Abstract: In 2011, California Governor Jerry Brown recognized several of the state’s existing firm incentive policies aimed at catalyzing innovative activity in the state, to be ineffectual, citing poor incentive design – especially in the state’s main incentive vehicle, its California Enterprise Zone (CEZ) program – for lackluster outcomes associated with the policy. By 2013, the policy had been replaced by a state-wide $700 million investment incentive fund – better known as Cal Competes (2013-2019), designed to allocate funding via a competitive formula that would purportedly target only the most innovative and employment intensive firms through a competitive bidding process. The primary goal of this project is to compare the effects of this “innovation-targeted” incentive program to a prior random allocation mechanism (the California motion picture tax credit lottery 2009 – 2015) as a counterfactual. Analyzing hundreds of applicants to the program including winners such as Tesla, Macy’s.com, Aetna, Credit Karma, Alibaba USA, as well as less visible firms, I compare effects on firm productivity, firm patents and revenue, employment and wage outcomes, and relocation probability. I use a Regression Discontinuity Design that exploits the threshold application “score” used for accepting firm applicants as quasi-random variation to analyze each program’s allocative efficiency.