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Beyond Demo Day: Sorting and Value Added in Startup Accelerators
We study who joins startup accelerators, how founders sort across programs, and which accelerators improve startup outcomes. Using a comprehensive sample of about 750,000 U.S. startups linked to 329 accelerators, we adapt the teacher value-added framework from education economics to estimate accelerator value added (AVA) while accounting for sorting. Selection is systematic: observably better ventures are more likely to enter accelerators and to sort into higher-AVA programs. Yet accelerator performance is highly dispersed. Most accelerators have negative value added relative to a no-accelerator benchmark, while a small right tail generates large gains. High-AVA accelerators predict better long-term outcomes, including acquisition, employment, revenue, and valuation, and are also more likely to accelerate the shutdown of weaker ventures. We validate AVA using internal applicant data from a large U.S. non-equity accelerator.
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