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Improving commuting zones using the Louvain community detection algorithm

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  • Zhang, Whitney

Abstract

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.

Suggested Citation

  • Zhang, Whitney, 2022. "Improving commuting zones using the Louvain community detection algorithm," Economics Letters, Elsevier, vol. 219(C).
    • Handle: RePEc:eee:ecolet:v:219:y:2022:i:c:s0165176522003093
    DOI: 10.1016/j.econlet.2022.110827
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    References listed on IDEAS

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    1. David H. Autor & David Dorn & Gordon H. Hanson, 2013. "The China Syndrome: Local Labor Market Effects of Import Competition in the United States," American Economic Review, American Economic Association, vol. 103(6), pages 2121-2168, October.
    2. Christopher S Fowler & Leif Jensen, 2020. "Bridging the gap between geographic concept and the data we have: The case of labor markets in the USA," Environment and Planning A, , vol. 52(7), pages 1395-1414, October.
    3. Tolbert, Charles M. & Sizer, Molly, 1996. "U.S. Commuting Zones and Labor Market Areas: A 1990 Update," Staff Reports 278812, United States Department of Agriculture, Economic Research Service.
    4. Andrew Foote & Mark J. Kutzbach & Lars Vilhuber, 2021. "Recalculating ... : How Uncertainty in Local Labour Market Definitions Affects Empirical Findings," Applied Economics, Taylor & Francis Journals, vol. 53(14), pages 1598-1612, March.
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    Cited by:

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    2. Wang, Zhaojing & Ye, Xianxing & Ma, Xiaoping, 2024. "Does the clusters of high-speed railway network match the urban agglomerations? A case study in China," Socio-Economic Planning Sciences, Elsevier, vol. 95(C).

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    More about this item

    Keywords

    Local labor markets; Commuting; Clustering; Measurement error;
    All these keywords.

    JEL classification:

    • C13 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Estimation: General
    • J21 - Labor and Demographic Economics - - Demand and Supply of Labor - - - Labor Force and Employment, Size, and Structure
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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