The vast majority of spatial econometric research relies on the assumption that the spatial network structure is known a priori. This study considers a two-step estimation strategy for estimating the n(n-1) interaction effects in a spatial autoregressive panel model where the spatial dimension is potentially large. The identifying assumption is approximate sparsity of the spatial weights matrix. The proposed estimation methodology exploits the Lasso estimator and mimics two-stage least squares (2SLS) to account for endogeneity of the spatial lag. The developed two-step estimator is of more general interest. It may be used in applications where the number of endogenous regressors and the number of instrumental variables is larger than the number of observations. We derive convergence rates for the two-step Lasso estimator. Our Monte Carlo simulation results show that the two-step estimator is consistent and successfully recovers the spatial network structure for reasonable sample size, T.

Sustainable procurement is steering today’s supply chains towards responsible business practices. This research aims to examine the trend in the sustainability performance of large enterprises for supplier selection across supply chain tiers and geographic locations. Secondary data on 83 global, large enterprises discussing sustainable procurement practices are analysed using hierarchical multiple regression analysis. Dynamic capabilities view and stakeholder theory are utilised to develop the hypotheses. The results show that sustainable procurement performance for large enterprises varies across supply chain tiers and increases in the direction of the end customer. Due to standardisation of regulations and dynamic capabilities of global, large enterprises, no significant difference is observed across geographic regions.