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# Demand-pull, technology-push, and the direction of technological change

Kerstin Hötte111 Mail<EMAIL_ADDRESS>Postal address: Institute for New
Economic Thinking, Manor Road Building, Manor Road, OX13UQ Oxford
The Alan Turing Institute, London
Institute for New Economic Thinking, University of Oxford
Faculty of Business Administration and Economics, University of Bielefeld

###### Abstract

This paper studies the impact of Demand-pull (DP) and Technology-push (TP) on
growth, innovation, and the factor bias of technological change in a two-layer
network of input-output (market) and patent citation (innovation) links among
307 6-digit US manufacturing industries in 1977-2012. Two types of TP and DP
are distinguished: (1) DP and TP are between-layer spillovers when market
demand shocks pull innovation and innovation pushes market growth. (2) Within-
layer DP arises if downstream users trigger upstream innovation and growth,
while TP effects spill over from up- to downstream industries. The results
support between- and within-layer TP: Innovation spillovers from upstream
industries drive market growth and innovation. Within the market, upstream
supply shocks stimulate growth, but this effect differs across industries. DP
is not supported but shows a factor bias favoring labor, while TP comes with a
shift towards non-production work. The results are strongest after the 2000s
and shed light on the drivers of recent technological change and its factor
bias.

JEL Classification Codes: E23; L16; L6; O14; O3
Keywords: Technological Change; Network; Patent; Input-output; Innovation;
Labor

###  1 Introduction

Shaping technological change is high on the political agenda to cope with the
challenges of the twenty-first century, such as climate change or digitization
[(61), (24)]. Demand-pull (DP) and technology-push (TP) are drivers of
technological change [(94), (78), (77), (98), (40)] that can be stimulated by
different policies [(89), (80)]. Effective policymaking requires an
understanding of how these mechanisms interact and differ by impact.

TP arises when technological breakthroughs enable the development and
commercialization of novelties. DP emerges from market needs if inventors
adapt R&D efforts in response to the perceived market potential [(40), (98)].
TP and DP are interdependent because R&D objectives may be demand-selected and
market needs may arise in response to innovation [(68), (31), (80), (77),
(79), (92)].

This paper studies the impact of TP and DP on qualitative and quantitative
indicators of technological change in US manufacturing since the late 1970s.
This period was characterized by a qualitative change of industrial production
with rising capital intensity and shifts from production to non-production
labor with changing skill-requirements [(1), (66), (43)]. This raised concerns
about the decline of the labor share and job market polarization with
undesired effects on distribution and employment when low-skill and routine-
intensive occupations become obsolete [(11), (9), (49)]. Here, it is analyzed
how TP and DP contributed to industry growth, innovation, productivity, and
changing production factor requirements.

Figure 1: Stylized representation of the two types of demand-pull and
technology-push

Notes: This figure shows the two-layer network of a coupled input-output and
patent citation network. Nodes in the network are industries $i,j,k$, and
arrows connecting the nodes are flows of intermediate goods in the market
layer $\mu$ (blue color) and patent citations in the innovation layer $\tau$
(red color). Demand-pull (DP) and technology-push (TP) Type 1 are between-
layer spillovers, while Type 2 are within-layer effects arising from down- and
upstream industries.

Previous studies of TP and DP mostly relied on market shares and sizes as
proxies for DP and innovation outputs such as patents for TP to study their
impact on growth, innovation, and productivity [(64), (31), e.g.]. This study
relies on similar empirical proxies and analyzes TP and DP in a two-layer
network that captures interactions between markets and innovation.

The network is schematically illustrated in Fig. 1. Industries $i,j,k$ are
connected in the top layer, called “market layer” $\mu$ (red nodes), by cross-
industrial flows of intermediate goods, and in the bottom layer, called
“innovation layer” $\tau$ (blue nodes), by patent citations. The thick arrows
indicate the two conceptually different mechanisms of TP and DP that are
studied in this paper:

Type 1—Between-layer effects: TP1 arises from technological breakthroughs
reflected in surges of patenting in the innovation layer
($\tau\rightarrow\mu$) and DP1 arises from output shocks in the market
($\mu\rightarrow\tau$). TP1 is present if past innovation in an industry $i$
pushes growth in $i$’s market size, and DP1 is present if $i$’s market growth
pulls innovation.

Type 2—Within-layer effects: TP2 and DP2 arise from the supply (upstream) and
demand (downstream) side of an industry. Within the market, DP2 effects
($j\rightarrow i$) are present when a rising demand from downstream customers
$j$ for $i$’s outputs stimulates growth in the upstream industry $i$. Within
innovation, DP2 exists if an increased use of $i$’s inventions by downstream
industry $j$ induces innovation in $i$. TP2 effects are the opposite
($i\rightarrow j$) when changes in the availability of inputs from suppliers
$i$ induce downstream technological change in industry $j$. In the market, TP2
effects are present if an improved availability of production inputs drives
growth in the downstream industry. In innovation, TP2 effects are given if
upstream technological advance stimulates downstream innovation.

These effects are operationalized by network metrics that are empirically
calculated using a panel of 307 NAICS 6-digit US manufacturing industries
during 1977-2012. The market layer is inferred from empirical input-output
(IO) tables provided by the Bureau of Economic Analysis (BEA). The patent
citation network is compiled using US Patent and Trademark Office (USPTO) data
and mapped to NAICS 6-digit industries through their technology classes
[(51)]. The data is supplemented with data on productivity and production
factor use [(12), (14)].222The data and code are available under a CC-BY 4.0
licsense [(56)].

Using dynamic panel regressions, this paper analyzes the impact of both types
of TP and DP on technological change measured by changing industry sizes in
both layers (output and patent counts), productivity, and changing use of
production factors. Industry sizes inform about the rise and decline of
industries, productivity measures reflect the production efficiency [(82)],
and a changing factor input use (employment, wages, capital intensity, share
of production labor) informs about the bias of technological change [(1)]. To
address cross-industrial heterogeneity in patterns of innovation [(85)], the
analysis of TP and DP is repeated for different subgroups of industries and
subperiods.

The results support within- and between-layer TP: Industries experiencing an
expansion of innovation opportunities grow faster in the market (between-layer
TP1) and innovate more (within-innovation TP2). These effects mostly arise
from innovation spillovers from upstream industries. The support for these two
TP effects is consistent across industries and strongest in patent-intensives
industries.

The results also support within-market TP2 effects arising from upstream
market spillovers, which indicate positive supply shocks in the availability
of production inputs. However, within-market TP2 is heterogeneous across
industries and upstream network centrality shows a negative effect in some
industries. One explanation for the ambiguous role of upstream market linkages
lies in their interaction with the position of an industry in the supply chain
[(76), cf.] and a larger process of structural change towards increasingly
processed goods (e.g. electronics, machinery, ICT). The results are much
stronger during the post-2000s period, indicating that TP as driver of change
became more important over time.

DP is not supported as a driver of growth and innovation, but downstream
linkages in the market are associated with a factor bias in favor of labor. An
increasing centrality in the downstream network is associated with a higher
labor demand, more investment, higher productivity, and a lower use of
capital.333Downstream centrality indicates that an industry supplies goods
that are essential inputs in many other industries (who themselves are
important suppliers to others). Upstream centrality indicates that an
industry’s upstream suppliers are more specialized on few customers. TP
effects exhibit the opposite bias and are associated with a reallocation of
labor from production towards non-production activities.

Within the innovation layer, downstream spillovers (DP2) show a weak but
statistically significant negative association with innovation. An increasing
downstream use of innovations may indicate a higher level of technological
maturity and more applied research. The combined results of within-innovation
TP and DP effects suggest that technological breakthroughs trickle down the
innovation chain from up- to downstream inventive activity.

Finally, the analyses reveal path-dependence of market growth and innovation,
which is stronger in innovation with an autocorrelation coefficient larger
one. This indicates increasing returns to innovation, which may explain an
increasing concentration of innovative activities.

Four major limitations exist. First, patents are an indicator of radical
innovation but are less suited to capture patterns of technology diffusion and
use. They also suffer from a series of additional well-known limitations
[(65), (44), (69), e.g.]. Second, classification systems change over time,
which hampers the study of long-term technology-industry links [(72), (100)].
Third, drivers of technological change differ across firms, industries, time,
and industrial maturity [(99), (85)]. Fourth, this research is limited to US
manufacturing and neglects other structural changes in the US economy.

Nevertheless, this study offers relevant contributions and insights for
research and policy. The two-layer network and the data offer a rich basis for
future research that aims to understand how markets and technology interact
(see also Sec. 6). This understanding is essential as the societal challenges
of today directly interact with technological change: Large-scale
technological and economic change is necessary to cope with climate change and
can be steered by adequate policy [(61)]. Digitalization as a process of
technological change needs to be steered to alleviate undesirable
distributional effects and disruptions in the labor market. These challenges
require an understanding of how radical innovation can be used to influence
the evolution of markets for goods and labor, and how market forces can be
mobilized to shape technological change.

The results suggest that TP policies stimulating the creation of technological
knowledge may influence market growth and innovation. The results also show
that the distributional effects may depend on the driver of change. This is
important for policy design. For example, it is a long-standing debate whether
technology replaces or complements human labor and more recent research raised
concerns about job market polarization and an unequal distribution of gains
from technological change [(59), see]. This research suggests that the driver
of change matters: TP shows a bias in favor of capital, while DP dynamics
exhibit an opposite relationship. This is informative for innovation policy
design when making the choice between DP or TP instruments.

The study further shows that innovation is subject to increasing returns. This
is important as increasing returns are drivers of endogenous growth [(87)],
which may leverage the effectiveness of innovation policy. But increasing
returns may also cause technological lock-ins [(6)].

The remainder of the paper is structured as follows: The next section provides
an overview of the related literature. The theoretical framework is explained
in Sec. 3. Sec. 4 introduces the data. Sec. 5 summarizes the results, Sec. 6
offers a discussion, and Sec. 7 concludes.

###  2 Background

TP and DP as drivers of technological and economic change are the subject of a
long-lived debate [(94), (77), (84), (31), (92)]. DP suggests that R&D follows
the market: The perceived commercial potential of innovations offers an
incentive for targeted R&D. TP arises from technological opportunities that
enable the development of new products and subsequent innovation. The two
concepts differ by the assumptions made about the incentives that influence
R&D and production decisions, and about the sources of ideas for technological
improvements. While DP emphasizes the role of users and customers, TP builds
on external and internal research [(32), (68), (40)].

Previous studies found support for both effects [(31), (40), see]. For
example, using aggregate time series over the business cycle, [(48)] studied
interactions between manufacturing outputs and innovative activity. They
observed causal effects from outputs to innovation, but no support for the
other way round. They highlighted the critical role of stochastic
determinants, which may indicate supply shocks that point to TP from radical
innovation. [(99)] showed for the chemical industry that TP from radical
breakthroughs drives market growth, which creates DP that induces incremental
innovation.

The analysis in this paper is conceptually close to the seminal work by [(64)]
who operationalized DP through the market shares of a firm in different
industries and TP as the effect that arises from innovations in
technologically related fields. The author found that push and pull effects
cannot be distinguished empirically when explaining TFP growth.

This paper builds on an empirical two-layer network approach composed of an IO
and a patent citation layer. The two layers capture the co-evolution of
markets and technology [(79)], which occurs when market dynamics correlate
with innovation [(92)].

IO and patent citation networks can be used to qualitatively describe
technology by the network position of an industry, firm, or patent. The
position is determined by the bundle of input links pointing to the physical
production inputs or existing technological knowledge embodied in patents that
enables subsequent innovation. Two firms, industries, or patents are
technologically similar if they have many overlapping up- and downstream
links, indicating the capability to make use of similar inputs and to serve
the needs of similar users [(30), (4), (2), (26), (60), (8), (65), (33),
e.g.].

Technological similarity and direct network links enable cross-industrial
spillovers of knowledge and market shocks. Other research based on patent data
has shown that innovation spillovers may stimulate productivity growth [(4)],
innovation [(2)], employment growth and R&D investments [(25)], and may be a
source of path-dependence of technological trajectories [(67), (95), (60)].

In IO networks, the characteristics of an industry’s production technology are
reflected in the bundle of input used and outputs produced. Other researchers
observed that patterns of production input adoption [(30)] and product market
diversification [(21)] depend on pre-existing IO links. [(28)] showed that
relatedness through IO links can be a moderating factor of output
fluctuations.

