Automating Labor: Evidence from Firm-level Patent
Data
Antoine Dechezleprêtre

David Hémous

Morten Olsen

Carlo Zanella∗
October 2019

Abstract
Do higher wages lead to more automation innovation? To answer this question,
we first introduce a new measure of automation by using the frequency of certain
keywords in the text of patents to identify automation innovations in machinery.
We validate our measure by showing that it is correlated with a reduction inroutine tasks in a cross-sectoral analysis in the US. Then, we build a firm-level
panel dataset on automation patents. We combine macroeconomic data from 41
countries and information on geographical patent history to build firm-specificmeasures of low-skill and high-skill wages. We find that an exogenous increase in
low-skill wages leads to more automation innovation with an elasticity between
2 and 4. An increase in high-skill wages tends to reduce automation innovation.
Placebo regressions show that the effect is specific to automation innovations.
Finally, we use the Hartz labor market reforms in Germany as an event study andfind that they are associated with a relative reduction in automation innovations.
∗
Antoine Dechezleprêtre, Grantham Research Institute and Centre for Economic Performance,
LSE and OECD, antoine.dechezlepretre@oecd.org. David Hémous, University of Zurich and CEPR,
david.hemous@econ.uzh.ch, Morten Olsen, University of Copenhagen, mgo@econ.ku.dk. Carlo Zanella,
University of Zurich, carlo.zanella@econ.uzh.ch. David Hémous gratefully acknowledges the financial
support of the European Commission under the ERC Starting Grant 805007 AUTOMATION. We thankLorenzo Casaburi, Nir Jaimovich, Elias Papaioannou, Pascual Restrepo, Joachim Voth and Fabrizio
Zilliboti among others for helpful comments and suggestions. We also thank seminar and conference
participants at the University of Zurich, Swiss Macro workshop, the University of Copenhagen, the
TRISTAN workshop in Bayreuth, the University of Bath, London Business School, the NBER Macroeconomics Across Time and Space Conference, the NBER Summer Institute, the AlpMacro Conference,
LMU, Oxford University and Helsinki Graduate School of Economics. We thank Amedeo Andriollo,
Selina Schön and Suo for fantastic research assistance.

JEL: O31, O33, J20
KEYWORDS: Automation, Innovation, Patents, Income Inequality2

1

Introduction

Do higher wages lead to more labor-saving innovations? And if so, by how much?
At a time of fast technological progress in automation technologies such as robotics
and AI and of political campaigns pushing for higher minimum wages, answering thesequestions is of central importance. Even more so because the endogeneity of automation
innovations matters for the long-term effects of policy interventions (Hémous and Olsen,
2018). Yet, the literature on the effect of wages on labor-saving technological changeremains limited. In fact, the few existing papers focus on the effect of labor costs on the
adoption of automation technologies (e.g. Lewis, 2011, Hornbeck and Naidu, 2014, or
Acemoglu and Restrepo, 2018a). Our paper is the first to establish a causal effect of anincrease in wages on automation innovations.
Answering this question requires overcoming two challenges: identifying automation
innovations and finding a source of exogenous variation in wages from the perspective ofinnovating firms. To overcome the first challenge, we build a new method of classifying
automation patents using the existing assignment of patents to technological categories(IPC and CPC codes). We use the text of patents from the European Patent Office(EPO) and compute the frequency of certain keywords (such as “robot”, “automation”
or “computer numerical control”) for each technological category. Our identification
strategy is ideally suited for innovations in equipment and we restrict attention to thoseinnovations. We define “automation technological categories” as technological categories
where the frequency of the keywords is above a certain threshold. Finally, we identify as
automation patents those which belong to automation technological categories (including non-EPO patents). Our method presents at least two advantages: it is transparent
and covers a wide range of innovations across several sectors compared with more narrowmeasures such as the use of robots. According to our laxer measure, the share of automation innovations among innovations in machinery has increased from 12.8% in 1999
to 20.5% in 2014. We conduct a validation exercise based on Autor, Levy and Murnane(2003). We find that in the United States, sectors where the share of automation patents
filed in machinery was high, saw a decrease in routine tasks and an increase in the skill
ratio. Automation is uncorrelated with computerization and captures a different formof technological change but has similar effects.
At the country level, technology and wages are co-determined. To isolate exogenous variation in wages, we therefore exploit firm-level variations in the wages facedby the potential customers of innovating firms by exploiting variations in innovating1

firms’ exposure to international markets. We expand on the methodology of Aghion,
Dechezleprêtre, Hémous, Martin and Van Reenen (2016, henceforth ADHMV) and use
the PATSTAT database, which contains close to the universe of patents. For each firm
which undertakes automation innovations, we compute how much it has patented presample in machinery in each country. We take this information as a proxy for the
distribution of the firm’s international exposure and build firm-specific weighted averages of low- and high-skill wages using country-level data. These firm-specific wages
proxy for the average wage paid by the downstream firms of the innovating firms. As
a result, for, say, two German firms, we identify the effect of an increase in wages on
automation innovations, by comparing how much more automation innovations increase
for the firm which has the higher market exposure to the US when US low-skill wages
increase.
We conduct our main analysis over the sample period 1997-2011 and use wage datafor 41 countries with automation patents for 3,341 firms. We find a substantial effectof wages on automation innovations: higher low-skill wages lead to more automation
innovations with an elasticity between 2 and 4 depending on specification. Higher highskill wages tend to reduce automation, but with a smaller elasticity, a finding in line withthe capital-skill complementarity hypothesis (Krusell, Ohanian, Rios-Rull and Violante,2000). Our results are robust to the inclusion of domestic country-year fixed effects andcontinues to hold when we decompose firm-specific wages into a domestic and a foreign
part. Moreover, we use the geographical localization of firms’ inventors to compute thelocal knowledge stocks which firms are exposed to. We find strong evidence of local
knowledge spillovers which suggest that the long-term effects of an increase in wages on
automation innovations are larger than the short-term effects. Yet, more automation
innovations in a firm are associated with fewer future automation innovations. We runplacebo regressions with low-automation patents in machinery and find no effect of lowskill wages on automation innovation.
Finally, we look at the effect of the Hartz reform in Germany in 2002-2004, which
aimed at increasing labor market flexibility making the use of labor more attractive andconsequently lowering the incentive for automation innovation. We focus on patents fromthe countries with the highest exposure to Germany, excluding Germany itself. Whileforeign firms most exposed to Germany were increasingly doing automation innovations
relative to other innovations in machinery until the Hartz reform, the trend sharply
reversed thereafter.

2

The theoretical argument that higher wages should lead to more labor-saving technology adoption or innovation dates back to Habakkuk (1962) and is at the core ofseveral theoretical papers (e.g. Zeira, 1998, Acemoglu, 2010). More recently, a small
growth literature has emerged where endogenous innovation can take the form of either
automation or the creation of new tasks, in which case wages affect the direction of
innovation (Hémous and Olsen, 2018, Acemoglu and Restrepo, 2018b).
There is an extensive literature on the effects of technological change on wages andemployment,1 yet the empirical literature on the reverse question is much more limited.
A few papers show that labor market conditions affect labor-saving technology adoption
in health care (Acemoglu and Finkelstein, 2008), agriculture (Manuelli and Seshardi,
2014, Hornbeck and Naidu, 2014, and Clemens, Lewis and Postel, 2018), and manufacturing (Lewis, 2011). Lordan and Neumark (2018) find that minimum wage hikes
displace workers in automatable jobs and Acemoglu and Restrepo (2018a) relate demographic trends to robot adoption. Our paper differs in at least two ways. First, ouranalysis is broader since it covers a range of automation technologies and 40 countries.
Second, we focus on innovation instead of adoption,2 which matters because the economic drivers of innovation may differ from those of adoption: it may be less responsive
to macroeconomic variables such as wages and knowledge spillovers are likely to playa greater role. There is essentially no empirical literature on automation innovations:
Alesina, Battisti and Zeira (2018) find in cross-country regressions that labor market regulations are positively correlated with innovation in low-skill intensive sectors, which isconsistent with a model where innovation is low-skill labor-saving; and a recent working
paper by Bena and Simintzi (2019) shows that firms with a better access to the Chineselabor market decrease their share of process innovations after the 1999 U.S.-China trade
agreement.3
1
See for instance Autor, Katz and Krueger (1998), Autor et. al. (2003), Bartel, Ichniowski and Shaw(2007) or Autor and Dorn (2013), Gaggl and Wright (2017) for IT, Doms, Dunne and Totske (1997) forfactory automation, Graetz and Michaels (2017) or Acemoglu and Restrepo (2017) for robots, Blanas,
Gancia and Lee (2018) for different forms of capital, Mann and Püttmann (2018) or Bessen, Goos,
Salomons and van den Berge (2019) for broader measures of automation and Aghion, Jones and Jones(2017), Martinez (2018) or Gaggl and Eden (2018) for the effect on factor shares (see also Aghion,
Bergeaud, Boppart, Klenow and Li, 2019, and Akcigit and Ates, 2019, for other factors behind the dropof the labor share).
2
To be more precise, Acemoglu and Restrepo (2018a) also show some cross-country correlations
between demographic trends and patents in robotics.
3
Process innovations and automation innovations are not the same: certain process innovations may
involve reducing other costs than labor costs (for instance materials costs) and certain automation
innovations can be product innovations (for instance a new industrial robot is a product innovation for
its maker).

3

This is perhaps surprising because a large literature shows that the direction of
innovation is endogenous in other contexts: Acemoglu and Linn (2004) in the pharmaceutical industry; Hanlon (2015) in the 19th century cotton industry and several papers
in the context of energy-saving or green innovations (Newell, Jaffe and Stavins, 1999,
Popp, 2002 and Calel and Dechezleprêtre, 2016). Here, we build more specifically on
the methodology of ADHMV, who build firm-level variations in gas prices to show thathigher gas prices lead firms in the auto industry to engage more in clean and less in dirty
innovations.4
The use of text analysis using keywords has developed rapidly in economics since
Gentzkow and Shapiro (2010). More closely related, Mann and Püttman (2018) use
machine-learning techniques to classify automation patents. We compare our approaches
below.
Section 2 contains our first contribution: a classification of automation technologies
and compares it with existing measures. Section 3 introduces a simple model to motivate
the analysis. Section 4 describes our empirical strategy and the data we use. Section 5contains the results of the main analysis on the effect of wages on automation innovations.
Section 6 discusses the event study of the Hartz reform. Section 7 concludes. Appendix
B provides details on our automation classification and additional robustness checks.

2

Classifying automation patents

In the following we describe the patent data as well as our method for classifying automation patents. We then show how our measure of automation compares to previous
measures of automation, notably the use of computers in the framework of Autor et.
al. (2003). Our approach proceeds in three steps: i) We use the existing literature to
identify keywords related to automation. ii) We use those keywords and the text of EPO
patents to classify technological categories (based on the existing IPC and CPC codes) inmachinery as automation or not. iii) We then classify worldwide patents as automation
or not depending on whether they belong to an automation technology category.
4

Three other papers have used ADHMV’s methodology: Noailly and Smeets (2015) use it to look at
innovation in electricity generation, Coelli, Moxnes and Ulltveit-Moe (2018) use it to look at the effectof trade policy on innovation and Aghion, Bénabou, Martin and Roulet (2019) to look at the role ofenvironmental preferences and competition in innovation in the auto industry—as explained later inthe text, we methodologically extend this work by looking separately at the effect of the domestic andforeign variables.

4

2.1

Patent data

We use two patent databases maintained by the European Patent Office (EPO). For mostof our empirical analysis, we use the World Patent Statistical Database (PATSTAT) from
Autumn 2018 which contains the bibliographical information of patents from 90 patentissuing authorities (covering nearly all patents in the world) but not the text of individual
patents. Since text analysis is essential to our approach, we supplement with the EP
full-text database from 2018, which contains the full text of EPO patent applications (asubset of the patents from PATSTAT).
PATSTAT allows us to identify “patent families”, a set of patent applications acrossdifferent patent offices which represent the same innovation. For each patent family, weknow the date of first application (which we use as the year of an innovation), the patentoffices where the patent is applied for (which indicates its geographical breadth), theidentity of the applicants and the inventors and the number of citations received by thepatent family. In addition, to identify the technological characteristics of patents we use
their IPC and their CPC codes (henceforth C/IPC codes).5 Importantly each patent
usually has several C/IPC codes. The C/IPC codes form a hierarchical classification
systems. Certain types of technologies (for instance fossil fuel engines) can readily be
identified to existing groupings of C/IPC codes. Such a grouping does not exist for
automation and it is our goal in the following to create it.
Our strategy to identify automation innovations relies on first identifying automation
C/IPC codes (and combinations thereof) by computing the frequency of certain keywords
in the text of patents belonging to those C/IPC codes. We then use this information
to identify automation patents as those with automation C/IPC codes. This strategy
has two advantages over classifying patents directly. First, it allows us to include nonEPO patents in our analysis, for which PATSTAT does not contain the text.6 Second,
technological codes by themselves are informative and one should think of the particularwording of a patent as a signal of its underlying characteristics. Patents are written indifferent styles, and often do not expand on the purpose of the invention, so that the sameinnovation can often be described with or without using our keywords. In other words,
5

The IPC is the International Patent Classification and the CPC the Cooperative Patent Classification used by the USPTO and the EPO. The CPC is an extension of the IPC and contains around
250,000 codes in its most disaggregated form.
6
To give an idea of the increase in sample, over the period 1997-2011 there are 3.19 million patentfamilies with patent applications in at least two offices (a condition we will impose in our main analysis).
Among those only around 740 thousand have an EPO patent with a description in English.

5

if a patent does not contain one of our keywords but belongs to a C/IPC code where
patents most of the time do, there is a high likelihood that it is actually an automation
patent (see examples in Figures 2a and 2b below). Conversely, if a patent uses one of our
keywords but does not belong to any C/IPC codes where this is common, the inclusionof this keyword is frequently uninformative about the nature of the innovation.7

2.2

Choosing automation keywords

In the following we explain how we choose our automation keywords. Most of our
keywords come from the automation technologies identified in Doms, Dunne and Troske(DDT, 1997) and Acemoglu and Restrepo (AR, 2018).8 We complemented this list as
described below. Naturally, we seek to capture as many patents truly associated with
automation as possible without too many false positives. Table 1 describes the list of
keywords together with their origin (Appendix B.1 provides additional details).
We have eight categories of keywords. Five of these, Robot*, numerical control,
computer-aided design and manufacturing, flexible manufacturing and programmable
logic control are automation technologies in DDT or AR. Simply applying these keywords
may result in false positives. For instance “NC” can refer to either “numeric control” or“North Carolina”. To address this issue, we require that those keywords are either in thesame patent or the same sentence as a list of secondary words which indicate that the
text describes a machine. We add 3D printing, which was in its infancy when DDT was
written. We also add “labor” which indicates that an innovation reduces labor costs.
We similarly add “automation” and “automatization”. The stem “automat*” gather
too many false positives such as “automatic transmission”. We resolve this in two ways:
either by restricting attention to patents where the frequency is 5 or more or by combining automat* with other words which largely come from technologies described in DDTor AR (we count patents where automat* and one of these words appear in the samesentence at least twice).
An alternative procedure would have been to read and classify a subset of patents
and use machine-learning techniques to classify patents (or technological categories) as
7

As a matter of fact, the World Intellectual Property Organization (WIPO) offers on its website a
simple tool based on a similar principle. A search engine allows to identify up to 5 IPC codes most
likely to correspond to a set of keywords using the text of the patents in its database.
8
Doms, Dunne and Troske (1997) measure automation using the Survey of Manufacturing Technology(SMT) from 1988 and 1993 conducted by the US Census. The survey asked firms about their use ofcertain automation and information technologies. Acemoglu and Restrepo (2018) include imports ofautomation technology and associate specific HS-categories from Comtrade with automation technology.

