The paper is by Aghion, Akcigit, and Howitt. They see creative destruction as less important in economies that are catching up to the state of the art in technology. It is more important for what they call “frontier economies,” meaning economies that require innovation for growth.
the theory points to appropriate growth policies, i.e. policies that match the particular context of a country or region. Thus we saw that more intense competition (lower entry barriers), a higher degree of trade openness, more emphasis on research education, all of these are more growth-enhancing in more frontier countries.
Read the paper, and think about China.
Finally finished skimming the linked paper. At first, I had to laugh when I saw how much symbolic math and calculus (!) they were using for the mathematical models. The notion that such simple math either helps elucidate what’s going on or makes it more precise is beyond stupid. Even in physics, such math is relegated to certain corner cases, either after a lot of physical study to back it up or simply as a reductionist effort to at least begin to reason about a system. It is understood that the vast majority of physical activity is too complex to model with such simple models, but the physicists themselves often lose sight of this, because of how successful physics has been over the last century and because they get blindered by their little domain where the math works.
For the economists to then use those same toy models to model the vastly more complex human economy is sheer ignorance, a level of breath-taking stupidity that has to be seen to be believed. The notion that any of the math in this paper has any relevance whatsoever is mind-blowingly idiotic.
All that said, they do lay out some good insights from the literature in the descriptive portions of their paper. I thought this section was very good, the answer to Tyler Cowen and the Great Stagnation dimbulbs:
“Bresnahan and Trajtenberg (1995) define a General-Purpose Technology (GPT) as a technological innovation that affects production and/or innovation in many sectors of an economy. Well-known examples in economic history include the steam engine, electricity, the laser, turbo reactors, and more recently the information-technology (IT) revolution. Three fundamental features characterize most GPTs. First, their pervasiveness: GPTs are used in most sectors of an economy and
thereby generate palpable macroeconomic effects. Second, their scope for improvement: GPTs tend to underperform upon being introduced; only later do they fully deliver their potential productivity growth. Third, innovation spanning: GPTs make it easier to invent new products and processes— that is, to generate new secondary innovations- of higher quality.
Although each GPT raises output and productivity in the long run, it can also cause cyclical fluctuations while the economy adjusts to it. As David (1990) and Lipsey and Bekar (1995) have argued, GPTs like the steam engine, the electric dynamo, the laser, and the computer require costly restructuring and adjustment to take place, and there is no reason to expect this process to proceed smoothly over time. Thus, contrary to the predictions of real-business-cycle theory, the initial effect of a ‘positive technology shock’ may not be to raise output, productivity, and employment but to reduce them.
Note that GPTs are Schumpeterian in nature, as they typically lead to older technologies in all sectors of the economy to be abandoned as they diffuse to these sectors. Thus it is no surprise that Helpman and Trajtenberg (1998) used the Schumpeterian apparatus to develop their model of GPT and growth. The basic idea of this model is that GPTs do not come ready to use off the shelf. Instead, each GPT requires an entirely new set of intermediate goods before it can be implemented. The discovery and development of these intermediate goods is a costly activity, and the economy must wait until some critical mass of intermediate components has been accumulated before it is profitable for firms to switch from the previous GPT. During the period between the discovery of a new GPT and its ultimate implementation, national income will fall as resources are taken out of production and put into R&D activities aimed at the discovery of new intermediate input components.”
This is a perfect description of the slowdown during the last decade, after the initial introduction of the GPTs of the PC and internet in the previous decades.
What any of this has to do with third-world countries not benefiting from democracy and trade, I have no idea. If anything, it’s possible that that may have been true in the past, when frontier technology required heavy capital investment, like manufacturing equipment, but certainly isn’t true today, when any group of software developers all over the world can compete with the software giants. So by looking back at the past, without adjusting for what’s different today, their predictions are wholly wrong about what non-frontier economies should do.
The Economist had a good article a couple months ago about the likelihood of growth coming back.