This study combines both types of networks. Existing concordance mappings of
patents to industries often rely on the industrial classification of the firms
that own patents in specific technological fields [(70), (93), (41), (97),
(74), (51), e.g.]. Concordances make it possible to study interactions between
the evolution of patented technology and industries. Proving the economic
validity of their concordance, [(51)] showed that industry-technology
relationships are relatively stable.

This study is not the first that considers the market and innovation network
positions simultaneously. Next to [(64)]’s (64) seminal paper,
bloom2013identifying (19) build on a similar framework but study the role of
market rivalry and knowledge spillovers on firms’ performance.
bloom2013identifying used output linkages to capture rivalry in product
markets and patent links to compute knowledge spillovers. They found that
market rivalry has a negative on firm performance, while the impact of
knowledge spillovers is positive.

###  3 Technological and economic change in a two-layered network

This section introduces the conceptual framework of the two-layer network,
explains how the network is used to identify TP and DP, and introduces the
measures of technological change. It concludes by a short outline of the steps
that are undertaken in the empirical analysis.

####  3.1 The economy as a two-layer network

Technology is the capability to transform a bundle of inputs into outputs.
Technological change occurs when the quality and/or quantity of inputs or
outputs changes (saviotti1997black, 91, 90). Industries use intermediate goods
as production inputs and build on pre-existing knowledge encoded in cited
patents to innovate, in addition to production factors such as labor and
capital.

Patent citations do not necessarily represent a direct flow of knowledge.
Citations are a legal requirement to describe prior art and to limit the scope
of the new patent. However, a patent citation still serves as an indicator of
technological relatedness revealing that the knowledge encoded in the cited
patent contributed to the technological basis onto which a patent builds
(jaffe2019patent, 65, 83).

In this paper, IO flows represent the market and patents are interpreted as
innovations. The IO and patent citation links span a weighted, directed two-
layer network consisting of a market layer $\mu$ and an innovation layer
$\tau$. A node in the network represents an industry $i\in N$, which is
connected with other industries $j\in N$ through IO links in the market and
patent citations in the innovation layer, where $N$ is the set of industries
in the economy. The layers $\alpha=\mu,\tau$ are linked as a duplex network
where each industry has a representation in each layer.

The links in the layers are given by the flow of goods $flow^{\mu}_{ij,t}$ and
patent citations $flow^{\tau}_{ij,t}$ from an industry $j$ to $i$ with $i,j\in
N$ in time $t$. These flows reveal two types of information about the
technology used by $i$ and $j$: An input flow from $j$ to $i$ indicates that
$i$ has the capability to use the outputs produced by $j$. It also reveals
that $j$ has the capabilities to produce outputs that are valuable for $i$.
Hence, the bundle of upstream (input) links and downstream (output) links
contains qualitative information about the technology that is used in these
industries.

Here, the flows of goods and citations are transformed into input shares
$w^{\alpha,up}_{ij,t}$ dividing the monetary flow (patent citation count)
$flow^{\alpha}_{ij,t}$ by the sum of inputs $\sum_{j}flow^{\alpha}_{ij,t}$.
Analogously, output shares $w^{\alpha,dw}_{ij,t}$ are obtained by dividing
$flow^{\alpha}_{ij,t}$ by the sum of all outputs produced by industry $i$,
i.e. $\sum_{k}flow^{\alpha}_{ki,t}$. Note that $w^{\alpha,up}_{ij,t}\neq
w^{\alpha,dw}_{ji,t}$ due to the different weighting. They reflect different
concepts: The input share $w^{\alpha,up}_{ij,t}$ reflects $j$’s relevance as
an input provider for $i$, while $w^{\alpha,dw}_{ij,t}$ reflect $j$’s
relevance as a customer or knowledge user of $i$. The normalization to shares
improves the comparability of different data types (monetary flows, patents)
and of industries that are heterogeneous by size.

Each network layer can be represented as a quadratic, asymmetric
$|N|\times|N|$ matrix $W_{t}^{\alpha,d}=\\{w_{ij,t}^{\alpha,d}\\}_{i,j\in N}$
with positive non-zero entries if a link from $i$ to $j$ exists in time $t$.
The superscript $d=up,dw$ indicates the direction of the links, i.e.
$w^{\alpha,up}_{ij,t}$ ($w^{\alpha,dw}_{ij,t}$) indicates an upstream
(downstream) link. The two-layer network is given by the set of both matrices:
one representing the “market layer” and the other presenting the “innovation
layer” as illustrated in Fig. 1.

####  3.2 Network spillovers and centrality

The network data is used to derive indicators to capture up- and downstream
TP2 and DP2 effects.

#####  3.2.1 Spillovers

Industries are connected in both layers and shocks in one industry may spill
over to industries that are sufficiently close by their up- and downstream
linkages (see e.g. carvalho2014input, 30, 2, 19).444For a short discussion of
the theoretical foundations of spillovers and the underlying metrics, see
bloom2013identifying (19).

Closeness in networks and spillovers can be empirically captured in different
ways. This paper focuses on spillovers from direct up- or downstream links
$l^{\alpha,d}_{ij,t}=\mathbbm{1}(w^{\alpha,d}_{ij,t}\geq 0.05)$ that equal one
if the weight of the link $w^{\alpha,d}_{ij,t}$ exceeds a threshold of five
percent and to zero otherwise. Hence, it is assumed that upstream spillovers
do only arise from those industries $j$ that are important for $i$ as a
supplier and the goods produced by $j$ account for five or more percent in
$i$’s input mix. Analogously, downstream spillovers arise only from those
industries from which $i$ receives at least five percent of its market revenue
or patent citations.555The threshold level of 5% restricts the network to a
maximal number of 20 most important up- and downstream linkages. Robustness
checks confirm that the main regression results are qualitatively consistent
for alternative threshold levels (2.5%, 10%, 20%) and for spillovers
calculated by using the weights $w^{\alpha,d}_{ij,t}$ instead of binary links.
Spillovers could also be calculated on the basis of technological
similarities. This paper focuses on direct links as this most directly
captures the impact of an industry’s neighbors in the network and as they show
sufficient variation in the innovation layer to disentangle up- and downstream
effects (see Sec. 5.2). Restricting the analysis to spillovers from direct
neighbors is a common approach in economic studies of the transmission of
shocks from up- and downstream industries (see e.g. carvalho2019production,
29, 39, 46). This procedure also helps reduce noise in the data arising from
the application of concordances when mapping patents to industries and
harmonizing different vintages of the NAICS.

Spillovers are calculated as

$\displaystyle Spill(A)^{\alpha,d}_{i,t}=\sum_{j\neq
i}^{N}l^{\alpha,d}_{ij,t}\cdot A^{\alpha}_{j,t}$ (1)

with $\alpha=\mu,\tau$, $d=up,dw$. $A^{\tau}_{j,t}$ is the number of patents
and $A^{\mu}_{j,t}$ is the amount of goods produced by $j$ in $t$. SThe level
of $Spill(A)^{\alpha,d}_{i,t}$ changes either by an output shock in related
industries $A^{\alpha}_{j,t}$ or by a change in the $i$’s up- or downstream
network $l^{\alpha,d}_{ij,t}$.

Increasing upstream spillovers in the market suggest that either existing
suppliers grew by market size $A^{\mu}_{j,t}$ or that $i$ connected to a new
and potentially larger supplier. Analogously, increasing upstream spillovers
in the patent layer indicate that the knowledge pool —proxied by patents
$A^{\tau}_{j,t}$— of the upstream industry increased or that new sources of
patented knowledge were acquired through new citation links. Upstream
spillovers are related to TP2 effects as $Spill(A)^{\alpha,up}_{i,t}$ reflects
a change in the availability of production and innovation inputs. Generally, a
high level of upstream spillovers suggests that an industry receives a high
share of its inputs from large industries.

In contrast, increasing downstream spillovers suggest that an industry’s
customers grew by market size or new customers were acquired. In the patent
layer, $Spill(A)^{\tau,dw}_{i,t}$ indicates that patents by $i$ are either
cited by industries with a growing number of patents or by novel groups of
knowledge users. Downstream spillovers indicate DP2 if they drive
technological change in the upstream industry.

#####  3.2.2 Centrality

An industry is central in the network if it is well-connected with other
industries. Network centrality is an indicator for the relevance of an
industry as input provider or output user (cf. jackson2008social, 62, 28).
Different approaches exist to measure network centrality (jackson2008social,
62). Here, the PageRank $PR^{\alpha,d}_{i,t}$ is used. It is calculated by a
recursive algorithm that ranks industries by the number and quality of link,
whereby the quality is higher when the link connects to an industry that is
itself ranked high by the PageRank (brin1998anatomy, 23). This study relies on
a version of the PageRank that accounts for the weighted and directed nature
of the links (csardi2006igraph, 37).666Originally, the PageRank was developed
by brin1998anatomy (23) and used in the early versions of the Google search
engine to rank websites by their relevance to the users based on upstream
links that are weighted by the relevance of the websites from which the links
are coming. The PageRank is used because it can be computed on the basis of
up- and downstream links and shows a sufficiently high variation. Exploratory
analyses have shown that the results across different measures are robust, and
correlation statistics show a high correlation between the PageRank and other
measures of network centrality (see A.3).

An industry can be central in two ways, and changes in the centrality are
indicative of within- and between-layer DP and TP effects: (1) Downstream
centrality indicates that an industry is a critical supplier. An increasing
downstream centrality in the market (innovation) layer indicates that an
industry $i$ became more important as a provider of goods (source of
knowledge) in the network. This is associated with DP2 because it reflects an
increasing importance of $i$’s outputs that are demanded by downstream
industries. Support for DP2 is found if an increasing downstream centrality is
associated with subsequent growth and innovation.

(2) Upstream centrality indicates that an industry is a critical customer. It
uses goods or patents supplied by a great number of other industries and/or
that account for a large share in their output bundle. If
$PR^{\alpha,up}_{i,t}$ increases, the upstream market power of $i$ increases.
This may imply a higher diversity of input sources, but it may also reflect a
pattern of vertical specialization when upstream industries become more
specialized to supply goods to $i$. An increasing upstream centrality can
indicate TP2 because it reflects an improved input availability for industry
$i$. TP2 is supported if this stimulates growth and innovation in the same
layer.

Changes in the centrality may also inform about between-layer effects because
centrality adds explanatory power to the pure size effect of an industry by
controlling for the quality of $i$’s outputs. For example, an increasing
number of patents of $i$ indicates a pure increase of innovation outputs
$A^{\tau}_{i,t}$, while an increasing centrality $PR^{\tau,d}_{i,t}$ suggests
that the innovations of the industry became more relevant. As we shall see in
the empirical analysis in Sec. 5.2.1, the number of patents is not
significantly related to market growth, but centrality is, which offers
support for TP1.777Between-layer effects (DP1/TP1) as conceptualized in this
paper include both: effects that come directly from an industry itself and
effects that arise from the network. For example, increasing patents,
spillovers, and centrality in the innovation layer suggest rising
technological opportunities in an industry $i$. These opportunities may come
from the industry itself, but also from its position in the innovation
network. TP1 is supported if any of these effects is a driver of market
growth. Analogously, DP1 is supported if rising output or rising market
opportunities and improved input availability in the market positively
correlate with subsequent innovation.

####  3.3 Technological change

At the industry level, technological change is reflected in a changing
composition of in- and outputs used to produce goods and for innovation. This
leads to changes in industry sizes: Some industries grow, others shrink in
relative and absolute terms.

Technological change also manifests in a changing use of production factors,
which is measured by productivity, labor requirements, capital and investment
intensity. This _qualitative_ dimension of technological change is referred to
as non-neutral or directed technological change.

Patterns of innovation and technological change are heterogeneous across
industries (pavitt1984sectoral, 85, 68, 31) and may differ over time. These
sectoral patterns are taken into account by analyzing the role of TP and DP
separately for different subsets of industries and separately for the first
and second half of the period covered. The second subperiod was characterized
by a major trade shock caused by Chinese import competition, which triggered
larger disruptions in the US economy (pierce2016surprisingly, 86). Splitting
the sample by the pre- and post-2000s helps understand whether the drivers of
technological change have changed over time.

Wrapping up, this paper studies technological change in the following ways:

  1. 1.

