Technology Dynamics and Growth

According to the Solow Model, productivity growth is the key to long-term, sustained economic growth. In practice, this is quantified by a blunt measure of economic efficiency called total factor productivity (TFP). Referred to as a “measure of our ignorance” by Robert Solow himself, TFP is calculated by summing up all growth that is not accounted for by physical and human capital accumulation. Historically, TFP has been conceptualized as codifiable technical knowledge — how to produce steel, how fertilizers affect plant growth, how medicines combat disease, etc. — that is close to instantaneously-available. As such, countries that lag behind the global technological leaders should be able to achieve rapid TFP growth, and thus economic convergence, by transplanting existing knowledge developed elsewhere, but available everywhere.

This has been the story in a number of developing countries, including pre-World War II Russia and Japan. More recently, China and Southeast Asian countries achieved rapid economic growth in the latter half of the 20th Century by transplanting technologies and practices that had been developed by the global technological leaders, and adapting them to the region. East Asia was once considered doomed to poverty — Stephen Enke dismissed it as having an “uneconomic culture” in 1963, Paul Ehrlich asserted that Indonesia “could not be saved” in 1967, and Gunnar Myrdal deemed it hopelessly volatile in 1968. Despite such predictions, the 23 economies of East Asia grew at a faster rate than all other regions of the world between 1965 and 1990, during which time roughly one-third of the region’s economic growth came from TFP growth.

In contrast to the experience of East Asia, many countries have not experienced TFP convergence with the global technological leaders, even where the rate of adoption for new technology has been relatively rapid. Anecdotally, many Westerners are surprised on their first trip to countries in Central America or Africa to find both urbanites using computers at internet cafés, and rural farmers using beasts of burden to plow fields. Empirically, TFP in Africa, Latin America, and the Middle East grew slower than in advanced economies during the latter half of the 20th Century.

In a new paper, Diego Comin and Martí Mestieri Ferrer propose replacing the conception of TFP as readily available knowledge with a two-part notion consisting of “technological adoption” — the time it takes for a technology to arrive in a country — and “intensity of use” — how widely used that technology becomes. In most cases, TFP growth is constrained by the intensity piece. That is, new technology arrives quickly in developing countries, but TFP fails to converge because the number of adopters remains small.

If TFP growth is constrained by the intensity of use for new technology, then the question becomes why is technology diffusion so much lower in developing countries. The answer will not be identical in every case, and probably has to do with the fundamental causes of growth. These underlying variables are endogenous to a given society, and provide a conceptual framework for thinking about complex interactions, but are decidedly not codifiable.

Fundamental causes are inherently difficult to quantify, and one should be skeptical of attempts to use them empirically to explain observed phenomena. For example, it is currently in vogue to use “strong institutions” as an explanatory variable. While institutions definitely matter for development, the notion of “strong institutions” can be used to refer to a high marriage rate, independent central banking, the persistence of slavery, or a consistent regulatory environment among other things. This is not to say that fundamental causes are not worth addressing — they certainly are. Improvements to them on the margin can make large differences over time. However, it is not clear how to measure them or compare them across countries in any useful manner.

 

 

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