compilation are published under a CC-BY 4.0 license as hotte2023data (56).
Note that the number of CPC codes per patent are very heterogeneous, which may
lead to double counts. Here, the heterogeneity is ignored, as a higher number
of co-classifications is often coinciding with a higher value of the patent
(lerner1994importance, 73). This may justify that the patent enters the
aggregate data with a higher weight compared to patents with fewer CPC
codes.222222It should be noted that the final analyses rely on citation-
weighted patents, which may lead to a repeated double counting when patents
map to multiple CPC classes and are frequently cited. However, the non-
citation-weighted and the weighted patent stock are very high (see Fig. B.1).

Patent stocks rely on the time windows prior to the benchmark year. For
example, for the patent stock in 1977, all patents granted in 1973-1977 are
summed up. However, one could also argue to use the subsequent time window
1977-1981 to compile patents for 1977. The IO data is a time snapshot of the
last year in the time window. The preceding time window is used for four major
reasons: (1) This paper uses granted patents, and the time lag between patent
application and grant often accounts for a few years. (2) Innovation is a
dynamic concept comprising the process of invention, innovation and
commercialization, and diffusion. Using the earlier time window takes account
of the diffusion lag. (3) Patents are seen as a proxy for the stock of
available technological knowledge, and patents that will be granted in future
are not yet available as knowledge for current use. (4) This approach is
consistent with other research where discounted patent stocks were used as
proxies of innovation and technological knowledge (e.g. antony2012technology,
4, 60).

The same procedure is applied to the citation data, where both the citing and
the cited patent both are mapped to NAICS codes. In numbers, more than 37.66 M
citation links between 3.75 M individual patents are first expanded to the CPC
4-digit level and then aggregated into citation counts for each NAICS-to-NAICS
pair in the relevant period.

These NAICS-to-NAICS citation counts are transformed into a symmetric matrix
where the entries $flow^{\tau}_{ij,t}$ correspond to the flow of citations
from $i$ to $j$, i.e. the number of times that $i$ cites patents from industry
$j$. As above, the entries of $flow^{\tau}_{t}$ are transformed into input
shares $w_{ij,t}^{\tau,d}$ through division by the row sum
$\sum_{j}flow^{\tau,d}_{ij,t}$. Output shares $w^{\tau,dw}_{i,t}$ are obtained
by division by column sums. Additional detail on the data processing is
provided in SI.1.2.

#####  A.1.3 Supplementary data and processing

The data is supplemented with data from the NBER-CES Manufacturing
Productivity Database (becker2013nber, 14,
12).232323https://www.nber.org/research/data/nber-ces-manufacturing-industry-
database For the main analyses, 6-digit level data is used and the subset of
manufacturing industries. More aggregate level and additional data on non-
manufacturing sectors have been used in earlier exploratory analyses and
validation tests.242424The data is available in the accompanying research data
publication (hotte2023data, 56). Robustness checks with more aggregate data
aim to cope with concerns about the reliability of the classification
approach, as classification systems change over time and many sequential
transformations were necessary.

The data is unbalanced panel data, i.e. some industries have no data entry for
output flows or patent counts in some periods. For the main analysis,
industries with incomplete coverage were removed. The final data is
characterized by $A^{\alpha}_{i,t}>0\;\;\forall\;\;t,\alpha$. This reduces the
sample size from 473 to 307 6-digit manufacturing industries. All variables
that are measured in monetary terms are deflated using the price deflator for
the value of shipment (_piship_) from the NBER-CES productivity database.

Both networks (cross-industrial flow and share matrices) and the raw patent
data are used to construct industry level variables. Using the raw patent
data, aggregate citation-weighted patent stocks $A^{\tau}_{i,t}$ at the
industry level are compiled. The citation weights are used to control for the
heterogeneity of patents by value (e.g. jaffe2019patent, 65).

Using the IO flow network, the sum of output given by the column sum
$A^{\mu}_{i,t}=\sum_{k}flow^{\mu}_{ki,t}$ is used as a measure for the market
size. To cope with potential inconsistency across time, additional robustness
checks are made with normalized data, dividing all entries by the cross-
industry average $\tfrac{1}{|N|}\sum_{j}{A}^{\alpha}_{j,t}$ for each $t$. The
normalized size measures the size relative to other industries and is used to
illustrate the evolution of size ranking of industries over time (see Sec.
5.1). In the normalized data, the cross-industry average equals one.

The networks $W^{\alpha,d}_{t}$ are used to compile the network centrality
$PR^{\alpha,d}_{i,t}$. Other centrality measures (degree, strength) are used
for robustness checks.

The weight matrices $W^{\alpha,d}_{t}$ are further used to compute the cosine
similarity matrices
$\Sigma^{\alpha,d}_{t}=\\{\sigma_{ij,t}^{\alpha,d}\\}_{i,j\in N}$ using a
sparsity-robust version by qlcMatrix2018 (38). The similarity matrix is used
to show how technological similarities in the network evolved (see below and
Table B.2). The matrices $W^{\alpha,d}_{i,t}$ and industry sizes
$A^{\alpha}_{i,t}$ are used to compute cross-industry spillovers
$Spill(A)^{\alpha,d}_{i,t}$.

In the robustness checks at other aggregation levels, all measures are re-
compiled from the network data at the respective aggregation level as network
properties (e.g. centrality, density, clustering) may change unsystematically
when the aggregation level changes (kymn1990aggregation, 71).

####  A.2 Network plots

In the network plots shown in Sec. 5.1, links between two industries $i$ and
$j$ if $j$ is a sufficiently important input supplier to $i$ and the weight
$w^{\alpha,up}_{ij,t}$ exceeds a threshold level defined by the average of
weights across all industry-pairs and periods plus one standard deviation.

The node size is scaled proportionally to $A^{\alpha}_{i,t}$, which is the
average value over the time window. To make the sizes in the market and
innovation layer comparable, the average value is normalized by division
through the cross-industry standard deviation over the full time horizon.
After this normalization, the log is taken using a $log(1+x)$ formula to deal
with $<1$ values.

The plots are generated using the plotting function of the _igraph_ package in
R using the Fruchtermann-Reingold algorithm for the layout. This algorithm
bases on the principle to minimize the number of crossing edges while keeping
them at roughly equal lengths. This comes with the side effect that nodes with
similar linking patterns tend to group together.

####  A.3 Data transformations

Before performing the statistical analyses, a series of data transformations
was undertaken to harmonize the data by scale and to cope with outliers and
skewness.

Table A.1: Overview statistics of regression variables

| Before transformation | After transformation
---|---|---
| Mean | Min | Max | Median | Mean | Min | Max | Median
$A^{\tau}_{i}$ | 2557 | 0.05 | 141771 | 504.30 | 5.873 | 0.05 | 11.86 | 6.22
$A^{\tau*}_{i,t}$ | 47145 | 0.02 | 2741069 | 6722.00 | 8.445 | 0.02 | 14.82 | 8.81
$A^{\mu}_{i,t}$ | 5447 | 0.30 | 480728 | 2498.00 | 7.594 | 0.26 | 13.08 | 7.82
$A^{\mu*}_{i,t}$ | 5862 | 0.36 | 698224 | 2870.00 | 7.726 | 0.31 | 13.46 | 7.96
$PR^{\tau,up}_{i,t}$ | 0.0033 | 0.00 | 0.0699 | 0.00 | 1.056 | 0.40 | 4.261 | 0.79
$PR^{\tau,dw}_{i,t}$ | 0.0033 | 0.00 | 0.0697 | 0.00 | 1.058 | 0.40 | 4.258 | 0.82
$PR^{\mu,up}_{i,t}$ | 0.0033 | 0.00 | 0.2095 | 0.00 | 1.016 | 0.40 | 5.349 | 0.74
$PR^{\mu,dw}_{i,t}$ | 0.0033 | 0.00 | 0.1344 | 0.00 | 1.084 | 0.40 | 4.908 | 0.83
$Spill(A)^{\tau,up}_{i,t}$ | 646738 | 197324.00 | 1321936 | 543613.00 | 4.064 | 3.03 | 4.892 | 4.01
$Spill(A)^{\tau*,up}_{i,t}$ | 12140991 | 3280630.00 | 28901043 | 9762547.00 | 6.941 | 5.80 | 7.969 | 6.88
$Spill(A)^{\mu,up}_{i,t}$ | 477533 | -129887.00 | 2505736 | 350955.00 | 3.587 | -2.64 | 5.528 | 3.59
$Spill(A)^{\mu*,up}_{i,t}$ | 537552 | -134443.00 | 2505413 | 435077.00 | 3.753 | -2.67 | 5.528 | 3.80
$Spill(A)^{\tau,dw}_{i,t}$ | 271075 | -9.07 | 1367711 | 144102.00 | 2.352 | -0.00 | 4.926 | 2.73
$Spill(A)^{\tau*,dw}_{i,t}$ | 4989650 | -124.90 | 29603733 | 2218575.00 | 4.574 | -0.01 | 7.993 | 5.41
$Spill(A)^{\mu,dw}_{i,t}$ | 232683 | -30240.00 | 3479138 | 88113.00 | 2.217 | -1.39 | 5.855 | 2.28
$Spill(A)^{\mu*,dw}_{i,t}$ | 258969 | -113022.00 | 3175549 | 106248.00 | 2.343 | -2.51 | 5.764 | 2.45
$TFP_{i,t}$ | 4.136 | 0.04 | 326.2 | 0.96 | 0.6717 | 0.04 | 2.428 | 0.67
$(VA/L)_{i,t}$ | 105.1 | 10.46 | 2404 | 75.76 | 4.353 | 2.44 | 7.785 | 4.34
$(VA/L)^{*}_{i,t}$ | 4053 | 19.62 | 366784 | 1995.00 | 7.615 | 3.03 | 12.81 | 7.60
$L_{i,t}$ | 36.43 | 0.74 | 469.5 | 22.67 | 3.197 | 0.55 | 6.154 | 3.16
$W_{i,t}$ | 29.85 | 5.42 | 101.4 | 27.85 | 3.309 | 1.86 | 4.629 | 3.36
$W^{*}_{i,t}$ | 1278 | 9.38 | 84494 | 687.90 | 6.548 | 2.34 | 11.34 | 6.54
$(K/L)_{i,t}$ | 117.2 | 5.15 | 1958 | 75.10 | 0.6649 | 0.05 | 3.025 | 0.56
$(K/L)^{*}_{i,t}$ | 4506 | 33.45 | 222749 | 2110.00 | 3.15 | 0.29 | 7.709 | 3.10
$(I/L)_{i,t}$ | 7.125 | 0.20 | 221.6 | 4.42 | 1.774 | 0.18 | 5.405 | 1.69
$(I/L)^{*}_{i,t}$ | 276.4 | 1.10 | 12254 | 123.00 | 4.829 | 0.74 | 9.414 | 4.82
$(L^{P}/L)_{i,t}$ | 0.716 | 0.29 | 0.931 | 0.74 | 0.5378 | 0.26 | 0.658 | 0.56
$(W^{P}/W)_{i,t}$ | 1.226 | 0.86 | 2.581 | 1.17 | 1.226 | 0.86 | 2.581 | 1.17
$(W^{P}/W)^{*}_{i,t}$ | 0.858 | 0.48 | 1.195 | 0.87 | 0.858 | 0.48 | 1.195 | 0.87
$Vship_{i,t}$ | 240.2 | 20.16 | 10509 | 158.50 | 5.097 | 3.05 | 9.26 | 5.07
$Vship^{*}_{i,t}$ | 8939 | 33.80 | 565041 | 4149.00 | 8.366 | 3.55 | 13.24 | 8.33
$(M/L)_{i,t}$ | 135.8 | 5.59 | 9122 | 77.90 | 4.412 | 1.89 | 9.119 | 4.37
$(M/L)^{*}_{i,t}$ | 4575 | 37.19 | 322902 | 2025.00 | 7.649 | 3.64 | 12.69 | 7.61
$(E/L)_{i,t}$ | 5.183 | 0.12 | 160.6 | 2.06 | 1.323 | 0.12 | 5.085 | 1.12
$(E/L)^{*}_{i,t}$ | 152.9 | 0.47 | 7031 | 58.73 | 4.143 | 0.38 | 8.858 | 4.09

Notes: This table shows the overview statistics of the variables included in
the regression equations before and after data transformation. The last block
of rows shows the data entries of the patent counts when the data is not
weighted by patent citations. * indicates that the row entry refers to the
citation-weighted value for patents and deflated data for market data in
monetary terms.

Table A.1 shows an overview of the variables included in the regression
analyses. The columns at the left-hand side show the raw data values, and the
right-hand side columns show the values after a series of data transformations
that are done to make the data more comparable and to cope with outliers and
highly skewed distributions. The transformation steps include in sequential
order:

  1. 1.

Network variables are scaled by their standard deviation to make the size
$A^{\alpha}_{i,t}$, centrality $PR^{\alpha,d}_{i,t}$, and spillovers
$Spill(A)^{\alpha,d}_{i,t}$ comparable across the three variables and across
the two layers.

  2. 2.

All variables except for the share of production labor ($(L^{P}/L)_{i,t}$
transformed to log values using the formula $\log(1+x)$ to cope with $<1$
values. The log-linearization is done to cope with highly skewed data.

  3. 3.

Outliers are removed according to an interquartile range (IQR) based formula.
Those values are treated as outliers that are beyond the 25/75% quantile
values minus/plus $(a\cdot IQR)$ with $a=30$ in the baseline models.
Robustness checks are made with more restrictive (i.e. $a=5$ and $a=10$)
removal rules. The regression results are qualitatively consistent with the
baseline.

The full code that was used to compile and process the data will be made
available upon publication of this paper.

###  Appendix B Additional descriptive information

Table B.2: Aggregate network statistics over time at the 6-digit level.

| Input-output | Patent
---|---|---
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012 | 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
_Flow matrix - upstream network_
Density | 0.07 | 0.07 | 0.07 | 0.10 | 0.14 | 0.15 | 0.16 | 0.17
Avg. degree | 22.02 | 21.86 | 20.08 | 29.02 | 44.43 | 46.39 | 49.84 | 51.33
Avg. weight | 0.87 | 0.83 | 0.88 | 0.75 | 0.36 | 0.35 | 0.34 | 0.34
Reciprocity | 0.15 | 0.14 | 0.12 | 0.33 | 0.36 | 0.34 | 0.32 | 0.29
Transitivity | 0.34 | 0.34 | 0.30 | 0.34 | 0.42 | 0.43 | 0.45 | 0.46
Diameter | 3.00 | 3.00 | 4.00 | 2.00 | 2.00 | 2.00 | 2.00 | 2.00
Mean dist. | 1.43 | 1.41 | 1.43 | 1.49 | 1.07 | 1.04 | 1.02 | 1.02
Assort. by degree | -0.25 | -0.24 | -0.19 | -0.33 | -0.12 | -0.10 | -0.07 | -0.06
Assort. by size | -0.00 | -0.02 | -0.03 | -0.02 | -0.02 | -0.01 | -0.00 | -0.00
_Flow matrix - downstream network_
Density | 0.08 | 0.07 | 0.07 | 0.09 | 0.14 | 0.15 | 0.16 | 0.16
Avg. degree | 24.15 | 22.50 | 20.91 | 28.39 | 43.72 | 45.42 | 48.95 | 50.33
Reciprocity | 0.17 | 0.17 | 0.13 | 0.30 | 0.36 | 0.35 | 0.33 | 0.30
_Cosine similarity - upstream network_
Density | 0.28 | 0.27 | 0.28 | 0.31 | 0.38 | 0.40 | 0.40 | 0.40
Avg. degree | 86.33 | 83.22 | 85.20 | 94.89 | 116.73 | 121.32 | 122.52 | 123.42
Avg. weight | 12.84 | 11.60 | 12.20 | 9.37 | 23.69 | 26.35 | 28.40 | 31.50
Transitivity | 0.67 | 0.68 | 0.75 | 0.63 | 0.72 | 0.74 | 0.75 | 0.74
_Cosine similarity - downstream network_
Density | 0.25 | 0.24 | 0.23 | 0.29 | 0.39 | 0.40 | 0.41 | 0.41
Avg. degree | 76.85 | 73.10 | 71.95 | 88.92 | 118.84 | 121.95 | 124.19 | 125.52
Avg. weight | 9.59 | 8.82 | 7.72 | 9.47 | 25.35 | 26.52 | 29.41 | 30.93
Transitivity | 0.61 | 0.62 | 0.58 | 0.67 | 0.72 | 0.74 | 0.74 | 0.73

Notes: The upper part of the table shows a series of network statistics
compiled at the basis of the up- and downstream links in the market- and
innovation layer for different time windows. The links in these time windows
are averaged. The lower parts of the table summarize the network
characteristics of the cosine similarity network given by the symmetric
$|N|\times|N|$ cosine similarity matrix $\Sigma^{\alpha,d}_{t}$, where the
pairwise similarities $\sigma^{\alpha,d}_{ij,t}$ are the weights of a link
connecting $i$ and $j$. The metrics are compiled using the R-package _igraph_
(csardi2006igraph, 37). For an introduction to the use of these metrics see
also jackson2008social (62). Those variables that are identical for the up-
and downstream network are shown only once. They are identical because of the
normalization of weights (Avg. weight) to shares or by the nature of the data
(Diameter, Transitivity, Mean distance, Assortativity).