The analysis begins with a description of the rise and decline of industries
and shows how the characteristics of the networks evolved over time.

  2. 2.

Next, regressions are used to identify the impact of both types of TP and DP
on industry growth $A^{\mu}_{i,t}$ and innovation $A^{\tau}_{i,t}$.

  3. 3.

Then, the direction of technological change is analyzed through a series of
regressions of productivity indicators (value added per employee
$(VA/L)_{i,t}$ as measure of labor productivity, total factor productivity
$TFP_{i,t}$), and indicators about the use of different production factors
(employment $L_{i,t}$, wages $W_{i,t}$, capital intensity $(K/L)_{i,t}$,
investment per capita $(I/L)_{i,t}$, the share of production labor
$(L^{P}/L)_{i,t}$, and the relative wage paid for production labor
$(W^{P}/W)_{i,t}$).

  4. 4.

Finally, the analyses are repeated for different groups of industries that
differ by innovation intensity $(A^{\tau}/A^{\mu})_{i}$, market size
$A^{\mu}_{i}$, centrality in the patent network $PR^{\tau,dw}_{i}$, up- and
downstream centrality in the market network $PR^{\mu,d}_{i}$, by broad
industry group (defined by their 2-digit NAICS code), and by Pavitt industry
group using the coding proposed by bogliacino2016pavitt (22).

###  4 Data

The two-layer network is inferred from two different data sets covering the US
manufacturing sector during the period 1977-2012. The data is available in
five-year snapshots. The market layer is compiled with national account data
provided by the Bureau of Economic Analysis (BEA). The data is combined with
data on patents granted by the US Patent and Trademark Office (USPTO), which
are classified by the Cooperative Patent Classification (CPC) system. Using
the concordance tables by goldschlag2020tracking (51), 4-digit CPC codes are
mapped to industries and aggregated into five-year windows in accordance with
the IO data (see A.1.2 for more detail). This enables the compilation of a
cross-industry patent citation network for different periods.

The networks are given by symmetric matrices, where the entries are flows of
goods $flow^{\mu}_{ij,t}$ and patent citations $flow^{\tau}_{ij,t}$. The data
on cross-industrial flows is harmonized to shares. The networks are used to
compile the industry level citation-weighted patent stocks $A^{\tau}_{i,t}$,
output $A^{\mu}_{i,t}$, centrality $PR^{\alpha,d}_{i,t}$, and spillovers
$Spill(A)^{\alpha,d}_{i,t}$.

The network data is complemented with the Manufacturing Productivity Database
from the National Bureau of Economic Research (NBER) and US Census Bureau’s
Center for Economic Studies (CES) (becker2013nber, 14, 12). The industry level
variables extracted from this data include five factor productivity
$TFP_{i,t}$, value added per employee $(VA/L)_{i,t}$ which is used as a proxy
of labor productivity, employment $L_{i,t}$, labor costs per employee
(hereafter called “Wage”) $W_{i,t}$, investment per employee $(I/L)_{i,t}$,
capital intensity $(K/L)_{i,t}$, the share of production workers
$(L^{P}/L)_{i,t}$, and the relative wage for production labor
$(W^{P}/W)_{i,t}$.888Production labor includes jobs that are related to tasks
like fabricating, processing, maintenance, repair, product development, and
similar activities. Non-production labor covers tasks associated with
supervision, sales, delivery, advertising, finance, legal issues, and similar
services (see https://www2.census.gov/programs-surveys/asm/technical-
documentation/questionnaire/2021/instructions/MA_10000_Instructions.pdf
[accessed on 14/11/2022]). The data in monetary terms ($A^{\mu}_{i,t}$,
$VA_{i,t}$, $W_{i,t}$, $W^{P}_{i,t}$, $K_{i,t}$, $I_{i,t}$) is deflated using
the industry level price deflator for the value of shipment from the NBER
database.

The final data consists of a balanced panel of 307 6-digit manufacturing
industries. More aggregate data is used for additional robustness checks. The
most important steps of the data compilation are summarized in A.1. Additional
detail is provided in SI.1.

###  5 Results

This section begins with a descriptive analysis of the two network layers. In
Sec. 5.2, it follows a series of regressions studying the impact of TP and DP.

####  5.1 Descriptive analysis

Figure 2: Upstream networks at the 4-digit level for different periods

1977-1992

(a) Input-output

(b) Patent citations

1997-2012

(c) Input-output

(d) Patent citations

Notes: These figures show the network of upstream links (suppliers) at the
4-digit level for two different periods. Self-citations and within-sector IO
flows are not shown. The colors indicate broad industrial categories given by
groups of 3-digit level industries, i.e. Food (311-312), Textiles (314-316),
Fiber (321-323), Petroleum (324), Chemicals (325-327), Metals (331,332),
Machinery (333), Electronics (334-335), Transport (336), Other (337-339). A
link between industries $i$ and $j$ is shown if $j$ is an important supplier
to $i$, i.e. if the average of the weight $w^{in,\alpha}_{ij,t}$ during a
subperiod exceeds a threshold level given by the average weight over all
industry pairs and both subperiods plus one standard deviation
($\text{mean}(w^{in,\alpha}_{ij,t})+\text{sd}(w^{in,\alpha}_{ij,t})$). The
size of the nodes is proportional to the log size of an industry
$A^{\alpha}_{i,t}$. Before taking logs, $A^{\alpha}_{i,t}$ had been scaled by
its standard deviation over all industries and periods. This enables a
comparison of sector sizes and their distribution over time and across layers.
The figure is generated using the R-package _igraph_ , which makes use of the
Fruchtermann-Reingold algorithm to allocate the nodes. The algorithm aims to
minimize the crossing of links while keeping the length of the links similar.
Technical detail is provided in A.2.

Fig. 2 shows a series of upstream network plots at the 4-digit level for the
first and the second half of the period. A link between two industries $i$ and
$j$ is shown if the connecting weight $w^{\alpha,up}_{ij,t}$ is strong. The
node sizes are proportional to the industry size $A^{\alpha}_{i,t}$. The
algorithm that creates the plots tends to group industries with similar
linking patterns together (see Notes in Fig. 2 or A.2 for technical details).
The node colors indicate the broad industry group.

This visual analysis shows a more balanced size distribution of industries in
the market compared to the innovation layer, which is strongly dominated by
electronics (blue), machinery (dark violet), and chemical manufacturing (red).
The black color (“Other”) captures residual economic activities. The large
size of black colored nodes in the innovation layer is an artifact of the
concordance table, as this category captures many patents with CPC codes that
are not straightforward to attribute to more specific industries.999“Other”
includes sectors with the 3-digit NAICS codes 337 and 339, which cover a wide
range of residual manufacturing sectors ranging from special types of
furniture, medical supplies, buttons, brushes, and miscellaneous other
categories that do not fit well into the other 3-digit groups. Many of these
industries map to a relatively high number of 4-digit CPC codes, which leads
to a high number of patents attributed to these industries. The weighting
scheme does not fully offset this effect. This does also affect the ranking of
industries by patent count (see below in Table 1). An illustrative example
offers the sector “Fastener, Button & Pin”, which ranks very high by the
number of patents. Its NAICS 6-digit code maps to almost 50 different 4-digit
CPC codes, which is about five times more than the average number of CPC links
per 6-digit industry. This industry falls into one of the residual categories
starting with 33999.  The comparison over time suggests an increasing
concentration in both layers.

In the market layer, groups of industries with similar color tend to cluster
together. The position of the clusters interacts with the position in the
supply chain (“trophic level” (mcnerney2018production, 76)). Industries that
are close to more primary resources (food processing (greenish), textiles
(yellow)) or close to final consumers (food, textiles, electronics (blue),
transportation (gray)) are located at the margins, while chemicals (red),
metals (violet) and petroleum products (brown) with intermediate positions in
the supply chain between primary input providers and end users take central
positions.

This pattern is different in the innovation layer where the largest nodes
(electronics (blue), chemistry (red), and machinery (dark violet)) take the
most central position, suggesting that they are important providers and users
of innovations. It is also clearly visible that the electronics sector
(including computer industries) grew most strongly over time.

Statistics of the properties of the two network layers (see Table B.2) and the
industry size rankings (Table 1) confirm the visual impressions. The
connectivity in the innovation layer is higher: An industry is connected to
roughly 40-50 other industries by patent citations, but only 20-30 industries
by IO links. While the characteristics of the IO network fluctuate without any
clear trend, the innovation layer became increasingly connected and industries
became more similar by their patent citation patterns. Both layers show a
negatively valued, but stable assortativity: Larger and more connected
industries tend to be linked to smaller and less connected industries (see
B.2).

Table 1: Top-10 ranking of industries by output and patent stock at the
6-digit level.

| _Top-10 industries by Aggr. output ( $A^{\mu}_{i,t}$)_
---|---
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
|  324110 |  Petroleum Refineries |  30.55 |  324110 |  Petroleum Refineries |  17.24 |  324110 |  Petroleum Refineries |  19.13 |  334413 |  Semiconductor |  39.65
|  324199 |  Petroleum & Coal Prod |  8.98 |  325211 |  Plastics Material, Resin |  8.76 |  334413 |  Semiconductor |  15.48 |  324110 |  Petroleum Refineries |  20.15
|  331111 |  Iron & Steel Mills |  8.28 |  311611 |  Animal Slaughter |  7.57 |  331111 |  Iron & Steel Mills |  11.24 |  334111 |  Electr Computer |  18.98
|  311611 |  Animal Slaughter |  8.13 |  324199 |  Petroleum & Coal Prod |  6.43 |  325211 |  Plastics Material, Resin |  8.13 |  336112 |  Light Utility Vhcl |  8.03
|  325211 |  Plastics Material, Resin |  7.36 |  331111 |  Iron & Steel Mills |  5.56 |  326191 |  Plastics Plumb Fixture |  4.78 |  331111 |  Iron & Steel Mills |  7.44
|  322121 |  Paper Mills |  5.20 |  322130 |  Paperboard Mills |  5.43 |  321113 |  Sawmills |  4.73 |  325211 |  Plastics Material, Resin |  5.36
|  322122 |  Newsprint Mills |  5.19 |  322122 |  Newsprint Mills |  5.39 |  332710 |  Machine Shops |  4.68 |  336391 |  Motor Vhcl Air-Cond |  4.75
|  325510 |  Paint & Coating |  4.54 |  322121 |  Paper Mills |  5.38 |  334418 |  Print Circuit Assembly |  4.09 |  336411 |  Aircraft Mnft |  4.27
|  322130 |  Paperboard Mills |  4.41 |  325510 |  Paint & Coating |  4.82 |  311119 |  Other Animal Food |  3.68 |  336111 |  Automobile Mnft |  4.09
|  327320 |  Ready-Mix Concrete |  3.82 |  327320 |  Ready-Mix Concrete |  3.98 |  322121 |  Paper Mills |  3.61 |  336350 |  Vhcl Power Train Parts |  3.38
| Quartiles:
| 0.21, 0.54, 1.11 | 0.21, 0.65, 1.3675 | 0.22, 0.545, 1.19 | 0.28, 0.46, 0.79
| _Top-10 industries by Patent stock ( $A^{\tau}_{i,t}$)_
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
|  325520 |  Adhesive Mnft |  16.23 |  334413 |  Semiconductor |  19.33 |  334413 |  Semiconductor |  25.97 |  334111 |  Electr Computer |  30.12
|  325998 |  Misc Chem Prod |  15.31 |  325520 |  Adhesive Mnft |  15.52 |  334111 |  Electr Computer |  23.77 |  334413 |  Semiconductor |  29.65
|  334413 |  Semiconductor |  13.73 |  334111 |  Electr Computer |  14.87 |  339112 |  Surgical & Medical Instr |  14.09 |  339112 |  Surgical & Medical Instr |  15.08
|  339993 |  Fastener, Button, Pin |  11.62 |  325998 |  Misc Chem Prod |  14.60 |  339993 |  Fastener, Button, Pin |  13.60 |  333314 |  Optical Instr & Lens |  13.34
|  334111 |  Electr Computer |  9.47 |  339993 |  Fastener, Button, Pin |  14.00 |  333314 |  Optical Instr & Lens |  12.48 |  334510 |  Electromedical Instr |  11.79
|  333314 |  Optical Instr & Lens |  9.05 |  333314 |  Optical Instr & Lens |  11.59 |  325520 |  Adhesive Mnft |  12.33 |  339993 |  Fastener, Button, Pin |  11.27
|  333613 |  Power Transmission Equ |  9.01 |  339112 |  Surgical & Medical Instr |  9.07 |  325998 |  Misc Chem Prod |  11.48 |  334518 |  Watch, Clock, & Part |  11.14
|  333911 |  Pump & Pumping Equ |  6.08 |  333613 |  Power Transmission Equ |  8.04 |  334510 |  Electromedical Instr |  11.02 |  334220 |  Radio, TV, Communic |  11.04
|  333612 |  Speed Changer & Gear |  5.99 |  334510 |  Electromedical Instr |  7.61 |  334518 |  Watch, Clock, & Part |  9.47 |  325520 |  Adhesive Mnft |  9.98
|  334518 |  Watch, Clock, & Part |  5.67 |  334518 |  Watch, Clock, & Part |  6.57 |  334210 |  Telephone Apparatus |  8.02 |  325998 |  Misc Chem Prod |  9.23
| Quartiles:
| 0.0575, 0.33, 0.99 | 0.06, 0.28, 0.8625 | 0.06, 0.22, 0.7375 | 0.05, 0.19, 0.6925

