A General Purpose Technology at Work: The Corliss Steam Engine in the late 19th Century US

The steam engine is widely regarded as the icon of the Industrial Revolution and a prime example of a 'General Purpose Technology,' and yet its contribution to growth is far from transparent. This paper examines the role that a particular innovative design in steam power, the Corliss engine, played in the intertwined processes of industrialization and urbanization that characterized the growth of the US economy in the late 19th century. Waterpower offered abundant and cheap energy, but restricted the location of manufacturing just to areas with propitious topography and climate. Steam engines offered the possibility of relaxing this severe constraint, allowing industry to locate where key considerations such as access to markets for inputs and outputs directed. The enhanced performance of the Corliss engine as well as its fuel efficiency helped tip the balance in favor of steam in the fierce contest with waterpower. With the aid of detailed data on the location of Corliss engines and waterwheels and a two-stage estimation strategy, we show that the deployment of Corliss engines indeed served as a catalyst for the massive relocation of industry away from rural areas and into large urban centers, thus fueling agglomeration economies, and attracting further population growth. This illustrates what we believe is an important aspect of the dynamics of GPTs, whether it is electricity in the early 20th century or Information Technologies in the present era: the fact that GPTs induce the widespread and more efficient relocation of economic activity, which in turn fosters long-term growth.

The Diffusion of Innovations: A Methodological Reappraisal

Studies of diffusion have traditionally relied on specific distributions-primarily the logistic- to characterize and estimate those processes.We argue here that such approach gives rise to serious problems of comparability and interpretation, and may result in large biases inthe estimates of the parameters of interest. We propose instead the Gini's expected mean differenceas ameasure of diffusion speed, discuss its advantages over the traditional approach, and tackle with it the problems of truncated processes, inter-group comparisons, and related issues. We also elaborateon the use of the hazardrate, and suggest some possible extensions. The diffusion of CT scanners is presented as an illustration.

Quality-Adjusted Prices for the American Automobile Industry: 1906-1940

We push the span of hedonic price calculations for automobiles backwards towards the industry's birth. Most of the real change that occurred between 1906 and 1982 occurred between 1906 and 1940. During these years, hedonic prices fell at an average annual rate of 5%. The pace was brisker still during the first 8-12 years. Our measured declines can be decomposed into price and quality components. Our calculations suggest that 60% of the overall decline 1906-1940 was due to process innovation and only 40% to product innovation or quality change per se. Regressors representing mechanical systems matter in these calculations.

Uncovering GPTS with Patent Data

This paper asks the question: Can we see evidence of General Purpose Technologies in patent data? Using data on three million US patents granted between 1967 and 1999, and their citations received between 1975 and 2002, we construct a number of measures of GPTs, including generality, number of citations, and patent class growth, for patents themselves and for the patents that cite the patents. A selection of the top twenty patents in the tails of the distribution of several of these measures yields a set of mostly ICT technologies, of which the most important are those underlying transactions on the internet and object-oriented software. We conclude with a brief discussion of the problems we encountered in developing our measures and suggestions for future work in this area.

General Purpose Technologies "Engines of Growth?"

Whole eras of technical progress and economic growth appear to be driven by a few key technologies, which we call General Purpose Technologies (GPT's). Thus the steam engine and the electric motor may have played such a role in the past, whereas semiconductors and computers may be doing as much in our era. GPT's are characterized by pervasiveness (they are used as inputs by many downstream sectors), inherent potential for technical improvements, and innovational complementarities', meaning that the productivity of R&D in downstream sectors increases as a consequence of innovation in the GPT. Thus, as GPT's improve they spread throughout the economy, bringing about generalized productivity gains. Our analysis shows that the characteristics of GPT's imply a sort of increasing returns to scale phenomenon, and that this may have a large role to play in determining the rate of technical advance; on the other hand this phenomenon makes it difficult for a decentralized economy to fully exploit the growth opportunities offered by evolving GPT's. In particular; if the relationship between the GPT and its users is limited to arms-length market transactions, there will be "too little, too late" innovation in both sectors. Likewise, difficulties in forecasting the technological developments of the other side may lower the rate of technical advance of all sectors. Lastly, we show that the analysis of GPT's has testable implications in the context of R&D and productivity equations, that can in principle be estimated.

Flows of Knowledge from Universities and Federal Labs: Modeling the Flowof Patent Citations Over Time and Across Institutional and Geographic Boundari

The extent to which new technological knowledge flows across institutional and national boundaries is a question of great importance for public policy and the modeling of economic growth. This paper develops a model of the process generating subsequent citations to patents as a lens for viewing knowledge diffusion. We find that the probability of patent citation over time after a patent is granted fits well to a double-exponential function that can be interpreted as the mixture of diffusion and obsolescence functions. The results indicate that diffusion is geographically localized. Controlling for other factors, within-country citations are more numerous and come more quickly than those that cross country boundaries.

The "Names Game": Harnessing Inventors' Patent Data for Economic Research

The goal of this paper is to lay out a methodology and corresponding computer algorithms, that allow us to extract the detailed data on inventors contained in patents, and harness it for economic research. Patent data has long been used in empirical research in economics, and yet the information on the identity (i.e. the names and location) of the patents’ inventors has seldom been deployed in a large scale, primarily because of the “who is who” problem: the name of a given inventor may be spelled differently across her/his patents, and the exact same name may correspond to different inventors (i.e. the “John Smith” problem). Given that there are over 2 million patents with 2 inventors per patent on average, the “who is who” problem applies to over 4 million “records”, which is obviously too large to tackle manually. We have thus developed an elaborate methodology and computerized procedure to address this problem in a comprehensive way. The end result is a list of 1.6 million unique inventors from all over the world, with detailed data on their patenting histories, their employers, co-inventors, etc. Forty percent of them have more than one patent, and 70,000 have more than 10 patents. We can trace those multiple inventors across time and space, and thus study the causes and consequences of their mobility across countries, regions, and employers. Given the increasing availability of large computerized data sets on individuals, there may be plenty of opportunities to deploy this methodology to other areas of economic research as well.

Ivory Tower Versus Corporate Lab: An Empirical Study of Basic Research and Appropriability

We explore the use of patent citations to measure the "basicness" and appropriability of inventions. We propose that the basicness of research underlying an invention can be characterized by the nature of the previous patents cited by an invention; that the basicness of research outcomes relates to the subsequent patents that cite an invention; and that the fraction of citing patents that are assigned to the same organization as the original invention is a measure of appropriabiity. We test the validity of these presumptions by comparing the value of our measures for university and corporate patents, and find that many of the measures do conform to our a priori belief that university research and research outcomes are more basic and harder to appropriate than those of corporations. We also find some evidence that basicness of outcomes is correlated with basicness of research, and that appropriability is lower for basic outcomes.