Deriving the number of jobs in proximity services from the number of inhabitants in French rural municipalities

TL;DR

This paper introduces a method to estimate the number of proximity service jobs per inhabitant in French rural municipalities by classifying them based on travel distance to main service locations and applying quantile regression.

Contribution

It presents a novel minimum requirement approach combined with classification and quantile regression to estimate proximity service jobs in rural areas.

Findings

Smaller municipalities have fewer proximity service jobs per inhabitant.

Distance to main service location correlates with increased proximity jobs per inhabitant.

Method provides insights into rural service employment patterns.

Abstract

We use a minimum requirement approach to derive the number of jobs in proximity services per inhabitant in French rural municipalities. We first classify the municipalities according to their time distance to the municipality where the inhabitants go the most frequently to get services (called MFM). For each set corresponding to a range of time distance to MFM, we perform a quantile regression estimating the minimum number of service jobs per inhabitant, that we interpret as an estimation of the number of proximity jobs per inhabitant. We observe that the minimum number of service jobs per inhabitant is smaller in small municipalities. Moreover, for municipalities of similar sizes, when the distance to the MFM increases, we find that the number of jobs of proximity services per inhabitant increases.