Total Cost of Labor
In evaluating cost of labor for a particular region, it is important to take into account total payroll costs, not just wages. The net impact of the cost of labor is magnified when payroll taxes are entered into the equation. Payroll taxes - including unemployment insurance, workers' compensation, FICA, and FUI (Federal Unemployment Insurance), and, in some locations, local payroll taxes - add significantly to a business' recurring operating costs. In addition to payroll and payroll taxes, add the cost of holidays, vacation, and sick leave, and one can experience a significant differential in total costs for labor between various locations. For companies employing large numbers of workers, these differentials can represent millions of dollars over a 10- to 20-year period.
While the above payroll and associated costs can be calculated and compared directly from one location to another using simple formulas and mathematics, how does one go about estimating, projecting, or determining the base payroll cost for a given occupation or cluster of occupations for a specific geographical region? It is the base payroll cost that drives the supplemental cost of payroll taxes and other associated costs.
BLS is a good general resource and reference for wage information and data, and can be used for generalization purposes. But be aware that total average wages for a geographical region include both public and private employment. Additionally, the information broken down by employment classification within BLS is a compilation of both hourly and salaried employees (total payroll) within a given employment classification, and includes everyone from entry-level clerk to CEO for a given occupation. Taking an average of payroll expenditures that range from $8 per hour to $800 per hour is not exactly the best way to determine "average wages" or potential exposure for wages for a new employer. Nor is it the best way to compare wage data across geographical regions.
One significant "look out" in using BLS data relates to its integrated use within many geographical information systems (GIS) databases. It is quite common for businesses, agencies, organizations, and consultants to use BLS data to screen geographical regions based on average wages. Geographical screening may be by county, by city, by MSA, or by clusters of counties. In many instances, this screening is used to "qualify" or "eliminate" defined regions.
By way of example, if a particular business doing a site location search does not want to pay more than $13/hour on average for hourly wages, the business may decide to eliminate from consideration all counties with an average wage that exceeds $13/hour. The fallacy of this strategy is twofold: first, what about counties where the average wage is $13.01 per hour? Should they be summarily eliminated? Is there really a significant difference based on a penny per hour? Secondly, what about the use of all payroll to determine average wages? Does this really represent average hourly wages? It is critical to understand not only the data being used, but also how to interpret the data once it has been identified.