easier recruiting, and the resulting mismatch is often difficult to correct.
- The unemployment rate is often used as a proxy for recruiting difficulty, but the unemployment rate alone has not been a sufficient signal to reprogram resources.
- Many of the Army's recruiting tools (e.g., recruiters, advertising campaigns) take time to develop in order to become fully productive.
To build a conceptual model of direct and indirect influences on desirable/adverse recruiting outcomes, researchers identified economic, world-event, and Army policy variables that predicted the recruiting environment with a sufficient lead time
- Key outcomes for the model include an accession mission and contracts written, DEP levels, and Training Seat Vacancies.
- Key variables for the model include economic and labor market conditions, news stories, recruiting resources, enlistment waivers, and recruit quality.
- Should the Army change its priorities (e.g., by eliminating waivers or accepting reductions in quality), the performance of the Recruiting Difficulty Index will likely decline and would have to be reoptimized in light of the new Army objectives.
- Recruiting difficulty predictions can be combined with the Recruiting Resource Model to inform policymakers preparing for resourcing requirements under alternative recruiting environments.
Table of Contents
Review of Factors Affecting Recruiting Difficulty
Optimizing the Forecast Model
Forecast of Army Recruiting Difficulty
Recommendations for Leveraging Recruiting Difficulty Index Forecasts
Summary and Conclusions
Model Specification and Estimated Parameters
Detailed Discussion of Data Sources
Instructions for Incorporating New Data and Making Forecasts