Jovanovic et al (2005) analyzed the impact of two general-purpose technologies (GPTs) on the US economy: electricity and information technology. They found that there were considerable time lags between each technology’s invention and widespread adoption. As a result, it took over 20 years until each GPT had a material impact on economy-wide productivity.
Picture by Alexandre Debieve, Unsplash
What are general-purpose technologies?
General-purpose technologies (GPTs) are those technologies that transform both household life and business conduct. More specifically, according to Bresnahan and Trajtenberg (1996), a GPT should have the following three characteristics:
Pervasiveness: the technology should spread to most sectors.
Improvement: it should get better over time, lowering the costs of its users.
Innovation spawning: it should make it easier to invent and produce new products or processes.
Some examples that meet these criteria are steam, internal combustion, electricity, and information technology. In this short article, we will be focusing on the latter two.
When did each GPT spread?
Jovanovic et al (2005) allocated each GPT to its own respective era, which spanned from its initial adoption to its establishment as a widespread technology. Each period’s start and endpoints were determined by each GPT’s adoption rate: an era began when the GPT achieved a 1% diffusion in the median sector, and it ended once the diffusion curve flattened. As a result, they argued the Electrification era started in 1894, with the completion of the first hydroelectric facility at Niagara Falls, and that it ended in 1930. For the Information Technology (IT) era, they claimed it began in 1971 with the invention of Intel’s ’4004’ microprocessor, the key component of the personal computer. The IT diffusion curve hadn’t flattened at the time of their paper, so they viewed the era as ongoing.
There were lags in each technology’s adoption…
A key message from the Jovanovic et al (2005) paper was that there can be considerable time lags between the invention of a GPT and its widespread usage. In fact, both for electric appliances and personal computers, it took almost 30 years from their invention to reach 50% of all households (see Figure 1, below).
Figure 1: It took almost 30 years for appliances and PCs to reach 50% of households
Sources: Jovanovic and Rousseau (2005).
What’s more, in manufacturing, it took many businesses over 20 years they complete the electrification of their processes (see Figure 2, below).
Figure 2: The electrification of manufacturing was a lengthy process
Sources: DuBoff (1964), Jovanovic and Rousseau (2005).
Jovanovic et al (2005), offered various explanations for these lags. For one, it took time until the technologies were cheap enough to be accessed by the wider public. Affordable PCs, for example, only came out in the 1980s, when the technology was some 15 years old. And for electrification, the durability of old manufacturing plants which relied on water and steam, often didn’t make it economical to replace them. For this reason, as noted by David (1989), it was expanding industries that tended to electrify first. In the early twentieth century, these were tobacco, fabricated metals, transportation equipment, and electrical machinery.
…productivity gains were also late to materialize
These lags form a part of the reason why economy-wide productivity only accelerated 20 years after the invention of each GPT (see Figure 3, below). But they were not the only reasons. As highlighted by David (1989), GPTs may lead to qualitative changes not captured by productivity statistics. Shorter traveling times for workers via electric trams are such an example. What’s more, due to the revolutionary nature of such technologies, their initial adoption may have an exploratory character that doesn’t take advantage of all the available efficiencies.
Figure 3: After the invention of each GPT, it took ~20 years for productivity growth to accelerate
Sources: Federal Reserve, Bonsai Economics.
Conclusion
Looking ahead, in the case of artificial intelligence, it would be unwise to assume that its adoption must follow a similar pattern to previous GPTs. However, these past experiences may help us be open to the possibility that new technologies may take some time until their revolutionary impact is fully felt. And once this occurs, productivity growth may accelerate considerably.
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