Notes: Industries are ranked by deflated output (citation-weighted patent
stock) $A^{\alpha}_{i,t}$ averaged across the time window indicated in the
column header in decreasing order, i.e. showing the largest industries on top.
The 6-digit number in the second column of each block shows the NAICS code of
the corresponding industry, and the third column shows the value of
$A^{\alpha}_{i,t}$. The values $A^{\alpha}_{i,t}$ were normalized before
through division by the economy-wide average output (patent stock) in $t$,
i.e. the mean value for each period equals one. The bottom lines of each sub-
table show the quartile values as indicators for the skewness of the
distribution. Deviations of the median from the average indicate skewness.

Table 1 shows the Top-10 ranking of industries by output and patents.
Petroleum Refineries (dark red color in the plots) rank persistently at the
top position in the market until the 2007 when the semiconductor industry took
over the first rank. We can also observe persistently high ranks for Iron &
Steel, Plastics Material & Resin, and Semiconductors. Over time, industries
associated with natural product processing (Paper Mills, Newsprint,
Paperboard, Slaughtering) gradually disappeared from the top ranks. This was
accompanied by the rise of machinery and electronics (Machine Shops, Aircraft,
Vehicle Air-Conditioning, Automobile).

The top ranks in the innovation layer are dominated by metal, machinery and
electronics manufacturing, as indicated by the leading 2-digit code 33. Only
two chemical industries rank high, but declined during the period covered
(2-digit code 32). The patent ranks show an increasing dominance of ICT-
related industries and specialist instruments (Semiconductors, Electronic
Computers, Medical & Optical Instruments, Watch & Clock, Wireless
Communication). The time trends in the patent ranking are more monotonous over
time, while the rise of Semiconductors (during the 1990s) and Electronic
Computers (2000s) came more abrupt in the market, as these two sectors did not
occupy any of the Top-10 ranks during the decades before. The high rank of the
“Fastener, Button & Pin” industry is likely an artifact of the concordance
table (see Footnote 9).

The bottom lines in the tables show the quartile distribution of the industry
sizes. The data is normalized and the average size in each period equals one.
A median value that deviates from one indicates a skewed distribution. Both
layers show a skewed distribution with concentration at the top ranks. The
table reveals a higher and increasing concentration in the innovation layer,
while market concentration does not show any clear trend. These observations
are consistent across different levels of aggregation (see B and SI.2).

####  5.2 Demand-pull, technology-push, and technological change

This section presents three sets of results showing first how different types
of TP and DP affect industry growth and innovation, and second how these
drivers interact with the direction of technological change reflected in the
use of labor and capital. Finally, the results for different subgroups of
industries are summarized.

#####  5.2.1 Demand-pull and technology-push

To test the presence of DP and TP, the following regressions are run:

$\displaystyle
Y_{i,t}=\sum_{\alpha=\mu,\tau}\Bigl{[}\beta^{\alpha}_{A}A^{\alpha}_{i,t-1}+\beta^{\alpha}_{PR}PR^{\alpha,d}_{i,t-1}+\beta^{\alpha,d}_{S}Spill(A)^{\alpha,d}_{i,t-1}\Bigr{]}+\mathbf{\beta}^{\prime}\mathbf{X}_{i,t-1}$
(2)

where $Y_{i,t}$ is a placeholder for output $A^{\mu}_{i,t}$ and patents
$A^{\tau}_{i,t}$. $\mathbf{X}_{i,t-1}$ is a vector of industry level controls
explained below. This specification allows testing for TP and DP in the
following ways:

Type 1—Between-layer effects:

Support for TP1 is found if lagged innovation is positively related to market
growth $A^{\mu}_{i,t}$ reflected in significant, positive coefficients of
$\beta^{\tau}_{A}$, $\beta^{\tau,d}_{PR}$, and $\beta^{\tau,d}_{S}$ in the
regression of $A^{\mu}_{i,t}$.

Analogously, support for DP1 is found if lagged market dynamics stimulate
innovation $A^{\tau}_{i,t}$ reflected in significant, positive coefficients
$\beta^{\mu}_{A}$, $\beta^{\mu,d}_{PR}$, and $\beta^{\mu,d}_{S}$ in the
regression of $A^{\tau}_{i,t}$.

Type 2—Within-layer effects:

TP2 is supported if upstream supply shocks positively affect downstream
growth, as reflected in significant, positive coefficients of
$\beta^{\alpha,up}_{PR}$ and $\beta^{\alpha,up}_{S}$ in the regression of
$A^{\alpha}_{i.t}$ within the same layer $\alpha$.

Support for DP2 is found if downstream changes in demand patterns stimulate
upstream growth and $\beta^{\alpha,dw}_{PR}$ and $\beta^{\alpha,dw}_{S}$ show
positive and significant coefficients in the regression of $A^{\alpha}_{i,t}$.

A few technical details need to be mentioned. The up- and downstream PageRank
in the innovation layer are highly correlated, with a correlation coefficient
of 98.4% (see Fig. B.1). To avoid multicollinearity, the analysis includes
only $PR^{\tau,dw}_{i,t-1}$, which is not informative about within-innovation
DP2. To detect within-innovation TP2 and DP2, this study relies exclusively on
up- and downstream spillovers $Spill(A)^{\tau,d}_{i,t-1}$. However, the
innovation centrality $PR^{\tau,dw}_{i,t}$ is informative about TP1 effects as
it indicates the importance of $i$’s innovation outputs for other industries,
and it can be considered as an indicator for the quality of $i$’s innovations.

To deal with unobserved cross-industrial heterogeneity and uniform time
trends, the regressions include two-ways time and industry fixed effects (FE).
The lagged dependent variables $A^{\alpha}_{i,t-1}$ capture path dependence
and industry level time trends in market growth and innovation, that are not
driven by the two types of TP and DP under study.101010Note that the impact of
lagged changes of market size $A^{\mu}_{i,t-1}$ on current market size could
be interpreted as an alternative type of within-industry DP effect as an
increasing $A^{\mu}_{i,t}$ suggests a higher demand for the products of $i$.
In the innovation layer, the impact of past on current innovation could be
seen as an alternative type of TP when own technological breakthroughs
stimulate subsequent innovation. This effect is not included in the conceptual
framework to limit the scope of analysis and discussion of concepts. Instead,
the impact of the autoregressive terms is referred to as path dependence.
Between-layer DP and TP effects could be also conceptualized contemporaneously
without a time lag interpreting DP1 as impact of current innovation on current
market growth and TP1 as impact of current innovation on current market size.
Here, this approach is not followed, and technological change is considered as
a dynamic process, with past changes having an impact on future development.
In a contemporaneous setting, the causal mechanisms would operate through
expectations, as both processes (innovation and diffusion) are sluggish.

Combining lagged dependent variables with individual FE violates the
assumption of strict exogeneity as the demeaning process of the FE causes a
correlation of the lagged regressor with the error term (nickell1981biases,
81). This bias may be non-negligible, as the number of periods is small while
the number of industries is large. The endogeneity can be overcome by using an
instrumental variable (IV) approach (arellano1991some (5) (AB),
blundell1998initial (20) (BB)). The main analyses presented in this section
rely on the BB estimator with robust standard errors.111111The AB and BB
estimator rely on deeper lags as instruments for the lagged dependent variable
to overcome the endogeneity. The BB specification is chosen because the BB
model builds on stronger instruments compared to an AB estimator. It estimates
the outcome variable in levels, includes time dummies, and it is estimated
using a one-step procedure. The one-step method is preferred over a two-step
routine for computational reasons. The maximal number of lags and all
exogenous variables are used as IVs. To avoid concerns about instrument
proliferation, the model is re-estimated with collapsed IVs as a robustness
check. Additional robustness checks include, for example, deeper lags, one-
and two-step estimation procedures and different specifications of the
regression equation and empirical variables. The results are qualitatively
robust, but sometimes with changing significance levels. Additional results
from an AB estimator and a weighted FE regression are available in C.1. The
regressions are run using the _pgmm_ function of the R-package _plm_
(croissant2008panel, 36).

Market growth and innovation may also be driven by industry-specific
structural changes and price shocks that are unrelated to TP and DP. The
control variables $\mathbf{X}_{i,t-1}$ aim to capture these effects. The
controls include employment $L_{i,t-1}$, wages $W_{i,t-1}$, capital intensity
$(K/L)_{i,t-1}$, the employment share of production labor $(L^{P}/L)_{i,t-1}$,
relative wages for production labor $(W^{P}/W)_{i,t-1}$, energy intensity
$(E/L)_{i,t-1}$, and material costs per employee $(M/L)_{i,t-1}$. These
variables control for the industry-specific and time variant exposure to
changes in factor markets, which are not captured by industry and time FE. The
controls also include lagged investment per capita $(I/L)_{i,t-1}$, as it
might be the source of an increase in outputs and innovation. To cope with
skewness, all variables (except for $(L^{P}/L)_{i,t-1}$) were log-linearized
and outliers were removed. The network variables (node size
$A^{\alpha}_{i,t}$, centrality $PR^{\alpha,d}_{i,t}$, spillovers
$Spill(A)^{\alpha,d}_{i,t}$) are scaled by their standard deviation before
logs were taken to make them quantitatively comparable. A detailed description
of the data transformations is available in A.3.