6

Table 1: Choice of automation keywords
Key words
Automat*

Robot*

Comments
Automation, automatization
or automat* at least 5 times

SourceOwn /Doms, Dunne
and Troskeor (automat* or autonomous) with (secondary words or warehouse (DDT) /or operator or arm or convey* or handling or inspect or knitting or Acemoglu
manipulat* or regulat* or sensor or storage or store or vehicle
and Restrepo
system or weaving or welding) in the same sentence at least twice (AR)Not surgical or medical
DDT and AR

Numerical Control

CNC or numeric* control* or(NC in the same sentence as secondary words)
Computer-aided design Computer-aided/-assisted/-supported
and manufacturing
In the same patent as secondary words
CAD or CAM in same sentence as secondary words
Flexible manufacturing

DDT and AR

Programmable logic
control
3D printerProgrammable logic control or
PLC and not (powerline or "power line")
Including additive layer manufacturing

DDT

Labor

Including laborious

Own

Secondary words

Machine or manufacturing or equipment or apparatus or machining

DDT

DDT

Own

Notes: "In the same sentence as control words" refers to at least one control word. Keywords include i)natural adjacent words (i.e. numerical control includes NC, numerically controlled and numeric control), ii)
British/American spelling (i.e. labour/labor) and iii) hyphenated adjectives (i.e. computer aided / computeraided design). We added words in italics, the others come from AR or DDT. See Appendix for details.

7automation or not. This is the procedure in Mann and Püttmann (2018). We believe ourapproach has several advantages. First, we found that classifying patents as automation
is a difficult task: often looking at a single patent in isolation is not enough, and oneneeds to look at several patents within the same technological group to find patterns
suggesting that a patent is likely an automation patent. Therefore, the task of manuallyclassifying patents cannot be easily systematized and therefore outsourced. Second,
patents are written in a technical language and do not primarily discuss the goal of an
innovation, so that only a few words within the text are informative. Consequently, amachine-learning algorithm would require a large set of classified data to classify patents
correctly. Third, once the classification is done it can easily be applied to patents without
text and future patents. Fourth, our method is more transparent and can easily be
replicated or modified.

2.3

Defining automation technological categories and automation patents

As discussed above we use the keywords to associate technological categories, and notpatents directly, to automation. These technological categories are defined as: 6-digit
C/IPC codes, all pairs of 4-digit C/IPC codes and pairs combining the union of the 3 digit
codes G05 and G06 with any 4-digit C/IPC codes (outside codes in G05, G06).9 The
code G05 corresponds to “controlling; regulating” and G06 to “computing; calculating;counting”. Using combinations of G05 and G06 code with 4-digit C/IPC codes is inspiredby Aschhoff et al. (2010) who use these codes to identify advanced manufacturing
technologies. We restrict attention to categories which contain at least 100 patents toensure that the prevalence of keywords measure is based on a sufficiently large numberof patents.10
We then measure the prevalence of our keywords within technological categories for
those patent applications from 1978 onward which contain a description in English (a total of 1,538,370 patent applications). In Appendix B.1.4, we verify that the choice of the9Technically, the structure of the C/IPC classification is as follows: C/IPC “classes” have 3 digit
codes (for instance B25: “hand tools; portable power-driven tools; handles for hand implements; workshop equipment and manipulators”), “subclasses” have 4 digit codes (for instance B25J: “manipulators;
chambers provided with manipulation devices”) and main groups have 5 to 7 digit codes (for instance
B25J 9: “programme-controlled manipulators”). In the following, we will slightly abuse language andrefer to classes, subclasses and main groups as 3 digit, 4 digit and 6 digit codes respectively.
10
We group 6-digit codes with less than 100 patents into codes at the 4-digit level.

8

starting year does not affect our classification much. To select automation C/IPC codes,
we further restrict attention to C/IPC codes which belong to technological fields which
are associated with equipment. There are 34 technological fields (see Figure A.7) and
we focus on “machine tools”, “handling”, “textile and paper machines” and “other special
machines”, which we refer to as “machinery” patents (we use machinery and equipment
interchangeably). Our classification scheme captures a broader set of automation technologies than what is relevant for our empirical analysis including Roombas and militarydrones. We adjust the set of technological codes accordingly.11 For pairs of 4 digit IPC
codes, we assume that they belong to the relevant technological field when at least one of
the 4 digit codes belongs to the relevant technological field. Similarly, the combinationsof 4 digit IPC code and G05 or G06 belong to the relevant technological fields if the 4digit code belongs to that group.
We extensively checked the C/IPC codes and sampled patents from each category toensure that the procedure delivered reasonable results. However, the validation exercises
and the main empirical exercise where carried out after the classification was set.
Table 2 gives some examples of 6-digit C/IPC codes in machinery with the prevalenceof automation keywords including their rank within machinery 6 digit codes with at least
100 patents. It also shows the prevalence of some of the most important subcategories(automat*, robots and CNC) in the patents linked to each C/IPC code. C/IPC codes
associated with robotics (B25J) have the highest prevalence numbers with up to 91%
patents in B25J5 which contain at least one of the keywords. Yet, there are also codes
associated with machine tools other than robots at the top of the distribution such as
B23Q15 and codes associated with devices used in the agricultural sector such as A01J7.
B24B49 is a code close to the threshold we use to delimit automation patents. The last
four C/IPC codes are examples with a low prevalence of automation keywords. The tablealso shows that the different sub-measures do not capture the same technologies: therobotic codes are ranked highly thanks to their share of patents with the word “robot”,
B23Q15 is high because a lot of patents contain words related to CNC, and B65G1,
11

Roombas are already excluded since they are not in the four technological fields. We further
exclude F41 and F42 which correspond to weapons and ammunition and are in “other special machines”.
In addition, we include B42C which corresponds to machines for book production and B07C whichcorresponds to machines for postal sorting as both correspond to equipment technologies and contain 6digit codes with a high prevalence of automation keywords; the 6-digit code G05B19 which corresponds
to “programme-control systems” and contains a large number of NC and CNC (computer numericallycontrolled) machine tools which are not attributed IPC codes in the machine tools technological field;
and the 6-digit code B62D65 which concerns engine manufacturing even though the rest of the B62D
code is about the vehicle parts themselves.

9

Code

Description

Number of

All share

patents

Rank

Robot

Automat*

CNC(over 1009)

share

share

share

– High prevalence –B25J5

Manipulators mounted on wheels or on car-

504

0.91

1

0.87

0.27

0.01

1001

0.89

2

0.85

0.22

0.04

riages.

B25J19

Accessories fitted to manipulators, e.g. formonitoring or for viewing; safety devices
combined with or specially adapted for use
in connection with manipulators.

B25J13Controls for manipulators.

857

0.88

3

0.81

0.27

0.03

B25J9

Programme-controlled manipulators.

2809

0.86

4

0.79

0.29

0.07

Automatic control or regulation of feed

591

0.79

7

0.09

0.36

0.65

B23Q15movement, cutting velocity or position oftool or work.

A01J7

Accessories for milking machines or devices.

395

0.77

9

0.62

0.52

0

G05B19Programme-control systems.

7133

0.70

16

0.22

0.39

0.25

B65G1

Storing articles, individually or in orderly ar-

1064

0.58

29

0.18

0.46

0.01

608

0.42

75

0.12

0.18

0.19

rangement, in warehouses or magazines.

B24B49Measuring or gauging equipment for controlling the feed movement of the grinding toolor work; Arrangements of indicating or measuring equipment, e.g.

for indicating thestart of the grinding operation.

– Low prevalence –B65H7Controlling article feeding, separating, pile-

736

0.28

228

0.01

0.25

0.00

Restoring or reconditioning objects.

613

0.26

266

0.07

0.06

0.05

Lifting or adjusting devices or arrangements

264

0.24

306

0.01

0.20

0

215

0.13

677

0.02

0.07

0.00

advancing, or associated apparatus, to takeaccount of incorrect feeding, absence of articles, or presence of faulty articles.

B23P6

A01B63for agricultural machines or implements.

B66D3

Portable or mobile lifting or hauling appliances.

Table 2: Examples of 6-digit C/IPC codes in relevant technological fields

10(a) For all C/IPC 6 digit codes(b) For C/IPC 6 digit codes in machinerywith at least 100 patents

Figure 1: Histogram of the prevalence of automation keywords for C/IPC 6 digit codes

because a lot of patents contain words associated with automation directly.
Figure 1 gives the histograms of the prevalence of automation keywords for all C/IPC
6 digit codes (panel a) and C/IPC 6 digit codes in the “machinery” technological field(panel b). The histograms show that most C/IPC codes have a low prevalence of automation keywords and that the distribution is shifted to the right for the relevant
technological fields. Yet, a few codes have a high prevalence measure. Appendix B.1
gives additional statistics on the prevalence measures.
Consequently, we define automation technological categories as those with a prevalence measure above some threshold. As our baseline, we choose thresholds at the 90th
and 95th percentiles of the 6 digit code distribution within the machinery technological field, which are given by 0.386 and 0.477 respectively. We then define a patent
as an automation patent if it belongs to at least one automation technological group(that is a 6 digit code, a pair of 4 digit codes, or a combination of 4 digit code and
G05/G06).12 We refer to the two classifications as auto90 and auto95 depending on thethreshold used. We can similarly define subcategories of automation patents such as
robot90 which correspond to patents which contain at least one technological group forwhich the frequency of the keywords related to robots (uniquely) is above the thresholddefining auto90. By definition all robot90 patents are also auto90 patents.
12

In practice, most automation patents in our dataset are automation patents because they belong
to at least one 6 digit automation code—see Appendix B.1 for more details.

11Figure 2 shows two automation patents. Both are automated storage cabinets andare counted as automation patents because they contain the IPC 6 digit code B65G 1.
As described in Table 2, B65G 1 corresponds to devices for storing articles and has a highprevalence of automation keywords (0.58, which is above the 95th percentile threshold).
The patent of Figure 2a contains our keywords: a sentence with the words “automatic”
and “storing,” and another sentence with the word “robot.” The description strongly
suggests that this is indeed an automation patent. The patent of Figure 2b does notcontain any of the keywords, but the description of the text still describes a labor-saving
innovation.

2.4

Trends in automation innovations

To ensure that we only capture innovations of a sufficiently high quality, we restrict
attention to patent families with patent applications in at least two countries in our
main empirical analysis and for the trends depicted here. We refer to these as biadic
patents.13 Several studies have documented that biadic patents are of higher quality
and fundamentally different from patents applied for in only one office (e.g. Harhoff,
Scherer and Vopel, 2003, van Pottelsberghe de la Potterie and van Zeebroeck, 2008,
De Rassenfosse, Dernis, Guellec, Picci and van Pottelsberghe de la Potterie, 2013, andDechezleprêtre, Ménière and Mohnen, 2017). In addition, patents can be more or lessbroad across countries: for instance the same invention may be covered by two patents
in Japan but only one in the US. By focusing on biadic patents, we only count such acase as one innovation.14
Figure 3 below shows the evolution of automation patents in the set of biadic patents.
Panel (a) shows that worldwide, the share of automation patents declines in the 1990s
from 17.4% to 12.8% for the laxer auto90 measure and from 8.8% to 6.4% for thestricter auto95 measure before increasing quickly to reach 20.5% for auto90 and 9.5%
for auto95 in 2014—Figure A.8 in the Appendix shows that automation patents inmachinery represent between 1.9 and 3.5% of all patents with the auto90 definition andit also reports the raw numbers of auto90 and auto95 patents. One interpretation is that
13

The original definition of biadic patents correspond to patents in at least 2 of the 3 main offices(EPO, USPTO and JPO). Our definition is a generalization counting all patent offices. We check that
our results are robust to the original definition of biadic in section 5.6.
14
We count patent applications and not granted patents because in certain patent offices, notably
in Japan, a patent is only formally granted if the rights of the applicant are challenged. To restrict
attention to patent families of even higher quality, we carry out robustness checks where we use patentcitations, or patents applied to more than two offices.

12

(a) Example with keywords(b) Example without keywords
Figure 2: Examples of automation patents from technological code B65G1, which are bothautomated storage cabinets.

13

.24

.3

.25

.12

.18

.2
.15

.06

.1
.05
1980

1985

1990

1995

year

2000

2005

2010

United States
United Kindom

00

auto95
auto90
2015

1980

1985

1990

1995

Germany
Japan
year

2000

2005

France2010(a) Share of automation patents in machinery(b) Share of automation patents (auto95) in ma-

worldwide. Automation technological categories are defined at the 90th percentile ofthe distribution of 6 digit C/IPC codes inmachinery (for auto90) or the 95th percentile(auto95).

chinery conditional on the patent being protected in the designated countries.

2015

Figure 3: Share of automation patents in machinery. Shares are computed for biadic patents.

globalization made cheap low-skill labor abroad available in the 1990s and contributed
to a temporary decline in automation, which has since reversed. Panel (b) computes theshare of automation patents for the auto95 measure for biadic patents conditional on
the patent being protected in certain countries. The graphs show that for UK, French,
German and US patents, the decline of the 1990s is less pronounced and the rise of
the 2000s is very stark. In Japan, the decline of the 1990s is more pronounced andthe recent growth more timid there. As a result while the share of automation patents
was the highest in Japan in the 1980s and early 1990s, it is now the lowest among
these countries. In the Appendix, Figure A.9 reports the share of automation patents inmachinery according to the nationality of applicants, the trends are roughly similar but
the share of Japanese patents remains higher (suggesting that the relative decline in theshare of automation patents at the JPO is due to foreign firms). These country trends
are similar with the auto90 measure.

