Association for Public Policy Analysis & Management

The traditional economics models in manufacturing usually look at factors like capital and plant spending, worker education, and research and development funding to measure changes in productivity levels. What they tend to ignore is the role of innovation in driving productivity. Patents have traditionally been recognized as an excellent proxy for levels of innovation, but are usually not present in economics productivity models.

Using data from 2004 to 2014, the authors focused on six key manufacturing industries -- semiconductors, apparel, food, computer parts, automobiles, and motor vehicle transmissions -- that represent a large portion of American manufacturing. Using U.S. Patent and Trademark Office data on patents, multi-factor productivity numbers from the Bureau of Labor Statistics, and NAICS code data from the Census Bureau, we created a model in R Studio that tried to determine the relative importance of innovation (as represented by patent activity) with respect to the more traditionally accepted components of economic productivity. The model incorporated multi-year data from each of the six industries. To account for possible interaction between the industries in our model, we utilized dummy variables. To account for time lags between when a patent is issued and when it makes a real impact on productivity levels, we created one and two year time lags in the model. The model also used natural logarithms. We controlled for scale by accounting for the different sizes of the six manufacturing industries.

What we found from our model was that patents have a real and statistically significant impact on productivity in a way that the other traditional components of productivity do not. We hope this paper will lead to further exploration of the role of innovation in driving productivity gains and economic growth.