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

Samarth Gupta

Journal of Quantitative Economics, 2023, vol. 21, issue 3, No 8, 662 pages

Abstract: Abstract Do firms benefit more from agglomeration-based spillovers than the technical know-how obtained through inter-firm collaboration? Quantifying the relative value of the industrial policy of cluster development vis-à-vis firm’s internal decision of collaboration can be valuable for policy-makers and entrepreneurs. I observe the universe of Indian MSMEs inside an industrial cluster (Treatment Group 1), those in collaboration for technical know-how (Treatment Group 2) and those outside clusters with no collaboration (Control Group). Conventional econometric methods to identify the treatment effects would suffer from selection bias and misspecification of the model. I use two data-driven, model-selection methods, developed by (Belloni, A., Chernozhukov, V., and Hansen, C. (2013). Inference on treatment e ects after selection among high-dimensional controls. Review of Economic Studies, 81(2):608 650.) and (Chernozhukov, V., Hansen, C., and Spindler, M. (2015). Post selection and post regulariza- tion inference in linear models with many controls and instruments. American Economic Review, 105(5):486 490.), to estimate the causal impact of the treatments on GVA of firms. The results suggest that ATE of cluster and collaboration is nearly equal at 30%. I conclude by offering policy implications.

Keywords: Firm Performance; Entrepreneurship; Formal and Informal Sectors; Shadow Economy; Institutional Arrangements (search for similar items in EconPapers)
JEL-codes: L25 L26 (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s40953-023-00349-8

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