Picking Winners or Marking Them? Timing-Based Evaluation of Innovation Certification

Angelo Leogrande, Mauro Di Molfetta, Valeria Nortarnicola and Maria Giovanna Trotta

No t4rxy_v1, SocArXiv from Center for Open Science

Abstract: Voluntary certification policies — which let firms self-nominate for innovative or privileged status — increasingly channel fiscal, financial, and regulatory support, yet evaluating them is deceptively hard: certified firms may outperform because certification works, or simply because firms apply when already rising. We show that voluntary certification can certify winners precisely because rational firms time entry to moments of transitory expansion: the measured premium largely reflects when firms participate, not what participation does. We formalise this as anticipatory take-up: because the benefits are most valuable while a firm scales, forward-looking firms register at the crest of a growth run-up. The mechanism yields three predictions — a cross-sectional premium, a premium that largely predates certification, and entry driven by recent growth, not profitability. We test them in the Italian innovative-SME regime, linking panel data for roughly 2,900 certified and 1,200 non-certified SMEs to each firm's registration date and sector, and treating performance as a six-dimensional vector. Certified firms grow far faster than balanced peers yet are financially less solid, especially when smaller and capital-intensive. Once registration timing is exploited through event-study and staggered difference-in-differences designs, the premium proves largely selection: about 81 per cent of the revenue advantage predates registration, and the residual merely continues a pre-existing trend. A hazard model confirms it — entry rises with recent growth and smaller size, not profitability. The paper reframes the evaluation of voluntary certification from average effects to observable dynamic selection: such schemes mark firms already on distinctive trajectories rather than create them. The lesson generalises — robustness to omitted variables is not robustness to selection on trends, and cross-sectional evaluations mislead unless they exploit the timing of take-up.

Date: 2026-06-19
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Persistent link: https://EconPapers.repec.org/RePEc:osf:socarx:t4rxy_v1

DOI: 10.31219/osf.io/t4rxy_v1

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