CRIW: Measuring and Accounting for Innovation in the 21st Century Spring 2017

Miranda and Zolas link USPTO patent data to U.S. Census Bureau administrative records on individuals and firms. The combined dataset provides a directory of patenting household inventors as well as a time-series directory of self-employed businesses tied to household innovations. The researchers describe the characteristics of household inventors by race, age, gender and U.S. origin, as well as the types of patented innovations pursued by these inventors. Business data allows them to highlight how patents shape the early life-cycle dynamics of nonemployer businesses. The researchers find household innovators are disproportionately U.S. born, white and older relative to business innovators. Data shows there is a deficit of female and black inventors. Household inventors tend to work in consumer product areas compared to traditional business patents. While patented household innovations do not have the same impact of business innovations their uniqueness and impact remains surprisingly high. Back of the envelope calculations suggest patented household innovations might generate between $7.2B and $8.2B in revenue.

Considerable progress has been made in tracing expenditures on intangibles in the macroeconomy. But much less is known about their returns. In this paper Los, de Vries, and Timmer outline a new strategy to estimate returns to intangibles in the context of globalised production networks. The researchers view intangibles as inputs that allow a firm to generate surplus value from tangible factor inputs. This is in contrast to the standard treatment of intangible capital as yet another factor of production which can be separately valued. The researchers propose an instrumental definition of the returns to intangibles as the residual value after subtracting the costs of labour and tangible capital. Given international fragmentation of production processes, this residual can only be measured when all stages of production (including distribution) are considered. To this end, they rely on the global value chain (GVC) approach introduced by Timmer et al. (2014). This approach allows for a decomposition of the value of a product into value added at each stage of production. The researchers extend this approach by splitting value added into returns to labour, tangible and intangible capital. They focus on final manufacturing products for the period 1995-2007 using the world input-output database (WIOD) and additional data derived from national accounts statistics. The researchers' main finding is that the share of intangibles in the value of final products has increased from 2000 onwards. This is found for all manufacturing product groups. They also find that for buyer-driven GVCs (like food, textiles and furniture) returns to intangibles are increasingly realized in the distribution stage, that is, in delivery of the final product to the consumer. In contrast, in producer-driven GVCs (like machinery, automotive and electronics) the returns are shifting to activities before the final production stage.

Goldschlag, Jarmin, Zolas, and Lane expand the data infrastructure available to build evidence on public and private investments in science and R&D and utilize it to examine the links between startup performance and new measures of workforce human capital. They apply machine-learning techniques to a rich new source of longitudinally-linked data to characterize the research-experienced workforce of new businesses. Startups with a more research-experienced workforce are more likely to survive and grow.

Income inequalities have increased in most OECD countries over the past two to three decades; particularly the income share of the top 1% has soared. In this paper Paunov and Guellec argue that the increasing importance of digital innovation — new products and processes based on software code and data — has increased market rents, which benefit disproportionately the top income groups. In line with a Schumpeterian vision, innovation gives rise to rents from market power and scale economies. This is magnified with digital innovation, in which the intangible component (the source of rents) is much larger than in traditional manufacturing innovation. Highly concentrated market structures ("winner-take-all") allow rent extraction. In addition, digital innovation tends to increase risks because even only marginally superior products can take over the entire market, hence rendering market shares unstable. Instability commands risk premia, hence higher expected revenues, for investors. Market rents accrue mainly to investors and top managers and less to the average workers, hence increasing income inequality. Market rents are needed to incentivize innovation and compensate for its costs, but beyond a certain level they become detrimental as rent seeking then substitutes to innovation in business strategies. Public policy may stimulate innovation and welfare by eliminating ex ante the market conditions which allow rent extraction that comes from anti-competitive practices.

Previous work (Byrne and Corrado, 2017a,b) reassessed the link between ICT investment prices and productivity to help understand how digital technology contributes to labor productivity growth. This paper extends the analysis by considering consumer spending on digital durable goods as investment. When consumer digital stocks are treated as investment, their asset prices have implications for productivity, and the researchers find mismeasurement patterns similar to those found in their earlier work on private ICT investment goods. They also consider whether and how households’ increased use of its stocks of digital goods should be folded into the measurement of income from those stocks. Without considering use rates, the researchers find that real services from consumer use of their digital stocks has increased nearly 20 percent per year since 1985. After adjusting for the increase in utilization of those stocks since 1995, real consumption of digital services is estimated to have grown 35 percent per year. The utilization adjustment is especially potent between 2005 and 2012.

This paper reports on the construction of a new dataset that combines data on trademark applications and registrations from the U.S. Patent and Trademark Office with data on firms from the U.S. Census Bureau. The resulting dataset allows tracking of various activity related to trademark use and protection over the life-cycle of firms, such as the first application for a trademark registration, the first use of a trademark, and the renewal, assignment, and cancellation of trademark registrations. Facts about firm-level trademark activity are documented, including the incidence and timing of trademark registration filings over the firm life-cycle and the connection between firm characteristics and trademark applications. Dinlersoz, Goldschlag, Fila, and Zolas also explore the relation of trademark application filing to firm employment and revenue growth, and to firm innovative activity as measured by R&D and patents.

Dinlersoz, Goldschlag, Fila, and Zolas report on the construction of a new dataset that combines data on trademarks from the U.S. Patent and Trademark Office with the microdata on firms from the U.S. Census Bureau. The methodology for merging the data and identifying matches between trademarks and firms is described. The resulting dataset allows tracking of various activity related to trademarks over the life-cycle of firms, such as the first application for a trademark, the first use of a trademark, and the renewal, assignment, and abandonment of trademarks. Some facts about firm-level trademarking activity are documented, including the incidence and timing of trademarking activity over the firm life-cycle, the connection between firm characteristics and trademarking, and the relation of trademarks to firm growth. The dataset offers new possibilities for research on how trademarks are related to firm dynamics and performance, firm-level innovative activity and product introductions, and firm strategies aimed at acquiring customers, generating loyalty, building brands and reputation, and signalling quality.

Polder, Mohnen, and van Leeuwen investigate with Dutch micro data whether ICT investment boosts total factor productivity, whether it does so because it increases the return to R&D, and whether ICT requires organizational innovations to increase productivity.

The large dispersion in labor productivity across firms within narrowly defined sectors is driven by many factors including, potentially, the underlying innovation dynamics in an industry. One hypothesis is that periods of rapid innovation in products and processes are accompanied by high rates of entry, significant experimentation and, in turn, high paces of reallocation. From this perspective, successful innovators and adopters will grow while unsuccessful innovators will contract and exit. Foster, Grim, Haltiwanger, and Wolf examine the dynamic relationship between entry, within-industry labor productivity dispersion and within-industry labor productivity growth at the industry level using a new comprehensive firm-level dataset for the U.S. economy. The researchers examine the dynamic relationships using a difference-in-differences analysis including detailed industry moments and focus on differences between High Tech and all other industries. They find a number of distinct patterns. First, they find that a surge of entry within an industry yields an immediate increase in productivity dispersion and then a lagged increase in productivity growth. Second, they find these patterns are more pronounced for the High Tech sector. Third, they find that these patterns change over time suggesting that other forces are at work in the latter part of their sample. The researchers devote considerable attention to discussing the conceptual and measurement challenges for understanding these relationships. The findings are intended to be exploratory and suggestive of the role innovation plays in the dynamic patterns of entry, productivity dispersion and productivity growth. Given the difficulties in directly measuring innovation, the findings could be used to help identify areas of the economy where innovation may be taking place. Alternatively, the researchers' findings suggest a useful cross check for traditional measures of innovation.