Table B.2 shows the properties of the two network layers, constructed on the
basis of up- and downstream links. In addition, the bottom part of the table
shows the network characteristics of the up- and downstream similarity
network. This is a network that shows industries as being connected if they
are very similar by their bundle of upstream (or downstream) links.

Upstream similarity in the market $\sigma^{\mu,up}_{ij,t}$ indicates that a
pair of industries relies on a similar bundle of intermediate goods as
production inputs. An increase in $Spill(A)^{\mu,up}_{i,t}$ indicates a rise
in the competition for these inputs as either, competitors that use the same
inputs grew ($A^{\mu}_{j,t}\uparrow$) or the extent to which input
requirements overlap increased ($\sigma^{\mu,up}_{ij,t}\uparrow$).

Downstream similarity in the market $\sigma^{\mu,dw}_{ij,t}$ measures the
overlap of $i$’s and $j$’s customer links, which indicates that the outputs of
$i$ and $j$ serve similar customer needs. This can be also an indicator for
competition if the outputs produced by $i$ and $j$ are substitutes, but it may
also indicate demand synergies if the outputs are complements. An increasing
similarity of two industries over time may be an indicator of technological
and economic convergence.

The first lines of each section of the table show the network density, which
measures the connectivity. It shows the ratio of actual over potential network
links. Both layers are sparsely connected. The innovation layer is denser and
shows an increase in the up- and downstream density over time, while
connectivity in the market does not exhibit any clear trend. The average
degree (second line of each block in the table) indicates the average number
of industries to which an industry is connected. On average, an industry is
connected to 20-30 customers and suppliers in the market and cites patents
from 44-51 other industries. Both network layers show a negatively valued
assortativity: Larger and more connected industries tend to link more often to
smaller and less connected industries.

The density in the cosine similarity networks is an indicator of technological
convergence, measuring whether industries became more similar on average. In
line with the connectivity trends, similarity in the market fluctuates without
any clear trend, while industries became increasingly similar by patent
citations.

(a) 1977-1992

(b) 1997-2012

Figure B.1: Pairwise correlations of network indicators in two subperiods

Notes: These figures show a correlation plot between different pairs of
network variables at the 6-digit level. Figure 1(a) (1(b)) shows the average
correlations during the first (second) half of the period that is studied. To
calculate the average correlations, the data is averaged over the respective
periods before the correlation coefficient is computed. $PR$ is short for
PageRank, $D$ for degree, $S$ for strength. $Patents(w)$ is the weighted
patent stock. The correlation at the diagonal is by definition equal one. The
colors and the shape of the ellipses indicate the strength of correlation.

Figure B.1 shows the pairwise correlation of different indicators used to
describe the network across and within layers. The figure illustrates a that
patent up- and downstream network properties are by far higher correlated
compared to the equivalent metrics in the market. The correlation among the
network metrics in the innovation layer is always positive, while there are
some negative correlations in the market, e.g. between the up- and downstream
centrality or upstream spillovers and market size. The figure also shows that
the correlation patterns are fairly robust over time when comparing the two
subperiods.

###  Appendix C Additional regression results

####  C.1 Demand-pull and technology-push

Table C.3 show the same results as discussed in Sec. 5.2.1 but including
industry level controls. Most of the coefficients of the controls are not
significant, suggesting that these effects are likely already captured by the
FE or the autoregressive term. Only wages, investment per capita, and energy
intensity are significant. Wages exhibit a negative association with market
growth, energy intensity shows a negative one with innovation, and investment
per capita shows a positive relationship with both market growth and
innovation.

| 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)
$W_{i,t-1}$ |  | -0.0173 |  | -0.0047. |  | -0.0276** |  | -0.0056 |  | -0.0333** |  | -0.0036
|  | (0.0139) |  | (0.0028) |  | (0.0104) |  | (0.0042) |  | (0.0121) |  | (0.0034)
$(K/L)_{i,t-1}$ |  | 0.0238 |  | -4e-04 |  | 0.0207 |  | -0.0065 |  | 0.0152 |  | -0.0029
|  | (0.0157) |  | (0.0034) |  | (0.0132) |  | (0.0044) |  | (0.0145) |  | (0.0037)
$(L^{P}/L)_{i,t-1}$ |  | 0.2489. |  | -0.0135 |  | 0.1575 |  | -0.0215 |  | 0.1662 |  | -0.0247
|  | (0.1279) |  | (0.0323) |  | (0.1089) |  | (0.0424) |  | (0.1241) |  | (0.0341)
$(I/L)_{i,t-1}$ |  | 0.0289* |  | 0.0118*** |  | 0.0315** |  | 0.0108*** |  | 0.0216. |  | 0.0107***
|  | (0.0114) |  | (0.0029) |  | (0.0104) |  | (0.0032) |  | (0.0112) |  | (0.0029)
$(E/L)_{i,t-1}$ |  | -0.0032 |  | -0.0084** |  | 8e-04 |  | -0.0116** |  | 0.0048 |  | -0.0086**
|  | (0.0143) |  | (0.003) |  | (0.0122) |  | (0.0037) |  | (0.013) |  | (0.003)
$(M/L)_{i,t-1}$ |  | 0.0011 |  | -9e-04 |  | 0.0024 |  | 0.0049 |  | 0.014 |  | 4e-04
|  | (0.0161) |  | (0.0037) |  | (0.0127) |  | (0.005) |  | (0.0134) |  | (0.0036)
$(W^{P}/W)_{i,t-1}$ |  | -0.2207. |  | -0.0413 |  | -0.2831** |  | 0.0278 |  | -0.2376* |  | 0.011
|  | (0.1287) |  | (0.0251) |  | (0.1071) |  | (0.0252) |  | (0.116) |  | (0.0226)
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 C.3: Demand-pull and technology-push effects including controls.

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).

| 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)
$A^{\mu}_{i,t-1}$ | 0.3431*** | 0.306*** | 0.0108 | 0.0314 | 0.8299*** | 0.6401*** |  |  | 0.5894*** | 0.5672*** | 0.0391 | 0.0401
| (0.082) | (0.0596) | (0.0243) | (0.0218) | (0.0918) | (0.0795) |  |  | (0.1084) | (0.0832) | (0.0261) | (0.027)
$A^{\tau}_{i,t-1}$ | -0.2727 | -0.2537 | 0.8206*** | 0.6053*** |  |  | 0.7537*** | 0.3797** | -0.3969 | -0.1191 | 0.7526*** | 0.3715**
| (0.4007) | (0.304) | (0.0567) | (0.0755) |  |  | (0.1279) | (0.1365) | (0.527) | (0.3246) | (0.126) | (0.1255)
$PR^{\mu,up}_{i,t-1}$ |  |  | -0.0015 | -0.0018 | -0.0686* | -0.055. |  |  | -0.0806** | -0.0682* | -0.0081 | -0.0094
|  |  | (0.0052) | (0.0051) | (0.0304) | (0.0322) |  |  | (0.0308) | (0.0278) | (0.0056) | (0.0057)
$PR^{\mu,dw}_{i,t-1}$ |  |  | -6e-04 | -7e-04 | -0.0916** | -0.0607* |  |  | -0.0762** | -0.052* | -0.0036 | -0.0019
|  |  | (0.0052) | (0.0044) | (0.0295) | (0.0289) |  |  | (0.0281) | (0.0244) | (0.005) | (0.0042)
$PR^{\tau,dw}_{i,t-1}$ | 0.5732* | 0.377* |  |  |  |  | 0.0285 | 0.0095 | 0.7663** | 0.4663** | -0.02 | -0.0046
| (0.2659) | (0.173) |  |  |  |  | (0.045) | (0.0432) | (0.2921) | (0.1773) | (0.0419) | (0.0427)
$Spill(A)^{\mu,up}_{i,t-1}$ |  |  | 0.0017 | 2e-04 | -0.059 | -0.0373 |  |  | -0.0132 | -0.0207 | 0.0074 | 0.0032
|  |  | (0.0086) | (0.008) | (0.0486) | (0.0457) |  |  | (0.0522) | (0.0411) | (0.009) | (0.0096)
$Spill(A)^{\mu,dw}_{i,t-1}$ |  |  | 0.0064 | 0.0025 | -0.2074*** | -0.1591*** |  |  | -0.1184** | -0.12** | 0.0099 | 0.0115
|  |  | (0.0057) | (0.006) | (0.0402) | (0.0367) |  |  | (0.0452) | (0.0405) | (0.0061) | (0.0071)
$Spill(A)^{\tau,up}_{i,t-1}$ | 10.37** | 6.151** |  |  |  |  | 1.853*** | 1.598*** | 11.62*** | 8.457*** | 1.517*** | 1.322***
| (3.25) | (2.199) |  |  |  |  | (0.4343) | (0.4336) | (2.676) | (1.961) | (0.355) | (0.3967)
$Spill(A)^{\tau,dw}_{i,t-1}$ | 0.2045 | 0.0152 |  |  |  |  | -0.0145 | 0.0341 | 0.1705 | 0.0418 | 0.0013 | 0.0329
| (0.1595) | (0.1065) |  |  |  |  | (0.0326) | (0.0326) | (0.1544) | (0.1052) | (0.0327) | (0.0317)
$W_{i,t-1}$ |  | 0.112* |  | -0.0202. |  | 0.1671** |  | 0.0024 |  | 0.1328** |  | 1e-04
|  | (0.0568) |  | (0.0114) |  | (0.0567) |  | (0.0163) |  | (0.0471) |  | (0.0155)
$(K/L)_{i,t-1}$ |  | -0.0374 |  | 0.02 |  | 0.0069 |  | 0.0317. |  | -0.0112 |  | 0.0384*
|  | (0.0767) |  | (0.0153) |  | (0.0717) |  | (0.0186) |  | (0.0763) |  | (0.0175)
$(L^{P}/L)_{i,t-1}$ |  | -0.7078 |  | 0.2495. |  | -0.3813 |  | 0.1315 |  | -0.0839 |  | 0.2425
|  | (0.666) |  | (0.1496) |  | (0.5939) |  | (0.1788) |  | (0.6121) |  | (0.1692)
$(I/L)_{i,t-1}$ |  | 0.044 |  | 0.0055 |  | 0.0387 |  | 0.0053 |  | 0.0558. |  | 0.0015
|  | (0.04) |  | (0.0061) |  | (0.0338) |  | (0.0073) |  | (0.0326) |  | (0.0068)
$(E/L)_{i,t-1}$ |  | -0.1974*** |  | -0.028** |  | -0.2233*** |  | -0.0383** |  | -0.1512** |  | -0.0376**
|  | (0.0536) |  | (0.0106) |  | (0.0476) |  | (0.014) |  | (0.0471) |  | (0.0122)
$(M/L)_{i,t-1}$ |  | 0.0141 |  | 0.0321* |  | -0.0345 |  | 0.0185 |  | -0.0803 |  | 0.0201
|  | (0.0746) |  | (0.0137) |  | (0.064) |  | (0.0201) |  | (0.0665) |  | (0.0172)
$(W^{P}/W)_{i,t-1}$ |  | 0.0194 |  | -0.0642 |  | -0.247 |  | -0.0824 |  | -0.2412 |  | -0.0821
|  | (0.2929) |  | (0.0584) |  | (0.2868) |  | (0.0626) |  | (0.2703) |  | (0.0632)
AR(1) | 0 | 0 | 2e-04 | 4e-04 | 0 | 0 | 3e-04 | 0.0136 | 0 | 0 | 5e-04 | 0.0125
AR(2) | 0.4028 | 0.3798 | 0.7767 | 0.5895 | 0.8789 | 0.8175 | 0.5217 | 0.3777 | 0.4567 | 0.523 | 0.5125 | 0.3298
Sargan | 1e-04 | 0 | 0 | 3e-04 | 0 | 0 | 0 | 1e-04 | 4e-04 | 6e-04 | 0 | 1e-04
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.0034 | 0.0137 | 0.6304 | 0.6608 | 0.0027 | 0.0027 | 0.6421 | 0.6892 | 0.0054 | 0.0151 | 0.6462 | 0.6883

Table C.4: Demand-pull and technology-push effects — Arellano-Bond estimator.

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 Arellano-Bond (AB) first-difference model.
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.

| 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)
$A^{\mu}_{i,t-1}$ | 0.3838*** | 0.3538*** | 0.0111. | 0.0046 | 0.4142*** | 0.3616*** |  |  | 0.3997*** | 0.3601*** | 0.0128* | 0.0048
| (0.0213) | (0.0223) | (0.0061) | (0.0065) | (0.0259) | (0.0275) |  |  | (0.0259) | (0.0273) | (0.006) | (0.0064)
$A^{\tau}_{i,t-1}$ | 0.2423*** | 0.1392. | 0.7802*** | 0.7295*** |  |  | 0.8407*** | 0.7835*** | 0.2542*** | 0.1491* | 0.8375*** | 0.7838***
| (0.0718) | (0.0753) | (0.0134) | (0.0152) |  |  | (0.0202) | (0.0212) | (0.0712) | (0.0745) | (0.0202) | (0.0212)
$PR^{\mu,up}_{i,t-1}$ |  |  | -0.0019 | -0.0015 | 0.0228** | 0.0277*** |  |  | 0.0221** | 0.0267** | -0.0022 | -0.0017
|  |  | (0.0021) | (0.0021) | (0.0084) | (0.0084) |  |  | (0.0083) | (0.0083) | (0.0021) | (0.0021)
$PR^{\mu,dw}_{i,t-1}$ |  |  | -0.0019 | -0.0014 | 0.0326*** | 0.0393*** |  |  | 0.0314*** | 0.0375*** | -0.0019 | -0.0013
|  |  | (0.0017) | (0.0017) | (0.007) | (0.007) |  |  | (0.0069) | (0.007) | (0.0017) | (0.0017)
$PR^{\tau,dw}_{i,t-1}$ | 0.0775* | 0.0541. |  |  |  |  | -0.0094 | -0.0159* | 0.0727* | 0.0514 | -0.0124. | -0.0177*
| (0.0316) | (0.0328) |  |  |  |  | (0.0074) | (0.0075) | (0.0314) | (0.0326) | (0.0074) | (0.0076)
$Spill(A)^{\mu,up}_{i,t-1}$ |  |  | -0.0061 | -0.0058 | 8e-04 | -0.0016 |  |  | 0.0107 | 0.0048 | -0.0052 | -0.0053
|  |  | (0.0042) | (0.0041) | (0.0169) | (0.0167) |  |  | (0.0169) | (0.0168) | (0.0042) | (0.0041)
$Spill(A)^{\mu,dw}_{i,t-1}$ |  |  | 0.0024 | 0.0026 | -0.0278** | -0.0242* |  |  | -0.0275** | -0.0259* | 0.0021 | 0.0027
|  |  | (0.0026) | (0.0026) | (0.0106) | (0.0106) |  |  | (0.0106) | (0.0106) | (0.0025) | (0.0025)
$Spill(A)^{\tau,up}_{i,t-1}$ | 1.268*** | 1.216*** |  |  |  |  | 0.4938*** | 0.509*** | 1.343*** | 1.268*** | 0.4822*** | 0.4925***
| (0.3483) | (0.349) |  |  |  |  | (0.0924) | (0.0924) | (0.3466) | (0.3461) | (0.0928) | (0.0928)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0915** | -0.0824** |  |  |  |  | -0.0222** | -0.0146. | -0.0887** | -0.0766** | -0.021** | -0.0143.
| (0.0288) | (0.0289) |  |  |  |  | (0.0077) | (0.0077) | (0.0286) | (0.0286) | (0.0077) | (0.0077)
$W_{i,t-1}$ |  | 0.0246* |  | -0.0014 |  | 0.0237* |  | 9e-04 |  | 0.025* |  | 6e-04
|  | (0.0102) |  | (0.0024) |  | (0.0102) |  | (0.0023) |  | (0.0103) |  | (0.0024)
$(K/L)_{i,t-1}$ |  | 0.0033 |  | 0.0042 |  | 0.0053 |  | 0.005. |  | 2e-04 |  | 0.0049
|  | (0.0127) |  | (0.003) |  | (0.0123) |  | (0.003) |  | (0.0126) |  | (0.003)
$(L^{P}/L)_{i,t-1}$ |  | -0.0915 |  | 0.0142 |  | -0.1232 |  | 0.0174 |  | -0.0646 |  | 0.0166
|  | (0.1251) |  | (0.0292) |  | (0.1231) |  | (0.0291) |  | (0.1237) |  | (0.0291)
$(I/L)_{i,t-1}$ |  | 0.0174* |  | 0.0057** |  | 0.0149. |  | 0.006** |  | 0.0174* |  | 0.0059**
|  | (0.0088) |  | (0.0021) |  | (0.0087) |  | (0.0021) |  | (0.0087) |  | (0.0021)
$(E/L)_{i,t-1}$ |  | -0.0672*** |  | -0.0151*** |  | -0.0782*** |  | -0.0149*** |  | -0.0733*** |  | -0.0143***
|  | (0.0113) |  | (0.0027) |  | (0.0111) |  | (0.0027) |  | (0.0112) |  | (0.0027)
$(M/L)_{i,t-1}$ |  | 0.0223. |  | 0.0101*** |  | 0.0325** |  | 0.0087** |  | 0.0257* |  | 0.0084**
|  | (0.0123) |  | (0.0029) |  | (0.012) |  | (0.0029) |  | (0.0122) |  | (0.0029)
$(W^{P}/W)_{i,t-1}$ |  | -0.0021 |  | -0.0431* |  | 0.0035 |  | -0.041* |  | 0.0135 |  | -0.0417*
|  | (0.0766) |  | (0.0182) |  | (0.076) |  | (0.018) |  | (0.0757) |  | (0.018)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.8118 | 0.8175 | 0.9955 | 0.9957 | 0.8114 | 0.8197 | 0.9956 | 0.9958 | 0.816 | 0.8224 | 0.9956 | 0.9958

