WHAT COLOR IS YOUR NOMEX?:CATEGORIZING AND QUANTIFYING THEWILDLAND FIRE RESPONSE WORKFORCE

Experts have acknowledged that there is “imperfect data on the full composition of the wildfire risk reduction, response, and recovery workforce.”1 In this paper, we improve upon the data available around the wildfire response workforce, specifically by quantifying the capacity and examining recent trends associated with this workforce. The fire response workforce is spread between federal, state, Tribal, local, and private agencies. While these sectors work together through a fire response system that emphasizes resource coordination, sharing, and interoperability, there is no one system that accounts for each individual firefighter across agencies participating in fire response. Rather, personnel qualifications and mobilizations are tracked differently depending on a person’s employer, employment type, the type of incident to which they are responding, and in what capacity they are assisting response. Not only do these variations contribute to the imperfect data on the fire response workforce, but each segment of the workforce also has a unique history, operating context, set of values and norms, and fire protection responsibility that present additional challenges in quantifying workforce composition. The lack of data on the full size and composition of the fire response workforce hinders critical analyses needed to inform policy decisions. At the fire response system level, imperfect data on workforce composition may obscure the availability of potential resources at a time when they are most needed to help address the wildfire crisis. At the personnel level, it may leave certain segments of the workforce vulnerable to unequal compensation, protection, and benefits. A better understanding of who is contributing to local, state, and national fire response is required to ensure that wildfire response frameworks and policy are effectively utilizing resources and properly accounting for the needs of a diverse workforce operating within unique environments.