Model-Selection Inference for Causal Impact of Clusters and Collaboration on MSMEs in India

Area of Science:

• Economics
• Industrial Organization
• Econometrics

Background:

• Industrial policy often promotes cluster development, assuming agglomeration spillovers benefit firms.
• Firms also engage in inter-firm collaboration to acquire technical know-how, another potential growth driver.
• Quantifying the relative impact of these two strategies is crucial for effective policy and business decisions.

Purpose of the Study:

• To compare the causal impact of industrial cluster participation versus inter-firm technical collaboration on firm performance.
• To provide empirical evidence for policymakers and entrepreneurs on the value of cluster development versus firm-level collaboration.

Main Methods:

• Utilized data on Indian Micro, Small, and Medium Enterprises (MSMEs).
• Employed advanced econometric techniques, specifically data-driven model selection methods (Belloni et al., 2013; Chernozhukov et al., 2015), to address potential selection bias and model misspecification.
• Estimated the Average Treatment Effect (ATE) on Gross Value Added (GVA) for firms in clusters, firms in collaboration, and a control group.

Main Results:

• Both cluster participation and inter-firm technical collaboration showed a statistically significant positive impact on firm GVA.
• The Average Treatment Effect (ATE) for both cluster development and technical collaboration was found to be nearly equal, around 30% increase in GVA.
• The study highlights that neither strategy significantly outperforms the other in terms of GVA enhancement.

Conclusions:

• Industrial cluster development and inter-firm technical collaboration offer comparable benefits to MSMEs in terms of GVA growth.
• Policymakers can consider supporting both cluster initiatives and fostering inter-firm collaborations, as they yield similar economic returns.
• Entrepreneurs can evaluate these strategies based on their specific contexts, as both approaches are effective for enhancing firm performance.