| Type 1 | Type 2 | Both
---|---|---|---
| $\tau\rightarrow\mu$ | $\mu\rightarrow\tau$ | Market | Innovation |
| $A^{\mu}_{i,t}$ | $A^{\mu}_{i,t}$ | $A^{\tau}_{i,t}$ | $A^{\tau}_{i,t}$ | $A^{\mu}_{i,t}$ | $A^{\mu}_{i,t}$ | $A^{\tau}_{i,t}$ | $A^{\tau}_{i,t}$ | $A^{\mu}_{i,t}$ | $A^{\mu}_{i,t}$ | $A^{\tau}_{i,t}$ | $A^{\tau}_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
$A^{\mu}_{i,t-1}$ | 0.6255*** | 0.6073*** | 0.0035 | -0.0058 | 0.7321*** | 0.6514*** |  |  | 0.6867*** | 0.6603*** | 0.0021 | -0.0058
| (0.0291) | (0.0242) | (0.0136) | (0.0127) | (0.0385) | (0.034) |  |  | (0.039) | (0.0344) | (0.0128) | (0.0117)
$A^{\tau}_{i,t-1}$ | -0.0637 | -0.0246 | 1.034*** | 1.016*** |  |  | 1.044*** | 1.034*** | -0.0197 | -0.0351 | 1.061*** | 1.049***
| (0.0947) | (0.0849) | (0.0076) | (0.0068) |  |  | (0.0403) | (0.0314) | (0.0862) | (0.0778) | (0.0359) | (0.0303)
$PR^{\mu,up}_{i,t-1}$ |  |  | -0.0055 | -1e-04 | 0.0066 | 0.01 |  |  | 0.0088 | 0.0047 | -0.0062 | -0.0021
|  |  | (0.0046) | (0.0041) | (0.015) | (0.0131) |  |  | (0.0145) | (0.0127) | (0.0046) | (0.0039)
$PR^{\mu,dw}_{i,t-1}$ |  |  | 0.0021 | 0.0041 | -0.001 | 0.0036 |  |  | -0.0039 | 0.005 | 9e-04 | 0.0039
|  |  | (0.0033) | (0.0031) | (0.0145) | (0.0121) |  |  | (0.0137) | (0.0118) | (0.0033) | (0.003)
$PR^{\tau,dw}_{i,t-1}$ | 0.1536** | 0.0928* |  |  |  |  | 0.0482** | 0.0314* | 0.1119** | 0.0924** | 0.031* | 0.0183.
| (0.0478) | (0.0391) |  |  |  |  | (0.0167) | (0.0136) | (0.0376) | (0.0322) | (0.013) | (0.0109)
$Spill(A)^{\mu,up}_{i,t-1}$ |  |  | -0.0126* | -0.0081 | 0.1054*** | 0.1043*** |  |  | 0.0975*** | 0.0868*** | -0.0133* | -0.0088
|  |  | (0.006) | (0.006) | (0.02) | (0.0189) |  |  | (0.0207) | (0.0195) | (0.0066) | (0.0063)
$Spill(A)^{\mu,dw}_{i,t-1}$ |  |  | 0.0044 | 0.006 | -0.0394. | -0.007 |  |  | -0.0429* | -0.0178 | 0.002 | 0.0068
|  |  | (0.0058) | (0.005) | (0.0228) | (0.0192) |  |  | (0.02) | (0.0181) | (0.0053) | (0.0048)
$Spill(A)^{\tau,up}_{i,t-1}$ | 2.435*** | 2.572*** |  |  |  |  | 0.7004** | 0.3476* | 2.363*** | 2.642*** | 0.4757** | 0.2599.
| (0.7017) | (0.5889) |  |  |  |  | (0.2131) | (0.1672) | (0.6273) | (0.5391) | (0.156) | (0.1388)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0606 | -0.0573 |  |  |  |  | -0.0539*** | -0.0369** | -0.0695. | -0.059. | -0.0514*** | -0.0378**
| (0.0417) | (0.035) |  |  |  |  | (0.016) | (0.013) | (0.0369) | (0.0337) | (0.0144) | (0.0126)
AR(1) | 0 | 0 | 1e-04 | 2e-04 | 0 | 0 | 7e-04 | 5e-04 | 0 | 0 | 5e-04 | 7e-04
AR(2) | 0.9373 | 0.9079 | 0.899 | 0.8057 | 0.9237 | 0.9583 | 0.6394 | 0.7465 | 0.9656 | 0.978 | 0.7307 | 0.7026
Sargan | 0 | 0 | 0 | 0.001 | 0 | 0 | 0 | 0 | 0 | 1e-04 | 0 | 0.0056
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9284 | 0.9354 | 0.996 | 0.9962 | 0.9363 | 0.9402 | 0.9952 | 0.9958 | 0.9332 | 0.9374 | 0.9957 | 0.996

Table 2: Demand-pull and technology-push effects.

Notes: The table shows the regression results of output $A_{i,t}^{\mu}$ and
patents $A_{i,t}^{\tau}$ on demand-pull and technology-push effects. The
estimation is based on a two-ways Blundell-Bond (BB) system GMM model using a
one-step estimation procedure. The controls included in all regressions are
wages $W_{i,t}$, capital intensity $(K/L)_{i,t}$, investment per capita
$(I/L)_{i,t}$, relative wages for production labor $(W^{P}/W)_{i,t}$, energy
intensity $(E/L)_{i,t-1}$, and material inputs per capita $(M/L)_{i,t-1}$.
Spillovers are calculated on the basis of first-order links. Variables
measured in monetary terms are deflated using the industry level price
deflators for the value of shipment obtained from the NBER-productivity
database (becker2013nber, 14). To cope with skewness and to obtain tractable
coefficients, most variables are pre-processed (taking logs, removing
outliers, scaling). Data in logs are patents and output $A^{\alpha}_{i,t}$,
centrality $PR^{\alpha,d}_{i,t}$, spillovers $Spill(A)^{\alpha,d}_{i,t}$,
employment $L_{i,t}$, $(K/L)_{i,t}$, $(I/L)_{i,t}$, $W_{i,t}$,
$(E/L)_{i,t-1}$, $(M/L)_{i,t-1}$ with $\alpha=\mu,\tau$ and $d=up,dw$.
$A^{\alpha}_{i,t}$, $PR^{\alpha,d}_{i,t}$, and $Spill(A)^{\alpha,d}_{i,t}$ are
scaled by division by their standard deviation to obtain comparable
coefficients across the different network effects. A detailed description of
the transformations and descriptive statistics of the regression data before
and after the transformations are provided in A.1. The rows AR(1), AR(2), and
Sargan show the test statistics of the specification tests, i.e. testing for
first- and second-order autocorrelation and the results of a Sargan test for
validity of instruments (see roodman2009xtabond2, 88). The $R^{2}$ is proxied
by the squared correlation between the fitted and observed values of the
dependent variable.

The regression results are presented in Table 2. Even numbered columns show
the results when industry level controls $\mathbf{X}_{i,t-1}$ are included.
Their coefficients are not shown here but can be found in C.1.121212Among the
controls, only wages, investment per capita, and energy intensity are
significant. Wages exhibit a negative association with market growth, energy
intensity a negative one with innovation, and investment per capita shows a
positive relationship with both market growth and innovation.

The first two columns show the isolated impact of TP1 on market growth. The
coefficients of the patent centrality $PR^{\tau,dw}_{i,t-1}$ and upstream
innovation spillovers $Spill(A)^{\tau,dw}_{i,t-1}$ enter both with positive
and significant coefficients. This observation persists when within layer
effects are included (column (9)-(10)). An increase in the normalized
centrality by one percent is associated with an 0.1 percent increase in the
normalized market size. Innovation spillovers exhibit a quantitatively
stronger effect: A one percent increase is associated with 2.6 percent
increase in market size. Note that the network variables $A^{\alpha}_{i,t}$,
$PR^{\alpha,d}_{i,t}$, $Spill(A)^{\alpha,d}_{i,t}$ are all scaled and similar
by their means (see Table A.3). This improves the comparability of the
coefficients. However, the variation of the spillover measure is very low and
small deviations may have large effects, which needs to be taken into
consideration when comparing the coefficients.

The results show that there is no direct effect of within-sector innovation
outputs $A^{\tau}_{i,t-1}$ on market growth when controlling for other
innovation network effects. However, exploratory analyses have shown that
$A^{\tau}_{i,t-1}$ exhibits a positive effect when other innovation layer
effects are excluded. This may be explained by the high correlation between
$A^{\tau}_{i,t-1}$ and $PR^{\tau,dw}_{i,t-1}$, while the centrality is more
powerful in explaining market growth.

A rising patent centrality $PR^{\tau,dw}_{i,t-1}$ indicates that $i$’s
innovations became increasingly relevant in the innovation network, which may
be considered as a quality indicator of $i$’s innovations. Rising spillovers
$Spill(A)^{\tau,d}_{i,t-1}$ suggest an increase in technological opportunities
arising from neighboring industries. Both measures can be interpreted as an
increase of technological opportunities, and thus the positive coefficients in
column (1)-(2) and (9)-(10) offer support for TP1. This effect is mostly
driven by the network effects, but less from the innovations produced by the
industry itself. Note that the industry that produces innovations is not
necessarily the main user of these inventions. This may explain why TP1 mainly
arises from network effects and less from within-sector innovation effects.
Downstream innovation spillovers $Spill(A)^{\tau,dw}_{i,t-1}$ enter with a
negative coefficient, which is only weakly significant and becomes
insignificant when within-market effects are added (column (10)).

Column (3)-(4) show the isolated effect of DP1 on innovation. None of the
market variables ($A^{\mu}_{i,t-1}$, $PR^{\mu,d}_{i,t-1}$,
$Spill(A)^{\mu,d}_{i,t-1}$) shows any significant interaction with innovation
$A^{\tau}_{i,t}$, suggesting the absence of DP1 effects. This does not change
when simultaneously controlling for within-innovation layer dynamics (column
(11)-(12)) or altering the model specification (see C.1).

The next two columns (5)-(6) show the results for within-market DP2 and TP2
effects. They offer support for TP2 in the market layer: Spillovers from
upstream suppliers $Spill(A)^{\mu,up}_{i,t-1}$ enter with a significant
positive coefficient. Market growth in upstream industries can be interpreted
as a positive supply shock, which is positively correlated with downstream
growth. This finding persists when between-layer effects are added (column
(12)-(13)). The effect is small: Compared to TP1 arising from innovation in
upstream industries, it plays an economically less important role. Within the
market, there is no evidence of DP2 effects arising from downstream
customers.131313Type 2 effects are not robust if a weighted FE instead of an
AB or BB estimator is used. In an FE specification (see e.g. Table C.5 in C.1)
market up- and downstream centrality enter with significant positive
coefficients while the effect of spillovers diminishes. Column (7)-(8) inform
about the existence of within-innovation DP2 and TP2. The results offer
support for TP2: Upstream spillovers $Spill(A)^{\tau,up}_{i,t-1}$ enter with a
small but positive coefficient. This observation is robust across different
estimation methods (AB, FE) with even more significant effects.

The empirical findings contradict the existence of within-innovation DP2:
Downstream innovation spillovers $Spill(A)^{\tau,dw}_{i,t-1}$ show a negative
coefficient, which is not significant in an AB and weighted FE model (see
C.1). The positive effect of upstream spillovers quantitatively dominates and
is about six times larger than the negative effect of
$Spill(A)^{\tau,dw}_{i,t-1}$.

Beyond TP and DP, the results in Table 2 show that growth in both layers is
path dependent: The lagged dependent variable $A^{\alpha}_{i,t-1}$ enters with
a positive and strongly significant coefficient. Path dependence is stronger
in innovation compared to the market and enters with an autocorrelation
coefficient, which is larger than one. One percent more patents
$A^{\tau}_{i,t-1}$ is associated with approximately 1.05 percent more
innovation five years later. The coefficient of output $A^{\mu}_{i,t-1}$
scores persistently below one, suggesting a decreasing explanatory power of
the autoregressive term over time. This indicates increasing returns to
innovation, which is in line with the observation that concentration in the
innovation layer grew stronger compared to the market (see Sec. 5.1).

#####  5.2.2 The direction of technological change

This section studies whether and how TP and DP interact with the direction of
technological change. This is analyzed through a series of regression analyses
showing how productivity, labor demand, capital use, and production labor
interact with TP and DP effects arising from both layers.

The dependent variables are not directly associated with one of the two
layers, which inhibits the distinction of within- and between-layer effects.
Instead, it is referred to DP (TP) whenever an effect is driven by the market
(innovation) layer.