2.5

Automation patents and robots

Recent papers (Graetz and Michaels, 2018, or Acemoglu and Restrepo, 2017) have useddata on industrial robots from the International Federation of Robotics (IFR) to measureautomation. The IFR reports stocks of robots by country and sectors based on yearlysurveys of robot suppliers.
14

We first compare our automation measure with robotization at the country level. To
measure robotization in a given country, we follow Acemoglu and Restrepo (2017) and
use the stock of industrial robots in 2011 minus the stock of robot in 1997 divided bytotal employment in manufacturing in 1997 (employment data come from the OECD
database). Table 3 reports the correlation across 27 countries between this measure ofrobotization and our measures of automation, namely the shares of auto95 and auto90
patents within machinery among biadic patents applied for in each country and computed
over the years 1997-2011. The correlation is quite high with a coefficient of 0.38 for the
auto95 measure. When we correlate robotization with the shares of robotic patents
in machinery (robot90 and robot80) we find a somewhat larger coefficient (0.46) for
robot80.
We then compare our two measures of automation at the sector level for the US andGermany. The IFR data contain consistent stocks of industrial robots for 17 sectors
according to the ISIC Rev 4 classification between 1997 and 2011 for Germany andbetween 2004 and 2011 for the US (technically the IFR data aggregate the robot stocks at
the level of US, Canada and Mexico). We compute robotization in each sector by taking
the difference between the stocks in the two years and dividing by employment in the first
period (still using OECD data). We allocate patents to these sectors according to their(family-level) 4-digit C/IPC codes using a concordance table provided by Lybbert andZolas (2014), and similarly measure the share of auto95, auto90, robot90 and robot80
patents in machinery for each sector over the same time periods.15 Appendix Table
A.15 reports shares of auto95 patents in machinery for patents granted at the USPTO,
patents protected in Germany (i.e. granted German patents or granted EPO patents
protected in Germany) in 1997-2011 and for all biadic patents across sectors. The sharesof automation patents are very similar in the US, Germany and for the world. The
three sectors with the highest shares for auto95 are always the automotive, “computer,electronic, optical and electrical products” and “other transport equipment” industries.
In addition, Table 3 reports correlations across sectors for these measures in the US andin Germany. We find higher levels of correlations with coefficients of 0.60 and 0.56 forboth US and German industries with the auto95 measure. When we use our method to15

Lybbert and Zolas (2014) present several probabilistic concordance tables, which are based onmatching industry descriptions with the title and the abstract of patents within an IPC code. Thismethodology cannot a priori distinguish between the sector of use of a patent and the industry of
manufacture, we verify however on a few simple examples that within machinery, the classification
seemed to assign patents to the sector of use (for instance textile machines are assigned to the textile
industry not the equipment industry).

15

Table 3: Correlations between our automation measures and robot intensity(1)
Across Countries(2)
Across US
Industries(3)
Across German
Industries

Share of automation
patents in machinery (auto95)

0.383

0.602

0.560

Share of automation
patents in machinery (auto90)

0.377

0.483

0.426

Share of robot patents inmachinery (robot90)

0.365

0.682

0.546

Share of robot patents inmachinery (robot80)

0.461

0.740

0.780

Number of observations

27

17

17

Note: This table reports correlations across countries or industries between shares of automation
patents in machinery, robots patents in machinery and robot intensity. Robot intensity is measured
as the difference between the stock of robots in 2011 and 1997 (columns 1 and 3) or 2004 (column2) over employment in each country (column 1) or each sector (columns 2 and 3) in 1997 (columns
1 and 3) or 2004 (column 2). Shares of automation and robot patents are computed over the time
period 1997-2011 for columns (1) and (3) and over 2004-2011 for column (2).

focus specifically on robotic patents, we find correlation coefficients up to 0.74 and 0.78
for the robot80 measure.

2.6

Validating our automation measure

To validate our automation measure, we use it in the framework of Autor et. al. (2003)(henceforth ALM), who show how computerization has been associated with a decreasein routine tasks at the industry level in a cross-sectional analysis on U.S. data from 1960
to 1998. Here, we provide a brief description of what we do, and we refer the reader
to Appendix B.2 for details. To measure automation innovations at the sectoral level,
we use USPTO granted patents which belong to the machinery technological field. As
before, we allocate patents to sectors according to their 4-digit C/IPC codes using aconcordance table provided by Lybbert and Zolas (2014). For each sector j and eachperiod τ , we compute the share of automation patents among machinery patents applied
for during this period. We denote this variable autjτ . We then run regressions of thetype:∆Tjkτ = β0 + βC ∆Cj + βaut autjτ ,(1)where ∆Tjkτ represents the change in tasks of type k in industry j during period τ and∆Cj is the measure of the change of computerization in sector j (it is computed over
the years 1984-1997 and used for all time periods θ). We do not first difference ourmeasure of automation because patenting is already a measure of the flow of knowledge. We take our tasks measures directly from ALM, and therefore consider 5 types
16

10

172

101

351

460
362
206

236
470

380

140 252261
471 231
130
230
152
42
111
22016
470 462
150
110
146
222
241
636
271
132
100
142
40 262161
212
460
242151
270
66
162250160282181
176
251
121391
211
201
236
346
221
102281 50
166
30
112
120
200
141
190
362
360
172 41
380

0

152
271
462

Δ Routine manual0220
111 16
112
261
252
31
471
212
150
636 230100
142
140
222 231102160
181
50
161
120
146
251
162
13228166
40110
242360
262
42200
176
270 151241
41
346
250
30
190
391
121
166
201
211 282
141
221

-10

Δ Routine cognitive

31

101
351
206
246

-10

10

130

246

186

-20

-20

186

381
0.0

0.1

0.2

381
0.3

0.4

0.0

Share of automation patents in machinery (95th threshold)Industries

0.1

0.2

0.3

0.4

Share of automation patents in machinery (95th threshold)Fitted values

Industries(a) Change in routine cognitive tasks andautomation intensity

Fitted values(b) Change in routine manual tasks and automation intensity

Figure 4: Scatter plots of routine tasks changes and automation intensity (auto 95) in 19801998 in the United States. The list of sectors is given in Table B.4

of tasks: nonroutine analytic, nonroutine interactive, routine cognitive, routine manual and nonroutine manual. ∆Tjkτ is measured as 10 times the annual within-industry
change in task input measured in percentile of the 1960 task distribution (as in ALM).
We consider 3 time periods for which we can compute our automation intensity measure:
1970-1980, 1980-1990 and 1990-1998 (ALM also considers 1960-1970), and the joint time
period 1980-1998. The initial concordance table mostly assigns our machinery patents
to manufacturing sectors (see full list in Table B.4, we restrict attention to sectors withat least 50 machinery patents per decade). As a result, we can measure automation
intensity for between 67 and 69 sectors most of them in manufacturing. Our automation
measures auto90 and auto95 are strongly correlated with each other (the coefficient is0.86) but not correlated with computerization (the coefficient is 0.016 for auto95 and0.05 for auto90).
Figure 4 first provides simple scatter plots of the changes in routine tasks and theshare of automation patents in machinery (according to the auto95 definition) over theyears 1980-1998. The list of sectors plotted (which are also the sectors in the regressions)is given in Appendix Table B.4.16 Sectors with a high share of automation patents
16

At this level of disaggregation, the five sectors with the highest share of automation patents are:scientific and controlling instruments, optical and health services (246), dairy products (101), electronic computing equipment, office and accounting machines (186), household appliances, radio, TV& communications equipment, electric machinery, equipment & supplies, n.e.c., not specified electrical17

experience a decline in routine cognitive and routine manual tasks. Given our focus on
automation in machinery a decline in routine cognitive tasks might seem surprising at
first sight, but several machines replace workers for tasks such as inspection and control(such as in the example given in Figure 2b).
Table 4, columns (1) to (5) report the results of regression (1) for the auto95 measure.
The means of the share of automation in machinery are 0.06, 0.08 and 0.07 in the 70s,
80s and 90s. Columns (3) and (4) show that sectors with a high share of automation
patents in machinery experienced a large reduction in both cognitive and manual routine
tasks in each decade. The coefficients of column (3) and (4) in panel B indicate that a
10 pp increase in the share of automation patents is associated with a 3 centiles and 2.2
centiles decrease in labor input of routine cognitive and manual tasks in the 1980s. To
interpret a 10 pp increase, note that the standard deviation in the share of automation
patents in the 1980s is 0.09, so that a 1 standard deviation increase in the automation
share is associated with a decrease in routine cognitive and routine manual tasks of 2.7
and 1.9 centiles respectively. The corresponding effect of a 1 standard deviation increasein computerization is associated with a decrease in routine cognitive tasks of 0.8 centiles
and essentially no change in routine manual tasks (the computerization variable hasa larger effect in the 90s). We obtain similar results when we restrict attention to
biadic patents (as in our main regression exercise of section 5) or when we exclude theequipment sector, which could be contaminated if patents are assigned to the industryof manufacture instead of the sector of use (176 in the Census classification).
Since we are interested in the effect of low- and high- skill wages on automation but
do not measure the price of tasks directly, we also use the ratio of high-skill to lowskill workers (defined as college graduates over high-school dropouts and high-school
graduates) as our dependent variable in cross-section regressions similar to 1.17 Column(6) of Table 4 shows that sectors with a higher automation share also experienced a large
increase in the ratio of high-skill to low-skill workers. Panel B, for instance suggests thata 10 pp increase in the share of automation patents is associated with an increase of 1.33
in the ratio of high-skill to low-skill workers in the 1980s.
In the Appendix, Table B.5 reproduces the same exercise for our laxer measure(auto90) and obtains similar results. Finally, Table B.6 reproduces the same analysis
separately for each education category (as ALM) and shows that automation leads to amachinery, equipment & supplies (206) and transport equipment (351).
17
The results are similar for the ratio of college graduates over high-school dropouts or college graduates and some college over high school graduates and dropouts.

18

Table 4: Correlation between changes in task intensity or skill ratio across sectors and automation (auto95)(1)
D Nonroutine
analytic(2)
D Nonroutineinteractive(3)
D Routine
cognitive(4)
D Routine
manual(5)
D Nonroutine
manual(6)
D H/LShare of automation
patents in machinery-1.29(5.10)

5.42(6.27)***-17.27***(6.59)**-11.43**(5.59)-1.15(7.46)***0.27***(0.07)D Computer use
1984 - 1997

-6.86
(5.72)-3.13(7.04)***-19.51***(7.41)-3.46(6.28)*14.87*(8.38)

0.07(0.08)

Intercept

1.06(0.95)**2.31**(1.17)**3.07**(1.23)***2.69***(1.04)-1.75(1.39)***0.05***(0.01)

0.02
-0.05

0.01
2.17

0.20
-0.90

0.07
1.49

0.05
0.42

0.21
0.07

10.09
(7.14)**19.05**(8.12)***-30.00***(6.76)***-21.61***(5.42)***16.78***(6.04)***1.33***(0.23)**24.80**(10.43)*22.21*(11.85)-13.24(9.87)-0.42(7.91)-6.49(8.82)

0.29(0.33)

Intercept-2.62(1.70)-0.65(1.93)

2.15(1.61)

1.20(1.29)-2.13(1.44)-0.04(0.05)

R2
Weighted mean D

0.12
1.86

0.14
4.17

0.27
-2.22

0.20
-0.59

0.11
-1.74

0.37
0.11

*11.06*(6.08)*16.02*(8.18)***-22.81***(6.54)**-12.53**(5.42)

6.66(6.28)***0.77***(0.15)***26.77***(8.35)**27.00**(11.23)**-23.15**(8.98)***-24.87***(7.44)

7.48(8.62)***0.66***(0.20)

Intercept*-2.36*(1.37)-1.43(1.84)

1.72(1.47)*2.27*(1.22)*-2.40*(1.41)*-0.06*(0.03)

R2
Weighted mean D

0.19
2.45

0.15
3.79

0.25
-3.44

0.23
-2.36

0.03
-0.79

0.41
0.09

Panel A: 1970 - 80, n=67

2

R
Weighted mean D
Panel B: 1980 - 90, n=67
Share of automation
patents in machinery
D Computer use
1984 - 1997

Panel C: 1990 - 98, n=67
Share of automation
patents in machinery
D Computer use
1984 - 1997

Standard errors are in parentheses. Colums (1) to (5) of Panels A to C each presents a separate OLS regression of ten times theannual change in industry-level task input between the endpoints of the indicated time interval (measured in centiles of the 1960 task
distribution) on the share of automation patents in machinery (defined with the 95th percentile threshold) and the annual percentage
point change in industry computer use during 1984 - 1997 as well as a constant. In Column (6), the dependent variable is the ratio of
high-skill (college graduates) to low-skill (high-school graduates and dropouts) workers. Estimates are weighted by mean industry shareof total employment in FTEs over the endpoints of the years used to form the dependent variable. * p<0.1; ** p<0.05; *** p<0.01

19

reduction of routine tasks and an increase in non-routine manual tasks for high-school
graduates (but in line with column (6) of Table 4, a large share of the task changes at
the industry level are explained by changes in educational composition - see Panel F).
Overall, these results suggest that our automation measure captures a form of skillbiased technical change, distinct from computerization and associated with a decreasein routine tasks by low-skill workers. We can therefore use it to analyze the effect ofwages on automation innovation incentives.

3

A simple model

Before carrying out our main empirical analysis, we present a simple one-period model
to clarify our argument. The model is motivated by the business structure of the largest
automation innovator. In 2018, Siemens, the biggest innovator in our sample, had 31%
of its work force in Germany, but only 14% of total revenue from customers based inGermany. During this year the strongest growing division of Siemens was the Digital Factory Division which provides a broad range of automation technology to manufacturers
across the globe. The annual report describes how “The Digital Factory Division offers acomprehensive product portfolio and system solutions for automation technologies usedin manufacturing industries, such as automation systems and software for factory automation, industrial controls and numerical control systems, motors, drives and inverters
and integrated automation systems for machine tools and production machines...”. The
report is centrally interested in how “Changes in customer demand [for automation technology by downstream manufacturers] are strongly driven by macroeconomic cycles” anddiscusses a number of such drivers including changes in cost of capital and political development towards trade protectionism.18 Siemens further directly discusses how such
macroeconomic trends affect its R&D decisions.
We incorporate these business features into a model built on the task framework of
Acemoglu and Autor (2011) and more precisely on the growth model of Hémous andOlsen (2018). A manufacturing good is produced with a continuum of intermediate
18

Interestingly, the report never mentions “cost of labor” as a reason for automation, but instead
used a number of euphemisms such as “increase competitiveness”, “enhance efficiency”, “improve costposition” and “stream line production”.

20inputs according to the Cobb-Douglas production function:
Z 1
Y = exp

ln y (i) di ,(2)0where y(i) denotes the quantity of intermediate input i. The manufacturing good isthe numeraire. Each intermediate input is produced competitively with high-skill labor(h1,i and potentially h2,i ), low-skill labor li and potentially machines xi , according to the
production function:1−νγ (i) li + α (i) ν ν (1 − ν)1−ν xνi h2,iyi = h1−β
1,i

β

,

where γ(i) is the productivity of low-skill workers and α(i) is an index which takes thevalue 0 for non-automated intermediates and 1 for automated intermediates. ν and β
are fixed share parameters in (0, 1). Machines are specific to the intermediate input i.
If a machine is invented, it is produced monopolistically, 1 for 1 with the final good sothat the monopolist charges a price px (i) ≥ 1.
At the beginning of the period, for each non-automated intermediate i, there is aninnovator (Siemens). The innovator manages to create a machine specific to intermediate
i with probability λ if it spends θλ2 Y /2 units of manufacturing good, where θ is a
productivity parameter.
We solve the model in two steps, first we derive the profits realized by machine
producers, second we solve for the innovation decision. Consider an automated intermediate input (that is α(i) = 1), then the downstream intermediate input producer isindifferent between using low-skill workers or machines together with high-skill workers
in production whenever:ν 1−ν
wH
px = wL /γ(i).

