Innovation, Productivity Dispersion, and Productivity Growth

Abstract

We examine whether underlying industry innovation dynamics are an important driver of the large dispersion in productivity across firms within narrowly defined sectors. Our hypothesis is that periods of rapid innovation are accompanied by high rates of entry, significant experimentation and, in turn, a high degree of productivity dispersion. Following this experimentation phase, successful innovators and adopters grow while unsuccessful innovators contract and exit yielding productivity growth. We examine the dynamic relationship between entry, productivity dispersion, and productivity growth using a new comprehensive firm-level dataset for the U.S. We find a surge of entry within an industry yields with a lag an increase in productivity dispersion and then after a subsequent lag an increase in productivity growth. These patterns are more pronounced for the High Tech sector where we expect there to be more innovative activities. These patterns change over time suggesting other forces are at work during the post-2000 slowdown in aggregate productivity.

Changing Business Dynamism and Productivity: Shocks vs. Responsiveness

“…In practice, we find that TFPR and RPR are highly correlated (about 0.8), consistent with the findings in Foster et al (2017). Moreover, Syverson (2008, 2016) find that TFPR and TFPQ are highly correlated (about 0.75) for the selected set of products where P and Q data are available to construct direct measures of TFPQ.…”

“…This implies that (as discussed in detail in Foster et al (2017)) RPR can exhibit positive dispersion regardless of frictions and distortions. We estimate RPR by estimating the revenue function in (2) using the GMM approach in Wooldrige (2009) (see Appendix C for more details).1 5F…”

“…Our diff-in-diff counterfactual achieves this objective and, since it relies on actual (estimated) firm policy functions, is reasonably robust to the parameters of the economic environment. by revenue function estimation are revenue elasticities (not factor elasticities) capturing both production and demand parameters (Foster et al (2017)). In this appendix, we discuss the estimation of RPR and the results using the RPR measure of TFP.…”

“…Given the possible presence of demand shocks, RPR should be interpreted as reflecting both TFPQ and demand shocks. Foster et al (2017) find that the Woolridge residuals are sensitive to outliers; pooling across a large number of observations mitigates this sensitivity, so we estimate revenue elasticities that vary at the 3-digit NAICS level.5 3F 54 After estimating the elasticities, we compute the revenue productivity residuals and deviate the latter from 6-digit NAICS industry by year means. We find that has a correlation of 0.76 with .…”

“…However, with endogenous prices, variation in dispersion of TFPR will reflect not only shocks to fundamentals such as TFPQ but also adjustment costs. Given possibly endogenous plant-level prices, we also consider an alternative measure of TFP that has been increasingly used in recent literature (e.g., Gopinath et al (2017) and Foster et al (2017)). Consider a simple plant-level demand function = −1 (where is an idiosyncratic demand shock and − 1 is the inverse demand elasticity), a plant production function that is Cobb-Douglas with factor elasticity for factor i equal to , and TFPQ equal to .…”