Table C.5: Demand-pull and technology-push effects — weighted FE model

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 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. 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 $A^{\alpha}_{i,t}$,
$PR^{\alpha,d}_{i,t}$, $Spill(A)^{\alpha,d}_{i,t}$, $L_{i,t}$, $(K/L)_{i,t}$,
$(I/L)_{i,t}$, $W_{i,t}$, $(K/L)_{ij,t-1}$, $(E/L)_{ij,t-1}$,
$(M/L)_{ij,t-1}$, $W^{P}_{ij,t-1}$ 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.

Table C.4 and C.5 show the results using an AB and a weighted FE regression.
The weights in the FE regression are output for the regression testing the
impact of TP and DP on market growth $A^{\mu}_{i,t}$ and citation-weighted
patents for the regression assessing the impact on innovation
$A^{\tau}_{i,t}$.

Table C.6 and C.7 show the results for the two different subperiods. The data
of the subperiods is technically taken the six 5-year snapshots covering
1977-1997 and 1992-2012. This leads to an overlap of two observations (1992
and 1997) in the estimation procedure. The BB estimator makes use of the
second time lag of the dependent variable to build an instrument for the first
lag of the dependent variable (blundell1998initial, 20, 88). This leads to an
exclusion of the first two observations. Hence, the second stage models for
the two subperiods do not overlap in time and effectively cover the period
from 1987-1992 and 1997-2012.

| 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)
$A^{\mu}_{i,t-1}$ | 0.5807*** | 0.5313*** | 0.0252 | 0 | 0.5993*** | 0.4622*** |  |  | 0.5934*** | 0.4794*** | 0.0281 | 0.0035
| (0.0854) | (0.0946) | (0.0208) | (0.0196) | (0.098) | (0.0932) |  |  | (0.0854) | (0.0926) | (0.0206) | (0.0202)
$A^{\tau}_{i,t-1}$ | -0.0539 | -0.0557 | 1.092*** | 1.045*** |  |  | 0.9694*** | 0.9981*** | 0.0116 | 0.0648 | 1.027*** | 1.027***
| (0.1777) | (0.1536) | (0.0218) | (0.02) |  |  | (0.1289) | (0.0688) | (0.1535) | (0.1416) | (0.0844) | (0.056)
$PR^{\mu,up}_{i,t-1}$ |  |  | -0.0128 | -0.0047 | 0.0072 | -0.0051 |  |  | 0.0091 | -0.013 | -0.0197 | -0.0068
|  |  | (0.0127) | (0.0075) | (0.0309) | (0.029) |  |  | (0.0314) | (0.0294) | (0.0159) | (0.0085)
$PR^{\mu,dw}_{i,t-1}$ |  |  | -0.0035 | 0.0018 | 0.0492 | -0.0506 |  |  | 0.0037 | -0.05 | -0.0052 | 9e-04
|  |  | (0.0117) | (0.0077) | (0.0384) | (0.0363) |  |  | (0.0368) | (0.0352) | (0.0135) | (0.008)
$PR^{\tau,dw}_{i,t-1}$ | 0.1011 | 0.0809 |  |  |  |  | 0.1253 | 0.0574 | 0.0759 | 0.0421 | 0.06 | 0.0267
| (0.0932) | (0.0742) |  |  |  |  | (0.0872) | (0.0373) | (0.0651) | (0.0595) | (0.0459) | (0.0255)
$Spill(A)^{\mu,up}_{i,t-1}$ |  |  | 0.0022 | 0.0028 | 0.0757 | -0.0686 |  |  | 0.0173 | -0.092 | -0.0038 | -0.0136
|  |  | (0.0369) | (0.0262) | (0.0984) | (0.086) |  |  | (0.0998) | (0.0913) | (0.0381) | (0.0285)
$Spill(A)^{\mu,dw}_{i,t-1}$ |  |  | 0.0036 | 0.003 | 0.0481 | 0.0641 |  |  | 0.0242 | 0.0569 | 5e-04 | 0
|  |  | (0.0156) | (0.0104) | (0.0427) | (0.044) |  |  | (0.0418) | (0.0462) | (0.0139) | (0.0111)
$Spill(A)^{\tau,up}_{i,t-1}$ | 1.827 | 2.831* |  |  |  |  | -0.2011 | -0.6991 | 1.827. | 2.389* | -0.4824 | -0.6271*
| (1.163) | (1.165) |  |  |  |  | (0.5279) | (0.4404) | (1.029) | (1.015) | (0.348) | (0.2954)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0337 | -0.0037 |  |  |  |  | -0.0314 | -0.0066 | -0.0539 | -0.049 | -0.0194 | -0.0054
| (0.0679) | (0.0695) |  |  |  |  | (0.0308) | (0.0236) | (0.0641) | (0.0676) | (0.0251) | (0.0212)
$W_{i,t-1}$ |  | -0.0192 |  | -0.011* |  | -0.004 |  | -0.0085 |  | -0.0136 |  | -0.0053
|  | (0.0211) |  | (0.0049) |  | (0.019) |  | (0.0069) |  | (0.02) |  | (0.0059)
$(K/L)_{i,t-1}$ |  | 0.0791. |  | 0.0074 |  | 0.0611 |  | 0.0164 |  | 0.0553 |  | 0.0082
|  | (0.0473) |  | (0.009) |  | (0.038) |  | (0.014) |  | (0.0377) |  | (0.0099)
$(L^{P}/L)_{i,t-1}$ |  | 0.535. |  | -0.0097 |  | 0.0544 |  | 0.0562 |  | 0.0925 |  | 0.0252
|  | (0.315) |  | (0.0721) |  | (0.2134) |  | (0.0893) |  | (0.2318) |  | (0.0676)
$(I/L)_{i,t-1}$ |  | 0.0493* |  | 0.0084. |  | 0.0422* |  | 0.0066 |  | 0.0375* |  | 0.0075
|  | (0.0204) |  | (0.0044) |  | (0.0173) |  | (0.0071) |  | (0.0178) |  | (0.0057)
$(E/L)_{i,t-1}$ |  | -3e-04 |  | -0.0055 |  | -0.027 |  | -0.028* |  | -0.0276 |  | -0.0075
|  | (0.035) |  | (0.0062) |  | (0.0238) |  | (0.0111) |  | (0.0242) |  | (0.0075)
$(M/L)_{i,t-1}$ |  | -0.0498 |  | 0.0023 |  | 1e-04 |  | 0.0133 |  | 0.0062 |  | 8e-04
|  | (0.0403) |  | (0.0074) |  | (0.026) |  | (0.0122) |  | (0.0279) |  | (0.0075)
$(W^{P}/W)_{i,t-1}$ |  | -0.4005 |  | -0.1213* |  | -0.3473 |  | -0.1095 |  | -0.1988 |  | -0.1154.
|  | (0.2947) |  | (0.0557) |  | (0.2158) |  | (0.0834) |  | (0.2344) |  | (0.0605)
AR(1) | 0 | 0 | 0.0378 | 0.0384 | 0 | 2e-04 | 0.0227 | 0.0477 | 0 | 1e-04 | 0.0479 | 0.0534
AR(2) | 0.5155 | 0.6397 | 0.8436 | 0.8225 | 0.5133 | 0.4159 | 0.8396 | 0.8501 | 0.5298 | 0.4975 | 0.8122 | 0.818
Sargan | 0.0232 | 2e-04 | 0.0011 | 4e-04 | 1e-04 | 1e-04 | 0 | 3e-04 | 9e-04 | 0.0063 | 1e-04 | 0
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9435 | 0.9398 | 0.995 | 0.9958 | 0.9476 | 0.949 | 0.9908 | 0.9942 | 0.9466 | 0.9467 | 0.9944 | 0.9954

Table C.6: Demand-pull and technology-push effects — first subperiod.

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. 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).

| 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)
$A^{\mu}_{i,t-1}$ | 0.4469*** | 0.4156*** | -0.006 | -9e-04 | 0.522*** | 0.5089*** |  |  | 0.5242*** | 0.5032*** | -0.0074 | -0.0026
| (0.0409) | (0.0323) | (0.0213) | (0.0201) | (0.0459) | (0.0519) |  |  | (0.0514) | (0.049) | (0.0236) | (0.0193)
$A^{\tau}_{i,t-1}$ | -0.1027 | 0.057 | 1.022*** | 1.007*** |  |  | 1.053*** | 1.077*** | -0.001 | 0.0045 | 1.084*** | 1.07***
| (0.1537) | (0.1308) | (0.0111) | (0.0124) |  |  | (0.0536) | (0.0482) | (0.1241) | (0.1255) | (0.0468) | (0.0388)
$PR^{\mu,up}_{i,t-1}$ |  |  | -0.0014 | -8e-04 | 0.0076 | 0.0183 |  |  | 0.0115 | 0.0135 | 0.0014 | -0.0011
|  |  | (0.0057) | (0.0059) | (0.0162) | (0.0135) |  |  | (0.0171) | (0.0142) | (0.0058) | (0.0056)
$PR^{\mu,dw}_{i,t-1}$ |  |  | 0.0017 | 0.004 | -0.0045 | 0.0034 |  |  | 0.0026 | 0.0033 | 0.004 | 0.0036
|  |  | (0.004) | (0.004) | (0.016) | (0.0133) |  |  | (0.0164) | (0.0134) | (0.0041) | (0.0037)
$PR^{\tau,dw}_{i,t-1}$ | 0.3035*** | 0.1018. |  |  |  |  | 0.0681* | 0.0098 | 0.2048*** | 0.1362** | 0.0293 | 0.0047
| (0.0835) | (0.0567) |  |  |  |  | (0.0272) | (0.0169) | (0.0563) | (0.05) | (0.0195) | (0.014)
$Spill(A)^{\mu,up}_{i,t-1}$ |  |  | -0.0118* | -0.0085 | 0.1106*** | 0.0956*** |  |  | 0.0989*** | 0.0831*** | -0.0148* | -0.0101
|  |  | (0.006) | (0.0062) | (0.0216) | (0.0217) |  |  | (0.0225) | (0.0209) | (0.0067) | (0.0065)
$Spill(A)^{\mu,dw}_{i,t-1}$ |  |  | 0.0015 | 0.0058 | -0.0266 | -0.0373 |  |  | -0.0342 | -0.0442. | -0.0029 | 0.0032
|  |  | (0.0066) | (0.0061) | (0.0247) | (0.0245) |  |  | (0.0237) | (0.0227) | (0.0063) | (0.0058)
$Spill(A)^{\tau,up}_{i,t-1}$ | 3.601** | 2.055* |  |  |  |  | 1.973*** | 1.071*** | 2.738** | 2.793*** | 1.366*** | 0.9312***
| (1.185) | (0.8783) |  |  |  |  | (0.3517) | (0.2817) | (0.882) | (0.8156) | (0.299) | (0.2517)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.1629. | -0.1361* |  |  |  |  | -0.0883*** | -0.0581** | -0.1768** | -0.1412* | -0.0737*** | -0.0517***
| (0.0874) | (0.0579) |  |  |  |  | (0.0255) | (0.0181) | (0.0636) | (0.0572) | (0.019) | (0.0151)
$W_{i,t-1}$ |  | -0.0125 |  | -0.0013 |  | -0.0389. |  | -0.0012 |  | -0.0382* |  | -0.0027
|  | (0.0261) |  | (0.0051) |  | (0.0214) |  | (0.0082) |  | (0.0191) |  | (0.0056)
$(K/L)_{i,t-1}$ |  | 0.0077 |  | 9e-04 |  | -0.0078 |  | -0.0038 |  | -0.0139 |  | -0.0015
|  | (0.0246) |  | (0.005) |  | (0.0203) |  | (0.0066) |  | (0.0217) |  | (0.0054)
$(L^{P}/L)_{i,t-1}$ |  | 0.3218 |  | 0.0316 |  | 0.385 |  | -0.0255 |  | 0.2817 |  | -0.0374
|  | (0.2343) |  | (0.0499) |  | (0.2443) |  | (0.0715) |  | (0.2319) |  | (0.0523)
$(I/L)_{i,t-1}$ |  | 0.0249 |  | 0.0099* |  | 0.0177 |  | 0.0089. |  | 0.0162 |  | 0.01*
|  | (0.0193) |  | (0.0048) |  | (0.0225) |  | (0.0046) |  | (0.0195) |  | (0.0045)
$(E/L)_{i,t-1}$ |  | 0.0293 |  | -0.0121** |  | 0.0456* |  | -0.0067 |  | 0.0472* |  | -0.0089.
|  | (0.0211) |  | (0.0044) |  | (0.023) |  | (0.0062) |  | (0.0195) |  | (0.0048)
$(M/L)_{i,t-1}$ |  | 0.0011 |  | 0.001 |  | 0.0178 |  | 0.003 |  | 0.0216 |  | 0.0032
|  | (0.0276) |  | (0.0052) |  | (0.0211) |  | (0.0076) |  | (0.0213) |  | (0.0049)
$(W^{P}/W)_{i,t-1}$ |  | -0.4345* |  | -0.0334 |  | -0.7856*** |  | 0.0127 |  | -0.5908** |  | 0.0176
|  | (0.1802) |  | (0.0409) |  | (0.2207) |  | (0.0399) |  | (0.1873) |  | (0.0341)
AR(1) | 1e-04 | 1e-04 | 2e-04 | 3e-04 | 0 | 0 | 1e-04 | 0.0016 | 0 | 0 | 0.001 | 0.0019
AR(2) | 0.5792 | 0.3485 | 0.3267 | 0.2549 | 0.4284 | 0.2814 | 0.301 | 0.176 | 0.5271 | 0.4214 | 0.2331 | 0.1869
Sargan | 0 | 0 | 0 | 0 | 0 | 0 | 0.0234 | 4e-04 | 0 | 0 | 5e-04 | 4e-04
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9009 | 0.929 | 0.9964 | 0.9964 | 0.9308 | 0.9316 | 0.9943 | 0.9963 | 0.9184 | 0.9275 | 0.9958 | 0.9964

Table C.7: Demand-pull and technology-push effects — second subperiod

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. 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).

####  C.2 The direction of technological change

This section provides a sample of additional results and robustness checks
about the impact of TP and DP on the direction of technological change. Table
C.8 shows the same results as discussed in Sec. 5.2.2 including industry level
controls and autoregressive terms.

Table C.9 and C.10 the regression results from models relying on alternative
estimators (one-step System-GMM à la blundell1998initial (20) and two-step
difference GMM à la arellano1991some (5)). Both control for two-ways FE, i.e.
include time and industry dummies. As the test statistics suggest, these
models severely suffer from weak instruments. Additional tests, which are not
presented here, suggest that these estimation difficulties cannot be resolved
if instruments are collapsed or deeper lags are used as instruments.

