Improving commuting zones using the Louvain community detection algorithm

Well-defined commuting zones are essential for accurate research on US local labor markets. To develop commuting zones, one must construct edge weights - a measure of commuting flows between counties - and then use the edge weights to partition counties into clusters. I improve upon currently used "ERS"commuting zones in two ways. First, it is unclear if ERS commuting zones use the best edge weights. Therefore, I test multiple edge weights. Second, the algorithm to produce ERS commuting zones requires specifying a theoretically-unguided cutoff parameter; results may be sensitive to the parameter choice. Instead, I use the Louvain algorithm, which optimizes for "modularity", a graph-intrinsic parameter that is greater when there is higher intra-commuting zone flow and lower inter-commuting zone flow. I call my new delineations "TS Louvain", which uses the ERS commuting flow definition to construct edge weights, and "Sum Louvain", which uses the total number of commuters as edge weights. Compared to ERS, TS Louvain and Sum Louvain have 0.05 to 0.15 greater modularity, Sum Louvain has a 0.01 to 0.02 higher share of people who work and live in the same commuting zone, and in a case study, TS Louvain produces greater estimates and t-statistics. These metrics suggest that these new commuting zones improve upon the existing delineations. Researchers can access these commuting zone definitions at bit.ly/LouvainCZ.(c) 2022 Elsevier B.V. All rights reserved.