The regression models look similar as above and are given by

$\displaystyle
Y_{i,t}=\beta_{Y}Y_{i,t-1}+\sum_{\alpha=\mu,\tau}\Bigl{[}\beta^{\alpha}_{A}A^{\alpha}_{i,t-1}+\beta^{\alpha}_{PR}PR^{\alpha,d}_{i,t-1}+\beta^{\alpha,d}_{S}Spill(A)^{\alpha,d}_{i,t-1}\Bigr{]}+\mathbf{\beta}^{\prime}\mathbf{X}_{i,t-1}$
(3)

where $Y_{i,t}$ is a placeholder for productivity measured as $TFP_{i,t}$, and
value added per worker $(VA/L)_{i,t}$, labor $L_{i,t}$, wages $W_{i,t}$,
capital intensity $(K/L)_{i,t}$, per capita investments $(I/L)_{i,t}$, the
share of production labor $(L^{P}/L)_{i,t}$, and the relative wage for
production labor $(W^{P}/W)_{i,t}$. $\mathbf{X}_{i,t-1}$ is a vector of
industry level controls explained above.

| Productivity | Labor & Wages | Capital & Investment | Production labor
---|---|---|---|---
| $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8)
$A^{\mu}_{i,t-1}$ | 0.0275. | -0.1469* | -0.0895* | -0.2082*** | 0.0492* | -0.1578. | -0.0149*** | -0.0186*
| (0.0159) | (0.0629) | (0.0438) | (0.0527) | (0.0213) | (0.0923) | (0.0043) | (0.0075)
$A^{\tau}_{i,t-1}$ | 0.0096 | -0.0482 | -0.019 | -0.0632 | 0.0924 | -0.1016 | -0.0265* | -0.0306
| (0.0446) | (0.1675) | (0.1171) | (0.1402) | (0.0568) | (0.2458) | (0.0115) | (0.0201)
$PR^{\mu,up}_{i,t-1}$ | 0.0082. | 0.047* | 0.0223. | 0.0596*** | -0.0048 | 0.0602* | 0.0013 | 0.0069**
| (0.0048) | (0.0196) | (0.0136) | (0.0164) | (0.0067) | (0.0288) | (0.0013) | (0.0024)
$PR^{\mu,dw}_{i,t-1}$ | 0.0123** | 0.057*** | 0.0265* | 0.0398** | -0.0096. | 0.0619** | -1e-04 | 0
| (0.0041) | (0.0163) | (0.0112) | (0.0136) | (0.0055) | (0.0239) | (0.0011) | (0.0019)
$PR^{\tau,dw}_{i,t-1}$ | -0.0166 | -0.0463 | 0.0136 | 0.0841 | 0.0384 | 0.1945. | -0.0064 | 0.0265**
| (0.0192) | (0.0745) | (0.0514) | (0.0623) | (0.0252) | (0.1093) | (0.0051) | (0.0089)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0117 | -0.0815* | -0.0555. | -0.0672. | 0.0319* | -0.1051. | -0.0025 | 0.0088.
| (0.0097) | (0.0411) | (0.0284) | (0.0344) | (0.0139) | (0.0604) | (0.0028) | (0.0049)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.0135* | -0.0176 | -0.0079 | -0.0085 | -0.0035 | -0.0165 | 7e-04 | 0.0033
| (0.0062) | (0.0252) | (0.0175) | (0.0212) | (0.0086) | (0.0371) | (0.0017) | (0.003)
$Spill(A)^{\tau,up}_{i,t-1}$ | -0.7479*** | -1.207 | -0.4783 | -1.336* | 0.0448 | 1.124 | -0.201*** | -0.2156*
| (0.2002) | (0.7875) | (0.5451) | (0.661) | (0.2676) | (1.159) | (0.0543) | (0.0946)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0225 | 0.0382 | 0.0322 | 0.0451 | -0.0286 | 0.1815. | 0.0043 | -0.0041
| (0.0167) | (0.066) | (0.0459) | (0.0554) | (0.0224) | (0.0971) | (0.0045) | (0.0079)
Controls | Y | Y | Y | Y | Y | Y | Y | Y
$R^{2}$ | 0.7746 | 0.9024 | 0.9359 | 0.927 | 0.927 | 0.8339 | 0.8832 | 0.6776

Table 3: Productivity, labor, capital, and production labor

Notes: The table shows the regression results of productivity, labor demand,
capital use, and production labor on demand-pull and technology-push effects.
The estimation is based on a two-ways weighted fixed-effects (FE) model. The
weights used in the regressions are $A^{\mu}_{i,t}$ in the TFP regression and
$L_{i,t}$ in all other regressions. Each regression include the respective
lagged dependent variable as a control. The controls included in all
regressions are wages $W_{i,t}$, capital intensity $(K/L)_{i,t}$, investment
per capita $(I/L)_{i,t}$, relative wages for production labor
$(W^{P}/W)_{i,t}$, energy intensity $(E/L)_{i,t-1}$, and material inputs per
capita $(M/L)_{i,t-1}$. Spillovers are calculated on the basis of first-order
links. Variables measured in monetary terms are deflated using the industry
level price deflators for the value of shipment obtained from the NBER-
productivity database (becker2013nber, 14). To cope with skewness and to
obtain tractable coefficients, most variables are pre-processed (taking logs,
removing outliers, scaling). Data in logs are patents and output
$A^{\alpha}_{i,t}$, centrality $PR^{\alpha,d}_{i,t}$, spillovers
$Spill(A)^{\alpha,d}_{i,t}$, employment $L_{i,t}$, $(K/L)_{i,t}$,
$(I/L)_{i,t}$, $W_{i,t}$, $(E/L)_{i,t-1}$, $(M/L)_{i,t-1}$ with
$\alpha=\mu,\tau$ and $d=up,dw$. $A^{\alpha}_{i,t}$, $PR^{\alpha,d}_{i,t}$,
and $Spill(A)^{\alpha,d}_{i,t}$ are scaled by division by their standard
deviation to obtain comparable coefficients across the different network
effects. A detailed description of the transformations and descriptive
statistics of the regression data before and after the transformations are
provided in A.1.

The regression models again include lagged dependent variables and may be
subject to a dynamic panel bias (nickell1981biases, 81). This bias may be
addressed using a BB or AB estimator as before, but these estimators suffer
from weak instruments. Hence, a weighted FE approach with industry and time FE
and two-ways clustered standard errors are used. The weights in the regression
of $TFP_{i,t}$ are the market size $A^{\mu}_{i,t}$ to capture the impact on
productivity for an average good produced. All other regressions rely on
employment $L_{i,t}$ as weights to capture the different relevance of
industries for the US labor market. Hence, the results inform on how the labor
productivity, wages, or capital intensity of an average employee are affected
instead of capturing the effect on an average industry. Results using
alternative estimators are reported in C.2. The findings are qualitatively
consistent across the different specifications, sometimes with variation in
their statistical significance.

The results are shown in Table 3. To keep it short, only the results of the
full models that include market and patent layer effects simultaneously are
presented.141414Additional results with separate layers can be found in the
SI.3. All regressions include industry level controls $\mathbf{X}_{i,t-1}$,
which are not shown here but can be found in C.2.

Columns (1)-(2) show the results for productivity measured as five factor TFP
and value added per employee. Columns (3)-(4) inform about labor demand as
reflected by employment and wages. Columns (5)-(6) show the effects on capital
use and investment intensity. The last two columns (7)-(8) show to which
extent an industry relies on production and non-production labor.

An increasing downstream centrality in the market $PR^{\mu,dw}_{i,t-1}$ shows
a positive association with productivity growth and a factor bias in favor of
labor. It is positively correlated with labor demand and wages (column
(3)-(4)), negatively with capital use (column (5)), and positively with
investments per capita (column (6)). The positive impact on labor demand
appears to dominate the investment effect, leading to a net negative
relationship between market downstream centrality and capital use per
employee. Market upstream centrality $PR^{\mu,up}_{i,t-1}$ shows similar
effects, namely a positive correlation with wages and employment, relative
wages for production labor, labor productivity, and investment.

Further, the results suggest that upstream spillovers
$Spill(A)^{\alpha,up}_{i,t-1}$ from both layers $\alpha=\mu,\tau$ are
associated with a decline in productivity (column (1)-(2)), wages (column
(4)), and a decreasing demand for production labor (column (7)-(8)). These
spillovers are positively correlated with a higher capital intensity. This
finding is qualitatively robust across different estimators, but the
significance varies across estimators.151515It may seem inconsistent that
innovation spillovers show a positive relationship with industry output (Table
2) but a negative association with productivity and labor, while the effect on
capital is insignificant (Table 3). The results in these two tables rely on
different estimation methods, and the effects in Table 3 are weighted. As
mentioned in the main text, the findings are qualitatively consistent across
different estimation methods, but the statistical significance of the
observations on productivity, labor, and capital varies (see Sec. C). For
example, the positive correlation between spillovers and capital is
statistically stronger when using an AB estimator. The qualitative consistency
supports the explanation that the increase of output is associated with a
shift from labor to capital. Moreover, TFP is five factor productivity, and
energy and material use are not considered here. Moreover, TP arising from
more innovation $A^{\tau}_{i,t-1}$, higher upstream innovation spillovers
$Spill(A)^{\tau,up}_{i,t-1}$ and centrality $PR^{\tau,dw}_{i.t-1}$ is
associated with a shift from production to non-production labor (columns
(7)-(8)) as they both show a positive correlation with the share of production
labor $(L^{P}/L)_{i,t}$ but no significant effect on labor $L_{i,t}$.

Market growth $A^{\mu}_{i,t-1}$ is associated with decreasing employment and
wages, a lower share of production labor, and less significantly, with an
increasing capital intensity, higher TFP, and lower labor productivity. These
findings are qualitatively robust across different estimators, but less or not
significant in other model specifications. The previous section (5.2.1) has
shown that TP1 was a key driver of market growth, and Table 3 indicates that
market growth is associated with a factor bias in favor of capital. The
combined results suggest that TP-driven technological change is biased in
favor of capital and associated with a labor demand shift towards non-
production labor.

#####  5.2.3 Sectoral patterns of innovation

Patterns of innovation, technological change, and sources of knowledge differ
across industries and may change over time (pavitt1984sectoral, 85, 68). To
capture this, the regressions are repeated for different subsets of industries
and subperiods (1987-1997, 2002-2012).161616Technically, the two subperiods
cover the periods 1977-1997 and 1992-2012. They overlap in two 5-year
snapshots. The overlap only matters for the first stage of the IV regressions,
as the BB estimator uses the second lag as in instrument for the first lag.
Hence, the first two periods are lost due to the estimation procedure. To
simplify the representation, the main observations are only verbally
summarized. The regression results for the two subperiods are provided in
C.1-C.2 and those for the industry subgroups in SI.4.

Table 4 gives an overview of the industry subsets and shows whether TP and DP
are supported. The mapping of NAICS 6-digit codes to subgroups can be found in
Table LABEL:tab:NAICStoSubsample_map. The plus symbols (+) indicate support,
minus symbols (-) indicate that opposite, i.e. negative effects were found,
and tilde symbols ($\sim$) suggest that the results were ambiguous across
variables and/or model specifications.171717In Table 4, it is refrained from
showing exact coefficients for two reasons: (1) Using symbols instead of
coefficients simplifies the representation as it summarizes multiple variables
that track DP and TP effects. For example, TP1 is tracked through
$A^{\tau}_{i,t-1}$, $PR^{\tau,d}_{i,t-1}$ and $Spill(A)^{\tau,d}_{i,t-1}$.
Hence, it would be necessary to show all three coefficients. (2) Showing
coefficients could suggest more precision than present, as the significance
and strength of the effects varies across different estimation methods. A
discussion of the limitations and consistency across methods can be found in
the previous sections (5.2.1, 5.2.2). For brevity, an analogous discussion is
omitted here. Next, the results are summarized stepwise for each effect (TP1,
DP1, TP2, DP2).

Name |  Definition |  N |  TP1 |  DP1 |  TP2μ |  TP2τ |  DP2μ |  DP2τ
---|---|---|---|---|---|---|---|---
All sectors |  |  2456 |  +++ |  |  +++ |  + |  |  -
First subperiod |  1987-1997 |  1535 |  + |  |  |  |  |
Second subperiod |  2002-2012 |  1535 |  +++ |  |  +++ |  +++ |  - |  \- - -
Food Processing |  2-digit 31 |  520 |  ++ |  |  |  |  |
Non-Metallic |  2-digit 32 |  656 |  |  |  |  ++ |  |
Metallic and Machinery |  2-digit 33 |  1280 |  +++ |  $\sim$ |  +++ |  |  - |  \- -
Innovation-intensive |  $>$ md($A^{\tau}/A^{\mu})$ |  1224 |  +++ |  + |  |  ++ |  $\sim$ |  \- - -
Non-Inno.-intensive |  $\leq$ md($A^{\tau}/A^{\mu})$ |  1232 |  + |  - |  \- - |  + |  - |  -
Big |  $>$ md($A^{\mu})$ |  1224 |  |  + |  |  + |  |  \- -
Small |  $\leq$ md($A^{\mu})$ |  1232 |  ++ |  |  |  ++ |  - |  \- -
Science-intensive |  BP (2016) |  360 |  |  |  |  |  |  \- - -
Supplier-dominated |  BP (2016) |  840 |  ++ |  |  |  |  - |  \- - -
Production-intensive |  BP (2016) |  1208 |  +++ |  $\sim$ |  |  |  - |  -
Patent-central |  $>$ md($PR^{\tau,dw})$ |  1224 |  +++ |  + |  |  +++ |  - |  \- - -
Not Patent-central |  $\leq$ md($PR^{\tau,dw})$ |  1232 |  ++ |  \- - - |  \- - - |  + |  - |  -
Market-central (up) |  $>$ md($PR^{\mu,up})$ |  1224 |  |  + |  |  |  - |  \- -
Not Market-central (up) |  $\leq$ md($PR^{\mu,up})$ |  1232 |  ++ |  |  |  + |  \- - |  -
Market-central (dw) |  $>$ md($PR^{\mu,dw})$ |  1224 |  ++ |  $\sim$ |  ++ |  |  - |  -
Not Market-central (dw) |  $\leq$ md($PR^{\mu,dw})$ |  1232 |  |  - |  |  |  |  -