Notably, the performance of these models is much better if different
subperiods or subgroups of industries are considered suggesting non-trivial
patterns over time and industries, which are hard to identify in these linear
models. However, a deeper search for suitable identification strategies is
beyond the scope of this paper. Lastly, the results for the two subperiods are
provided in C.11 and C.12.

| Productivity | Labor & Wages | Capital & Investment | Production labor
---|---|---|---|---
| $TFP_{i,t}$ | $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
$A^{\mu}_{i,t-1}$ | 0.0459** | 0.0275. | -0.1465* | -0.1469* | -0.2349*** | -0.0895* | -0.2227*** | -0.2082*** | 0.1234*** | 0.0492* | -0.1769* | -0.1578. | -0.0099* | -0.0149*** | -0.0138. | -0.0186*
| (0.0166) | (0.0159) | (0.0624) | (0.0629) | (0.0426) | (0.0438) | (0.0526) | (0.0527) | (0.0216) | (0.0213) | (0.0886) | (0.0923) | (0.0041) | (0.0043) | (0.0072) | (0.0075)
$A^{\tau}_{i,t-1}$ | 0.0966* | 0.0096 | -0.1031 | -0.0482 | -0.4202*** | -0.019 | -0.0747 | -0.0632 | 0.2042*** | 0.0924 | -0.598* | -0.1016 | -0.038*** | -0.0265* | -0.0669*** | -0.0306
| (0.0452) | (0.0446) | (0.1587) | (0.1675) | (0.1152) | (0.1171) | (0.1337) | (0.1402) | (0.0587) | (0.0568) | (0.237) | (0.2458) | (0.0112) | (0.0115) | (0.0195) | (0.0201)
$PR^{\mu,up}_{i,t-1}$ | 0.0034 | 0.0082. | 0.042* | 0.047* | 0.0258. | 0.0223. | 0.0585*** | 0.0596*** | -0.0017 | -0.0048 | 0.0577. | 0.0602* | 0.0013 | 0.0013 | 0.0068** | 0.0069**
| (0.0051) | (0.0048) | (0.0196) | (0.0196) | (0.0142) | (0.0136) | (0.0166) | (0.0164) | (0.0072) | (0.0067) | (0.0294) | (0.0288) | (0.0014) | (0.0013) | (0.0024) | (0.0024)
$PR^{\mu,dw}_{i,t-1}$ | 0.0063 | 0.0123** | 0.046** | 0.057*** | 0.024* | 0.0265* | 0.0371** | 0.0398** | -0.0027 | -0.0096. | 0.0629** | 0.0619** | -7e-04 | -1e-04 | -3e-04 | 0
| (0.0043) | (0.0041) | (0.0162) | (0.0163) | (0.0117) | (0.0112) | (0.0136) | (0.0136) | (0.0059) | (0.0055) | (0.0242) | (0.0239) | (0.0011) | (0.0011) | (0.002) | (0.0019)
$PR^{\tau,dw}_{i,t-1}$ | -0.0041 | -0.0166 | -0.1013 | -0.0463 | -0.155** | 0.0136 | 0.0284 | 0.0841 | 0.1312*** | 0.0384 | 0.1126 | 0.1945. | -0.0026 | -0.0064 | 0.0248** | 0.0265**
| (0.0196) | (0.0192) | (0.0734) | (0.0745) | (0.052) | (0.0514) | (0.0619) | (0.0623) | (0.0262) | (0.0252) | (0.1078) | (0.1093) | (0.005) | (0.0051) | (0.0087) | (0.0089)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0111 | -0.0117 | -0.0742. | -0.0815* | -0.0743* | -0.0555. | -0.0718* | -0.0672. | 0.0424** | 0.0319* | -0.0883 | -0.1051. | -9e-04 | -0.0025 | 0.0107* | 0.0088.
| (0.0103) | (0.0097) | (0.0412) | (0.0411) | (0.0297) | (0.0284) | (0.0348) | (0.0344) | (0.0151) | (0.0139) | (0.0615) | (0.0604) | (0.0029) | (0.0028) | (0.005) | (0.0049)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.016* | -0.0135* | -0.0161 | -0.0176 | 0.0032 | -0.0079 | -0.004 | -0.0085 | -0.0046 | -0.0035 | -0.0041 | -0.0165 | -2e-04 | 7e-04 | 0.0032 | 0.0033
| (0.0065) | (0.0062) | (0.0252) | (0.0252) | (0.0181) | (0.0175) | (0.0212) | (0.0212) | (0.0092) | (0.0086) | (0.0376) | (0.0371) | (0.0018) | (0.0017) | (0.0031) | (0.003)
$Spill(A)^{\tau,up}_{i,t-1}$ | -0.7711*** | -0.7479*** | -1.471. | -1.207 | -0.754 | -0.4783 | -1.533* | -1.336* | 0.1511 | 0.0448 | 0.3971 | 1.124 | -0.2311*** | -0.201*** | -0.3043** | -0.2156*
| (0.2122) | (0.2002) | (0.7862) | (0.7875) | (0.5666) | (0.5451) | (0.6627) | (0.661) | (0.288) | (0.2676) | (1.174) | (1.159) | (0.0551) | (0.0543) | (0.0961) | (0.0946)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0424* | -0.0225 | 0.0341 | 0.0382 | 0.0839. | 0.0322 | 0.0474 | 0.0451 | -0.0202 | -0.0286 | 0.2051* | 0.1815. | 0.0043 | 0.0043 | -0.0014 | -0.0041
| (0.0176) | (0.0167) | (0.0659) | (0.066) | (0.0478) | (0.0459) | (0.0556) | (0.0554) | (0.0242) | (0.0224) | (0.0988) | (0.0971) | (0.0046) | (0.0045) | (0.0081) | (0.0079)
$TFP_{i,t-1}$ | 0.9444*** | 0.8321*** |  |  |  |  |  |  |  |  |  |  |  |  |  |
| (0.0162) | (0.0246) |  |  |  |  |  |  |  |  |  |  |  |  |  |
$(VA/L)_{i,t-1}$ |  |  | 0.8153*** | 0.6924*** |  |  |  |  |  |  |  |  |  |  |  |
|  |  | (0.0143) | (0.0426) |  |  |  |  |  |  |  |  |  |  |  |
$L_{i,t-1}$ |  |  |  |  | 0.8415*** | 0.96*** |  |  |  |  |  |  |  |  |  |
|  |  |  |  | (0.0145) | (0.0275) |  |  |  |  |  |  |  |  |  |
$W_{i,t-1}$ |  | 0.0579*** |  | 0.1659*** |  | -0.0373* | 0.8332*** | 0.8407*** |  | 0.013 |  | 0.0688. |  | 0.0052** |  | 0.0119***
|  | (0.0091) |  | (0.048) |  | (0.0174) | (0.0129) | (0.0201) |  | (0.0081) |  | (0.0353) |  | (0.0017) |  | (0.0029)
$(K/L)_{i,t-1}$ |  | 0.0363*** |  | -0.0544. |  | -0.111*** |  | -0.0604* | 0.8275*** | -0.0434*** |  | -0.2473*** |  | 0.0048* |  | -0.0146***
|  | (0.0077) |  | (0.0297) |  | (0.0209) |  | (0.0249) | (0.016) | (0.0128) |  | (0.0437) |  | (0.002) |  | (0.0036)
$(I/L)_{i,t-1}$ |  | -0.0473*** |  | 0.0411. |  | 0.0605*** |  | 0.0718*** |  | 0.0808*** | 0.5464*** | 0.4823*** |  | -0.005*** |  | -3e-04
|  | (0.005) |  | (0.0212) |  | (0.0151) |  | (0.0176) |  | (0.0074) | (0.0208) | (0.0308) |  | (0.0014) |  | (0.0025)
$(L^{P}/L)_{i,t-1}$ |  | 0.1587* |  | 1.114*** |  | 1.336*** |  | 1.127*** |  | -0.7374*** |  | 1.161** | 0.6768*** | 0.6286*** |  | 0.182***
|  | (0.0735) |  | (0.304) |  | (0.209) |  | (0.253) |  | (0.1028) |  | (0.4435) | (0.0193) | (0.0208) |  | (0.0362)
$(W^{P}/W)_{i,t-1}$ |  | 0.0349 |  | -0.2356 |  | -0.4672*** |  | -0.3293* |  | 0.2112*** |  | -0.379 |  | 0.0909*** | 0.5492*** | 0.4784***
|  | (0.0446) |  | (0.1882) |  | (0.1296) |  | (0.1571) |  | (0.0638) |  | (0.2753) |  | (0.0129) | (0.0216) | (0.0225)
$(E/L)_{i,t-1}$ |  | -0.048*** |  | -0.0856** |  | -0.0476* |  | -0.0861*** |  | -0.0147 |  | 0.2205*** |  | 0.0039* |  | 0.011***
|  | (0.007) |  | (0.0274) |  | (0.022) |  | (0.0228) |  | (0.0096) |  | (0.0399) |  | (0.0019) |  | (0.0033)
$(M/L)_{i,t-1}$ |  | -0.0012 |  | 0.0118 |  | -0.0769*** |  | 0.0225 |  | 0.0297** |  | 0.0139 |  | -0.0062** |  | -0.0043
|  | (0.0073) |  | (0.0294) |  | (0.0206) |  | (0.0247) |  | (0.0102) |  | (0.0432) |  | (0.002) |  | (0.0035)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.7438 | 0.7746 | 0.9007 | 0.9024 | 0.929 | 0.9359 | 0.9251 | 0.927 | 0.9135 | 0.927 | 0.8259 | 0.8339 | 0.8778 | 0.8832 | 0.662 | 0.6776

Table C.8: 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.

| Productivity | Labor & Wages | Capital & Investment | Production labor
---|---|---|---|---
| $TFP_{i,t}$ | $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
$A^{\mu}_{i,t-1}$ | -0.0033 | 0.0358 | -0.359* | -0.0498 | 0.0606 | 0.2302* | -0.2898* | -0.0156 | 0.0782. | 0.0113 | -0.1454 | 0.0202 | 0 | 1e-04 | -0.0171 | -0.0121
| (0.0269) | (0.0256) | (0.1435) | (0.1334) | (0.0875) | (0.0914) | (0.1267) | (0.1125) | (0.0453) | (0.0401) | (0.163) | (0.1756) | (0.0087) | (0.0077) | (0.0147) | (0.0135)
$A^{\tau}_{i,t-1}$ | -0.0456 | -0.0725 | -0.4094 | -0.929*** | -0.3931** | -0.5861*** | -0.5227* | -0.6681*** | 0.1251 | 0.1459* | -0.4306 | -0.4793 | -0.0441** | -0.0392* | -5e-04 | 0.0197
| (0.0737) | (0.0535) | (0.254) | (0.2354) | (0.1456) | (0.1568) | (0.2376) | (0.1951) | (0.0848) | (0.0689) | (0.2926) | (0.2939) | (0.0154) | (0.0161) | (0.0283) | (0.0243)
$PR^{\mu,up}_{i,t-1}$ | 0.034*** | 0.0199* | 0.1175** | 0.078. | -0.0312 | -0.0077 | 0.0369 | 0.0529 | 0.0066 | 0.0043 | 0.001 | 0.0178 | 0.0029 | 0.0024 | 0.0098. | 0.0106*
| (0.0093) | (0.009) | (0.0416) | (0.0401) | (0.0249) | (0.026) | (0.0369) | (0.0383) | (0.0169) | (0.0139) | (0.0571) | (0.0545) | (0.0023) | (0.0022) | (0.005) | (0.0047)
$PR^{\mu,dw}_{i,t-1}$ | 0.0299*** | 0.0287*** | 0.1951*** | 0.1946*** | 0.0679*** | 0.0969*** | 0.1466*** | 0.1326*** | -0.0291* | -0.0476*** | 0.1526*** | 0.1777*** | 7e-04 | 0.0013 | 0.0039 | 0.0039
| (0.0086) | (0.0085) | (0.0402) | (0.039) | (0.0198) | (0.0202) | (0.0308) | (0.0291) | (0.0128) | (0.0124) | (0.0405) | (0.0396) | (0.0022) | (0.0023) | (0.0045) | (0.0041)
$PR^{\tau,dw}_{i,t-1}$ | 0.1629*** | 0.1313*** | 0.5601*** | 0.4807*** | -0.0242 | 0.0193 | 0.645*** | 0.3945*** | 0.0887* | -0.0065 | 0.2954* | 0.208 | 0.0033 | 0.0088 | -0.0179. | -0.0195.
| (0.0461) | (0.0394) | (0.1325) | (0.0957) | (0.0685) | (0.0575) | (0.1311) | (0.0808) | (0.0367) | (0.0257) | (0.126) | (0.1343) | (0.0064) | (0.0058) | (0.0108) | (0.0102)
$Spill(A)^{\mu,up}_{i,t-1}$ | -8e-04 | -0.0068 | -0.0355 | -0.0593 | -0.0652 | -0.0675 | -0.1059. | -0.0435 | 0.03 | 0.0551* | -0.1245. | -0.1086 | -0.0014 | -0.0019 | 0.0019 | -0.005
| (0.016) | (0.0141) | (0.069) | (0.0645) | (0.0489) | (0.0481) | (0.0589) | (0.0577) | (0.0277) | (0.0271) | (0.0748) | (0.0782) | (0.0041) | (0.004) | (0.0078) | (0.0071)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.0121 | -0.0068 | 0.0973. | 0.065 | 0.0698* | 0.0513 | 0.1128** | 0.0667. | -0.0311. | -0.0225 | 0.1198. | 0.1246. | -0.0021 | -0.0018 | 0.0037 | -0.0053
| (0.0108) | (0.0108) | (0.0513) | (0.0512) | (0.0329) | (0.0321) | (0.0433) | (0.0397) | (0.0184) | (0.0163) | (0.0715) | (0.0671) | (0.0034) | (0.0031) | (0.0068) | (0.0062)
$Spill(A)^{\tau,up}_{i,t-1}$ | -0.6915 | -0.4913 | -3.865. | -2.116 | -2.37. | -2.604* | -2.985 | -0.6153 | 0.2543 | 0.8965 | -2.12 | -4.18. | -0.0994 | -0.0988 | -0.2208 | -0.1931
| (0.5408) | (0.445) | (2.161) | (2.084) | (1.304) | (1.231) | (1.905) | (1.591) | (0.6839) | (0.5913) | (2.159) | (2.158) | (0.093) | (0.0823) | (0.158) | (0.1577)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0858* | -0.0471 | -0.0865 | 0.2426* | 0.2694*** | 0.3773*** | -0.059 | 0.1556. | -0.1474*** | -0.1257*** | 0.1224 | 0.2247 | 0.0226*** | 0.0175* | 0.0105 | 0.0049
| (0.0363) | (0.0331) | (0.1115) | (0.1058) | (0.0727) | (0.0768) | (0.1019) | (0.0873) | (0.0418) | (0.0337) | (0.1416) | (0.1427) | (0.0064) | (0.0071) | (0.0129) | (0.0122)
AR(1) | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0
AR(2) | 0.0165 | 0.0222 | 0.0032 | 6e-04 | 3e-04 | 0 | 0.0461 | 0.0332 | 9e-04 | 0.0025 | 0.1265 | 0.0301 | 0.6521 | 0.5527 | 0.5203 | 0.3256
Sargan | 0 | 0.0059 | 0 | 0.0035 | 0 | 0.0024 | 0 | 5e-04 | 1e-04 | 9e-04 | 0.0025 | 6e-04 | 0 | 0.0015 | 0.083 | 0.0182
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9554 | 0.9625 | 0.9934 | 0.994 | 0.9877 | 0.9872 | 0.9929 | 0.9943 | 0.9466 | 0.953 | 0.9718 | 0.9724 | 0.9942 | 0.9944 | 0.993 | 0.9933

Table C.9: Productivity, labor, capital, and production labor — Blundell-Bond
estimator.

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 oneways Blundell-Bond (BB) system GMM model. 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 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).

