12 GOOLSBEEANDKLENOW creatively destroy by competitors, as hypothesized by Garcia-Macia, Hsieh and Klenow (2018). It lends support to the idea that firms can escape from competitionbyinnovatingasinAghionetal.(2005). 5 TheImpactofNewProductsonInflation Feenstra (1994) showed that a direct way to gauge the importance of new productsinaCESframeworkistolookatthegrowthrateofoverallspendingin a category minus the growth rate of spending for products that exist in both time periods. The higher this net growth rate, the lower the true inflation rate relative to the matched model inflation rate. As shown in Table 3, entering products do tend to have significantly bigger market shares than outgoing 6 products in the Adobe data, even outside apparel. Feenstra (1994) showed that the reduction in true inflation equals the net growth in spending on new varieties times 1/(σ − 1), where σ is the elasticity of substitution between varieties. We use a baseline value of σ = 4 based on Hottman, Redding and Weinstein (2016). We also consider a higher value of σ = 6 for robustness — a moreconservative value given new varieties are less valuable if they are closer substitutesforexistingvarieties. Table7presentsestimatesofnewgoodsbiasintheAdobeonlinedata. Even excluding apparel, the arrival of new goods is equivalent to 1.5 to 2.5 7 percentagepointslowerinflationthanwhatamatched-modelwouldindicate. This is much higher than the 0.6% per year new product bias estimated by the Boskin Commission, though that was for the CPI as a whole. The Adobe data may cover items with larger-than-average new goods bias. Outside apparel, new goods bias looks largest for recreation and ICT products, and lowest for medicineandfood. 6This should capture improvements in product quality in addition to brand new types of products,becausebothareassociatedwithnewproductIDcodesintheAdobedata. 7Weexcludeapparelbecauseincomingitemsmaysellalotmorethanoutgoingitemspurely duetoseasonal/fashioncycles.