Model-Selection Inference for Causal Impact of Clusters and Collaboration on MSMEs in India
Do agglomeration-based spillovers impact firms more than the technical know-how obtained through inter-firm collaboration? Quantifying the effect of these treatments on firm performance can be valuable for policy-makers as well as managers/entrepreneurs. I observe the universe of Indian MSMEs inside an industrial cluster but with no collaboration (Treatment Group 1), those in collaboration with other firms for technical know-how but outside a cluster (Treatment Group 2) and those outside cluster with no collaboration (Control Group). Selection of firms into these treatments and sub-sequent performance of the firm may be simultaneously driven by observable factors. To address selection bias and overcome model mis-specifcation, I use two data-driven, model-selection methods, developed in Belloni et al. (2013) and Chernozhukov et al.(2015), to estimate causal impact of the treatments on GVA of Ërms. The results suggest that ATE of cluster and collaboration is nearly equal at 30%. I conclude by offering policy implications of the results.