Table C.10: Productivity, labor, capital, and production labor — Arellano-Bond
estimator.

| Productivity | Labor & Wages | Capital & Investment | Production labor
---|---|---|---|---
| $TFP_{i,t}$ | $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
$A^{\mu}_{i,t-1}$ | 0.0311 | -0.005 | -0.1821 | -0.2775 | 0.108 | -0.0092 | 0.0303 | -0.1197 | 0.1205 | -0.0255 | 0.3788 | 0.5065 | -0.0213 | -0.028. | -0.0179 | -0.0567**
| (0.0474) | (0.0429) | (0.2127) | (0.2087) | (0.1802) | (0.1727) | (0.1913) | (0.2038) | (0.1109) | (0.0824) | (0.2382) | (0.353) | (0.015) | (0.0146) | (0.0252) | (0.0198)
$A^{\tau}_{i,t-1}$ | 0.0193 | -0.0353 | -0.8719 | -0.7764 | -1.201* | -0.8362. | -0.7636 | -0.3618 | 0.1326 | -0.0756 | 0.5511 | 0.6003 | -0.0741. | -0.082 | -0.1396* | -0.0846
| (0.2145) | (0.1451) | (0.7167) | (0.6369) | (0.466) | (0.4727) | (0.5821) | (0.5691) | (0.272) | (0.2861) | (0.9901) | (1.265) | (0.0437) | (0.0505) | (0.0692) | (0.0598)
$PR^{\mu,up}_{i,t-1}$ | 0.0064 | 0.0046 | 0.0374 | 0.0287 | 0.0292 | 0.0077 | 0.0246 | 0.0441 | -0.0157 | -0.0148 | -0.0469 | -0.0235 | 0.0076* | 0.0039 | 0.0105* | 0.0125*
| (0.0117) | (0.0129) | (0.0526) | (0.0526) | (0.0447) | (0.0443) | (0.0493) | (0.0626) | (0.0204) | (0.0173) | (0.0766) | (0.0947) | (0.0035) | (0.0037) | (0.0049) | (0.0054)
$PR^{\mu,dw}_{i,t-1}$ | 0.0084 | 0.0172 | 0.0707 | 0.0395 | 0.0802* | 0.0534 | 0.0482 | 0.0922* | -0.021 | -0.0244 | 0.0782 | 0.0653 | 0.0029 | 4e-04 | 0.0044 | 0.004
| (0.011) | (0.0118) | (0.0494) | (0.0548) | (0.0323) | (0.0339) | (0.0364) | (0.0428) | (0.0209) | (0.0164) | (0.0534) | (0.0825) | (0.0031) | (0.0031) | (0.0052) | (0.0054)
$PR^{\tau,dw}_{i,t-1}$ | 0.1399 | -0.0157 | 0.4922 | 0.1913 | 0.299 | -0.0805 | 0.7384. | 0.0626 | 0.1619 | 0.3589* | -1.003. | -0.8996 | -0.005 | -0.0166 | 0.0637 | 0.0939*
| (0.1577) | (0.0926) | (0.5016) | (0.4389) | (0.3487) | (0.2779) | (0.4425) | (0.3526) | (0.1698) | (0.1582) | (0.6092) | (0.7414) | (0.0308) | (0.0293) | (0.0579) | (0.0374)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0065 | -0.0023 | 0.0194 | 0.0535 | -0.085 | -0.1022 | -0.0321 | -0.0941 | 0.0486 | 0.0291 | -0.2236. | -0.1688 | -0.0035 | -0.0018 | -0.0106 | -0.0075
| (0.0228) | (0.0197) | (0.0953) | (0.1056) | (0.073) | (0.0667) | (0.0924) | (0.0812) | (0.0345) | (0.0332) | (0.1156) | (0.1537) | (0.0065) | (0.006) | (0.0098) | (0.0096)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.0223 | 0.0054 | 0.0348 | 0.0613 | -0.0138 | -0.0043 | 0.006 | 0.028 | -0.0038 | -0.0045 | -0.0617 | -0.0703 | -0.0061 | -0.0039 | -0.008 | -0.0052
| (0.0143) | (0.0139) | (0.0716) | (0.0673) | (0.046) | (0.0513) | (0.0561) | (0.0653) | (0.026) | (0.0259) | (0.094) | (0.1276) | (0.0041) | (0.0047) | (0.0076) | (0.0073)
$Spill(A)^{\tau,up}_{i,t-1}$ | -0.1603 | -1 | -1.109 | -2.832 | -3.598 | -5.354. | 1.017 | -6.405. | 1.578 | 2.85* | -3.161 | -4.32 | -0.7326** | -0.3763 | -1.012** | -0.7787.
| (0.9856) | (0.8618) | (4.315) | (4.866) | (3.29) | (2.957) | (3.697) | (3.878) | (1.687) | (1.357) | (6.068) | (8.63) | (0.2611) | (0.2475) | (0.3789) | (0.4169)
$Spill(A)^{\tau,dw}_{i,t-1}$ | 0.0371 | 0.0349 | 0.4588. | 0.2432 | 0.2026 | 0.0271 | 0.489* | 0.1453 | 0.127 | 0.0485 | 0.1322 | 0.4282 | -0.0097 | -0.0015 | 0.005 | 0.0114
| (0.0652) | (0.0499) | (0.2529) | (0.258) | (0.1997) | (0.1919) | (0.1991) | (0.1978) | (0.0983) | (0.086) | (0.324) | (0.4022) | (0.0146) | (0.0165) | (0.0259) | (0.02)
AR(1) | 1e-04 | 0 | 0.001 | 1e-04 | 0.2033 | 0.3687 | 5e-04 | 0 | 0.5601 | 0.0526 | 1e-04 | 0.0058 | 0.0032 | 0 | 0.0016 | 0
AR(2) | 0.0048 | 0.0042 | 0.0097 | 0.0202 | 0.0182 | 0.052 | 0.0737 | 0.1603 | 0.0468 | 0.0396 | 0.0468 | 0.0405 | 0.5783 | 0.71 | 0.1537 | 0.0632
Sargan | 4e-04 | 3e-04 | 6e-04 | 3e-04 | 3e-04 | 1e-04 | 1e-04 | 0 | 0.0014 | 0.019 | 0.0086 | 0 | 0.0803 | 0.1101 | 0.08 | 0.0966
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.1577 | 0.2142 | 0.2154 | 0.1667 | 0.2476 | 0.2564 | 0.2801 | 0.2852 | 0.1884 | 0.2518 | 0.0432 | 0.0688 | 0.019 | 0.0212 | 0.0028 | 0.0102

1977-1992 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 Arellano-Bond (AB) first-
difference model. 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 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).

| Productivity | Labor & Wages | Capital & Investment | Production labor
---|---|---|---|---
| $TFP_{i,t}$ | $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
$A^{\mu}_{i,t-1}$ | 0.0624* | 0.045. | -0.1046 | -0.18 | -0.2117** | 0.0222 | -0.2794** | -0.2751** | 0.1597*** | -0.0168 | 0.3646* | 0.1793 | -0.0201** | -0.0143. | -0.0236* | -0.0139
| (0.0247) | (0.0258) | (0.1232) | (0.1252) | (0.0795) | (0.0866) | (0.1061) | (0.1062) | (0.0349) | (0.034) | (0.1617) | (0.1757) | (0.0069) | (0.0078) | (0.0118) | (0.0134)
$A^{\tau}_{i,t-1}$ | 0.0812 | 0.068 | -0.038 | 0.0484 | -0.8527*** | -0.2916 | -0.3586 | -0.2032 | 0.3294*** | 0.0469 | -0.5414 | -0.2627 | -0.0322. | -0.0273 | -0.0544. | -0.0175
| (0.0568) | (0.0559) | (0.2749) | (0.2792) | (0.1933) | (0.1873) | (0.233) | (0.2369) | (0.0869) | (0.0756) | (0.3925) | (0.3917) | (0.0174) | (0.0175) | (0.0293) | (0.0299)
$PR^{\mu,up}_{i,t-1}$ | 7e-04 | -0.0062 | 0.0901. | 0.0806 | 0.0439 | 0.0255 | 0.0538 | 0.0502 | -0.0157 | 4e-04 | 0.1444. | 0.125. | 0.0045 | 0.004 | 0.0071 | 0.0069
| (0.0097) | (0.0093) | (0.0517) | (0.0514) | (0.0366) | (0.0345) | (0.0438) | (0.0436) | (0.0165) | (0.0139) | (0.074) | (0.0721) | (0.0033) | (0.0032) | (0.0056) | (0.0055)
$PR^{\mu,dw}_{i,t-1}$ | -0.0101 | -0.0056 | 0.0474 | 0.045 | 0.0655* | 0.0624* | 0.077* | 0.0718* | 0.0123 | -0.0151 | 0.1616** | 0.142** | -0.0051* | -0.0033 | -3e-04 | -7e-04
| (0.0084) | (0.0082) | (0.038) | (0.0385) | (0.0272) | (0.0258) | (0.0322) | (0.0326) | (0.0122) | (0.0105) | (0.0549) | (0.0539) | (0.0024) | (0.0024) | (0.0041) | (0.0041)
$PR^{\tau,dw}_{i,t-1}$ | 0.0186 | 0.0183 | 0.344* | 0.4048* | 0.2821* | 0.3528*** | 0.3352* | 0.3485** | -0.0487 | -0.108* | 0.7479*** | 0.6452** | -0.0045 | -0.0073 | 0.0025 | 0.0018
| (0.0321) | (0.0313) | (0.1577) | (0.1597) | (0.112) | (0.1061) | (0.1334) | (0.1346) | (0.0499) | (0.0429) | (0.2236) | (0.2227) | (0.0099) | (0.0099) | (0.0169) | (0.017)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0106 | 0.0177 | 0.0841 | 0.0908 | -0.0373 | -0.1198 | -0.0062 | -0.0436 | 0.1133. | 0.0512 | 0.3041 | 0.2015 | -0.0055 | -4e-04 | 0.0106 | 0.0031
| (0.0356) | (0.0342) | (0.1851) | (0.1846) | (0.1313) | (0.1233) | (0.1569) | (0.1567) | (0.059) | (0.05) | (0.2654) | (0.2591) | (0.0117) | (0.0116) | (0.0199) | (0.0198)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.0125 | -0.0015 | 0.068 | 0.0938 | 0.0626 | 0.0656 | 0.1125* | 0.1119* | 0.0284 | -0.0162 | 0.1453 | 0.1259 | -0.0064 | -0.0042 | 0.002 | 0.0021
| (0.0124) | (0.012) | (0.0623) | (0.0622) | (0.0443) | (0.0416) | (0.0527) | (0.0527) | (0.0198) | (0.0168) | (0.0895) | (0.0872) | (0.0039) | (0.0039) | (0.0067) | (0.0067)
$Spill(A)^{\tau,up}_{i,t-1}$ | 0.2507 | 0.1165 | 2.111 | 2.194. | 0.4105 | 1.458. | 0.5583 | 0.7734 | -0.2876 | -0.3668 | 5.391** | 5.734** | 0.0741 | 0.082 | -0.2779. | -0.1985
| (0.2681) | (0.2613) | (1.32) | (1.317) | (0.9356) | (0.8823) | (1.119) | (1.117) | (0.4207) | (0.3562) | (1.885) | (1.847) | (0.0836) | (0.0825) | (0.1423) | (0.1409)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0296 | -0.022 | -0.0187 | -0.0262 | 0.0554 | -0.0215 | 0.0219 | -0.0128 | -0.0017 | 0.0232 | 0.1863 | 0.14 | -4e-04 | 0.0022 | -0.0042 | -0.0081
| (0.0204) | (0.0195) | (0.1) | (0.0997) | (0.0709) | (0.0666) | (0.0848) | (0.0847) | (0.0319) | (0.027) | (0.1429) | (0.14) | (0.0063) | (0.0063) | (0.0108) | (0.0107)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.8645 | 0.8775 | 0.9242 | 0.9264 | 0.9511 | 0.9577 | 0.9442 | 0.9456 | 0.9406 | 0.9583 | 0.8871 | 0.8939 | 0.9317 | 0.9343 | 0.7803 | 0.7885

Table C.11: Productivity, labor, capital, and production labor — first
subperiod.

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 data includes only the first subperiod.

| Productivity | Labor & Wages | Capital & Investment | Production labor
---|---|---|---|---
| $TFP_{i,t}$ | $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12) | (13) | (14) | (15) | (16)
$A^{\mu}_{i,t-1}$ | 0.0382. | 0.0363. | -0.0647 | -0.0261 | -0.1156* | -0.053 | -0.1357* | -0.103 | 0.053. | 0.0187 | -0.1321 | -0.1174 | -0.0064 | -0.0113. | -0.0067 | -0.0095
| (0.0214) | (0.0203) | (0.0786) | (0.0783) | (0.0572) | (0.0566) | (0.0655) | (0.0649) | (0.0317) | (0.0306) | (0.1167) | (0.1158) | (0.0058) | (0.0059) | (0.0106) | (0.0104)
$A^{\tau}_{i,t-1}$ | 0.0749 | 0.0433 | -0.0348 | 0.0914 | -0.1823 | 0.0413 | 0.2227 | 0.1768 | 0.2483* | 0.171 | -0.3939 | 0.0974 | -0.1102*** | -0.084*** | -0.0569 | -0.015
| (0.0841) | (0.082) | (0.2989) | (0.3077) | (0.2217) | (0.2241) | (0.2496) | (0.2557) | (0.1224) | (0.1206) | (0.4499) | (0.456) | (0.0225) | (0.0231) | (0.041) | (0.0411)
$PR^{\mu,up}_{i,t-1}$ | 0.0025 | 0.0043 | 0.026 | 0.021 | 0.0182 | 0.0121 | 0.0452* | 0.0386. | 0.0054 | 0.002 | 0.0228 | 0.0306 | 0.0012 | 0.0012 | 0.0066* | 0.0067*
| (0.0066) | (0.0063) | (0.0243) | (0.024) | (0.0179) | (0.0174) | (0.0203) | (0.02) | (0.0099) | (0.0094) | (0.0366) | (0.0356) | (0.0018) | (0.0018) | (0.0033) | (0.0032)
$PR^{\mu,dw}_{i,t-1}$ | 0.0043 | 0.0092. | 0.0356. | 0.0453* | 0.0215 | 0.0174 | 0.0238 | 0.026 | -0.0025 | 0 | 0.0672* | 0.0571. | 4e-04 | 0.0011 | -7e-04 | -4e-04
| (0.0057) | (0.0055) | (0.0213) | (0.0213) | (0.0157) | (0.0153) | (0.0178) | (0.0176) | (0.0087) | (0.0083) | (0.032) | (0.0314) | (0.0016) | (0.0016) | (0.0029) | (0.0028)
$PR^{\tau,dw}_{i,t-1}$ | 0.0403 | 0.0138 | -0.1792 | -0.1153 | -0.2961*** | -0.1067 | 0.0137 | 0.0439 | 0.2378*** | 0.0961. | -0.2555 | 0.1664 | -0.0062 | -0.0052 | 0.0421** | 0.0589***
| (0.0339) | (0.0338) | (0.1212) | (0.1253) | (0.0875) | (0.0916) | (0.1018) | (0.1043) | (0.0493) | (0.0495) | (0.1795) | (0.1859) | (0.009) | (0.0094) | (0.0161) | (0.0167)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0074 | -0.0084 | -0.0855. | -0.099* | -0.0605. | -0.051 | -0.0545 | -0.0581 | 0.0375* | 0.0222 | -0.1449* | -0.1296. | -0.0036 | -0.005 | 0.0109. | 0.0111.
| (0.0122) | (0.0116) | (0.0467) | (0.0464) | (0.0344) | (0.0334) | (0.0391) | (0.0385) | (0.0191) | (0.0181) | (0.0703) | (0.0687) | (0.0035) | (0.0035) | (0.0064) | (0.0062)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.0143. | -0.0098 | -0.0135 | -0.0209 | 0.0079 | -0.002 | -0.0026 | -0.0072 | -0.0119 | -0.0065 | 0.0236 | 7e-04 | -0.0011 | -3e-04 | 0.0012 | 0.001
| (0.0086) | (0.0082) | (0.0316) | (0.0313) | (0.0233) | (0.0226) | (0.0264) | (0.026) | (0.0129) | (0.0123) | (0.0474) | (0.0464) | (0.0024) | (0.0024) | (0.0043) | (0.0042)
$Spill(A)^{\tau,up}_{i,t-1}$ | -0.8952** | -0.7984* | -3.417** | -3.681** | -2.234* | -2.566** | -3.156** | -3.433*** | 0.928. | 0.903. | -2.214 | -2.735 | -0.3505*** | -0.3291*** | -0.2169 | -0.2245
| (0.3441) | (0.3262) | (1.23) | (1.221) | (0.9085) | (0.8841) | (1.028) | (1.016) | (0.5075) | (0.4811) | (1.853) | (1.812) | (0.0925) | (0.0918) | (0.1675) | (0.1632)
$Spill(A)^{\tau,dw}_{i,t-1}$ | -0.0187 | -0.0357 | -0.0887 | -0.0904 | -0.0303 | -0.0211 | -0.0743 | -0.0538 | -0.034 | -0.0081 | 0.0859 | 0.1276 | 0.0082 | 0.0084 | -0.0117 | -0.0099
| (0.0293) | (0.0278) | (0.11) | (0.1088) | (0.0812) | (0.079) | (0.0919) | (0.0904) | (0.045) | (0.0429) | (0.1654) | (0.1612) | (0.0083) | (0.0082) | (0.015) | (0.0145)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.7926 | 0.8174 | 0.925 | 0.928 | 0.9423 | 0.9469 | 0.9442 | 0.9468 | 0.9207 | 0.9298 | 0.8616 | 0.8703 | 0.8892 | 0.8933 | 0.6905 | 0.7112

Table C.12: Productivity, labor, capital, and production labor — second
subperiod.

