Knowledge Complexity: A Trade-Off
Growing Meta #7
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Technological knowledge plays a central role in shaping the working of an economic system and its growth.
Economic growth theories are always changing. They are theories that speculate what makes an economy grow.
Most theories focus on inputs, capabilities and mechanical factors (factors that move the economy). One theory, the economic complexity theory [1], focuses on outcomes. They analyze data on economic activities (such as foreign trade) to infer how capable the economy is. An index, called the economic complexity index (ECI), measures complexity through technological difficulty, or knowledge complexity, of exported goods. You can explore the Observatory of Economic Complexity for more info.
This week I read a paper [2] on the diversity and size of knowledge complexity, and its role in growing an economy. Here is its summary:
Knowledge complexity and the mechanisms of knowledge generation and exploitation: The European evidence
Knowledge Complexity: A Trade-Off
Three Italian economists looked into existing economic growth theories.
Their question.
What is the effect on the COMPOSITION and SIZE of knowledge within a specific region on knowledge GENERATION and EXPLOITATION?
Why did they ask it?
No body examined COMPOSITION of knowledge-stock with respect to upstream (knowledge generation) and downstream (knowledge exploitation) functions.
They wanted to create a complete framework for analyzing knowledge and economic productivity.
How did they answer it?
They look at the complexity of the knowledge-base on productivity growth by region.
To measure the effects, they implemented two equations: one for knowledge generation, and another for productivity/exploitation.
To approximate knowledge complexity, they used patent data (Worldwide Statistical Patent Database).
This is how they define the relationship of the factors within the equations (refer to the paper for the full equation):
The Knowledge Generation Equation (KGE)
Knowledge Stock Per Employee (KSTOCK) = R&DSTOCK + KCI + MAN
R&DSTOCK: A measure of input into innovation
KCI: Knowledge Complexity Index (i.e. how hard are the patent contributions within a specific region - it factors diversity of contributions)
MAN: Share of manufacturing employees from total employees
In simplified English — Knowledge generated in a region is affected by total R&D investments, complexity/difficulty of patents in that region, and the % of employees in manufacturing.
The Technology Production Function (TPF)
Labor Production (LP) = LP from previous year + L + KCI + KL + KSTOCK + R&DSTOCK
L: Level of employment
KL: Fixed capital formation and employment
In simplified English — Technological labor production is affected by labor production of last year, total tech jobs available, complexity/difficulty of patents in that region, total capital and employment, knowledge generated, and investment into R&D.
They ran these equations on European regions from 1997–2009.
What did they find?
There are major differences in complexity of knowledge across European regions.
This affects the regions’ innovation and productivity performances positively.
The economy (social aspect) benefits from knowledge complexity more than firms (private aspect). Firms can get stuck trying to produce value from the generated knowledge due to its impact on their productivity.
This creates the “Knowledge Complexity Trade-Off”.
They recommend
Each region should have different innovation policies (that make a distinction between knowledge generation and application).
Economic policy should “help the system to achieve levels of knowledge complexity that go beyond the limits of private returns and are closer to its social returns.”
But
Their work only computes knowledge complexity through patents, but what about the research body? Patents cannot happen without a lot of dry research.
I think how they look at regions is misleading. They take the region of current address of the patent issuer, how clean is that data?
This space is for me to vent
Why do academic papers have to have such difficult language? Machine learning papers were relatively easier to read than this one.
Let me give you an example:
“A knowledge complexity trade-off can be identified if and when the complexity of the stock of knowledge engenders positive effects in the recombinant generation of new technological knowledge but negative ones in its exploitation in terms of productivity gains.”
This is the first line of the abstract, can you imagine the rest of the paper? Simple ideas made complicated.
The world will change if everyone becomes good communicators.
[1] Hidalgo, César A., and Ricardo Hausmann. "The building blocks of economic complexity." Proceedings of the national academy of sciences 106.26 (2009): 10570-10575.
[2] Antonelli, Cristiano, Francesco Crespi, and Francesco Quatraro. "Knowledge complexity and the mechanisms of knowledge generation and exploitation: The European evidence." Research Policy (2020): 104081.