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The Dynamo and the Computer: An Historical Perspective On the Modern Productivity Paradox

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Exxon Mobil and ConocoPhillips stock price has been predicted using the difference between core and headline CPI in the United States. Linear trends in the CPI difference allow accurate prediction of the prices at a five to ten-year horizon.
... Inventor and assignee addresses on granted patents are used to locate these innovations geographically in counties and commuting zones. Crucially, unlike most work on subnational spatial inequality, our approach enables description of two key waves of disruption: the 1920s, in which key electrical technologies of the Second Industrial Revolution began to profoundly reshape the US economy (David, 1990;Field, 2003); as well as the post-1970 rise of the Third Industrial Revolution. ...
... This means there can be considerable differences in how different scholars date the beginning and end of these waves. 3 Within each wave, a major new technology initially has a fallow period of slow productivity growth, later followed by a period of 'reaping', as the disruptive innovation begins to intensively reshape economic activity (David, 1990;Helpman & Trajtenberg, 1998b;Lipsey et al., 2005). In the case of the Second Industrial Revolution, David and Wright (2005) and Petralia (2020a) find that the 1920s was the major reaping period for the electricity and related technologies that were initially invented between 1880 and 1910. ...
... 4 Similarly, researchers were at first perplexed by the 'missing' productivity effects of the major innovations of the 1970s and 1980s, but they subsequently started finding them from the 1990s onward (Bresnahan et al., 2002). Historians agree that although the electrical dynamo was invented during the 1860s, and the 1880s witnessed the emergence of the first electrical power stations, it was not until the 1910s and 1920s that the effects of these innovations began to powerfully reshape the economy of the United States (David, 1990;Field, 2003;Freeman & Louçã, 2001). Similarly, though silicon semiconductors were conceptualized in the early 20th century, and key working transistors came out of Nobel Prize-winning work at Bell Labs in the 1940s and 1950s, it was not until the late 1970s and 1980s that computers, and subsequently the internet, begin to transform the organizational patterns of economic activity in the United States. ...
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Although technological change is widely credited as driving the last 200 years of economic growth, its role in shaping patterns of inequality remains under-explored. Drawing parallels across two industrial revolutions in the United States, this paper provides new evidence of a relationship between highly disruptive forms of innovation and spatial inequality. Using the universe of patents granted between 1920 and 2010 by the US Patent and Trademark Office (USPTO), we identify disruptive innovations through their rapid growth, complementarity with other innovations and widespread use. We then assign more and less disruptive innovations to subnational regions in the geography of the United States. We document three findings that are new to the literature. First, disruptive innovations exhibit distinctive spatial clustering in phases understood to be those in which industrial revolutions reshape the economy; they are increasingly dispersed in other periods. Second, we discover that the ranks of locations that capture the most disruptive innovation are relatively unstable across industrial revolutions. Third, regression estimates suggest a role for disruptive innovation in regulating overall patterns of spatial output and income inequality.
... In many respects, therefore, caselaw embodies the characteristics of a networked technology and exhibits the kinds of diffusion processes typical of such technologies. In caselaw, just like the dynamo (David 1990), a shock might take many years to reverberate across the economy, as indeed our VAR estimates show. From that perspective, it is not at all surprising that English men and women could in 1760 access a system of property rights that was much more economically productive than that available a century ago, while at the same time not being able to identify any single source for this change. ...
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We examine how pre-Industrial-Revolution English caselaw development on land, inheritance, and families affected, and was affected by, economic and demographic outcomes. Our yearly measures of caselaw development are derived from existing topic-model estimates that reflect a comprehensive corpus of reports on pre-1765 court cases. We estimate a structural VAR model using these caselaw time-series in combination with measures of real per-capita income and vital rates. Pre-industrial caselaw development profoundly shaped economic development. Strikingly, the areas of caselaw that stimulated real-income growth are on families and inheritance, not land. Caselaw on families and inheritance was especially important as a driver of real income and birth rates after 1710. Caselaw developments were spurred primarily by changes in real income, not by changes in vital rates. Incorporation of endogenous caselaw development leaves intact the findings of the existing literature that examines pre-industrial economic-demographic interactions. However, our findings do imply that any Malthusian trap that was present in pre-industrial England was made less severe as a result of developments in caselaw on families and inheritance.
... While different literature strands offer insights into one or more of these drivers, a synthetic understanding of their interactions has not yet been developed. Furthermore, while these interactions can create positive, amplifying, feedback loops in later transition phases, they can also generate negative, dampening feedback loops in early phases, when actors are reluctant to invest, develop and adopt new technologies because of inertia, high switching costs, and high technology costs (David, 1990;Aghion et al., 2019). But because they do not invest, low-carbon technologies remain high in costs and low in performance, which hinders and delays the transition. ...
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Understanding the relationship of economic growth, energy consumption, and pollution is critical for forecasting energy demand and environmental impacts. Energy technology innovations create opportunities for low- and middle-income countries to “leapfrog” over older technologies that dominated the development paths of today’s industrialized countries. Yet technology change may also increase consumption at earlier development stages, and institutions and policy choices can hinder energy-efficient development. Extending a recent paper by van Benthem (2015), I build a panel dataset over 136 countries and nearly 60 years to show that over this wider and longer panel, the long-run energy intensity of economic growth is substantially lower for today’s developing countries than in developing countries in the past.
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Artificial Intelligence (AI) is the latest General-Purpose Technology, inducing disruption to processes of economic and social production. This collection focuses attention on AI induced disruption to social production processes including contributions that note such in the Global South and the BRICs-Brazil, Russia, India, and China. Scrutiny of social production is long a part of political economy studies, being traced to the tradition of economic interpretation of history and society. Contemporary international political economy is partly built on the economic interpretation of history tradition affording sufficient analytical depth and breadth for this collection. A working definition of AI is provided before an overview of contributions to this collection.