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 data includes only the second subperiod.

## Supplementary Material

###  Supplementary Material SI.1 Data processing in detail

This analysis builds on two distinct sources of data brought into a consistent
form that enables the statistical analyses; this is (1) a series of time
snapshots of the network of cross-industrial IO flows and patent citations and
(2) a panel data set of aggregate statistical indicators at the industry
level. Industries are identified by 6-digit NAICS codes. The time snapshots
cover 5-year intervals from 1977 to 2012. Obtaining these data involved a
series of steps of re-formatting and harmonization, which are explained in
detail below.

####  SI.1.1 Input-output data

The IO data is constructed by the composition and harmonization of the
historical benchmark tables provided by Bureau of Economic Analysis
(BEA).252525https://www.bea.gov/industry/historical-benchmark-input-output-
tables [accessed on Dec 21, 2020] Since 1947, BEA publishes IO tables at the
detailed industry level every five years. The data is collected in BEA’s
quinquennial Survey of Current Business. A detailed manual on BEA’s IO data is
provided by BEA2009IO (55)

#####  SI.1.1.1 Overview

The raw data shows monetary transactions between industries. It also covers
final demand sectors and public services. For this project, use tables from
1977-2012 are used and harmonized in a series of conversions and processing
steps. Over time, industrial classification systems and technical methods of
data processing, formatting, and saving have changed. The earliest tables are
only available in text format that were edited manually to make it readable
for statistical software. A further challenge arises from changes in the
classification system, which are most pronounced in the conversion from SIC to
NAICS.

The final data structure is a series of quadratic matrices for each period
that show the monetary transactions between industries in NAICS 2002 codes.
The data is also used to create a panel of industry level indicators, i.e.
outputs, inputs, and growth rates.

#####  SI.1.1.2 Processing steps in detail

Here, the single steps of data processing are explained.

Step 1:

For each period, the IO data is downloaded separately. Some manual
harmonization and data conversions were made to obtain machine-readable,
harmonized data tables. For example, the very old data is only available in
text format, which is not ready to be read by statistical software. The more
recent table are Excel files with many macros and explanations in text. All
tables were reformatted individually. The scripts are available in the data
publication. After this step, all tables have a uniform format, which is a
long 3-column table with column (1) as producer ID, column (2) as user ID, and
column (3) indicating the monetary value of the goods that flow from producer
to user.

Step 2:

Large quadratic matrices with rows as producers and columns as users are
created. The entries $flow^{out,\mu}_{ij,t}$ are flows of goods from $i$ to
$j$. Hence, column-wise reading indicates the composition of inputs used by
sector $j$ and row-wise reading indicates the composition of customer
industries to which industry $i$ delivers.

Step 3:

This is an intermediate step. All concordance and IO-to-industry conversion
tables must be harmonized. Again, some tables are not machine-readable.
Moreover, all codes need to be harmonized to obtain a mapping from IO codes
for each period to 2002-NAICS codes. Some IO codes map to multiple industries.
In this step, tables were created where each row indicates an IO code and all
NAICS codes that are attributed to the respective IO.

Step 4:

NAICS-based IO tables were harmonized and consistency checks were done. For
example, it was tested whether the differences in the tables e.g. regarding
the sector coverage are negligible. Some normalizations of IO-flows to input
(output) shares were made through division by row (column) sum. The full
6-digit list is used as row and column names.

Not every time snapshot has a full sector coverage. This is a result of
reclassification issues, obsolescence, and introduction of new sectors. For
example, some finely granulate computer industries were not yet existing in
1977. For these cases, empty vectors are included to present missing sectors
to ensure that matrices have the same dimensionality.

Additional steps of harmonization are done. Rows represent the range of inputs
that is used, columns represent customers. After this step, NAICS 6-digit data
on IO flows, sector weights (row and column sums), input shares (measured in
percentage points), 6-digit distance matrix computed by the input-share
dissimilarity are obtained.

Step 5:

The data was harmonized to quadratic NAICS 2002 matrices. The matrices are
1179 $\times$ 1179 matrices of 6-digit industries.262626473 of 1179 NAICS
6-digit codes are manufacturing and out of these, 307 provide sufficient data
to form a balanced panel. These 307 industries are used in the final analyses.
Empty rows and columns are included for industries that are not producing in
some $t$, for example if an industry was not yet existing or disappeared over
time.

Step 6:

For each $t$, NAICS $\times$ NAICS matrices were created with flows of goods
$flow^{\mu}_{ij,t}$ as entries.

#####  SI.1.1.3 Technical and conceptual issues

###### General remarks about IO codes, NAICS and SIC

The original IO data in early years uses IO codes, which are an internal
metric of the accounting system used to construct social accounting matrices
(SAM). These codes are converted into industry codes (SIC and NAICS). The
classification system has changed over time. Fortunately, the IO-codes in the
raw IO tables are largely consistent across time. The accounting codes were
converted into SIC and from SIC into NAICS or directly into NAICS if such
mapping is available. For the data harmonization, all codes were converted
into the NAICS-2002 codes as they can be directly mapped to SIC 1987 codes.

###### How to deal with accounting codes that are mapped into multiple SIC
sectors?

Some of the accounting codes are associated with multiple SIC sectors, i.e.
multiple industries have been aggregated into one accounting position.
Information about the strengths of links to each of these subsectors is
missing. For reasons of simplification, it is assumed that the accounting
position is equally related to all of them. The strength of single links is
weighted uniformly by the number of sectors. For example, the IO code 020401
(“Fruits”) is linked to 9 SIC sectors (0171, 0172, 0174, 0175, *0179, *019,
*0219, *0259, *029). The links are weighted by factor 1/9.

###### How to deal with inconsistencies across time in changing classification
systems?

The accounting codes of 1977 and 1982 data are mapped to SIC 1987. All
mappings from accounting positions to SIC are based on the 1987 data, after
having ensured that the accounting codes are consistent across time. Also the
vast majority of IO-to-SIC-mappings is consistent in 1977 and 1992 data. For
1977 some minor deviations exist, but these can be largely explained by
adjustments in the SIC system between 1977 and 1987. Some old SIC industries
do not exist any longer. A reconstruction is practically not feasible with
reasonable effort, given that the expected value added of higher precision is
negligibly small, if existing at all. The 1977 IO-SIC mapping is only used
when 1987-data is not available.

In the 2002 NAICS file, some IO codes are mapped to a very high number of sub-
sectors. This is for example the case for aggregate positions such as retail
and wholesale trade and construction. They were kept in the mapping. It should
be noted that an accounting position that has a link to more than a hundred
6-digit NAICS industries is not necessarily meaningful. This problem was
addressed by a series of robustness checks using only a subset of the data,
higher levels of aggregation and rounding of IO links that fall below a
certain threshold.

The more recent versions of the classification systems are more detailed.
Equal weights were used when one coarse industry maps to several more detailed
industry when using another (typically more recent) classification system.
Hence, the transaction volume is equally distributed across sub-sectors.

####  SI.1.2 Patent data

#####  SI.1.2.1 Overview

The raw patent data classified by CPC codes are taken from an earlier project.
An extensive documentation of the data is provided along with the data, which
can be downloaded for reuse under a CC-BY-4.0 license (hotte2023data, 56).

From the raw data, the CPC classification data, citations among patents with
the grant number as ID, and data on the grant year of the patents were used.
Further, to map patents classified by CPC codes to NAICS 6-digit codes, the
concordance tables by goldschlag2020tracking (51) was
used.272727https://sites.google.com/site/nikolaszolas/PatentCrosswalk
[accessed in Oct 2021]. The _Cooperative Patent Classification (CPC)
Crosswalks - Version 1603_ file downloaded in October 2021 was used.

The patents were first reclassified into NAICS taking account of the weighting
scheme of goldschlag2020tracking’s (51) concordance. Next, a NAICS-to-NAICS
citation network for each 5-year time window was constructed.

#####  SI.1.2.2 Processing steps in detail

To construct a patent citation network among NAICS 6-digit industries, the
data were processed in a series of steps.

Step 1:

In a first step, patents were mapped to NAICS 2002 codes to create industry
level patent stocks as 5-year aggregates covering the period 1973-2012. First,
patents were sampled by time window. Then, patents in each time window were
aggregated into each 4-digit CPC class, ensuring uniqueness for each entry by
patent grant number and CPC 4-digit code. Hence, patents that map to multiple
more disaggregate CPC codes that belong to the same 4-digit aggregate were
treated as unique entry. The counts at the 4-digit CPC level were subsequently
mapped to NAICS 6-digit codes taking account of the weights, i.e. the patent
counts are multiplied by the weight whenever one CPC 4-digit class maps to
multiple NAICS 6-digit codes.

Step 2:

Next, the citation data is mapped from citations between individual patents by
grant number to citations between NAICS 6-digit industries. The NAICS-to-NAICS
edgelist also contains a column with the weight, which indicates the number of
citations that flow from one industry to another during each 5-year time
window. To obtain this edgelist, both the citing and the cited patent were
first mapped to CPC, ensuring the uniqueness of patent CPC 4-digit links.
Then, they were aggregated and the number of CPC 4-digit-to-CPC 4-digit
citations was obtained based on unique pairs of patents. Next, the CPC-to-CPC
citations were mapped to NAICS while applying the weight to both the citing
and cited NAICS industry.

Step 3:

The edgelist is used to construct an adjacency matrix with 6-digit industries
as row and column names. The data is harmonized with the format of the IO
adjacency matrices. Additionally, the adjacency matrices are also created for
other levels of aggregation.

###  Supplementary Material SI.2 Supplementary descriptives

####  SI.2.1 Network plots

Figure SI.1: Upstream networks at the 3-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. A link between a pair of industries
$i$ and $j$ is shown if $j$ is a sufficiently important supplier to $i$, i.e.
if the average of the weight $w^{in,\alpha}_{ij,t}$ during periods 1977-1992
and 1997-2012 exceeds a threshold level given by the average weight over all
industry pairs and all periods plus one standard deviation
($\text{mean}_{i,j,t}(w^{in,\alpha}_{ij,t})+\text{sd}_{i,j,t}(w^{in,\alpha}_{ij,t})$).
The size of the nodes is proportional to the size of an industry
$A^{\alpha}_{i,t}$ in the respective layer. The figure is generated using the
plotting functions of the R-package _igraph_ , which makes use of the
Fruchtermann-Reingold algorithm to allocate the nodes and edges of the
network. 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).

####  SI.2.2 Industry rankings

Tables SI.1 and SI.2 show the Top-10 ranking of industries by up- and
downstream centrality as measured by the PageRank.

Table SI.1: Top-10 ranking of industries by the upstream PageRank at the
6-digit level.

| _Top-10 industries by Pagerank ( $PR^{\mu,up}_{i,t}$)_
---|---
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
1 |  Petroleum Refineries |  324110 |  0.17 |  Petroleum Refineries |  324110 |  0.08 |  Petroleum Refineries |  324110 |  0.10 |  Copper Refineries |  331411 |  0.05
2 |  Copper Refineries |  331411 |  0.05 |  Plastics Mat. & Resin |  325211 |  0.05 |  Iron & Steel Mills |  331111 |  0.07 |  Iron & Steel Mills |  331111 |  0.05
3 |  Plastics Mat. & Resin |  325211 |  0.04 |  Copper Refineries |  331411 |  0.05 |  Semiconductor & Device |  334413 |  0.05 |  Automobile Mnft. |  336111 |  0.03
4 |  All Petrol. & Coal Prod. |  324199 |  0.04 |  All Petrol. & Coal Prod. |  324199 |  0.04 |  Sawmills |  321113 |  0.04 |  Biological Prod. |  325414 |  0.02
5 |  Iron & Steel Mills |  331111 |  0.04 |  Chem. Preparations |  325998 |  0.03 |  Plastics Mat. & Resin |  325211 |  0.04 |  Plastics Mat. & Resin |  325211 |  0.02
6 |  Chem. Preparations |  325998 |  0.02 |  Iron & Steel Mills |  331111 |  0.03 |  Copper Refineries |  331411 |  0.03 |  Ship Building & Repair |  336611 |  0.02
7 |  Paperboard Mills |  322130 |  0.02 |  Inorganic Dye & Pigm. |  325131 |  0.03 |  Gum & Wood Chem. |  325191 |  0.02 |  Aircraft Mnft. |  336411 |  0.02
8 |  Organic Chem. |  325199 |  0.02 |  Organic Chem. |  325199 |  0.03 |  Organic Chem. |  325199 |  0.02 |  Dog & Cat Food Mnft. |  311111 |  0.02
9 |  Inorganic Dye & Pigm. |  325131 |  0.01 |  Fats & Oils Refin. |  311225 |  0.01 |  Machine Shops |  332710 |  0.02 |  Petroleum Refineries |  324110 |  0.01
10 |  Metal Can Mnft. |  332431 |  0.01 |  Nitrogen. Fertl. Mnft. |  325311 |  0.01 |  Print Circuit Assembly |  334418 |  0.02 |  Semiconductor & Device |  334413 |  0.01
| Quartiles:
| 0.01, 0.01, 0.01 | 0.01, 0.01, 0.01 | 0.01, 0.01, 0.02 | 0.01, 0.01, 0.01
| _Top-10 industries by Pagerank ( $PR^{\tau,up}_{i,t}$)_
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
1 |  Adhesive Mnft. |  325520 |  0.05 |  Adhesive Mnft. |  325520 |  0.05 |  Semiconductor & Device |  334413 |  0.05 |  Semiconductor & Device |  334413 |  0.06
2 |  Chem. Preparations |  325998 |  0.05 |  Chem. Preparations |  325998 |  0.05 |  Adhesive Mnft. |  325520 |  0.05 |  Adhesive Mnft. |  325520 |  0.04
3 |  Semiconductor & Device |  334413 |  0.03 |  Semiconductor & Device |  334413 |  0.04 |  Chem. Preparations |  325998 |  0.04 |  Electr. Computer Mnft. |  334111 |  0.04
4 |  Power Transm. Equ. |  333613 |  0.03 |  Power Transm. Equ. |  333613 |  0.03 |  Laboratory Apparatus |  339111 |  0.03 |  Chem. Preparations |  325998 |  0.04
5 |  Fastener  & Pin |  339993 |  0.02 |  Fastener  & Pin |  339993 |  0.03 |  Electr. Computer Mnft. |  334111 |  0.03 |  Optical Instrum. & Lens |  333314 |  0.03
6 |  Laboratory Apparatus |  339111 |  0.02 |  Electr. Computer Mnft. |  334111 |  0.03 |  Fastener  & Pin |  339993 |  0.03 |  Fastener  & Pin |  339993 |  0.03
7 |  Speed Changer  & Gear |  333612 |  0.02 |  Laboratory Apparatus |  339111 |  0.02 |  Optical Instrum. & Lens |  333314 |  0.02 |  Wireless Communic. |  334220 |  0.02
8 |  Electr. Computer Mnft. |  334111 |  0.02 |  Speed Changer  & Gear |  333612 |  0.02 |  Power Transm. Equ. |  333613 |  0.02 |  Medical Instrum. |  339112 |  0.02
9 |  Boiler & Heat Exch. |  332410 |  0.02 |  Optical Instrum. & Lens |  333314 |  0.02 |  Speed Changer  & Gear |  333612 |  0.02 |  Power Transm. Equ. |  333613 |  0.02
10 |  Optical Instrum. & Lens |  333314 |  0.02 |  Boiler & Heat Exch. |  332410 |  0.02 |  Dental Equ. & Supplies |  339114 |  0.02 |  Elctrmed. Apparatus |  334510 |  0.02
| Quartiles:
| 0.01, 0.01, 0.0125 | 0.01, 0.01, 0.01 | 0.01, 0.01, 0.01 | 0.01, 0.01, 0.02

Notes: Industries are ranked by the PageRank compiled on upstream links
$PR^{\alpha,up}_{i,t}$ 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
$PR^{\alpha,up}_{i,t}$. The values $PR^{\alpha,up}_{i,t}$ were normalized
before through division by the economy-wide average 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 SI.2: Top-10 ranking of industries by the downstream PageRank at the
6-digit level.

