Spatial non-stationarity in son preference: a district-level geographically weighted regression analysis of NFHS-5 in India

Son preference remains a key driver of gender inequality in India, yet most studies treat its determinants as uniform across space, obscuring critical subnational variation. Addressing this gap, this study investigates the geographic heterogeneity of son preference and examines how its predictors vary spatially. Data and methods Using district-level data on 102,045 ever married women aged 15–49 from the National Family Health Survey-5 (2019–2021), we applied a spatially explicit analytical framework, including choropleth mapping, Global Moran’s I, hotspot analysis, Local Indicators of Spatial Association (LISA) cluster mapping, kriging interpolation, and Geographically Weighted Regression (GWR). The use of GWR was justified by significant spatial non-stationarity (Koenker BP = 32.78, p < 0.001) detected in OLS diagnostics. Son preference prevalence ranged 8.2%-42.3%. Global Moran’s I (0.397, z = 57.86, p < 0.001) confirmed significant clustering. OLS identified five significant predictors: parity 2, no mass media exposure, household size 5–8, illiterate mothers, and younger maternal age, explaining 64% variance. GWR demonstrated superior fit (AICc: 4459.98 vs 4478.46; adjusted R 2 : 0.65 vs 0.64). Local R 2 ranged 0.45–0.71, highest in northern/central districts. Women’s illiteracy (β: 0.40–0.62), large household size (β: 0.35–0.83), and younger mothers showed strongest associations in northern/central India, but negligible effects in southern regions, confirming spatial heterogeneity. Son preference is a spatially embedded social process, shaped by localized patriarchy, economy, and institutions. Geographically targeted, gender-transformative policies such as conditional incentives for girls’ schooling and localized media campaigns effectively address the structural devaluation of daughters.