How production networks amplify economic growth
GAICIL Insights
Two countries can enjoy exactly the same productivity gains in their factories and still grow at different speeds.1 How production networks amplify economic growth explains why, and the explanation rewrites the map of where technology pays off. James McNerney, Charles Savoie, Francesco Caravelli, Vasco M. Carvalho and J. Doyne Farmer begin from the premise that technological improvement is the most important cause of long-term economic growth. They then study it inside a production network where each producer buys inputs, converts them and sells to households or other producers. The network, they show, amplifies technological improvements as they travel along chains of production.
The mechanism works like compound interest for productivity. An industry benefits from its own gains and from those of its upstream suppliers, so improvements pile up along production chains. Longer chains bias an industry toward faster price reduction, and longer chains for a country bias it toward faster growth. The output multiplier, which recent studies read as the average length of an industry's production chains, becomes a growth determinant alongside productivity itself. The predictions, the authors report, agree well with data and improve with the passage of time.
So growth depends on what improves and on how far the improvement travels. A gain buried deep in a long chain reaches far more output than a gain at the end of a short one. The same invention can be a ripple or a wave depending on where it lands.
Here is where the paper meets the present. AI is the ideal test case. AI is a general-purpose input that enters production at many stages: design, manufacturing, coordination, services. Where it lifts the productivity of upstream inputs used across long chains, the network amplifies the gain, cutting prices downstream and raising output across many industries. The aggregate payoff of AI will hinge on where in the network adoption happens and how deeply the production structure is interconnected. Counting AI adopters will tell only half the story; mapping their position in the web tells the rest.
The paper also cracks an old puzzle. In the 1960s, Baumol observed that some industries, notably manufacturers, improve productivity faster than others, and that sustained differences reshape prices over long periods. Quickly improving industries see relative prices fall; slower ones, including many services, see prices rise even with some improvement. The effect is often cited for long-term price increases in health care and education. The authors' results suggest why manufacturing can keep improving faster: its production chains tend to be longer, so it inherits improvements from many upstream suppliers.
Here is the provocative extension. Many services have stagnated because their chains are short and labor-intensive. If AI becomes an input into services, inserted as software, data and automated tasks, it could lengthen and deepen those chains. Services might then start inheriting productivity gains from upstream technology producers, easing the long-run price pressure behind Baumol's observation. That remains a hypothesis for future research. The paper supplies the framework to test it, and the stakes are enormous: the classrooms and clinics that have resisted productivity growth for decades.
The policy lessons are concrete. Growth strategies obsessed with headline productivity in a few showcase industries miss the amplification that networks provide. Deepening integration into production networks, at home and across borders, raises the multiplier through which progress becomes growth. Barriers that shorten chains or cut industries off from innovative suppliers shrink it. For AI, openness to upstream innovation, including access to imported AI tools and inputs, becomes a growth policy in its own right.
The deeper message: economies are systems, and progress in one corner travels through the whole. Technological improvement does its work through the links between producers. AI policy that grasps this will focus on enabling adoption across the network so improvements can propagate along every chain they can reach. The payoff, the research shows, can be faster price declines and faster growth for decades. The smartest place to invest in AI may be the unglamorous supplier three steps upstream.
Introduction
Technological improvement is the most important cause of long-term economic growth. We study the effects of technology improvement in the setting of a production network, in which each producer buys input goods and converts them to other goods, selling the product to households or other producers. We show how this network amplifies the effects of technological improvements as they propagate along chains of production. Longer production chains for an industry bias it toward faster price reduction, and longer production chains for a country bias it toward faster growth. These predictions are in good agreement with data and improve with the passage of time, demonstrating a key influence of production chains in price change and output growth over the long term.