| _Top-10 industries by Pagerank ( $PR^{\mu,dw}_{i,t}$)_
---|---
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
1 |  Mobile Home Mnft. |  321991 |  0.07 |  Motor Home Mnft. |  336213 |  0.10 |  Aircraft Mnft. |  336411 |  0.04 |  Petroleum Refineries |  324110 |  0.07
2 |  Motor Home Mnft. |  336213 |  0.04 |  Ice  & Frozen Dessert |  311520 |  0.04 |  Mobile Home Mnft. |  321991 |  0.04 |  Copper Refineries |  331411 |  0.06
3 |  Frozen Spec. Food Mnft. |  311412 |  0.03 |  Mobile Home Mnft. |  321991 |  0.04 |  Automobile Mnft. |  336111 |  0.04 |  Iron & Steel Mills |  331111 |  0.04
4 |  Motor Vhcl. Body Mnft. |  336211 |  0.03 |  Missile & Space Vhcl. |  336414 |  0.03 |  Motor Home Mnft. |  336213 |  0.03 |  Die-Cut & Paper Office |  322231 |  0.03
5 |  Missile & Space Vhcl. |  336414 |  0.03 |  Aircraft Mnft. |  336411 |  0.03 |  Ship Building & Repair |  336611 |  0.03 |  Plastics Mat. & Resin |  325211 |  0.02
6 |  Ice  & Frozen Dessert |  311520 |  0.03 |  Motor Vhcl. Body Mnft. |  336211 |  0.02 |  Retail Bakeries |  311811 |  0.03 |  Soybean Processing |  311222 |  0.02
7 |  Ship Building & Repair |  336611 |  0.02 |  Frozen Spec. Food Mnft. |  311412 |  0.02 |  Commercial Bakeries |  311812 |  0.03 |  All Misc. Electr. Equ. |  335999 |  0.01
8 |  Automobile Mnft. |  336111 |  0.02 |  Ship Building & Repair |  336611 |  0.02 |  Travel Trailer & Camper |  336214 |  0.02 |  Graphite Prod. |  335991 |  0.01
9 |  Light & Utility Truck |  336112 |  0.02 |  Travel Trailer & Camper |  336214 |  0.02 |  Oth. Animal Food Mnft. |  311119 |  0.02 |  Motor Vhcl. Air-Cond. |  336391 |  0.01
10 |  Heavy Duty Truck Mnft. |  336120 |  0.02 |  Electr. Computer Mnft. |  334111 |  0.02 |  Dog & Cat Food Mnft. |  311111 |  0.02 |  Gum & Wood Chem. |  325191 |  0.01
| Quartiles:
| 0.01, 0.01, 0.02 | 0.01, 0.01, 0.02 | 0.01, 0.01, 0.01 | 0.01, 0.01, 0.01
| _Top-10 industries by Pagerank ( $PR^{\tau,dw}_{i,t}$)_
| 1977-1982 | 1987-1992 | 1997-2002 | 2007-2012
1 |  Adhesive Mnft. |  325520 |  0.05 |  Adhesive Mnft. |  325520 |  0.05 |  Semiconductor & Device |  334413 |  0.05 |  Semiconductor & Device |  334413 |  0.07
2 |  Chem. Preparations |  325998 |  0.05 |  Chem. Preparations |  325998 |  0.05 |  Adhesive Mnft. |  325520 |  0.05 |  Electr. Computer Mnft. |  334111 |  0.04
3 |  Semiconductor & Device |  334413 |  0.04 |  Semiconductor & Device |  334413 |  0.04 |  Chem. Preparations |  325998 |  0.05 |  Adhesive Mnft. |  325520 |  0.04
4 |  Power Transm. Equ. |  333613 |  0.03 |  Power Transm. Equ. |  333613 |  0.03 |  Electr. Computer Mnft. |  334111 |  0.03 |  Chem. Preparations |  325998 |  0.04
5 |  Fastener  & Pin |  339993 |  0.03 |  Fastener  & Pin |  339993 |  0.03 |  Fastener  & Pin |  339993 |  0.03 |  Optical Instrum. & Lens |  333314 |  0.03
6 |  Electr. Computer Mnft. |  334111 |  0.02 |  Electr. Computer Mnft. |  334111 |  0.03 |  Optical Instrum. & Lens |  333314 |  0.02 |  Fastener  & Pin |  339993 |  0.03
7 |  Speed Changer  & Gear |  333612 |  0.02 |  Speed Changer  & Gear |  333612 |  0.02 |  Power Transm. Equ. |  333613 |  0.02 |  Power Transm. Equ. |  333613 |  0.02
8 |  Urethane & Foam Prod. |  326150 |  0.02 |  Optical Instrum. & Lens |  333314 |  0.02 |  Speed Changer  & Gear |  333612 |  0.02 |  Medical Instrum. |  339112 |  0.02
9 |  Boiler & Heat Exch. |  332410 |  0.02 |  Boiler & Heat Exch. |  332410 |  0.02 |  Urethane & Foam Prod. |  326150 |  0.01 |  Misc. Food Mnft. |  311999 |  0.02
10 |  Optical Instrum. & Lens |  333314 |  0.02 |  Urethane & Foam Prod. |  326150 |  0.02 |  Dental Equ. & Supplies |  339114 |  0.01 |  Watch & Clock Mnft. |  334518 |  0.02
| Quartiles:
| 0.01, 0.01, 0.01 | 0.01, 0.01, 0.01 | 0.01, 0.01, 0.01 | 0.01, 0.01, 0.0175

Notes: Industries are ranked by the PageRank compiled on downstream links
$PR^{\alpha,dw}_{i,t}$ 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
$PR^{\alpha,dw}_{i,t}$. The values $PR^{\alpha,dw}_{i,t}$ were normalized
before through division by the economy-wide average 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.

###  Supplementary Material SI.3 Disentangled technology-push and demand-pull
and the direction of change

This section provides additional results for the effects of TP and DP on
productivity (TFP and labor productivity), labor demand (employment and
wages), capital use (capital intensity and investment per capita), and
production labor (share of production workers and relative wage paid for
production labor).

The tables below show the effects from a weighted FE regression, analogous to
the extract of the results shown in the main text 5.2.2. Additional results
relying on dynamic panels methods were used in a comprehensive series of
robustness checks. However, as explained above, these models severely suffer
from weak instruments. The results should be understood as analysis of
conditional correlations without any claims for causality.

| Total factor productivity | Labor productivity
---|---|---
| $\tau\rightarrow TFP_{i,t}$ | $\mu\rightarrow TFP_{i,t}$ | Both | $\tau\rightarrow(VA/L)_{i,t}$ | $\mu\rightarrow(VA/L)_{i,t}$ | Both
| $TFP_{i,t}$ | $TFP_{i,t}$ | $TFP_{i,t}$ | $TFP_{i,t}$ | $TFP_{i,t}$ | $TFP_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$ | $(VA/L)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
$TFP_{i,t-1}$ | 0.9532*** | 0.8322*** | 0.9584*** | 0.8454*** | 0.9444*** | 0.8321*** |  |  |  |  |  |
| (0.016) | (0.0245) | (0.0155) | (0.0244) | (0.0162) | (0.0246) |  |  |  |  |  |
$(VA/L)_{i,t-1}$ |  |  |  |  |  |  | 0.8092*** | 0.6916*** | 0.8066*** | 0.7052*** | 0.8153*** | 0.6924***
|  |  |  |  |  |  | (0.0134) | (0.0421) | (0.0134) | (0.0423) | (0.0143) | (0.0426)
$A^{\mu}_{i,t-1}$ | 0.0476** | 0.0278. |  |  | 0.0459** | 0.0275. | -0.1486* | -0.1468* |  |  | -0.1465* | -0.1469*
| (0.0166) | (0.016) |  |  | (0.0166) | (0.0159) | (0.0624) | (0.0629) |  |  | (0.0624) | (0.0629)
$A^{\tau}_{i,t-1}$ |  |  | 0.098* | 0.0058 | 0.0966* | 0.0096 |  |  | -0.1197 | -0.0679 | -0.1031 | -0.0482
|  |  | (0.0452) | (0.0447) | (0.0452) | (0.0446) |  |  | (0.1588) | (0.1677) | (0.1587) | (0.1675)
$PR^{\mu,up}_{i,t-1}$ | 0.0036 | 0.0082. |  |  | 0.0034 | 0.0082. | 0.0414* | 0.0463* |  |  | 0.042* | 0.047*
| (0.0051) | (0.0048) |  |  | (0.0051) | (0.0048) | (0.0196) | (0.0196) |  |  | (0.0196) | (0.0196)
$PR^{\mu,dw}_{i,t-1}$ | 0.006 | 0.0122** |  |  | 0.0063 | 0.0123** | 0.044** | 0.0557*** |  |  | 0.046** | 0.057***
| (0.0043) | (0.0041) |  |  | (0.0043) | (0.0041) | (0.0161) | (0.0162) |  |  | (0.0162) | (0.0163)
$PR^{\tau,dw}_{i,t-1}$ |  |  | 0.0021 | -0.0135 | -0.0041 | -0.0166 |  |  | -0.0782 | -0.0216 | -0.1013 | -0.0463
|  |  | (0.0195) | (0.0192) | (0.0196) | (0.0192) |  |  | (0.073) | (0.0742) | (0.0734) | (0.0745)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0095 | -0.0083 |  |  | -0.0111 | -0.0117 | -0.0622 | -0.0766. |  |  | -0.0742. | -0.0815*
| (0.0103) | (0.0097) |  |  | (0.0103) | (0.0097) | (0.0408) | (0.0408) |  |  | (0.0412) | (0.0411)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.0167* | -0.0137* |  |  | -0.016* | -0.0135* | -0.0182 | -0.02 |  |  | -0.0161 | -0.0176
| (0.0065) | (0.0062) |  |  | (0.0065) | (0.0062) | (0.0251) | (0.0251) |  |  | (0.0252) | (0.0252)
$Spill(A)^{\tau,up}_{i,t-1}$ |  |  | -0.7579*** | -0.7383*** | -0.7711*** | -0.7479*** |  |  | -1.399. | -1.154 | -1.471. | -1.207
|  |  | (0.2119) | (0.2002) | (0.2122) | (0.2002) |  |  | (0.7844) | (0.7871) | (0.7862) | (0.7875)
$Spill(A)^{\tau,dw}_{i,t-1}$ |  |  | -0.0418* | -0.0235 | -0.0424* | -0.0225 |  |  | 0.0353 | 0.0355 | 0.0341 | 0.0382
|  |  | (0.0176) | (0.0167) | (0.0176) | (0.0167) |  |  | (0.0658) | (0.066) | (0.0659) | (0.066)
$W_{i,t-1}$ |  | 0.059*** |  | 0.0565*** |  | 0.0579*** |  | 0.1687*** |  | 0.1349** |  | 0.1659***
|  | (0.009) |  | (0.009) |  | (0.0091) |  | (0.0477) |  | (0.0477) |  | (0.048)
$(K/L)_{i,t-1}$ |  | 0.0349*** |  | 0.0398*** |  | 0.0363*** |  | -0.056. |  | -0.0523. |  | -0.0544.
|  | (0.0074) |  | (0.0077) |  | (0.0077) |  | (0.0288) |  | (0.0297) |  | (0.0297)
$(L^{P}/L)_{i,t-1}$ |  | 0.1801* |  | 0.141. |  | 0.1587* |  | 1.17*** |  | 1.109*** |  | 1.114***
|  | (0.0731) |  | (0.0735) |  | (0.0735) |  | (0.3005) |  | (0.3041) |  | (0.304)
$(I/L)_{i,t-1}$ |  | -0.0474*** |  | -0.0473*** |  | -0.0473*** |  | 0.0425* |  | 0.0439* |  | 0.0411.
|  | (0.005) |  | (0.005) |  | (0.005) |  | (0.0211) |  | (0.0212) |  | (0.0212)
$(E/L)_{i,t-1}$ |  | -0.0479*** |  | -0.0454*** |  | -0.048*** |  | -0.0848** |  | -0.0739** |  | -0.0856**
|  | (0.0069) |  | (0.0069) |  | (0.007) |  | (0.027) |  | (0.0273) |  | (0.0274)
$(M/L)_{i,t-1}$ |  | -0.0019 |  | -0.0021 |  | -0.0012 |  | 0.0069 |  | 0.0115 |  | 0.0118
|  | (0.0072) |  | (0.0073) |  | (0.0073) |  | (0.0287) |  | (0.0294) |  | (0.0294)
$(W^{P}/W)_{i,t-1}$ |  | 0.0369 |  | 0.025 |  | 0.0349 |  | -0.2304 |  | -0.2732 |  | -0.2356
|  | (0.0446) |  | (0.0447) |  | (0.0446) |  | (0.1875) |  | (0.1886) |  | (0.1882)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.7408 | 0.7726 | 0.7413 | 0.7719 | 0.7438 | 0.7746 | 0.9003 | 0.9022 | 0.8998 | 0.9013 | 0.9007 | 0.9024

Table SI.3: Productivity effects.

Notes: The table shows the regression results of total factor productivity
$TFP$ and labor productivity $(VA/L)_{i,t}$ 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. 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 $TFP$,
$(VA/L)_{i,t}$$A^{\alpha}_{i,t}$, $PR^{\alpha,d}_{i,t}$,
$Spill(A)^{\alpha,d}_{i,t}$, $L_{i,t}$, $(K/L)_{i,t}$, $(I/L)_{i,t}$,
$W_{i,t}$, $(K/L)_{i,t-1}$, $(E/L)_{i,t-1}$, $(M/L)_{i,t-1}$, $W^{P}_{i,t-1}$
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.

