Lightcast’s diversity cluster definitions and ranking methodology come from C2ER (The Council for Community and Economic Research). These measures quantify how jobs are distributed across industry clusters in a select region compared to a typical one.
Background
A region’s economic function or functions represent the collection of broad economic activities in which the region’s workforce and firms engage. Practically, functions can be identified by grouping industries together into categories that are broadly similar on factors such as inputs, outputs, and/or the technological or skill requirements necessary to perform the work customary to these industries. Grouping industries according to function, rather than simply accepting the NAICS industry categories, can help to
Broadly characterize the economic roles a county plays in its region
Provide insight into the economic relationships and similarities counties have with other regions
Identify factors that make regions comparatively better fits for certain economic activities
Speak to the broader economic and demographic forces that are likely to impact a county’s economic prospects
Methodology
The entropy measure of diversity is used to calculate industry function and occupation knowledge-based measures of economic diversity across U.S. counties and a variety of other geographies.[1] These metrics were calculated according to the following formula:
where there are i = 1 to k industries and pi is the share of economic activity (e.g. employment or earnings) in the in the industry. The products of industry shares of economic activity and the natural log of the inverse industry shares of economic activity are summed to arrive at the final entropy index measurement. The index has a minimum value of 0 when all economic activity is within one industry, and the value increases as the number of industries increases and the distribution of economic activity across these industries becomes more equal.
Separate diversity rankings have been created based on geography type: county, microMSA, MetroMSA, and state.
Creating Industry Function Classifications
In an examination of the rise of services as a proportion of employment, Noyelle (1983) advanced a functional classification system for services based on the type of outputs (intermediate or final outputs) and the institutional setting under which services are provided (private, public, or nonprofit sectors).[2] Lawrence (1984) classified manufacturing industries on the basis of the primary end use of the product (e.g. intermediate goods; consumer durables; producer durables; consumer nondurables) and the necessary inputs to the industry (e.g. research and development expenditures; scientists and engineers; capital-, labor-, and resource-intensive).[3]
This analysis draws primarily from the work of Lawrence (1984) and Noyelle (1983) to categorize industries according to functional types. In an effort to focus on the economic base of counties, non-function industries or industries that often serve local populations, such as retail, trade, personal services, doctor’s offices, local government, and construction, were excluded from the analysis of functions.
[1] Malizia, E. E., & Ke, S. (1993). The influence of economic diversity on unemployment and stability. Journal of Regional Science, 33(2), 221-235.
[2] Noyelle, T. J. (1983). The implications of industry restructuring for spatial organization in the United States. In Regional analysis and the new international division of labor (pp. 113-133). Springer, Dordrecht.
[3] Lawrence, R. (1984). Sectoral Shifts and the Size of the Middle Class. The Brookings Review, 3(1), 3-11.