As a result, the machine producer is in “Bertrand competition” with low-skill workers. Given that a machine costs 1, the machine producer will charge a price px (i) =1− νmax (w/γ(i)) 1−ν wH 1−ν , 1 , and the intermediate input producer will use low-skill workν
ers whenever wL /γ(i) < wH
and machines otherwise. Therefore, the machine producer
can charge a higher price when low-skill wages are lower it has to charge a lower pricewhen high-skill wages are higher since high-skill workers and machines are complement.
Using that the manufacturing good is produced according to a Cobb-Douglas production

21

function, we have that p(i)y(i) = Y for all intermediates. Therefore, we can derive theprofits of the machine producer for intermediate i as:
πiA = max 1 −



γ(i)
wL

1
 1−ν

ν
1−ν

!

wH , 0 νβY.
2

In turn, at the beginning of the period, the potential innovator solves max λπiA −θ λ2 Y ,which gives the equilibrium innovation rate as:νβ
λ=max 1 −
θ



γ(i)
wL

1
 1−ν

ν
1−ν

!

wH , 0 .

As a result, the number of automation innovations is equal to:
νβ
Aut =
θ

Z 1
0


(1 − α (i)) max

1−

γ(i)
wL

1
 1−ν

ν
1−ν

wH

!

!
, 0 di.

This expression is increasing in the low-skill wage wL and decreasing in the high-skill
wage wH , with a smaller elasticity in absolute value. Intuitively, the incentive to replace
low-skill workers with machines (and high-skill workers) increases with low-skill wages
and make manufacturing firms better customers of machines and the reverse for highskill wages. An upward shift in the low-skill workers productivity function γ(i) also
reduces the number of automation innovations.
More generally, the defining characteristic of automation is that it allows for the
replacement of workers by machines in certain tasks. When intermediates have a unitelasticity of substitution as in (2), the aggregate production function is Cobb-Douglas
and automation corresponds to a change in factor shares. When intermediates have anelasticity of substitution lower than 1, the aggregate production function is CES andautomation corresponds to a combination of labor-augmenting and capital-depleting
technical changes (see Aghion et al., 2017).

4

Empirical Strategy and Data

4.1

Empirical strategy

We now take the predictions of our model to the data. As mentioned above, innovators
in automation technologies are often large companies (e.g. Siemens) which sell their22automation equipment internationally. Following the logic of our model, the incentivesof the downstream producers to adopt automation technology is determined by wages intheir local market. As a result, the decision of innovators such as Siemens to pursue automation research in the first place depends on the wages that their potential customers
face in different countries.19
In our baseline regression, we assume that a firm’s innovation in automation is givenby the following Poisson specification:

P ATAut,i,t= exp

βwL ln wL,i,t−2 + βwH ln wH,i,t−2 + βX Xi,t−2 + βKa ln KAut,i,t−2+βKo ln Kother,i,t−2 + βSa ln SP ILLAut,i,t−2 + βSo ln SP ILLother,i,t−2 + δi + δt

(3)!

P ATAut,i,t denotes the number of automation patents applied for by firm i in year t.
wL,i,t−2 and wH,i,t−2 denote the average low-skill and high-skill wages faced by the customers of firm i at time t − 2 (we explain below how we proxy for them). Section 3
predicts that βwL > 0: an increase in the average low-skill wage faced by the customersof firm i leads firm i to undertake more automation innovations. It also predicts that
βwH < 0 since high-skill workers are complementary to machines. Xi,t represents a vector of additional controls (average GDP per capita, GDP gap and labor productivity).
Controlling for GDP per capita or labor productivity allows us to control for changes
in productivity in the country where machines are potentially sold20 and controlling for
the GDP gap allows us to capture business cycle fluctuations and changes in demand.
We include this control because the literature finds that innovation in general is affectedby the business cycle (see for instance Aghion, Angeletos, Banerjee, and Kalina, 2010).
KAut,i,t−2 and Kother,i,t−2 denote the stocks of knowledge in automation and in othertechnologies of firm i at time t − 2. These knowledge stocks are computed using theperpetual inventory method.21 SP ILLAut,i,t−2 and SP ILLother,i,t−2 similarly denote thestocks of external knowledge (spillovers) in automation and in other technologies whichfirm i has access to at time t − 2 (we explain below how these are constructed). δi is
19

If the automation innovation is internal to the firm, then the argument follows if one interprets theinnovator’s customers as the different downstream production sites of the same firm.
20
GDP per capita could also captures non-homotheticity in preferences, for instance if higher quality
goods or services are less automated.
21
To be more specific we use ln(1 + K), a depreciation rate of 15% and add a dummy indicatorvariable for when each of knowledge stocks—automation and others—equals zero.

23+ i,t .

a firm fixed effect and δt is a time fixed effect. Finally, i,t is an error term, which, we
assume, is uncorrelated with the other right-hand side variables. The right-hand side
variables are lagged by 2 years in the baseline regressions to reflect the delay betweenchanges in R&D investments and patent applications—we investigate the role of ourtiming assumption in Section 5.4 below.
To control for firm-level fixed effects, we use several econometric techniques. Our
baseline specification uses the Hausman, Hall and Griliches (1984) method, denoted
HHG, which is the count data equivalent to the within-group estimator. Technically,
this method is inconsistent with equation (3) because it requires strict exogeneity andtherefore prevents the lagged dependent variable from appearing on the right-hand side
(which it does through the knowledge stock KAut,i,t−2 ). Yet, the bias is small with large
T , which is the case in our baseline regression (15 years). Second, we use the Blundell,
Griffith and Van Reenen (1999) method, which proxies for the fixed effect by using the
pre-sample average of the dependent variable.

4.2

Macroeconomic data

Our macroeconomic variables come primarily from the 2013 release of the World Input
Output Tables, henceforth, WIOD (Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer,
R. and de Vries, G. J., 2015). The database contains information on hourly labor costs
across groups of educational attainment – low-skill, middle-skill and high-skill workers– for the manufacturing sector from 1995 to 2009 as well as value added and producer
price indices. The dataset contains information on 40 countries, including all 27 EU
countries of 2009. We obtained similar data from the Swiss Federal Statistical Office to
add Switzerland, a large source of patents, to our analysis. For our baseline regressions,
we focus on labor costs in manufacturing since our analysis in section 2 showed thatmost of our patents (89% of biadic auto95 patents in 1997-2011) are associated with
manufacturing, but we check that our results are robust to using labor costs in the entire
economy. Although our measures cover all labor costs, we refer to those as wages fromhere on for simplicity. From the same dataset, we obtain measures of labor productivity(as value added divided by hours) and producer price indices (for the whole economy andmanufacturing). We obtain exchange rate and GDP data from UNSTAT and compute
the GDP gap to control for business cycles.22 Appendix B.4 provides additional details.
22
We use a HP filter with a smoothing parameter of 6.25 on ln(GDP ) to get the trend, and the GDP
gap is measured as the difference between ln(GDP ) and its trend.

24

Table 5: Low-skill wages and the skill-premium in manufacturing sector for selected countries
Country

India
MexicoBulgaria
USA
Belgium
SwedenFinlandLow-skill wages(1995$)

Skill-premium(HS wages/LS wages)

1995

2009

1995

2009

0.19
0.89
1.29
11.57
29.50
19.92
23.41

0.28
0.61
0.71
13.67
41.89
42.16
43.63

4.79
3.90
3.32
2.46
1.56
1.73
1.20

4.98
4.21
2.25
3.02
1.46
1.33
1.46

Note: Wages data, taken from the World Input Output
Database. The table shows manufacturing low-skill wages(technically labor costs) deflated by (manufacturing) producer price index and converted to US dollars using average
1995 exchange rates. Skill-premium is the ratio of high-skill
to low-skill wages (labor costs). The table shows the three
countries with the lowest low-skill wages in 2009, the threewith the highest and the United States.

All macroeconomic variables are deflated in the same way: In the baseline regression, we
first deflate nominal values by the local producer price index for manufacturing (indexed
to 1995), and then we convert everything into dollars using the average exchange rate
for 1995 the starting year of our regressions.
In the data low-skill workers are defined as those without a high-school diploma or
equivalent and high-skill workers as those with at least a college degree. Middle-skill
wages and low-skill wages are very highly correlated so in practice one should interpret
our low-skill wage variable as reflecting both low-skill and middle-skill (we look at middleskill wages in section 5.6).23
The countries with the highest low-skill wages in 1995 are Belgium, Sweden and
Finland with $41.9, $42.2 and $43.6 respectively (in 1995 dollars). The countries withthe lowest high-skill wages in 2009 are India, Mexico and Bulgaria with $0.28, $0.61 and$0.71, respectively. The corresponding number for the US is $13.7. Table 5 summarizes
these values for these seven countries. It further shows that the ratio of high-skill to lowskill wages varies considerably across countries, even among those that have relatively
similar low-skill wages. The skill-premium in the United States rose from 2.46 to 3.02
during this period while it slightly declined in Belgium from 1.56 to 1.46.
23

For our baseline sample of firms, included in Table 7 below, the correlation between low-skill andmiddle-skill wages is 0.94 controlling for firm and year fixed effects. It is only 0.6 for low-skill andhigh-skill wages. See Appendix Table A.26.

25
1

4.3

Computing firm’s market-specific wages and spillovers

Ideally, we would like to measure the wages paid by the (actual and potential) customersof automation innovators. We do not directly observe these, and we build a proxywhich is a weighted average of country-level wages where the weights reflect the market
exposure of innovators. We define the average low-skill wage faced by a firm’s customers
wL,i,t as
wL,i,t ≡X
ωi,c wL,c,t ,(4)cwhere wL,c,t is the low-skill wage in country c at time t and ωi,c is the fixed weightof country c for firm i. Firms have different exposure to different markets becauseof trade barriers, heterogeneous tastes of customers, or various historical accidents if
exporting involves sunk cost. This measure is a shift-share instrument (Bartik, 1991).
Since the weights are fixed, our identification relies on how country-level shocks affectfirms differently. In fact, had we observed the wages of the customers of automation
innovators, those would have suffered from reverse causality, and we would have used
our measure as an instrument. We discuss the recent literature on shift-share regressions
in detail in Section 5.5.24
To measure the weights, and in the absence of sales data for most firms involvedin automation innovations, we follow and expand on the methodology of Aghion et al.
(2016, ADHMV). We use the firm’s pre-sample history of patent filing as a proxy forthe market exposure of firms. When a firm applies for a patent, it applies for protection
in a specific jurisdiction, and it has to pay a fixed cost whenever it wants to expand
the geographic coverage of a patent. Therefore, whether a firm protects its innovations
in a country or not reflects its intent to sell or license its technology in that country(see e.g. Eaton and Kortum, 1996). Taking this into account, we compute for eachfirm, the fraction of its patents in the relevant technological field of machinery (not only
automation) protected in each country c, ω̃i,c during a pre-sample period.25 We only
count patents in the machinery because some of the biggest innovators in automation
24

As we keep the weights fixed we look at how wage changes in the countries where a firm alreadysells affect the firm’s automation innovation. A different question would have been to analyze how wagechanges affect a firm’s decision to enter a new market, this is beyond the scope of this paper.
25
In Europe, firms can apply both at national patent offices and at the European Patent Office (EPO).
In the latter case, firms still need to pay a fee for each country in which they want their patent to beprotected. We count a patent as being protected in a given European country if it is applied for either
directly in the national office or through the EPO.

26

technologies are large firms (Sony, Siemens, etc.) which produce a wide array of productswith different specialization patterns across industries. We restrict attention to patentfamilies with at least one citation (not counting self-citations) to exclude the lowest
quality patents.26
Patenting indicates whether the firm intends to sell in that market. However, a larger
market is likely to host more firms so that the market size per firm will generally not
grow 1 for 1 with size of total market. To account for this we weigh each market c
0.35
by GDP0,c, where GDP0,c is the 5 year average GDP of country c at the end of the
pre-sample period.27 As a result, the weight of country c for firm i is given by:0.35
ω̃i,c GDP0,cP
ωi,c =.
0.35
ω̃i,c0 GDP0,c0c0

We compute weights for the 41 countries for which we have wage data. The weights
are computed over the pre-sample period 1970-1994 to ensure that they are weakly
exogenous as patent location could be influenced by shocks to innovation. We use thesame weights to compute firm customers’ average high-skill wage, productivity or GDP
per capita.
ADHMV verify that a similar method accounts well for the sales distribution ofmajor auto manufacturers. Coelli, Moxnes and Ulltveit-Moe (2016) carry out a more
systematic exercise and verify that a similar method accounts well for aggregate bilateral
trade flows and firm exports across 8 country groups in a representative panel of 15,000
firms from 7 European countries (regressing patent weights on sales weights gives a
coefficient of 0.89 with a s.e. of 0.008). In Appendix B.3, we similarly show that ourpatent weights correlate well with trade flows. 28
Patent data also reports where firms’ innovators are located. Given that knowledge
spillovers have a geographical component (Hall, Jaffe and Trajtenberg, 1993), we can use
this information to build a measure of the stock of knowledge to which a firm is exposed.
26

Including all patents generally increases the weight of the country with the most patents, in line withthe finding that poor quality patents tend to be protected in fewer countries. However, further increasing
the threshold from 1 to more citations does not significantly change the distribution of weights.
27
Here we use Eaton et al. (2011) who estimate that the elasticity of French exports to the GDP ofthe destination country is 1 while the elasticity of the number of French exporters is 0.65, which gives
an elasticity of the average export by firm of 0.35. ADHMV use a power of 1 on GDP instead of 0.35.
We use different values in robustness checks in section 5.6
28
There are three differences between our weights and those of these previous papers: we use theempirically founded exponent of 0.35 on GDP, we restrict attention to cited patent families and topatents in certain technological fields.

