Measuring spatial dispersion: an experimental test on the M‐Index

Tidu, Alberto;Guy, Frederick;Usai, Stefano

2024-01-01

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Abstract

Despite representing a very accurate method for assessing spatial distribution, Marcon and Puech's M has been insufficiently exploited so far, most likely because its computation relies on pairing every point of interest (i.e., firms, plants) with every other point within the area under analysis. Such a figure rapidly grows to unmanageable levels when said area is larger than a neighborhood or when every industry is taken into account. Consequently, practical applications of M have been exclusively experimental and circumscribed to very limited areas or to a handful of industries. This seems much regrettable since M provides many advantages compared to conventional measures of spatial distribution and also to alternative distance measures. In this article, we assess the reliability of using small administrative units instead of exact postal addresses for the localization of plants, in order to reduce M's computational burden. Working with a dataset that provides the location, the specific industry and the number of employees for every single plant/establishment in Italy for both manufacturing and services, we can also draw a preliminary but certainly interesting picture of Sardinia's economic geography and its development through the Great Recession toughest years between 2007 and 2012.