| Employment | Wage
---|---|---
| $\tau\rightarrow L_{i,t}$ | $\mu\rightarrow L_{i,t}$ | Both | $\tau\rightarrow W_{i,t}$ | $\mu\rightarrow W_{i,t}$ | Both
| $L_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $L_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $W_{i,t}$ | $W_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
$L_{i,t-1}$ | 0.8379*** | 0.9597*** | 0.8334*** | 0.968*** | 0.8415*** | 0.96*** |  |  |  |  |  |
| (0.0145) | (0.027) | (0.0145) | (0.0273) | (0.0145) | (0.0275) |  |  |  |  |  |
$W_{i,t-1}$ |  | -0.0352* |  | -0.0498** |  | -0.0373* | 0.8381*** | 0.8487*** | 0.8171*** | 0.8172*** | 0.8332*** | 0.8407***
|  | (0.0171) |  | (0.0165) |  | (0.0174) | (0.0122) | (0.0199) | (0.0121) | (0.0194) | (0.0129) | (0.0201)
$A^{\mu}_{i,t-1}$ | -0.2697*** | -0.09* |  |  | -0.2349*** | -0.0895* | -0.2236*** | -0.2105*** |  |  | -0.2227*** | -0.2082***
| (0.0427) | (0.0438) |  |  | (0.0426) | (0.0438) | (0.0526) | (0.0527) |  |  | (0.0526) | (0.0527)
$A^{\tau}_{i,t-1}$ |  |  | -0.4525*** | -0.0185 | -0.4202*** | -0.019 |  |  | -0.0857 | -0.0645 | -0.0747 | -0.0632
|  |  | (0.1158) | (0.117) | (0.1152) | (0.1171) |  |  | (0.1344) | (0.1409) | (0.1337) | (0.1402)
$PR^{\mu,up}_{i,t-1}$ | 0.025. | 0.0218 |  |  | 0.0258. | 0.0223. | 0.0579*** | 0.059*** |  |  | 0.0585*** | 0.0596***
| (0.0143) | (0.0135) |  |  | (0.0142) | (0.0136) | (0.0166) | (0.0164) |  |  | (0.0166) | (0.0164)
$PR^{\mu,dw}_{i,t-1}$ | 0.0229. | 0.0261* |  |  | 0.024* | 0.0265* | 0.0363** | 0.0399** |  |  | 0.0371** | 0.0398**
| (0.0118) | (0.0112) |  |  | (0.0117) | (0.0112) | (0.0136) | (0.0136) |  |  | (0.0136) | (0.0136)
$PR^{\tau,dw}_{i,t-1}$ |  |  | -0.1631** | 0.0297 | -0.155** | 0.0136 |  |  | 0.0563 | 0.1111. | 0.0284 | 0.0841
|  |  | (0.0518) | (0.0511) | (0.052) | (0.0514) |  |  | (0.0617) | (0.0622) | (0.0619) | (0.0623)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0557. | -0.0563* |  |  | -0.0743* | -0.0555. | -0.071* | -0.0705* |  |  | -0.0718* | -0.0672.
| (0.0298) | (0.0281) |  |  | (0.0297) | (0.0284) | (0.0345) | (0.0342) |  |  | (0.0348) | (0.0344)
$Spill(A)^{\mu,dw}_{i,t-1}$ | 0.0076 | -0.0092 |  |  | 0.0032 | -0.0079 | -0.0072 | -0.0107 |  |  | -0.004 | -0.0085
| (0.0183) | (0.0174) |  |  | (0.0181) | (0.0175) | (0.0212) | (0.0211) |  |  | (0.0212) | (0.0212)
$Spill(A)^{\tau,up}_{i,t-1}$ |  |  | -0.653 | -0.434 | -0.754 | -0.4783 |  |  | -1.402* | -1.24. | -1.533* | -1.336*
|  |  | (0.5688) | (0.5437) | (0.5666) | (0.5451) |  |  | (0.6643) | (0.6627) | (0.6627) | (0.661)
$Spill(A)^{\tau,dw}_{i,t-1}$ |  |  | 0.0938. | 0.0302 | 0.0839. | 0.0322 |  |  | 0.045 | 0.0386 | 0.0474 | 0.0451
|  |  | (0.048) | (0.0459) | (0.0478) | (0.0459) |  |  | (0.0558) | (0.0556) | (0.0556) | (0.0554)
$(K/L)_{i,t-1}$ |  | -0.1079*** |  | -0.109*** |  | -0.111*** |  | -0.0502* |  | -0.0602* |  | -0.0604*
|  | (0.0205) |  | (0.0209) |  | (0.0209) |  | (0.0242) |  | (0.025) |  | (0.0249)
$(L^{P}/L)_{i,t-1}$ |  | 1.341*** |  | 1.345*** |  | 1.336*** |  | 1.126*** |  | 1.167*** |  | 1.127***
|  | (0.207) |  | (0.209) |  | (0.209) |  | (0.2509) |  | (0.2542) |  | (0.253)
$(I/L)_{i,t-1}$ |  | 0.0609*** |  | 0.0614*** |  | 0.0605*** |  | 0.0715*** |  | 0.0772*** |  | 0.0718***
|  | (0.0151) |  | (0.0151) |  | (0.0151) |  | (0.0175) |  | (0.0177) |  | (0.0176)
$(E/L)_{i,t-1}$ |  | -0.0483* |  | -0.0458* |  | -0.0476* |  | -0.0885*** |  | -0.0764*** |  | -0.0861***
|  | (0.0219) |  | (0.0219) |  | (0.022) |  | (0.0224) |  | (0.0228) |  | (0.0228)
$(M/L)_{i,t-1}$ |  | -0.0779*** |  | -0.0776*** |  | -0.0769*** |  | 0.0195 |  | 0.0236 |  | 0.0225
|  | (0.02) |  | (0.0206) |  | (0.0206) |  | (0.0241) |  | (0.0247) |  | (0.0247)
$(W^{P}/W)_{i,t-1}$ |  | -0.4702*** |  | -0.4825*** |  | -0.4672*** |  | -0.3355* |  | -0.3488* |  | -0.3293*
|  | (0.1292) |  | (0.1297) |  | (0.1296) |  | (0.1569) |  | (0.158) |  | (0.1571)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9273 | 0.9359 | 0.9276 | 0.9355 | 0.929 | 0.9359 | 0.9248 | 0.9266 | 0.9237 | 0.9257 | 0.9251 | 0.927

Table SI.4: Labor market outcomes.

Notes: The table shows the regression results of employment $L_{i,t}$ and
wages $W_{i,t}$ 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. 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.

| Capital intensity | Investment per capita
---|---|---
| $\tau\rightarrow(K/L)_{i,t}$ | $\mu\rightarrow(K/L)_{i,t}$ | Both | $\tau\rightarrow(I/L)_{i,t}$ | $\mu\rightarrow(I/L)_{i,t}$ | Both
| $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(K/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$ | $(I/L)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
$(K/L)_{i,t-1}$ | 0.69*** | 0.0212 | 0.664*** | 0.0234 | 0.6847*** | 0.0249 |  | -0.325*** |  | -0.3464*** |  | -0.3426***
| (0.0279) | (0.0161) | (0.0277) | (0.0161) | (0.0277) | (0.0162) |  | (0.0693) |  | (0.0699) |  | (0.0698)
$(I/L)_{i,t-1}$ |  | 0.0789*** |  | 0.0762*** |  | 0.0765*** | 0.2362*** | 0.1602*** | 0.2912*** | 0.1819*** | 0.23*** | 0.1708***
|  | (0.0076) |  | (0.0076) |  | (0.0077) | (0.0294) | (0.0393) | (0.0278) | (0.0391) | (0.0298) | (0.0393)
$A^{\mu}_{i,t-1}$ | 0.1776*** | -0.0122 |  |  | 0.1597*** | -0.0168 | 0.3471* | 0.1329 |  |  | 0.3646* | 0.1793
| (0.0348) | (0.034) |  |  | (0.0349) | (0.034) | (0.1622) | (0.1765) |  |  | (0.1617) | (0.1757)
$A^{\tau}_{i,t-1}$ |  |  | 0.3533*** | 0.0565 | 0.3294*** | 0.0469 |  |  | -0.6145 | -0.3806 | -0.5414 | -0.2627
|  |  | (0.0875) | (0.0749) | (0.0869) | (0.0756) |  |  | (0.3941) | (0.3912) | (0.3925) | (0.3917)
$PR^{\mu,up}_{i,t-1}$ | -0.0126 | 0.0021 |  |  | -0.0157 | 4e-04 | 0.1389. | 0.1222. |  |  | 0.1444. | 0.125.
| (0.0166) | (0.0139) |  |  | (0.0165) | (0.0139) | (0.0743) | (0.0725) |  |  | (0.074) | (0.0721)
$PR^{\mu,dw}_{i,t-1}$ | 0.0091 | -0.0157 |  |  | 0.0123 | -0.0151 | 0.164** | 0.1393** |  |  | 0.1616** | 0.142**
| (0.0123) | (0.0104) |  |  | (0.0122) | (0.0105) | (0.0548) | (0.0538) |  |  | (0.0549) | (0.0539)
$PR^{\tau,dw}_{i,t-1}$ |  |  | -0.0216 | -0.1096* | -0.0487 | -0.108* |  |  | 0.8232*** | 0.673** | 0.7479*** | 0.6452**
|  |  | (0.0504) | (0.0427) | (0.0499) | (0.0429) |  |  | (0.2259) | (0.2231) | (0.2236) | (0.2227)
$Spill(A)^{\mu,up}_{i,t-1}$ | 0.112. | 0.0492 |  |  | 0.1133. | 0.0512 | 0.351 | 0.2512 |  |  | 0.3041 | 0.2015
| (0.0593) | (0.0499) |  |  | (0.059) | (0.05) | (0.2662) | (0.2599) |  |  | (0.2654) | (0.2591)
$Spill(A)^{\mu,dw}_{i,t-1}$ | 0.0292 | -0.0184 |  |  | 0.0284 | -0.0162 | 0.1676. | 0.1421 |  |  | 0.1453 | 0.1259
| (0.0199) | (0.0168) |  |  | (0.0198) | (0.0168) | (0.0897) | (0.0874) |  |  | (0.0895) | (0.0872)
$Spill(A)^{\tau,up}_{i,t-1}$ |  |  | -0.4933 | -0.3114 | -0.2876 | -0.3668 |  |  | 4.67* | 5.254** | 5.391** | 5.734**
|  |  | (0.4265) | (0.3548) | (0.4207) | (0.3562) |  |  | (1.91) | (1.855) | (1.885) | (1.847)
$Spill(A)^{\tau,dw}_{i,t-1}$ |  |  | 0.0024 | 0.0225 | -0.0017 | 0.0232 |  |  | 0.222 | 0.1801 | 0.1863 | 0.14
|  |  | (0.0323) | (0.0269) | (0.0319) | (0.027) |  |  | (0.1444) | (0.1404) | (0.1429) | (0.14)
$W_{i,t-1}$ |  | -0.002 |  | -0.0052 |  | -0.0025 |  | 0.0979 |  | 0.1411* |  | 0.1022
|  | (0.0126) |  | (0.0116) |  | (0.0126) |  | (0.0651) |  | (0.0605) |  | (0.0647)
$(L^{P}/L)_{i,t-1}$ |  | -0.3684* |  | -0.3788** |  | -0.3712* |  | 0.867 |  | 1.237 |  | 1.062
|  | (0.1437) |  | (0.1442) |  | (0.1449) |  | (0.7488) |  | (0.7541) |  | (0.751)
$(E/L)_{i,t-1}$ |  | 0.0217. |  | 0.0267* |  | 0.0258* |  | 0.1936** |  | 0.1725** |  | 0.1787**
|  | (0.0127) |  | (0.0127) |  | (0.0128) |  | (0.0634) |  | (0.0642) |  | (0.0637)
$(M/L)_{i,t-1}$ |  | 0.0221 |  | 0.0205 |  | 0.023 |  | 0.1395. |  | 0.1526* |  | 0.1206
|  | (0.015) |  | (0.0149) |  | (0.0151) |  | (0.0773) |  | (0.0767) |  | (0.0773)
$(W^{P}/W)_{i,t-1}$ |  | 0.3604*** |  | 0.3477*** |  | 0.3495*** |  | -1.233** |  | -1.095* |  | -1.123*
|  | (0.0869) |  | (0.0869) |  | (0.0869) |  | (0.4468) |  | (0.4485) |  | (0.445)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9393 | 0.9579 | 0.938 | 0.958 | 0.9406 | 0.9583 | 0.8849 | 0.892 | 0.8827 | 0.8916 | 0.8871 | 0.8939

Table SI.5: Effects on capital use.

Notes: The table shows the regression results of capital intensity
$(K/L)_{i,t}$ and per-capital investment $(I/L)_{i,t}$ 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. 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.

| Share of production labor | Relative wage for production labor
---|---|---
| $\tau\rightarrow(L^{P}/L)_{i,t}$ | $\mu\rightarrow(L^{P}/L)_{i,t}$ | Both | $\tau\rightarrow(W^{P}/W)_{i,t}$ | $\mu\rightarrow(W^{P}/W)_{i,t}$ | Both
| $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(L^{P}/L)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$ | $(W^{P}/W)_{i,t}$
| (1) | (2) | (3) | (4) | (5) | (6) | (7) | (8) | (9) | (10) | (11) | (12)
$(L^{P}/L)_{i,t-1}$ | 0.499*** | 0.4891*** | 0.4871*** | 0.4796*** | 0.4854*** | 0.4863*** |  | 0.234*** |  | 0.2174*** |  | 0.2212***
| (0.0293) | (0.0333) | (0.0303) | (0.0335) | (0.0301) | (0.0335) |  | (0.0567) |  | (0.057) |  | (0.0573)
$(W^{P}/W)_{i,t-1}$ |  | 0.0557** |  | 0.0537** |  | 0.0539** | 0.4499*** | 0.3903*** | 0.4467*** | 0.3894*** | 0.4417*** | 0.3884***
|  | (0.0198) |  | (0.0199) |  | (0.0199) | (0.033) | (0.0338) | (0.033) | (0.0339) | (0.0331) | (0.034)
$A^{\mu}_{i,t-1}$ | -0.0225** | -0.0145. |  |  | -0.0201** | -0.0143. | -0.0249* | -0.0129 |  |  | -0.0236* | -0.0139
| (0.0069) | (0.0078) |  |  | (0.0069) | (0.0078) | (0.0117) | (0.0134) |  |  | (0.0118) | (0.0134)
$A^{\tau}_{i,t-1}$ |  |  | -0.0326. | -0.0235 | -0.0322. | -0.0273 |  |  | -0.0582* | -0.0163 | -0.0544. | -0.0175
|  |  | (0.0174) | (0.0174) | (0.0174) | (0.0175) |  |  | (0.029) | (0.0296) | (0.0293) | (0.0299)
$PR^{\mu,up}_{i,t-1}$ | 0.0044 | 0.0039 |  |  | 0.0045 | 0.004 | 0.0062 | 0.0064 |  |  | 0.0071 | 0.0069
| (0.0033) | (0.0032) |  |  | (0.0033) | (0.0032) | (0.0056) | (0.0055) |  |  | (0.0056) | (0.0055)
$PR^{\mu,dw}_{i,t-1}$ | -0.005* | -0.0031 |  |  | -0.0051* | -0.0033 | 8e-04 | -1e-04 |  |  | -3e-04 | -7e-04
| (0.0024) | (0.0024) |  |  | (0.0024) | (0.0024) | (0.0041) | (0.0041) |  |  | (0.0041) | (0.0041)
$PR^{\tau,dw}_{i,t-1}$ |  |  | -0.0094 | -0.0083 | -0.0045 | -0.0073 |  |  | 0.0014 | 0.0019 | 0.0025 | 0.0018
|  |  | (0.0099) | (0.0099) | (0.0099) | (0.0099) |  |  | (0.0167) | (0.0169) | (0.0169) | (0.017)
$Spill(A)^{\mu,up}_{i,t-1}$ | -0.0064 | -7e-04 |  |  | -0.0055 | -4e-04 | 0.0106 | 0.002 |  |  | 0.0106 | 0.0031
| (0.0117) | (0.0115) |  |  | (0.0117) | (0.0116) | (0.0199) | (0.0197) |  |  | (0.0199) | (0.0198)
$Spill(A)^{\mu,dw}_{i,t-1}$ | -0.007. | -0.0044 |  |  | -0.0064 | -0.0042 | 0.0021 | 0.0023 |  |  | 0.002 | 0.0021
| (0.0039) | (0.0039) |  |  | (0.0039) | (0.0039) | (0.0067) | (0.0066) |  |  | (0.0067) | (0.0067)
$Spill(A)^{\tau,up}_{i,t-1}$ |  |  | 0.1114 | 0.1007 | 0.0741 | 0.082 |  |  | -0.253. | -0.1876 | -0.2779. | -0.1985
|  |  | (0.084) | (0.0823) | (0.0836) | (0.0825) |  |  | (0.1416) | (0.1402) | (0.1423) | (0.1409)
$Spill(A)^{\tau,dw}_{i,t-1}$ |  |  | -0.0011 | 0.0016 | -4e-04 | 0.0022 |  |  | -0.0028 | -0.0076 | -0.0042 | -0.0081
|  |  | (0.0063) | (0.0062) | (0.0063) | (0.0063) |  |  | (0.0107) | (0.0106) | (0.0108) | (0.0107)
$W_{i,t-1}$ |  | -0.0012 |  | -0.0032 |  | -0.0013 |  | -0.0047 |  | -0.0061 |  | -0.0048
|  | (0.0029) |  | (0.0027) |  | (0.0029) |  | (0.0049) |  | (0.0046) |  | (0.0049)
$(K/L)_{i,t-1}$ |  | 0.0061* |  | 0.0067* |  | 0.0072* |  | -0.0102. |  | -0.0107* |  | -0.01.
|  | (0.0031) |  | (0.0031) |  | (0.0031) |  | (0.0052) |  | (0.0053) |  | (0.0053)
$(I/L)_{i,t-1}$ |  | -0.0071*** |  | -0.0077*** |  | -0.0072*** |  | -0.0013 |  | -0.0014 |  | -0.0013
|  | (0.0017) |  | (0.0017) |  | (0.0018) |  | (0.003) |  | (0.003) |  | (0.003)
$(E/L)_{i,t-1}$ |  | 0.0053. |  | 0.0058* |  | 0.0058* |  | 0.012* |  | 0.0113* |  | 0.0115*
|  | (0.0028) |  | (0.0028) |  | (0.0028) |  | (0.0048) |  | (0.0049) |  | (0.0049)
$(M/L)_{i,t-1}$ |  | -0.0039 |  | -0.0033 |  | -0.0034 |  | -3e-04 |  | 0.0016 |  | 7e-04
|  | (0.0034) |  | (0.0034) |  | (0.0035) |  | (0.0059) |  | (0.0058) |  | (0.0059)
Controls |  | Y |  | Y |  | Y |  | Y |  | Y |  | Y
$R^{2}$ | 0.9311 | 0.9338 | 0.93 | 0.9337 | 0.9317 | 0.9343 | 0.778 | 0.7878 | 0.7791 | 0.7879 | 0.7803 | 0.7885

Table SI.6: Effects on the share of production labor.

Notes: The table shows the regression results of the share of production labor
$(L^{P}/L)_{i,t}$ and relative wages for production labor $(W^{P}/W)_{i,t}$ 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.
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