Table 4: Overview - Sectoral patterns of innovation

Notes: This table shows how the industry subsets are named, defined, and it
summarizes the results for different effects. md is short for median. The
correspondence list of how 6-digit NAICS industries are grouped can be found
in LABEL:tab:NAICStoSubsample_map. The column N shows the number of
observations per sample. The plus (minus) symbols +++, ++, + (- - -, - -, -)
indicate strong, moderate, weak positive (negative) effects by statistical
significance and consistency across different model settings. A tilde $\sim$
is shown whenever the effects are ambiguous for different variables that
capture the same concept (e.g. qualitative ambiguity between
$A^{\alpha}_{i,t-1}$ and $PR^{\alpha,d}_{i,t-1}$). The summary of the results
is based on a large sample of regression analyses of different industry
subsets and different model specifications. Positive and negative effects are
only indicated, if the results are consistent across specifications. These
results are published along with the research data and statistical outputs
(hotte2023data, 56). An extract of these results is available in SI.4. The two
subperiods (1987-1997 and 2002-2012) technically cover the periods 1977-1997
and 1992-2012, which is due to the estimation procedure (see Footnote 16 and
C.1). Food, Non-Metal, Metal are short for the Food processing sector
identified by 2-digit NAICS codes 31, Non-metallic (Wood, Fiber, Chemical)
manufacturing (2-digit NAICS code 32), Metallic and Machinery manufacturing
(2-digit NAICS code 33). Innovation-intensive, Big, Patent and Market central
industries are defined by whether the corresponding variable taken from the
network data and averaged over time ranges above or below the cross-industry
median value. The classes Science-intensive, Supplier-dominated, Production-
intensive are categories motivated by the taxonomy introduced by
pavitt1984sectoral (85). The mapping to 6-digit NAICS industries is based on
the tables provided by bogliacino2016pavitt (22) (short: BP (2016)).

TP1:

This effect is consistently supported in all subsamples with varying levels of
significance. TP1 arises from both innovation centrality
$PR^{\tau,dw}_{i,t-1}$ and spillovers $Spill(A)^{\tau,up}_{i,t-1}$, while the
effect from upstream spillovers is larger and more significant in most groups.
The effect is statistically and economically strongest during the second
subperiod, and in Metallic and Machinery manufacturing, Innovation-intensive,
Small, Production-intensive sectors, those with a high innovation centrality,
and those with a low upstream but high downstream centrality in the market.

DP1:

Support for DP1 effects is —if at all— only weakly significant, ambiguous, or
even negative; hence market growth, centrality, and spillovers do not show a
clearly positive effect on innovation. Generally, but only poorly significant,
upstream market centrality tends to have a negative effect while downstream
centrality rather shows positive effects. The opposite holds for spillovers:
Downstream (upstream) spillovers are rather negatively (positively) correlated
with innovation.

TP2 in the market:

Upstream spillovers $Spill(A)^{\mu,up}_{i,t-1}$ show a positive association
with market growth during the second subperiod, in Metallic and Machinery
manufacturing and industries with high market downstream centrality. Upstream
market centrality $PR^{\mu,up}_{i,t-1}$ exhibits opposite effects: It shows
negative coefficients in sectors with a low innovation-intensity and low
innovation centrality.

TP2 in innovation:

Upstream innovation spillovers display an unambiguously positive association
with subsequent innovation, with varying significance. It is significant
during the second subperiod, in Non-Metallic manufacturing, Innovation-
intensive, Big, Production-intensive sectors, and sectors with a high and low
innovation centrality, and with a low upstream and high downstream market
centrality. The coefficients are small in Big industries and those with a low
innovation centrality, and high downstream market centrality.

DP2 in the market:

DP2 from downstream links is not supported. Downstream spillovers enter
unambiguously with negative coefficients. Only in Innovation-intensive
industries the effect is ambiguous as downstream centrality enters with a
weakly significant positive coefficient.

DP2 in innovation:

This effect is absent and, whenever a significant effect is observed, the
effect is negative, contradicting DP2 to be a driver of innovation. Almost all
industry subsamples show a statistically significant but quantitatively small
negative correlation between downstream spillovers
$Spill(A)^{\tau,dw}_{i,t-1}$ and innovation outputs. This effect is strongest
in Metallic manufacturing, Innovation-intensive, Big, Small, Science-
intensive, Suppliers-dominated, sectors with a high innovation centrality, and
a high downstream market centrality.

Summing up, the results further confirm that the effects from innovation on
the market are stronger than vice versa (TP1). The positive effects of
innovation mostly arise from upstream spillovers. In the market, up- and
downstream spillovers are ambiguous: Downstream spillovers show rather
positive interactions with innovation, while the opposite holds for upstream
effects.

There is clear support for TP2 in the innovation layer, while the effect of
TP2 in the market is ambiguous. The ambiguity largely comes from the
qualitatively different impact of upstream centrality and upstream spillovers.
Generally, the results suggest that DP2 is not supported. Most of the results
are much stronger for the second subperiod of the sample, and sometimes not or
only weakly significant in the years before.

The qualitative results are largely consistent across industry subgroups, but
the relative importance of TP and DP as drivers of innovation and market
growth differs. For example, TP1 and TP2 effects are qualitatively consistent
across all subsets of industries and subperiods. But their impact is strongest
in industries that are most reliant on patents, and belong to Metallic and
Machinery Manufacturing. This coincides with the rise of the ICT sector, which
grew strongly by market size and innovation activity (see Fig. SI.1). Many of
the industries belonging to this broad industrial group are classified as
Small industries, even though they have become large ones over time. The rise
of industries that are driven by TP may explain why the TP effects became
stronger in the post-2000s.

The results suggest a negative correlation between DP and TP forces, as in
many industries that are positively driven by TP show a negative effect of DP.
However, innovation-intensive and patent-central industries are an exception
and both DP in the market and TP in innovation simultaneously drive
technological change, suggesting a co-evolutionary dynamic between innovation
and market growth in these sectors.

###  6 Discussion

The key results can be wrapped up as follows:

  1. 1.

Both network layers show different dynamics: The innovation layer became
increasingly connected, technologically similar, and skewed, while
connectivity, similarity, and size rankings in the market are sluggish without
a clear trend. The size distribution in the market shares is more balanced
compared to innovation. Both layers show the rise of ICT-related and other
electronics industries.

  2. 2.

The results reveal path-dependence in both layers, which stronger in
innovation and indicates increasing returns. This may explain why innovation
became more concentrated than the market.

  3. 3.

The results support TP1: Past innovation shows a strong positive association
with subsequent market growth. TP1 mostly arises from upstream innovation
spillovers, which indicate an expansion of the technological knowledge base
upon which an industry relies.

Also the centrality in the innovation network is positively associated with
market growth but less significant. Innovation centrality shows how well an
industry’s own R&D activity is integrated in the innovation network and
informs about an industry’s access and exchange of technological knowledge.

  4. 4.

The results support within-innovation TP2: Upstream spillovers stimulate
subsequent innovation. This observation is consistent with previous research
(e.g. acemoglu2016innovation, 2, 4, 63).

  5. 5.

Within-market TP2 effects are ambiguous. There is support for a positive
effect of upstream market spillovers, which is strongest during the second
subperiod and in Metallic manufacturing sectors with a high downstream
centrality in the market. These spillovers indicate an increased availability
of production inputs.

Upstream effects in the market are heterogeneous across industries: Upstream
market centrality shows a negative effect in industries with a low reliance on
innovation.

  6. 6.

The results do not support DP effects. While DP1 effects are weak and
ambiguous across industries, the lack of support for DP2 effects is clear:
Across almost all industry groups, downstream effects enter with negative
coefficients.

  7. 7.

Up- and downstream innovation spillovers enter with opposite coefficients,
which might be explained by their different meaning. Upstream spillovers
inform about the pool of technological knowledge upon which the downstream
industry relies. Downstream spillovers suggest that an industry’s own
innovations are increasingly used (cited). This may reflect a higher level of
technological maturity, which may associated with a lower potential to grow.
However, the negative coefficients are small and only weakly significant.

  8. 8.

The analysis in Sec. 5.2.2 indicates that the factor bias of technological
change may depend on its driver. DP arising from a higher centrality in the
customer network in the market is associated with higher productivity, more
labor demand, and less capital use. This indicates a factor bias in favor of
labor, albeit DP does not show any positive effect on output or innovation. In
contrast, TP and pure size effects are associated with lower wages and a
reallocation from production to non-production labor, and show a statistically
weak positive relationship with capital use.

Further, TP from innovation spillovers and productivity show a negative
correlation, which might be attributed to the shift from production to non-
production labor. Previous research has shown that the increasing service
share in manufacturing coincided with lower productivity growth, which may be
an issue of mismeasurement as intangible capital is difficult to measure
(corrado2009intangible, 35, 50, 10, 13).

####  6.1 Demand-pull, technology-push, and the direction of technological
change

The analysis suggests that TP from between-layer effects and within-layer
upstream linkages is a key driver of industry growth and innovation in US
manufacturing since the late 1970s, especially since the 2000s. A major source
of TP are innovation spillovers from upstream industries. TP from innovation
is largest in those industries that rely on patents (Innovation-intensive,
high centrality in the innovation layer, Metallic and Machinery
manufacturing), and in Small industries, and those with a low upstream, but
high downstream centrality in the market. These industry groups strongly
overlap (see Table LABEL:tab:NAICStoSubsample_map) and many of them belong to
the group of Metallic and Machinery manufacturing industries. It is not
surprising to find TP driven by innovation to be strongest in industries, that
use patents as means of IP protection. Patents may perform worse as an
innovation indicator in other industries and TP effects from non-patented
innovations cannot be captured by this framework.

However, the analytical results are well in line with the descriptives. The
post-1980s in the US were characterized by the rise of ICT-related and
electronics industries, which largely belong to those industries that show the
strongest impact of TP. The rise of these industries accelerated in recent
years, coinciding with the observation that TP is most significant in the
post-2000s. Hence, the increasing relevance of TP as a driver of innovation
and market growth may be attributed to industrial change and the take-off of
ICT-related industries, whose growth is more strongly driven by TP compared to
other industries (especially Non-Metallic Manufacturing such as Textiles,
Wood, Fiber, and Paper).

The analyses suggest that innovation-induced TP comes with a shift from
production to non-production labor. This is consistent with previous research
that documented patterns of capital deepening driven by ICT
(corrado2009intangible, 35) and heterogeneous employment effects that are
positive for high-skill, non-routine, and service jobs but negative for low-
skilled production labor (see hotte2022technology, 59, for an overview).
However, this paper does not inform about the net impact of TP from innovation
on labor markets at the aggregate level, as it only analyzes within-
manufacturing effects and ignores the rise of services
(gallipoli2018structural, 47).

In contrast to TP, DP does not show any significant positive impact on market
growth or innovation, but the results indicate that DP arising from customer
relationships in the market is associated with productivity growth and more
employment. This observation points to the importance of the demand side for
the creation of new jobs (bessen2019automation, 16, 59).

Upstream linkages that reflect supply conditions and the availability of
technological knowledge and physical production inputs show positive effects
on industrial growth, while downstream spillovers show the opposite effect.
Despite not directly comparable, this finding has conceptual overlaps with the
observations made by bloom2013identifying (19). At the firm level, the authors
identified positive effects of upstream knowledge spillovers in R&D, and
negative effects of downstream spillovers, which they interpret as market
rivalry and cannibalization effects.

