Polycentrism as a sustainable development strategy: empirical analysis from the state of Maryland

Abstract

We present in this paper an analysis of economic centers and their role in shaping employment development patterns and travel behavior in the state of Maryland. We begin by identifying 23 economic centers in the Baltimore-Washington region. We then examine these centers first in their role as centers of economic activity and then in their role as nodes in the state’s transportation system. Finally, we identify the commute sheds of each center, for multiple modes of travel and travel times, and examine jobs–housing balance within these various commute sheds. We find that Maryland’s economic centers not only promote agglomerative economies and thus facilitate economic growth; they also generate a disproportionate number of trips and promote transit ridership. These results provide empirical support for policies that promote polycentric urban development, and especially policies that promote polycentric employment development. Further, they suggest that polycentrism as a sustainable development strategy requires careful coordination of regional transportation systems designed to balance jobs and housing within a center’s transit commute shed. Based on these findings we recommend that the Maryland state development plan, and regional sustainable communities plans across the nation, encourage the concentration of employment within economic centers and encourage housing development within the transit commute sheds of those centers.

Keywords:

• polycentrism
• sustainable development
• employment centers
• economic development
• transit-oriented development

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

1. For more information on PlanMaryland, see State of Maryland (2011).

2. For a thorough analysis of the history and evolution of employment cluster studies, see Cruz and Teixeira (2010).

3. The term economic centers is more commonly used in this line of research as the focus is less on interindustry relationships and more on relative employment density.

4. For more on the Maryland State Transportation model, see Mishra et al. (2013).

5. It is important to note that the QCEW data are derived from unemployment insurance records filed by each employer. This introduces a set of known limitations, including the omission of sole-proprietor firms and incomplete military and government employment information. Since these three groups do not purchase unemployment insurance, they are not accurately represented in the population. However, through a number of adjustment procedures, we estimate total military and government employment by comparing QCEW total employment in each industry with figures published by the Bureau of Economic Analysis and other trusted sources. We then use proportional allocation to distribute adjusted employment among known firm locations until our estimates are consistent with other sources. This adjustment process could impact our analysis; however, we believe it produces better results than if no adjustment had been made.

6. We choose a lower threshold than Giuliano and Small did because the state of Maryland is, obviously, a much larger geographic area than the city of Los Angeles and contains a broader diversity of development types. Therefore, we expect average employment density to be lower in the state of Maryland than in the city of Los Angeles and we adjust our threshold accordingly.

7. For a discussion of alternative ways of identifying economic centers see Casello and Smith (2008).

8. Because of the large geographic size of the center in the 270 corridor, we define two centers in this corridor based on a natural break in the geography.

9. “The Herfindahl-Hirschman index, better known as the Herfindahl index, is a statistical measure of concentration. It has achieved an unusual degree of visibility for a statistical index because of its use by the Department of Justice and the Federal Reserve in the analysis of competitive effects of mergers. The Herfindahl index can be used to measure concentration in a variety of contexts. For example, it can be used to measure the concentration of income (or wealth) in US households and also market concentration, that is, the degree of concentration of the output of firms in banking or industrial markets.” (Rhoades 1993)

10. For more information on MSTM, see Mishra et al. (2011, 2013).

11. In another paper (author suppressed) we conducted a statistical analysis of job growth in the state and found the probability of new firm start-ups to be significantly higher in the 23 employment centers than in other parts of the state, see Niu et al. (2014).