Takeaway
Economics has emphasized the outsized role of productivity in explaining cross-country differences in growth, and our theory features this. But production network models predict that variation in the output multiplier matters as well. These models generate a variety of predictions for how the output multiplier should shape price evolution and growth, with which observations from data are in good agreement. Recent studies [26, 28] have emphasized that the output multiplier can be understood as the average length of an industry’s production chains. This leads to an intuitive mechanism driving our results: An industry benefits from its own productivity gains, and that of upstream suppliers, and so the effects of productivity improvement accumulate along production chains. As a result, two countries realizing the same average micro-level productivity improvement can, if their production networks differ in depth, experience different aggregate growth rates. At a micro level, the results suggest one reason why some technologies improve more rapidly than others, especially a version of this question that arises in Baumol’s classic observation. As confirmed many times since, in the 1960s Baumol observed that some industries, particularly manufacturers, realize productivity gains more rapidly than others [21, 36], and that over long periods such sustained differences will significantly impact prices. The relative prices of quickly-improving industries will naturally fall, but those of slower-improving industries (including many services) will increase, even if these industries are realizing some improvement [21].1
This effect has often been cited as a cause of long-term price increases in health care [37] and education [38]. The results here point to a reason why manufacturing would be able to sustain faster improvement for long periods. Observations of output multipliers [19] reinforce the intuitive idea that manufacturing industries tend to have longer production chains than services. Manufacturing industries are advantaged by their network positions to benefit more greatly from productivity gains across the network. The results here suggest a nuanced way to help distinguish fast and slow segments of an economy, in that part of what helps define the fastimproving segment is the set of industries with large output multipliers rather than manufacturers per se. In particular, the fact seen earlier that output multipliers retain their predictive power within broad industries suggests that our analysis provides a more operational way to distinguish fast- and slow-improving industries. At the aggregate level, the results suggest a new perspective on the long process of structural change [22], emphasizing changes to the length of an economy’s production chains. One expects an undeveloped economy to have short chains of production. As manufacturing becomes more prominent, the average output multiplier increases. Finally, as service industries become more prominent, the average output multiplier decreases. The predictions here suggest that, all else equal, an economy will accelerate its growth during the manufacturing stage and relax to slower growth when it becomes more developed.
In Fig. 6a, developed economies have low average output multipliers and low growth rates while economies that are developing a strong manufacturing sector, such as China or Slovakia, tend to have high average output multipliers and high growth. The WIOD [20] does not contain data for undeveloped countries and we cannot confirm that their average output multipliers are low, though it would be surprising if it were otherwise. To get a feel for the amplification to growth associated with production chains we consider some rough figures. In Fig. 5, China’s average output multiplier increases from 3.8 to 5 from 1995 to 2009. Had it not done this, Eq. (5) implies that its growth rate at the end of the period would have been reduced by a factor 3.8/5 ≈ 0.76. More drastically, suppose China had the output multiplier of the United States of about 2.5, and as a rough figure take China’s output multiplier over the period to be its midpoint around 4.4. If China otherwise had the same average productivity improvement, its growth would have been smaller by a factor 2.5/4.4 ≈ 0.57, i.e. close to half of its growth during the period would have been lost. The results suggest that differences in the average output multiplier have been an important factor in the large income differences that exist across countries.
These income differences originated in changes that economies underwent during industrialization [39, 40], when we would expect production chains to have started becoming more developed. The potential for the average output multiplier to help explain disparate income levels has not gone unnoticed. Jones [5, 41] notes that accounting for intermediate goods in models of aggregate production can generate large multiplier effects, with values that help explain observed differences in income. Our results suggest that policy-makers may want to design network-aware industrial policies, targeting particular nodes in the production network.
Notes
- James McNerney et al., How production networks amplify economic growth, 119 Proc. Nat'l Acad. Sci. e2106031118 (2022), https://doi.org/10.1073/pnas.2106031118. Takeaway source: Discussion, reproduced from a free author or repository copy (working-paper version). ↩
Cite as: James McNerney et al., How production networks amplify economic growth, 119 Proc. Nat'l Acad. Sci. e2106031118 (2022), https://doi.org/10.1073/pnas.2106031118
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