A Spatially Weighted Relative Deprivation Index: Incorporating Spatial Structure Into Regional Inequality Measurement

ABSTRACT Regional inequality is conventionally assessed based on the distribution of economic outcomes across regions. However, regional disparities are spatially structured because regional economic outcomes are interdependent across space. Existing approaches generally capture either the spatial dimension of inequality at an aggregate level or unit‐specific disadvantage without incorporating spatial relevance. This study proposes a Spatially Weighted Relative Deprivation (SWRD) index that combines the one‐sided upward‐gap rule of relative deprivation (RD) with an empirically calibrated spatial weighting structure. Using real gross regional domestic product (GRDP) for 229 Korean districts from 2015 to 2021, the study estimates the distance decay of spatial dependence and constructs annual SWRD values. The empirical comparison shows that SWRD produces spatial patterns and regional rankings that differ from conventional RD. Spatial permutation tests indicate that high SWRD values are unlikely under random spatial reassignment. Monte Carlo simulations on a uniform lattice further show that the divergence between RD and SWRD increases with stronger spatial dependence, steeper distance decay, and greater dispersion and right skewness. These findings show that incorporating spatial structure can change assessments of regional disadvantage under varying spatial and distributional conditions.