Another interesting observation is the role of up- and downstream centrality
in the market. While upstream centrality tends to show negative correlations
with innovation and growth, the opposite holds for downstream centrality. The
effects are only weakly significant and matter only in certain subgroups of
industries. Nevertheless, this observation is interesting as it points to the
relevance of an industry’s position in the supply chain. Other research has
shown that industries with a deep embeddedness in the supply network tend to
grow faster as the positive effects of productivity growth accumulate along
the supply chain (mcnerney2018production, 76). Here, data limitations
prevented the calculation of output multipliers, which would be an adequate
indicator to capture this. However, the market up- and downstream centrality
exhibit similar properties, as industries with a high downstream centrality
often produce highly processed goods (transportation equipment, electronics,
chemicals, processed food). Those with a high upstream centrality are more
frequently found in sectors that process raw material (mills, raw plastics,
primary material processing) (cf. Table LABEL:tab:NAICStoSubsample_map and
SI.2.2). The statistical analyses control for sector FE. Hence, changes in the
market up- and downstream centrality reflect whether an industry moved up- or
down in the supply chain. It seems that a move down the supply chain is rather
positively associated with innovation, market growth, and labor.

The results of this study are consistent with acemoglu2016innovation (2) who
observed a strong positive effect of within-innovation TP2 arising from
upstream innovation spillovers and a weak negative effect of downstream
linkages, contradicting within-innovation DP2 effects. This is at odds with
taalbi2020evolution (95) who found support for both DP2 and TP2 as drivers of
innovation. taalbi2020evolution’s (95) analysis relies on an innovation output
network that captures innovations defined as significantly improved and
commercialized products and their actual use in downstream industries. This
indicates potential reasons for the lack of support for DP2 effects in this
and acemoglu2016innovation’s (2) study: Patent citations capture the flow of
knowledge between industries, which does not necessarily coincide with
technology supply and demand. The contradictory results indicate that whether
technology demand can pull upstream innovation may be sensitive to the way of
measurement.

Further, the dominance of TP may be specific for the period under study. The
strong support of TP appears to be driven by the rise of the ICT sector, which
characterized the four decades covered by this study. The dominance of TP
could be a feature of emergent technologies. walsh1984invention (99) showed
for the chemical industry that TP from radical breakthroughs may drive growth
in the market, which in turn creates DP effects that induce incremental
innovation. The analysis in this paper has shown that the rapid and radical
technological change that happened in the ICT sector during the past decades
was primarily driven by TP. However, this should not lead to the conclusion
that the TP dominance will persist in future when incrementally enhanced ICT
applications diffuse.

This study investigated innovation captured by patents. Patents are an
indicator of technological inventions, which are only the first step in the
chain of invention, innovation, and diffusion (dosi2010technical, 42). Other
research has shown that DP policy can effectively stimulate the diffusion and
incremental innovation in existing technologies, while proving less effective
as a stimulus of radical inventions (nemet2009demand, 80, 54). Thus, the lack
of support for DP may be due to the dominance of the ICT sector in its infancy
and the choice of patents as means of measurement. Innovation policy goals
(invention, innovation, diffusion) need to be carefully defined when it comes
to the choice of the appropriate instruments.

####  6.2 Limitations and research implications

So far, this study has ignored the role of trade. The exposure to import
competition from low wage countries can be a driver of the reallocation from
low- to high-tech industries (bernard2006survival, 15). The accession of China
to the WTO in 2001 was a large shock to the US manufacturing sector and
associated with a sharp drop in US manufacturing employment
(pierce2016surprisingly, 86). For European countries, bloom2016trade (18) have
shown that Chinese import competition was a driver of technological change,
inducing the reallocation of production towards more productive firms and
spurring innovation within trade exposed firms.

The increased trade exposure of US manufacturing after 2001 may be another
explanation for the take-off of TP during the second subperiod. However, the
rise of the ICT sector in innovation began already before the 2000s, even
though the impact of TP was weak and non-existent in innovation. Whether the
trade-shock caused by the Chinese WTO accession “activated” TP and accelerated
the market uptake of the ICT sector and decline of more primary manufacturing
activities is beyond the scope of this study.

It should be also noted that this study is limited to manufacturing. Various
studies documented the decline of US manufacturing since the 1980s (e.g.
elsby2013decline, 43, 45). It would be important to verify whether the
observed patterns are general or specific for the US manufacturing sector
during the period under study.

Further, the analysis is subject to three more technical limitations. First,
patents as a measure of innovation are imperfect. The use of patents to
protect IP varies across industries (fontana2013reassessing, 44, 7, 34),
patents vary by value, and not every patent indicates a technological
breakthrough (trajtenberg1990penny, 96, 69). Sometimes patents are only filed
for defensive purposes to protect a pre-existing, but not a new invention
(granstrand1999economics, 52). Over time, citation practices may have changed
(hall2005market, 53, 75). These limitations are partly addressed by
restricting the sample to manufacturing where patents are a common means of
intellectual property protection (blank2012intellectual, 17), by controlling
for industry and time FE, and by using citation-weighted patents.

Second, studying innovation and industrial evolution over time is challenging
because of non-static classification systems (marmor1980approach, 75, 100,
72). This analysis relies on NAICS industry codes that are purposely designed
to describe industries by their production processes. The codes are regularly
(quasi-endogenously) updated to meet this purpose. This can be one explanation
for the less skewed sector-size distribution, and possibly also for the higher
stability of the IO network.

The aim of this paper is the economic analysis of technological change, which
justifies the choice of NAICS codes rather than patent classes. Inferring from
patents to industrial dynamics is a challenging endeavor
(antonelli2014economics, 3, 42), not least because the industry where a patent
is filed is not necessarily the same industry where the patented invention is
used. Various concordances exist (lybbert2014getting, 74, 97, 51, 41), and a
systematic, dynamic comparison between these concordances and their
implications for economic studies would be a valuable research avenue. It
would be also interesting to compare the results of this study with an
approach using patent classes as means of description: Classifying IO flows by
their correspondence in patent classes can be insightful to understand the
impact of DP on the dynamics of patented innovations. But this is beyond the
scope of this paper.

Third, this paper studies TP and DP at the aggregate level. But patterns of
innovation, knowledge sources, and IP practices differ across firms,
industries, and technology fields (pavitt1984sectoral, 85, 27, 17).
walsh1984invention (99) documented that whether DP or TP dominates may be a
matter of industry maturity. The static dimension of sector heterogeneity is
captured by the FE approach and by the analysis of subsamples. Exploring the
dynamic dimension of industrial heterogeneity could be an interesting avenue
for future work. For example, future research may take account of the
distinction between radical and incremental innovation, and the chronology of
the technology cycle. This might also confirm whether the results of this
study are driven by the ICT sector, which was nascent during the early periods
under study.

###  7 Conclusions

Understanding and shaping technological change is a key task for policymakers
in the 21st century. Climate change mitigation requires a dramatic and fast
transition to climate-friendly technology, (ipcc2018special, 61) and
digitalization may disrupt labor markets with undesirable effects on income
distribution (brynjolfsson2012race, 24, 9). Understanding the impact of TP and
DP as drivers of technological change can help develop effective policies to
steer the process and to mitigate undesirable side effects.

This paper conceptualized TP and DP as between- and within-layer effects and
studied their impact on market growth, innovation, and the direction of
technological change in an empirical two-layer network of IO and patent
citation links between US manufacturing industries.

The results strongly support TP, suggesting that an increase of the available
technological knowledge base can stimulate growth in the market and subsequent
innovation. Increasing returns to innovation further suggest that this may be
a self-reinforcing process. This provides evidence that R&D policy, that
stimulates the creation of relevant technological knowledge, may be an
effective instrument to speed up industry level technological change. However,
the results also show that technological change driven by TP is factor-biased,
which may require supplementary policy to alleviate undesirable distributional
effects.

It was also seen that DP tends to show an opposite factor bias, which may help
develop political instruments that offset the labor-saving effect of TP. The
results bear important insights for the debate on whether and how
technological change is labor-saving: The results indicate that the factor
bias of technological change may be dependent on its driver.

In contrast to previous research relying on other measures of innovation, the
results do not support DP as a driver of innovation and market growth.
Innovation was measured by patents, which are indicative for radical
inventions but may be less suited to capture actual patterns of technology
diffusion and use. Innovation policymakers need to be clear about their goals
when it comes to the choice of DP or TP instruments. This study offers support
that TP is a driver of technological change pushed by patented novelties, but
it should not question existing evidence on the effectiveness of DP as a
driver of diffusion and incremental change.

### Acknowledgements

I want to thank Angelo Secchi and Antoine Mandel who facilitated this work at
an early stage. Further, many thanks to Herbert Dawid, Markus Trunschke,
Angelos Theodorakopoulos, Peter Persoon, Francois Lafond, and the participants
of the Annual GENED meeting 2020, the INET Complexity group, and the OMPTEC-
FoW seminar for helpful comments. Further gratitude is owed to the Competence
Center for Research Data at Bielefeld University. I am indebted to three
anonymous reviewers whose valuable feedback helped to significantly improve
this research. Moreover, I gratefully acknowledge the financial support by the
German Academic Foundation, Deutsch-Französische Hochschule, Bielefeld
Graduate School of Economics and Management, and from the EU Horizon 2020
Research and Innovation programme under grant agreement No 822330
TECHNEQUALITY.

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## Appendix

###  Appendix A Methods

####  A.1 Data compilation

#####  A.1.1 Input-output data

BEA provides detailed current and historical benchmark IO tables in
quinquennial frequency dating back to 1947.181818The data was downloaded from
https://www.bea.gov/industry/benchmark-input-output-data and
https://www.bea.gov/industry/historical-benchmark-input-output-tables [Both
accessed in Oct 2021]. The most disaggregate data at the 6-digit level is
used. The data is accounting data, which show monetary flows between
industries including final demand, and dummy positions that ensure the
financial closure. Accounting positions are largely but not perfectly
compatible with NAICS or Standard Industrial Classification (SIC) codes. The
data stepwise converted into a time-consistent and convenient format. First,
the data was transformed from accounting positions into industry codes, i.e.
SIC codes for the 1977-1987 data and into NAICS for later periods. The
industry codes are harmonized to the NAICS 2002 version using multiple
concordances provided by BEA.191919Detailed explanations of conceptual and
technical issues (e.g. changing classification systems, ambiguous mappings)
that arose during the compilation are available in SI.1.1.

After harmonizing the data, a matrix of monetary flows between 1179 distinct
6-digit NAICS industries has been obtained for each period. The entries of the
matrix are input flows $flow^{\mu}_{ij,t}$ indicating the monetary value of
the inputs that $i$ buys from $j$ in time $t$. Division of the flows by the
row sums $\sum_{j}flow^{\mu}_{ij,t}$ gives the input shares
$w^{\mu,up}_{i,t}$. The output shares $w^{\mu,dw}_{i,t}$ are obtained by
division by column sums. Note that some rows and columns are empty for some
$t$. This results from the harmonization procedure to uniform NAICS codes and
can happen when the classification changes. Industries can emerge or disappear
over time. For example, industries associated with computer technologies were
less granular in the 70s compared to the 90s. This is often associated with a
split (merge) of pre-existing industries.

#####  A.1.2 Patent data

The innovation layer is taken from a patent data set compiled for an earlier
project (hotte2021data_rise, 57, 58). The data contains a list of USPTO
patents including the grant year and CPC technology classes, relying on the
USPTO Master Classification File from January
2020.202020https://bulkdata.uspto.gov/data/patent/classification/ This file
offers a list that maps individual patents to one or more CPC classes at the
most disaggregate level.

To obtain NAICS level patent data, the patents-to-CPC and the CPC-to-NAICS
provided by goldschlag2020tracking (51) were merged. The latter is a
probabilistic mapping and comes along with probability weights whenever the
one CPC class maps to multiple NAICS. These weights are used when compiling
industry level patent stocks and industry-to-industry citation counts. To
compile the 5-year snapshot of patent stocks, weighted patents per NAICS class
were aggregated for each time window. To obtain the number of patents of an
industry $i$ and in time $t$, all patents of a given CPC 4-digit that show a
link to NAICS 6-digit sector $i$ are summed up after having been multiplied
with the corresponding weight. For example, patents classified into CPC class
A01B map to NAICS 6-digit sector 115112 with a weight of 0.9996819 and to
sector 237010 with a weight 0.0003181. Hence, all patents that were granted in
the time window $t$ and classified by A01B were summed up to get the patent
count, then multiplied by 0.99996819, and assigned to sector 115112. Industry
115112 does also map to other CPC 4-digit classes (A01C, A01G, E01H). The
patent counts from these classes are summed up in the same way and added to
the industry level patent count.212121The data and the R-code for the data