27More specifically and similarly to ADHMV, we compute the stocks of automation patents
and of other patents in each country. Then, for each firm, we build a weighted averageof country-level knowledge stocks, where the weights correspond to the location of theirinnovators pre-sample in 1970-1994.29
To link patents with their owners, we use Orbis Intellectual Property which links 40
million patents to companies available in the Orbis financial database. For companies
in the same business group, R&D decisions could happen at the group, though treatinga group as one agent is often too aggressive (for instance because subsidiaries may bein different sectors). Therefore, for firms within the same business group, we normalize
company names by removing non-firm specific words such as country names or legal
entity types from the name and then merge firms with the same normalized name. Allother firms are treated as separate entities.30

4.4

Descriptive statistics

Our basic dataset consists of applicants who have applied to at least one biadic automation patent between 1997 and 2011 (included), who have at least one patent prior to 1995
which can be used to compute weights, and who are not fully domestic (i.e. we exclude
firms which have only patented in one country pre-sample). For the auto95 measure thiscorresponds to 3, 341 firms, which are responsible for 35, 803, or 58% of the total numberof innovations (patent families). For auto90, 4, 905 firms are responsible for 61, 931, also
58% of the total. Table 6 gives some descriptive statistics on the number of automation
patents per year and the country weights for the firms in our sample. Over the period
1997-2011, the median firm in the sample has filed 2 auto95 and 3 auto90 patent applications. The distribution is very skewed and the 99th percentile firm in the sample
has filed 194 automation patents for auto90 and 173 for auto95. The largest country fora given firm has on average a weight of 0.47 (for auto95). To ensure that our results
are not driven solely by the largest country, which we refer to as the “domestic country”
of a firm, we will include in some regressions, domestic country-year fixed effects. The
second largest country has on average a weight of 0.17. The three countries with thelargest weights on average are the United States, Germany and Japan. Appendix Table
29

The country stocks are built using the perpetual inventory method with a depreciation rate of 15%.
We add dummy variables for when the spillover stocks are zero.
30
For instance, Siemens S.A., Siemens Ltd. or Belgian Siemens S.A. are merged, but Primetals Technologies Germany Gmbh which belongs to the same group remains a separate entity in our regressions.

28

Table 6: Descriptive statistics for firms in our baseline regression
Variable

Auto95

Auto90

Auto95

Automation pantents

per year

1997-2011

per year

1997-2011

Mean
Standard deviation
p50
p75
p90
p95
p99

0.7
3.46
0
0.27
1.4
3
12

11.22
48.71
2
6
19
41
173

0.84
4.04
0
0.33
1.6
3.27
13.73

13.24
56.76
3
7
22
50
194

Number of firms

3341

Auto90weights
Largest country
Second largest
US
Japan
Germany
France
UK

0.47
0.17
0.21
0.17
0.2
0.09
0.09

0.46
0.18
0.21
0.15
0.21
0.09
0.09

4903

Note: Summary statistics for the firms used in our baseline regression.

A.16 gives the list of the ten biggest automation patenters in our sample.31

5Main Empirical Results

We present our main results in three steps: First, our baseline regressions use the fullvariation of firm low-skill wages to estimate the effect of an increase in low-skill wages
on automation innovations. Second, we use country-year fixed effects to isolate thecontribution of foreign wages. Third, we contrast the results on automation innovations
with those on other types of machinery innovations. The rest of the section contains
additional results and robustness checks.

5.1

Baseline results

Our baseline results are contained in Table 7. The dependent variable is the number of1biadic patents that qualify as automation when we use a threshold of the 95th percentile
for 6 digit C/IPC codes (auto95). The regression is carried over the years 1997-2011 forthe dependent variable and 1995-2009 for the independent variables, a constraint imposedby the availability of wage data for a large number of countries. Skill-dependent wages
are measured in the manufacturing sector and we deflate by the producer price index in
the same sector.
Column (1) shows that without any controls except fixed effects, a higher low-skill
manufacturing wage for the customers of an innovating firm predicts more automation
innovation. The estimated coefficient is an elasticity so that an increase of 10% in the lowskill wage is associated with 22% more automation patents. Column (2) introduces high31

For instance, for Siemens the countries with the largest weights are Germany (0.37), the USA (0.12),
France (0.10), Japan (0.09) and the UK (0.07).

29

Table 7: Baseline regressions: effect of wage on automation innovations (auto95)
Dependent variable
Low-skill wage
High-skill wage

Auto95
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

2.2000***(0.5123)2.8254***(0.7332)-0.9210(0.7082)

1.8160**(0.7421)-0.9009(0.6715)-0.1275***(0.0495)
0.6311***(0.0579)

1.9058**(0.7729)-0.9695(0.6913)-0.1269**(0.0496)0.6296***(0.0581)0.0210(0.0159)

1.9992**(0.8223)-0.8698(0.7511)-0.1270**(0.0495)
0.6309***(0.0581)0.0214(0.0157)-0.2551(0.8644)2.2954***(0.8198)-0.2971(0.6802)-0.1239**(0.0495)
0.6260***(0.0574)
0.0179(0.0157)

2.4627***(0.8351)-1.6180**(0.8033)-0.1441***(0.0509)0.6408***(0.0600)
0.0279*(0.0158)

2.4266***(0.8658)-1.6700*(0.8634)-0.1443***(0.0510)0.6407***(0.0600)
0.0278*(0.0157)
0.1285(0.9199)

3.7365***(0.9116)-0.4838(0.7650)-0.1504***(0.0510)0.6489***(0.0595)
0.0265*(0.0156)

Stock automation
Stock other
GDP gap
Labor productivity
GDP per capita
Spillovers automation-1.5635*(0.8765)

Spillovers other0.5442*(0.3135)-0.3014(0.2248)0.5478*(0.3151)-0.3089(0.2315)-3.3618***(0.8917)0.8587***(0.3213)-0.5853**(0.2303)

Fixed effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG). All regressions include firm fixed effects and year dummies. All regressions with stock variables (resp.
spillover variables) include a dummy for no stock (resp. no spillover). Standard errors are clustered at the firm-level. * p < 0.1; ** p < 0.05;*** p < 0.01

skill wages as a control. As predicted by the model, high-skill wages enter with a negative
coefficient which is smaller in magnitude thanthe low-skill wage (though not statistically1significant). Column (3) adds control for the firm’s stock of knowledge: a higher stockof automation knowledge within the firm reduces the amount of automation innovation,suggesting that firms do not become more specialized in automation technologies overtime. Column (4) controls for the GDP gap, automation innovations appear to be mildly
pro-cyclical with a small elasticity which is only significant at the 10% level in somespecifications. Columns (5) and (6) add controls for labor productivity in manufacturing
and GDP per capita. Labor productivity does not have a significant effect and GDP per
capita has a negative effect, though its significance is not robust to the specifications tofollows. Columns (7) to (9) repeat columns (4) to (6) but include knowledge spillovers
and find that firms which are exposed to more knowledge in automation technologies
innovate more in automation (with an elasticity between 0.54 and 0.86 depending on
specifications). In all specifications, the coefficient on low-skill wages is highly significant
with elasticities between 1.8 and 2.8 for columns (1) to (8) and a larger elasticity of 3.7
in column (9).
Firms in the same country could be affected by common shocks, and we therefore wecluster standard errors at the domestic country (i.e. the country of largest weight) level30

in Appendix Table A.17. If anything clustering at the country level tends to reduce thestandard error on low-skill wages.32
Appendix Table A.18 repeats Table 7 for the auto90 measure of automation. The
results are very similar but the coefficients on low-skill wages tend to be of a smaller
magnitude, which is in line with auto95 measure being a stricter measure of automation.
This also helps explain the magnitude of our elasticities in Table 7: our analysis focuseson innovations with a high automation content (and therefore most likely to respond to
an increase in wages) for firms which introduce at least one of those innovations.

5.2

Focusing on foreign wages

Country-level shocks which we have not controlled for may affect both innovation andwages. Insofar as firms are mainly affected by the shock of their domestic country, we
can capture those through domestic country-year fixed effects. Country-year fixed effects would for instance control for a tax reform in Germany that would affect both theinnovation incentives of Siemens and low-skill wages. It would also control for a technology shock that leads German firms to introduce more automation innovations and affectwages. Our identification assumption then becomes that foreign wages are exogenous
to automation innovations given our set of controls. One remaining concern would arise
from shocks to the cost of innovation if firms innovate outside of their domestic country.
We address this issue directly in section 5.6 by including wages weighted by the locationof the firm’s inventors.33 Furthermore, in section 5.3 we look at the effect of wages on
low-automation machinery innovations and therefore any remaining bias would have toaffect both types of machinery innovations differently.
Columns (1), (2) and (3) of Table 8 reproduce the columns (7), (8) and (9) of Table
7 but adding country-year fixed effects, where the country of a firm is still defined as
the country with the largest weight. We still obtain a positive effect of low-skill wages
on automation innovations with similar elasticities (between 1.8 and 3.0). Columns (4)to (9) go further and only consider the foreign component of wages (and of the othermacroeconomic variables). In columns (4) to (9), the foreign low-skill wage variable is32

A potential explanation for the negatively correlated error terms, is that a successful automation
innovation by one firm will reduce the incentive for its competitors since the market has already been
captured.
33
A related concern arises from offshoring: the cost of machine production would then be correlatedwith foreign wages. Note, however, that higher foreign low-skill wages in production would increase theprice of machines and therefore bias our coefficient on low-skill wages toward 0.

31

Table 8: Country-year fixed effects
Dependent variable

Auto95
Domestic + Foreign

Low-skill wage
High-skill wage
GDP gap
Labor productivity

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.8852*(1.0367)-2.4820**(1.0115)
0.0623*(0.0343)

2.1429*(1.1505)-1.9117*(1.0157)
0.0620*(0.0342)-1.2851(1.6381)3.0411**(1.2232)-1.7526(1.1046)
0.0646*(0.0343)3.4891***(1.2958)-3.5161***(1.2515)0.0044(0.0492)

4.3023***(1.4482)-2.4740*(1.4209)
0.0016(0.0492)-1.7494(1.4131)3.7989**(1.6370)-3.3526**(1.3633)
0.0044(0.0492)

3.6420***(1.3146)-3.7549***(1.2805)
0.0031(0.0494)

4.3362***(1.4473)-2.8325**(1.4364)0.0001(0.0494)-1.5475(1.3896)

3.8663**(1.6288)-3.6398***(1.3692)
0.0031(0.0494)

GDP per capita-0.1511***(0.0528)0.6549***(0.0602)
1.4782***(0.4992)-1.2259***(0.3805)-0.1506***(0.0527)0.6556***(0.0602)
1.4762***(0.5000)-1.2020***(0.3820)-2.8260(2.0242)-0.1541***(0.0523)0.6555***(0.0598)
1.4715***(0.4998)-1.2436***(0.3789)

Fixed effects

F + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

Stock automation
Stock other
Spillovers automation
Spillovers other-0.1522***(0.0525)0.6494***(0.0602)1.4396***(0.4872)-1.2377***(0.3748)-0.1523***(0.0523)0.6471***(0.0601)
1.4128***(0.4895)-1.2268***(0.3730)-0.5289(1.9347)-0.1526***(0.0525)0.6490***(0.0600)
1.4355***(0.4899)-1.2436***(0.3716)-0.1530***(0.0524)0.6496***(0.0601)
1.4380***(0.4866)-1.2252***(0.3731)-0.1532***(0.0521)0.6475***(0.0601)
1.4161***(0.4896)-1.2141***(0.3725)-0.3829(1.8713)-0.1533***(0.0524)0.6493***(0.0599)
1.4357***(0.4887)-1.2300***(0.3697)

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson
regressions fixed-effects (HHG). All regressions include firm and country-year fixed effects. All regressions with stock variables include a dummy
for no stock and no spillover. In columns (4)-(6) domestic (resp. foreign) low-skill wages are interacted with the share of domestic (resp. foreign)
low-skill wages in total low-skill wages computed at the beginning of the sample, and similarly for high-skill wages, GDP per capita and VA per employee. In columns (7)-(9), they are interacted with the average shares over the sample period instead. In columns (4)-(9), domestic (resp. foreign)
GDP gap is interacted with the domestic (resp. foreign) weight. In columns (1)-(3), there is no such interactions. Standard errors are clustered at
the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

defined as the log of the weighted average of country-level wages excluding the domestic
country multiplied by the share of foreign low-skill1 wages in total wages. This share iscomputed at the beginning of the sample for columns (4) to (6) and as the average value
over the whole sample for columns (7) to (9). We pre-multiply the (log) foreign wage by
this share to take into allow for some firms being more affected by foreign wages thanothers, and to ensure that the reported coefficient corresponds to an elasticity on total
low-skill wages. The foreign macroeconomic control variables are defined similarly.34
Once again we find a positive effect of low-skill wages on automation innovations, with
34

Denote ωi,D the domestic weight and ωi,F = 1 − ωi,D the total foreign weight with wL,D,t the wage
in the domestic country and wL,F,t the average wage in the foreign country. Then we can decompose asmall change in log wL,i,t as:
d log wL,i,t = d log (ωi,D wL,D,t + ωi,F wL,F,t ) =ωi,D wL,D,0
ωi,F wL,F,0
d log wL,D,t +d log wL,F,t
wL,i,0wL,i,0where ωi,D wL,D,0 /wL,i,0 denotes the values around which the change is computed—which we take as
the the value at the beginning of the period or the average value over the sample period. This showsω
wL,F,0that if i,F
wL,i,0 d log wL,F,t increases by 0.01 then wL,i,t increases by 1%. The same reasoning applies
to high-skill wages or GDP per capita. In (3), GDP gap enters directly in levels as an average of logs
so we directly interact the domestic and foreign variables with ωi,D and ωi,F .

32

if anything slightly larger elasticities. Our coefficient captures the average effect of anincrease in foreign low-skill wages given our controls whatever the shock behind it. Relative to Table 7, the main difference is that high-skill wages are now the macroeconomic
control variable with the most explanatory power (neither labor productivity nor GDP
per capita have a significant effect once high-skill wages are introduced). Clustering at
the country-level (to account for correlation of errors across firms within a country overtime) tends to reduce standard errors (Appendix Table A.19). We also reproduced theregression for the auto90 measure, we obtain similar results with slightly smaller coefficients (Appendix Table A.20). Finally, we also replaced the country-year fixed effects
with the interaction of country-year dummies with the domestic weight of each firm toaccount that firms are more or less exposed to the domestic country. Here as well, we
obtain similar results although the magnitude of the coefficient on low-skill wages is a
bit smaller (Appendix Table A.21).

5.3

Non-automation innovations

Is the effect of wages on automation innovations specific to automation or does it affect machinery patents in general? To answer this question, we now look at “placebo
regressions” of the effect of wages on innovations with a low score on our automation
metric. Specifically, we consider the set of machinery patents and exclude any patent
which has a technological category with an automation score above a certain threshold.
We fix that threshold at the 60th percentile of the distribution of C/IPC 6 digit codesin the machinery technological fields (0.2091). We refer to these innovations as “placebomachinery” innovations and we recompute knowledge stocks and spillover variables for
those innovations (“own”) and for all innovations except those (“other”). Table 9 reports
the results. Columns (1) to (3) correspond to the baseline regressions with firm andyear fixed effects. Low-skill wages only have a positive and significant effect in column(3) when GDP per capita is included as a control variable, but even in that case thecoefficient is statistically significantly smaller than with automation (1.66 versus 3.74
in column 9 of Table 7).35 Columns (4) to (6) repeat the same regressions but add
country-year fixed effects and columns (7) to (9) focus on foreign wages (here defined asin columns (4) to (6) of Table 8). Neither low-skill wages nor any other macroeconomic
control variables have an effect on placebo machinery innovations. The sign of low-skill
35
Further, this positive coefficient in the placebo regression is sensitive to specifications, and unlike
for the regressions with automation, it loses significance with different deflators for wages (not shown).

33

Table 9: Non-automation innovations
Dependent Variable

Placebo Machinery
Domestic + Foreign

Low-skill wage
High-skill wage
GDP gap
Labor productivity

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

0.2962(0.6209)-0.1907(0.6953)-0.0307***(0.0105)

0.5837(0.7013)0.3251(0.6428)-0.0292***(0.0103)-1.1140(0.7467)

1.6587**(0.6573)
0.8911(0.7506)-0.0292***(0.0104)-0.0486(0.8089)-0.3499(0.9539)-0.0072(0.0188)

0.0964(0.9245)-0.0648(0.9122)-0.0071(0.0187)-0.6087(1.1021)0.6381(0.9903)
0.0238(1.0053)-0.0062(0.0188)-0.7470(1.2590)0.4969(1.3193)0.0117(0.0319)-1.0568(1.4477)
0.1238(1.3073)
0.0120(0.0319)0.6174(1.1452)-0.9430(1.3045)0.4016(1.4470)0.0114(0.0319)

GDP per capita
Stock own
Stock other
Spillovers own
Spillovers otherForeign(1)0.0866**(0.0408)0.4797***(0.0464)2.6849***(0.4153)-2.4198***(0.5298)

0.0879**(0.0411)
0.4811***(0.0464)2.7419***(0.4163)-2.4342***(0.5348)-3.4367***(0.8242)0.0892**(0.0405)
0.4758***(0.0463)1.9983***(0.4423)-1.8132***(0.5386)

0.0952**(0.0405)0.4854***(0.0460)
1.1394***(0.4410)-1.2443**(0.5052)

0.0956**(0.0406)
0.4861***(0.0459)
1.1505***(0.4435)-1.2469**(0.5056)-1.5038(1.3776)
0.0957**(0.0404)
0.4847***(0.0459)
1.0777**(0.4411)-1.1918**(0.5047)

0.0958**(0.0405)
0.4862***(0.0448)
1.1398***(0.4393)-1.2694**(0.4965)

0.0954**(0.0406)
0.4871***(0.0449)
1.1215**(0.4428)-1.2450**(0.5008)0.3079(1.3051)
0.0956**(0.0406)
0.4866***(0.0449)
1.1469***(0.4418)-1.2706**(0.4965)

Fixed effects

F+Y

F+Y

F+Y

F + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

1155757705

1155757705

1155757705

1155157701

1155157701

1155157701

1155157701

1155157701

1155157701

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG). Columns (1)-(3) include firm and year fixed effects, while (4)-(9) include firm and country-year fixed
effects. Stock variables are calculated with respect to the dependent variable. In columns (7)-(9) domestic (resp. foreign) low-skill wages areinteracted with the share of domestic (resp. foreign) low-skill wages in total low-skill wages computed at the beginning of the sample, andsimilarly for high-skill wages, GDP per capita and VA per employee. Domestic (resp. foreign) GDP gap is interacted with the domestic (resp.
foreign) weight. In columns (1)-(6), there is no such interactions. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; ***p < 0.01

wages even flip in columns (7) to (9).36 We therefore view this exercise as validating1
both our empirical approach and our measure of automation. In particular, if our resulton the effect of low-skill wages on automation innovations came from a bias, than thatbias would have to be absent for other types of machinery innovations.

5.4

Additional results

Innovation types. Building on the previous results contrasting automation innovations
and low-automation machinery innovations, we now look at subcategories of automation
innovations and a laxer measure in Table 10, which reproduces column (8) of Table 7
for various types of innovations. Column (1) is essentially a robustness check which
36

Conditioning on the 60th percentile is not important and we obtain similar results with machinery
innovations excluding auto95 or auto90. In fact, since automation innovations are a relatively small
share of all machinery innovations, a regression on all machinery innovation gives similar results. See
Appendix Table A.22.

34

Table 10: Innovation categories
Dependent Variable

AutoX95

Auto80Automat* 90

Automat* 80

Robot 90

Robot 80

CNC 90

CNC 80

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

1.9759**(0.9046)-1.2113(0.9265)0.0370**(0.0186)0.2216(0.9431)-0.1400**(0.0567)
0.6443***(0.0645)0.7068*(0.4072)-0.5863*(0.3036)1.3013**(0.6373)-1.2776**(0.5754)0.0047(0.0121)0.8058(0.6648)0.0263(0.0374)
0.5225***(0.0460)
0.9236*(0.5235)-0.6139(0.4435)

2.6151**(1.1768)-0.9885(1.0579)
0.0078(0.0214)-0.9351(1.1098)-0.1149*(0.0601)
0.6684***(0.0872)
0.3869(0.4365)-0.3800(0.2736)

1.7535*(0.9657)-0.9874(0.8395)-0.0052(0.0173)-0.2196(0.9161)-0.0861(0.0525)
0.6312***(0.0737)0.4415(0.4719)-0.3305(0.3469)

0.4046(1.6931)-0.8384(1.6053)0.0345(0.0365)
0.8059(1.9404)-0.3029***(0.0993)0.8200***(0.1334)
0.2346(0.5380)-0.0665(0.3529)

2.3998*(1.2440)-2.0705*(1.2334)0.0409(0.0264)
0.7937(1.3971)-0.1319*(0.0790)0.6329***(0.0994)
0.1891(0.3489)-0.2028(0.2887)-2.6476(2.0151)
2.0374(1.8923)
0.0317(0.0411)2.7221(2.3494)-0.3043**(0.1511)0.5642***(0.1303)0.7408**(0.3657)-1.5340***(0.5522)-1.5273(1.5877)
0.8833(1.5580)0.0214(0.0305)
1.9101(2.1381)-0.2888***(0.0999)0.6140***(0.0961)
0.4634*(0.2727)-0.7109(0.4478)

Fixed effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

48600
3240

97635
6509

34170
2278

50220
3348

17670
1178

24645
1643

8970
598

15000
1000

Low-skill wage
High-skill wage
GDP gap
Labor productivity
Stock own
Stock other
Spillovers own
Spillovers other

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson
regressions fixed-effects (HHG). Stocks and spillovers are calculated with respect to the dependent variable. All regressions include firm fixed effects andyear dummies. All regressions include a dummy for no stock and no spillover. Standard errors are clustered at the firm-level. * p < 0.1; ** p < 0.05;*** p < 0.01

removes the codes that we added to the definition of the machinery technological field
listed in footnote 11 (though, we continue to exclude the weapons categories). The
results are similar to the baseline (with a lower but not statistically so coefficient).
Column (2) presents a laxer definition of automation using the 80th percentile of the
distribution of the C/IPC 6 digit codes. We still get a positive effect of low-skill wages
1though with a coefficient smaller than for either auto90 or auto95. Columns (3) to (8)look at subcategories of automation innovations. Robot90 and Robot80 were alreadydefined in Section 2.5. The other types of innovations are similarly defined: for instance,
automat*90 covers patents which belong to technological categories with a frequencyof the “automat*” keywords above the threshold used to define auto90. Columns (3)and (4) show that the results are similar for automat* patents (note that by definition
automat*80 patents are all auto80 but 91.5% of them are auto90). Column (6) showsthat our results extend to robot80 patents (which are also all auto95) but not to robot90
maybe because the sample size is reduced. The sample size drops even more substantiallyfor the CNC categories in columns (7) and (8), and consequently the coefficient on lowskill wages is very imprecisely estimated.
Timing. We look at alternative lags for the macroeconomic and the spillover variables in Table 11—we keep a lag of 2 between patent applications and the stocks of35

Table 11: Lags and leads
Dependent variable

Auto95
(1)
-5

(2)
-4

1.4268*(0.8599)-0.0640(0.9033)
0.1931(1.1023)

2.0578**(0.8328)-0.9379(0.8937)
0.4055(1.0789)

Panel A: baseline
1.9681** 2.4266*** 2.0882**(0.8229)(0.8658)(0.8417)-1.6808*-1.6700*-2.0273**(0.9223)(0.8634)(0.7977)
1.12830.12850.0857(1.0884)(0.9199)(0.7871)

Fixed effects

F+Y

F+Y

F+Y

F+Y

Observations
Firms

47565
3171

48240
3216

49395
3293

50115
3341

Low-skill wage

0.9671(1.1012)0.4539(1.3522)-1.5193(1.8190)

1.3572(1.1353)-0.9749(1.1490)-0.8311(1.6338)

Lags (Leads)
Low-skill wage
High-skill wage
Labor productivity

High-skill wage
Labor productivity

Low-skill wage
High-skill wage
Labor productivity

1.5679(1.6579)
2.1192(1.8327)-2.3858(1.5235)

(3)
-3

(4)
-2

(5)
-1

(6)
0

(7)
1

(8)
2

2.0767**(0.8331)-2.5752***(0.8281)-0.0118(0.8022)2.2411***(0.8518)-2.5365***(0.7687)-0.2255(0.8265)

1.4514*(0.8251)-2.7223***(0.7828)0.4201(0.8912)

F+Y

F+Y

F+Y

F+Y

50670
3378

51315
3421

52470
3498

53940
3596

1.2360(1.1088)-2.7165**(1.0935)
0.3167(1.5761)

1.2538(1.1409)-2.1045**(1.0333)-0.1957(1.6158)

0.1282(1.0962)-1.6862(1.0682)
0.0676(1.5974)

Panel C: country-year fixed effects and foreign variables
2.5117* 3.1804** 4.3023*** 3.0459**
1.6943(1.4908) (1.4684)(1.4482)(1.4516)(1.5642)-1.0194-2.5135-2.4740*-3.2862** -3.8818***(1.6302) (1.6445)(1.4209)(1.4238)(1.4272)-0.9029-0.7200-1.74940.40101.8684(1.5420) (1.5937)(1.4131)(1.3247)(1.4493)

1.6996(1.7055)-3.3215**(1.3771)
1.6417(1.5255)

0.4034(1.7377)-2.5666*(1.4844)1.6644(1.6175)

Panel B: country-year fixed effects
1.54052.1429*1.6930(1.1175)(1.1505)(1.1222)-1.7245-1.9117*-2.0866**(1.0931)(1.0157)(1.0346)-0.2556-1.2851-0.5775(1.5444)(1.6381)(1.6431)

Fixed effects

F + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

47565
3171

48240
3216

49365
3291

50070
3338

50595
3373

51255
3417

52410
3494

53895
3593

Note: Marginal effects; Standard errors in parentheses. Each panel represents a different regression. All regressions contain controls for GDP gap, stocks and spillovers, for which we do not report the coefficient. The independent variables(wages, VAemp and GDP gap) are lagged by the number of periods indicated in lag, except for the stock variables which
are always lagged by 2 periods. Estimation is by conditional Poisson regressions fixed-effects (HHG). Panel A regressions
contain firm and year fixed effects. Panel B and C regressions contain firm and country-year fixed effects. In Panel C
regressions, wages are replaced with foreign wages interacted with the share of foreign wages in total wages at the beginning of the sample, and similarly for the other macro variables. Standard errors are clustered at the firm-level * p < 0.1;** p < 0.05; *** p < 0.01

1patents from the firm because otherwise the dependent variable would be included in
the stock of automation when we consider contemporaneous regressions or leads. Column (4) reproduces our baseline results with a 2 year lag. Panel A shows that the largest
coefficient on low-skill wages is obtained for a 2 year lag, but remains relatively stablebetween a 4 year lag and a 1 year lead. Both panels find an effect of low-skill wages moreclearly centered around lag 2 (ADHMV also found that the largest coefficient for theeffect of gas prices on innovations in the car industry was at a 2 year lag). Our baseline
regressions assume a 2 year lag between wages and patent applications.
Of course, innovators would not be interested about wages 2 years in the past perse, but only inasmuch as they are indicative of future wages. This is our interpretation
throughout our regressions, with the 2 year lag corresponding roughly to the time spent
between an effect on R&D and the first results materialized by a patent application.37
37

In that context, the difference between the significant lead coefficients in Panel A and the insignif-36

We push this logic further in Appendix Table A.23, where we compute predicted futurewages at time t − 2 based on an AR(1) process with country-specific trends. We findsimilar results.
Minimum wage. Given its policy relevance, we also look at the effect of minimum
wages using data on 22 countries.38 Importantly, we cannot use the minimum wage as
an instrument for low-skill wages: our regressions show that the high-skill wage has asignificant effect and therefore should be included in the regression. If low-skill wages
were to be instrumented so should high-skill wages, and we would need a second instrument. We report the results of reduced form regressions where we replace low-skill
wages with the minimum wage in Appendix Table A.24. We find a positive effect ofthe minimum wage on automation innovations which in specifications with country-year
fixed effects has p-values just above or below 0.1. Clustering standard-errors at thecountry-level gives significant coefficients (see Appendix Table A.25). Minimum wages
are unlikely to be a strong predictor of automation in our analysis: first because it only
captures part of the labor costs (contrary to our baseline measure), second because we
focus on automation innovations that largely happen in manufacturing where wages forlow-skill workers are often substantially higher than the minimum wage. An analysis on
automation in service industries might show a stronger relationship.

5.5

Shift-share set-up

A recent literature addresses the identifying assumptions behind the shift-share set-up in
linear regressions. In the language of our setting, Goldsmith-Pinkham, Sorkin and Swift(2019) show that the shift-share instrument is equivalent to a combination of weights
time country-year dummies. Our shift-share setting would then capture the effect of
low-skill wages on automation innovations if weights time country-year dummies only
affect automation through the controls that we have included. In this interpretation of
the shift-share set-up the exogeneity of the weights is important and we show below that
our results are robust to using weights from an earlier period.
Borusyak, Hull and Jaravel (2018) show that country-time shocks can also be a
icant ones in Panel B and C, could reflect that domestic wages may be easier to predict than foreign
wages.
38
We use data from the OECD. Importantly, not all countries have government-mandated minimum
wages, most notably Italy and, until 2015, Germany. For Germany, we follow Dolado, Kramarz, Machin,
Manning, Margolis, Teulings, Saint-Paul and Keen (1996) and use the the collectively bargained minimum wages which in effect constitute law.

37source of identification in the shift-share setting. The inference is valid if either there
is a large number of countries (such that the Herfindahl index tends toward 0) affectedby independent shocks (controlling for a year and firm fixed effects); or the correlationof shocks within a country decays sufficiently rapidly that a large number of country x
years is sufficient (see Appendix A2 in their paper). They advise practitioners to use
appropriate controls to capture omitted variables. We follow this approach partly by
including a large set of controls in our regressions and partly by including country-year
fixed effects. They further encourage practitioners to apply the standard error correctionof Adão, Kolesár and Morales (2019).
Adão et. al. (2019) show that standard applications with the shift-share design often
lead to an over-rejection of the null of no effect. In the language of our application, theproblem arises if the standard errors of firms with similar country-distributions havecorrelated residual errors. Though this problem is related to the correlation of standard
errors in clustered designs it is not solved by standard clustering. Adão et. al. (2019) use
Monte Carlo simulations in a standard Bartik setting and show that in a setting where
the true coefficient is zero by construction the commonly used approach rejects the nullof no effect up to 55% of the cases. They derive a formulae for standard errors in an
OLS setting that corrects for this problem. This formulae is not directly applicable inthe current setting since we employ a Poisson estimator and deriving the corresponding
correction for the Poisson estimator is beyond the scope of this paper. Instead we
implement a Monte Carlo simulation like the one used in Adão et. al. (2019) and showthat we do not have a similar problem of over-rejection.
Specifically, we replicate the regression in Column (9) in Table 7. For each firm wekeep the automation activity, the stocks of innovations, the spillover variables, as well as
the distribution of country-weights based on actual patents. For each country we sample
without replacement the entire path of wages and GDP from the existing set of wages
and GDP. Figure 5 shows a histogram of the t-statistic of the low-skill wages, the maincoefficient of interest, where the red line at 4.1 corresponds to the t-statistic of column(9). The empirical distribution has a heavier left tail than the expected standard normal
distribution but only in 0.33% of the cases did the absolute value of the t-statistic exceed
the realized t-statistic from our main regressions. In the language of Adão et al. (2019)the set of controls soaks up most country-specific shocks affecting the outcome variable
and, consequently, no shift-share structure is left in the regression residuals.

38

0

.05

.1

Density
.15

.2

.25

T-statistics for Monte Carlo on country wages

-4

-2

0
t-value2

4

Figure 5: The t-value on the low-skill wages from a Monte Carlo simulation sampling country
wages and GDP (see text for details)

5.6

Robustness checks

This section presents several robustness checks.
Controlling for the cost of innovation. Our measure of wages could still reflect
the cost of innovation if innovation does not solely take place in the domestic country.
To address this issue we re-build our firm-specific macroeconomic variable using theinventor weights of the firm instead of the patent weights. Table 12 reports the result.
The coefficient on low-skill wages remains positive and significant but the coefficient on
low-skill wages weighted by inventor weights is small and insignificant. These regressions
constitute a placebo test in that they are essentially treating the firms by the same
macroeconomic shocks but distributed according to where the firm innovates not where
it sells.
Multicollinearity and skill premium. Low-skill wages, high-skill wages and labor productivity are correlated, which could affect our regressions—although controllingfor firm fixed effects and year fixed effects, the correlation coefficient is only 0.6 (seeAppendix Table A.26). To deal with this issue, Appendix Table A.27 regresses automation innovation on the log of the ratio of low-skill to high-skill wages (the inverse of theskill premium) for firm fixed effects, country-year fixed effects and foreign wages withcountry-year fixed effects. The coefficient on the inverse skill premium is always of thesame magnitude as that on low-skill wages and highly significant. On the other hand,replacing low-skill and high-skill wages with their ratio in the regressions with placebomachinery innovations of Table 9 gives insignificant coefficients.
Including middle-skill wages. Previous work has often found that IT disproportionately affects middle-skill workers (e.g. Autor and Dorn, 2013). In this spirit,
39

Table 12: Wages weighted by inventor weights
Dependent Variable

Auto95
Domestic + Foreign

Low-skill wage
Low-skill wage (iw)High-skill wage
High-skill wage (iw)
GDP gap
GDP gap (iw)Labor productivity

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

2.6194***(0.9119)-0.2924(0.4461)-1.9307**(0.9171)
0.3960(0.3397)
0.0364(0.0229)-0.0076(0.0123)2.4897***(0.9549)-0.1985(0.4668)-2.1087**(1.0032)
0.5295(0.3869)
0.0366(0.0227)-0.0083(0.0121)0.4313(1.1116)-0.3065(0.5374)

3.7088***(1.0503)-0.0762(0.4805)-0.8557(0.8490)0.4991(0.3370)0.0314(0.0231)-0.0050(0.0124)

1.9136*(1.0705)-0.1439(0.4747)-2.5728**(1.0770)
0.1804(0.3249)
0.0616*(0.0362)0.0003(0.0122)2.0761*(1.1954)0.0552(0.4794)-2.2029**(1.0546)0.4874(0.3727)
0.0616*(0.0362)-0.0017(0.0120)-0.8383(1.6547)-0.7076(0.5066)

2.9547**(1.3173)
0.0944(0.4754)-1.9204*(1.1427)
0.2735(0.3451)
0.0630*(0.0361)
0.0022(0.0125)

4.7342***(1.5977)-0.1005(0.5772)-4.0721***(1.5497)-0.2895(0.4384)-0.0077(0.0567)
0.0186(0.0152)5.6526***(1.7376)
0.6363(0.6011)-3.4857**(1.6359)
0.7720*(0.4655)-0.0166(0.0565)
0.0126(0.0153)-1.5747(1.5093)-1.8365***(0.6146)

5.0494**(1.9638)
0.4886(0.5562)-4.2454**(1.6521)0.0817(0.4573)-0.0080(0.0565)0.0208(0.0153)

Labor productivity (iw)
GDP per capita
GDP per capita (iw)-3.0004***(0.9236)-0.4388(0.5746)-2.4889(1.9888)-0.4514(0.6508)-0.1854(2.1553)-1.1406**(0.5649)

stock + spillstock + spillstock + spillstock + spillstock + spillstock + spillstock + spillstock + spillstock + spill

Fixed Effects

F+Y

F+Y

F+Y

F + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

49305
3287

49305
3287

49305
3287

49245
3283

49245
3283

49245
3283

37395
2493

37395
2493

37395
2493

Control variables

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects(HHG). Columns (1)-(3) include firm and year fixed effects, while (4)-(9) include firm and country-year fixed effects. Stock variables are calculated with respect to the dependent variable. In columns (7)-(9) domestic (resp. foreign) low-skill wages are interacted with the share of domestic (resp. foreign) low-skill wages in total low-skill wages
computed at the beginning of the sample, and similarly for high-skill wages, GDP per capita and labor productivity. Domestic (resp. foreign) GDP gap is interacted withthe domestic (resp. foreign) weight. In columns (1)-(6), there is no such interactions. All regressions with patent-weighted low-skill wage variable include a corresponding
inventor-weighted low-skill wage variable, similarly for high-skill wage, GDP gap, GDP per capita and labor productivity. All inventor-weighted variables are denoted by (iw)after their names. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

Appendix Table A.28 adds middle-skill wages to our regressions. Low-skill wages con1tinue to have a large positive impact on automation,
whereas middle-skill wages have anegative (though not consistently significant effect). Low-skill and middle-skill wages arehighly correlated (with a coefficient of 0.94, see Appendix Table A.26), and consequently,
middle-skill wages have a positive coefficient when low-skill wages are not included.
Wages and deflators. Appendix Table A.29 shows that our results are robust to
deflating our macroeconomic variables differently: by converting to USD in a different
year (columns 1 and 2), every year (columns 3 and 4) or using the local GDP deflator
instead of the local PPI in manufacturing (columns 5 and 6). Further, we look at total
wages instead of manufacturing wages either with our baseline deflator (columns 7 and
8) or converting every year (columns 9 and 10). Our results remain largely robust butwith smaller coefficients when converting in USD every year. Converting in USD every
year makes our macroeconomic variables more correlated and increases the importanceof short-term fluctuations.
Weights. Table 13 reproduces the regressions of columns (7) and (8) of Table 7 butwith alternative firm-specific weights ωi,c . In columns (1) and (2), we compute the patent
weights over a more recent period (1985-1994 instead of 1970-1994) and obtain the same40

Table 13: Alternative weights
Dependent Variable

Auto95
1985 − 1994

GDP 01970 − 1989

GDP 1

(wL ∗ L)0.35

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

2.4739***(0.8691)-1.7055**(0.8288)
0.0226(0.0163)

1.8155*(0.9480)-0.8990(0.8354)0.0140(0.0164)-0.1429***(0.0511)
0.6385***(0.0598)
0.8056**(0.3340)-0.4680**(0.2265)

2.8825***(0.8953)-1.6405**(0.7547)
0.0264*(0.0159)-0.0482(0.8293)-0.1429***(0.0511)
0.6384***(0.0598)
0.8051**(0.3354)-0.4664**(0.2305)2.2007***(0.8125)-1.4445*(0.7847)
0.0283*(0.0156)-0.1436***(0.0509)
0.6414***(0.0600)
0.4091(0.3093)-0.1913(0.2311)1.7962**(0.8176)-1.4820*(0.8851)
0.0273*(0.0153)0.2559(0.8994)-0.1441***(0.0511)0.6410***(0.0600)
0.4178(0.3106)-0.2090(0.2366)

2.8690***(0.8855)-1.6609**(0.7114)0.0265*(0.0158)-0.1194**(0.0602)0.6900***(0.0769)
0.2618(0.3206)-0.3772(0.2435)

1.7192*(0.9544)-1.0259(0.9524)0.0138(0.0164)0.3240(1.0211)-0.1201**(0.0603)0.6895***(0.0769)
0.2719(0.3229)-0.3951(0.2518)

1.8685**(0.7776)-1.3791*(0.8226)
0.0276*(0.0154)-0.1337**(0.0524)0.6539***(0.0639)0.5655*(0.3154)-0.3401(0.2303)2.3626***(0.8876)-1.9002**(0.8899)
0.0224(0.0162)0.4484(0.9649)-0.1343**(0.0524)0.6540***(0.0639)0.5815*(0.3182)-0.3693(0.2401)-0.1428***(0.0509)0.6404***(0.0600)
0.4679(0.3103)-0.2577(0.2284)

2.1429**(0.8516)-1.5237*(0.8444)0.0280*(0.0154)
0.1983(0.9221)-0.1432***(0.0509)0.6403***(0.0600)
0.4744(0.3114)-0.2702(0.2353)

Fixed effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

45735
3049

45735
3049

35955
2397

35955
2397

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

Low-skill wage
High-skill wage
GDP gap
Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers other

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixedeffects (HHG). All regressions include firm fixed effects and year dummies. All regressions with stock variables (resp. spillover variables) include a dummy for no stock(resp. no spillover). In columns (1) and (2) firms’ country weights for the macroeconomic variables are computed over the period 1985-1994; and over the period 19701989 for columns (3) and (4). Columns (5) to (19) use the baseline pre-sample period of 1970-1994. Columns (5) and (6) do not adjust for GDP in the computationof the weights and columns (7) and (8) use GDP instead of GDP 0.35 to adjust for countries’ size in the computation of the weights. Columns (9) and (10) adjust for
total low-skilled payment instead of using GDP. Standard errors are clustered at the firm-level. * p < 0.1; ** p < 0.05; *** p < 0.01

results. Columns (3) and (4) on the other hand drop the 5 most recent years in computing
the weights. We lose a large number of firms, but still obtain a positive effect of lowskill wages on automation innovations, though with slightly smaller coefficients. This
regression addresses the potential concern that our weights could be endogenous because
firms which already intend to do automation innovations may decide to locate in places1
where they forecast an increase in low-skill wages: it is hard to see how firms’ location
decisions before 1989 could reflect increases in wages from 1995 onward.39 Columns (5)to (10) keep the patent weights as in our baseline analysis but instead of multiplying
them by GDPc0.35 , they do not multiply them (columns 5 and 6), multiply them by GDP(7 and 8) or by the total value of low-skill employment to the power 0.35 ((wL L)0.35 : one
could argue that this represents a better measure than GDP 0.35 of the potential market
for technology designed to automate low-skill work). We obtain similar results.
Quality. Appendix Table A.30 investigates whether our results are robust when
focusing on patents of higher quality. We look at patents which have been applied forat 2 of the 3 main patent offices (EU, Japan and US), or at triadic patents which have39

The same concern can be addressed by keeping our baseline weight but dropping the first few years.
See Appendix Table A.30 which reproduces Table 7 but only from 2000. Though the standard errors
are bigger, the results are essentially the same.

41

been applied for at the 3 offices. Triadic patents are generally considered to be patentsof very high quality. All of these give similar results. We also restrict attention to biadic
patents with at least one citation within 5 years and weigh patents by citations.40 This
weakens the results somewhat perhaps because whereas the decision to innovate is achoice variable of the firm the eventual quality of the innovation is largely random.
Nickell’s bias. Our regressions include the stock of automation innovations andtherefore may suffer from Nickell’s bias. To address this issue, in Appendix Table A.31,
we remove the stock of automation innovations with very little effect on the variable ofinterest. In addition, we use Blundell, Griffith and van Rennen (1999)’s method, which
proxies for the fixed effect by using the firm’s pre-sample average of the dependent
variable. We obtain very similar results.
Industry-year fixed effects. Appendix Table A.33 introduces industry-year fixed
effects where the industry of a firm correspond to its 2 digit industry in Orbis. The
results are very similar.

6

Event study: the Hartz reforms in Germany

In this section, we use the Hartz reform as an event study to complement our main
analysis. The Hartz reforms were a series of labor-market reforms in Germany designed
from 2002 onward and implemented between January 1st 2003 and January 1st 2005.
The reforms aimed at reducing unemployment and increasing labor-market flexibility
by reforming employment agencies to provide better job-search assistance, deregulating
temporary work, offering wage subsidies for hard-to-place workers, reducing or removingsocial contributions on low-paid jobs and reducing long-term unemployment benefits (seeJacobi and Kluve, 2007). The reforms have been widely credited with playing a majorrole in the remarkable performance of the German labor market since, in particular,
for increasing labor supply and improving matching efficiency (see Krause and Uhlig,
2012, Krebs and Scheffel, 2013 and 2017, or Burda and Seele, 2016). Such reforms
should reduce the incentive to automate low-skill labor by reducing labor costs (directly
through social contribution and indirectly through an increase in labor supply) but also40

We add to each patent the number of citations received within 5 years normalized by technologicalfield and year of application, in a similar fashion to, for instance, Kogan, Papanikolaou, Seru andStoffman (2017), who find a positive correlation between patent value and citations. Abrams, Akcigit
and Grennan (2018) on the other hand find an inverted U relationship between patent value andcitations.

42

by allowing for more flexible contracts and reducing the expected cost of vacancies.
We start from the same database linking firms and patents as in our main empiricalanalysis of Section 5, using the same weights to measure firms’ exposure to different
countries and focusing on biadic patent applications as a measure of innovation. We stilldefine the country of a firm as the country of largest weight, and restrict attention tofirms from the countries with the highest average exposure to Germany (Austria, France,
Italy, Japan, the Netherlands, Spain, Switzerland, the United Kingdom and the UnitedStates).
We first run the following Poisson regression, over the years 1995-2012, maintaining
a 2-year lag:

P ATAut,i,t+2 = exp (βDE · δt ωi,DE + βKa ln KAut,i,t + βKo ln Kother,i,t + δi + δc,t ) + i,t .
P ATAut,i,t+2 is still a count of automation patents, KAut,i,t and Kother,i,t continue represent firm knowledge stocks, δi is a firm fixed effect, δc,t is a country-year fixed effect,
ωi,DE is the (fixed) firm weight on Germany, δt is a set of year dummies (with 2003 as
the excluded year) and βDE is the full vector of coefficients of interest. The vector of
coefficients βDE determines by how much a firm more exposed to Germany tends to do
more automation patents in a given year relative to 2005 (with the 2 year lag). Figure
6.a reports the results. The coefficients can be interpreted as follows: a value of −2
in 2008 indicates that on average a firm with a German weight of 0.1 (the mean value
is 0.106) did 20% less automation innovations in 2010 than in 2005 (recall the 2 yearlag) compared to a firm with no German exposure. The figure suggests that from 2000
until 2004 firms highly exposed to Germany increased their propensity to introduce automation innovations. This trend reversed between 2006 and 2009 and resumed from2010. This is consistent with the Hartz reform increasing labor supply from 2002-2004,
and therefore decreasing the incentive to introduce automation innovations 2 years later.
2008 marks the beginning of the Great Recession which had a lower impact on German
labor markets than in other countries, so that German labor markets remained relatively
tight, potentially increasing the incentive to undertake automation innovations.
The previous figure clearly shows that the behavior of firms highly exposed to Germany differs over time from that of other firms. To show that the trends above are
specific to automation innovations, we then run the following Poisson regression:

43

4

6

2

4

-2

0

2
0

-4

-2

the interaction between the German weight and a set of year fixed effects in a Poisson regressionof auto95 innovations controlling for a full set of fixed effects and firm innovation stocks. Panel(b) reports coefficients on the triple interaction between the German weight, a dummy for auto95
innovations and a set of year fixed effects in a Poisson regression of auto95 and other machinery
innovations controlling for a full set of fixed effects, firm innovation stocks and the interaction
between the German weight and a set of year fixed effects.

2012

2011

2010

(b) Effect of German exposure on automation
innovations relative to other machinery innovations.

Figure 6: Effect of German exposure on automation innovations. Panel (a) reports coefficients on

44

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

2012

2011

2010

2009

2008

2007

2006

2005

2004

2003

2002

2001

2000

1999

1998

1997

1996

1995

-6

-4

(a) Effect of German exposure on automation
innovations

P ATk,i,t+2 = exp

aut
βDE · δt ωi,DE + βDE· δt ωi,DE 1k=aut+βKa · δk ln KAut,i,t + βKo · δk ln Kother,i,t + δk,i + δk,c,t!
+k,i,t . (5)k denotes the type of an innovation which is either auto95 or another machinery innovation, δk,i represents a full set of innovation type firm fixed effects and δk,c,t innovation
type country year fixed effects and 1k=aut is a dummy for an auto95 innovation. Standard
aut
errors are clustered at the firm level. βDE
is the vector of coefficients of interests, for eachyear, they measure how much exposure to Germany increases the relative propensity tointroduce automation innovations instead of other forms of machinery innovations compared to 2005 (as 2003 is still the excluded year). Figure 6.b reports the results: thepattern is similar to Figure 6.a but more striking.
To formally test that the Hartz reform created a trend break in the relative propensityof firms highly exposed to Germany to introduce automation innovations relative toother types of machinery innovation, we replace the full set of year fixed-effects δt inaut· δt ωi,DE 1k=aut in (5) with a time trend t − 2003 and a time trend interacted with aβDEpost 2003 dummy (t − 2003)1t>2003 . We focus on the years 1998-2008 (i.e. 2000-2010 for
the innovation variable) to have a panel centered on 2003 and avoid the Great Recession.
Table 14 reports the result. Column (2) corresponds exactly to the specification we
discussed: it shows a significant time trend in the effect of German exposure on therelative propensity to carry automation innovation two years later between 1998 and2003, this time trend sharply reversed in the following 5 years. Column (1) carries out thesame regression but omits the controls for the stock variables. Column (3) adds a control
for the triple interaction of the German weight, a dummy for automation innovations
and a dummy for post-2003. This tests whether the break in time trends is associatedwith a shift in levels. The coefficient is insignificant. Column (4) replaces the German
weight by a dummy indicating that the firm is in the top quartile of exposure to Germany
among innovating firms: the results are of similar magnitude since the 75th percentileof German weight is 0.16. Column (5) uses the low-automation innovations of section
5.3 instead of all other machinery innovations. The results are similar. Finally, column(6) considers three types of innovations by separating non-auto95 machinery innovations
into the low-automation innovations of the previous columns and the rest. There areno significant trends distinguishing low-automation innovations from other non-auto95
machinery innovations. Overall, this exercise suggests that the Hartz reforms reduced45

Table 14: Innovation and exposure to Germany
Dependent variables
time trend*dummy auto95*German exposure
time trend*dummy auto95*post_2003*German exposure
dummy auto95*post_2003*German exposure

Auto 95 and other + low auto

Auto95 and low auto

(2)

(3)

(4)

(5)

(6)

0.6309**(0.2502)-1.2330***(0.4473)

0.6245***(0.2296)-1.2322***(0.4291)

0.7726*(0.3957)-1.3229**(0.5273)-0.7289(1.0856)

0.0929**(0.0366)-0.1810**(0.0766)

0.6486***(0.2464)-1.2500***(0.4605)

0.6523***(0.2322)-1.2826***(0.4300)

Y
Y
Y
Y

Y
Y
Y
Y

time trend*dummy low auto*German exposure

0.0081(0.1278)-0.0386(0.1835)

time trend*dummy low auto*post_2003*German exposureyear dummy*German exposurefirm innovation stocks * innovation types
firm *innovation types fixed effects
country * year * innovation types fixed effects

Auto95 and other and low auto

(1)

Y
N
Y
Y

Y
Y
Y
Y

Y
Y
Y
Y

Y
Y
Y
Y

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects(HHG). All regressions include firm innovation types fixed effects, country year innovation types fixed effects and controls for the year dummy times the measure of German
exposure. German exposure is measured by the German weights in all regressions except for column (4) where it is replaced by a dummy signaling that the firm is in the top
quartile of Germany exposed firms. Innovation types are auto95 and (other + low auto) in columns (1) to (4), auto 95 and low auto in column (5) and auto 95, other and low
auto in column (6). All regressions with stock variables include a dummy for no stock. Standard errors are clustered at the firm-level.* p < 0.1; ** p < 0.05; *** p < 0.01

the propensity of foreign firms highly exposed to Germany to introduce automation
innovations.

7

Conclusion
1

In this paper, we have used patent text data to identify patents which correspond toautomation innovations and provide a new measure of automation. Across sectors, ourmeasure is uncorrelated with computerization but positively correlated with robotization. We also find that our measure is associated with a decline in routine tasks across
US sectors. We then use our classification to analyze for the first time the effect of wages
on automation innovations in machinery. We find that automation innovations are very
responsive to changes in low-skill wages with elasticities estimated between 2 and 4.
This result does not extend to other types of innovations in machinery. Furthermore,
we show that the Hartz reforms in Germany were associated with a relative increase inautomation innovations by foreign firms with a high exposure to Germany.
These results suggest that policies which increase labor costs for low-skill workers willlead to an increase in innovations which aim at saving on low-skill workers. Therefore,
with endogenous technological change, such policies are likely to be less costly for the
economy in terms of overall welfare, but they introduce additional negative effects forlow-skill workers. By how much then would an exogenous increase in low-skill wages
be undone in a couple of years through innovation? Answering this question requires
finding the effect of an increase in automation patents on low-skill wages.

46

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Analysis of biological materials
Audio−visual technology
Basic communication processes
Basic materials chemistry
Biotechnology
Chemical engineering
Civil engineering
Computer technology
Control
Digital communication
Electrical machinery, apparatus, energy
Engines, pumps, turbines
Environmental technology
Food chemistry
Furniture, games
Handling
IT methods for management
Machine tools
Macromolecular chemistry, polymers
Materials, metallurgy
Measurement
Mechanical elements
Medical technology
Micro−structural and nano−technology
OpticsOrganic fine chemistry
Other consumer goods
Other special machines
Pharmaceuticals
Semiconductors
Surface technology, coating
Telecommunications
Textile and paper machines
Thermal processes and apparatus
Transport

0

2

4

6

Percent
Figure A.7: Share of biadic patent applications in the different technical fields in 1997-2011

A

Appendix Figures and Tables

53

(thousands)
60 80

.04

40

.032

20

.024
.016
.008
0

auto95
auto90
1980

1985

1990

1995

year

2000

2005

2010

2015

(a) Share of automation patents in machinery out

auto95
auto90
1980

1985

1990

1995

year

2000

2005

2010

(b) Number of automation patents worldwide ac-of total patents. Automation technological categories are defined at the 90th percentile of the
distribution of 6 digit C/IPC codes in machinery(for auto90) or the 95th percentile (auto95).

cording to the auto90 and auto95 definitions

.06

.12

.18

.24

.3

Figure A.8: Trends in automation (for biadic applications)0

United States
United Kindom
1980

1985

1990

1995

Germany
Japan
year

2000

2005

France

2010

2015

2015

Figure A.9: Share of automation patents (auto95) in machinery by applicant’s nationality.

54

Table A.15: Share of automation patents in machinery across sectors

ISIC Rev. 4 Title

Share of automation patents in machinery 1997 - 2011 (in %)Germany
United States
All Countries
auto95
auto90
auto95
auto90
auto95
auto90

A
B
10-12
13-15

5.7
10.0
4.6
3.9

12.4
17.6
12.9
9.0

6.4
9.9
5.6
4.7

14.8
18.2
15.2
11.4

6.8
9.8
5.0
4.2

13.8
17.2
12.6
10.3

4.3
2.6
2.9

9.3
6.8
6.9

4.7
2.8
3.8

11.9
7.5
8.2

4.9
2.8
3.0

10.9
7.6
7.0

6.1
10.8
7.7
30.7

11.7
26.0
22.3
39.4

6.7
12.4
8.8
30.1

13.9
29.4
24.3
40.1

5.9
11.1
8.4
29.4

12.0
27.0
23.7
39.1

17.4
32.6
24.5
15.7

30.5
36.8
29.3
23.2

18.1
30.0
22.8
18.7

30.7
35.7
29.1
27.9

18.8
31.9
26.1
18.9

31.5
36.8
31.9
27.7

6.6

13.2

8.2

16.5

7.9

14.7

7.7

11.7

9.4

15.5

8.4

13.3

16
17-18
19-22
23
24
25
26-27
28
29
30
91
E
F

Agriculture, forestry and fishing
Mining and quarrying
Food, beverages and tobacco products
Textiles, wearing apparel, leather andrelated products
Wood and products of wood and cork
Paper, paper products and printing
Coke, chemicals, pharmaceuticals,
rubber and plastic products
Other non-metallic mineral products
Basic metals
Fabricated metal products
Computer, electronic, optical andelectrical products
Machinery and equipment n.e.c.
Motor vehicles, trailers and semi-trailers
Other transport equipment
All other manufacturing
branches
Water supply; sewerage, waste
management and remediation activities
Construction

Table A.16: Top 10 auto95 innovators in our sample
Number of biadic auto95
patents in 1997-2011

Company
Siemens S.A.
Honda Motor Co., Ltd.
Fanuc Co.
Samsung Electronics Co., Ltd.
Robert Bosch AG
Mitsubishi Electric Europe B.V.
Tokyo Electron Europe, Ltd.
Murata Machinery, Ltd.
Kabushiki Kaisha Toshiba
General Electric Canada

55

1738
810
777
706
655
652
578
501
473
464

Table A.17: Baseline regressions for auto95 with country-level clustering
Dependent variable
Low-skill wage
High-skill wage

Auto95
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

2.2000***(0.5464)2.8254***(0.7421)-0.9210(0.6234)

1.8160***(0.6310)-0.9009**(0.3519)-0.1275***(0.0336)0.6311***(0.0495)

1.9058***(0.6863)-0.9695***(0.3701)-0.1269***(0.0339)0.6296***(0.0506)
0.0210***(0.0081)1.9992**(0.9001)-0.8698(0.7025)-0.1270***(0.0335)
0.6309***(0.0483)
0.0214**(0.0088)-0.2551(1.0309)2.2954***(0.5383)-0.2971(0.2972)-0.1239***(0.0355)
0.6260***(0.0518)0.0179**(0.0074)

2.4627***(0.7170)-1.6180***(0.4701)-0.1441***(0.0358)0.6408***(0.0493)0.0279***(0.0091)2.4266***(0.8727)-1.6700**(0.7968)-0.1443***(0.0365)0.6407***(0.0492)
0.0278***(0.0096)
0.1285(0.9693)

3.7365***(0.6582)-0.4838*(0.2831)-0.1504***(0.0389)
0.6489***(0.0501)
0.0265***(0.0076)

Stock automation
Stock other
GDP gap
Labor productivity
GDP per capita
Spillovers automation-1.5635*(0.8207)

Spillovers other0.5442***(0.1831)-0.3014(0.2573)

0.5478***(0.1931)-0.3089(0.2395)-3.3618***(0.8952)
0.8587***(0.1270)-0.5853***(0.1790)

Fixed effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG). All regressions include firm fixed effects and year dummies. All regressions with stock variables (resp.
spillover variables) include a dummy for no stock (resp. no spillover). Standard errors are clustered at the country-level. * p < 0.1; ** p < 0.05;*** p < 0.01

Table A.18: Baseline regressions: effect of wage on automation innovations (auto90)
1

Dependent variable
Low-skill wage
High-skill wage

Auto90
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.7307***(0.4953)

2.4414***(0.6610)-1.0613*(0.5844)

1.3357**(0.6363)-0.7746(0.5311)-0.0347(0.0405)
0.5682***(0.0496)

1.3715**(0.6610)-0.8019(0.5480)-0.0345(0.0405)
0.5676***(0.0497)
0.0081(0.0137)

1.4738**(0.6778)-0.6844(0.6068)-0.0348(0.0404)
0.5690***(0.0495)
0.0085(0.0134)-0.2904(0.7011)1.8797***(0.7051)
0.0911(0.5491)-0.0328(0.0406)
0.5611***(0.0495)
0.0038(0.0135)

1.9059***(0.6883)-1.4074**(0.6296)-0.0475(0.0403)
0.5773***(0.0508)0.0152(0.0133)

1.8309***(0.7008)-1.5340**(0.6850)-0.0479(0.0403)
0.5770***(0.0508)0.0151(0.0133)
0.2911(0.7224)

3.1623***(0.7486)-0.0865(0.6114)-0.0538(0.0403)
0.5814***(0.0504)
0.0127(0.0132)

Stock automation
Stock other
GDP gap
Labor productivity
GDP per capita
Spillovers automation-2.0568***(0.7380)

Spillovers other0.8903**(0.4162)-0.6079**(0.3050)

0.9102**(0.4190)-0.6342**(0.3140)-3.5341***(0.7721)1.2870***(0.4170)-1.0159***(0.3174)

Fixed Effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

73545
4903

73545
4903

73545
4903

73545
4903

73545
4903

73545
4903

73545
4903

73545
4903

73545
4903

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG). All regressions include firm fixed effects and year dummies. All regressions with stock variables(resp. spillover variables) include a dummy for no stock (resp. no spillover). