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Econometric connectedness as a measure of urban influence: evidence from Maine

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  • Thomas F. P. Wiesen

    (University of Maine School of Economics)

  • Todd Gabe

    (University of Maine School of Economics)

  • Lakshya Bharadwaj

    (University of Maine School of Economics)

Abstract

We propose a new indicator of urban influence, which builds from the concept of econometric connectedness that is commonly used to measure market integration in finance and macroeconomics. This metric, based upon a vector autoregression model, quantifies the fraction of the forecast error variance of a non-urban area’s economic activity that can be explained by the economic shocks of urban regions. As an illustration, we apply this metric to data on monthly total taxable sales for regions in the US state of Maine, with urban influence scores calculated relative to the state’s three largest cities. Additionally, we investigate how this measure of urban influence is related to a non-urban area’s population size, its distance to one of the state’s three largest cities, whether the region is coastal, and the importance of visitors to the area.

Suggested Citation

  • Thomas F. P. Wiesen & Todd Gabe & Lakshya Bharadwaj, 2023. "Econometric connectedness as a measure of urban influence: evidence from Maine," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-16, December.
    • Handle: RePEc:spr:lsprsc:v:16:y:2023:i:1:d:10.1007_s12076-023-00353-9
    DOI: 10.1007/s12076-023-00353-9
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    More about this item

    Keywords

    Connectedness; Market integration; Rurality; Variance decomposition; Urban influence;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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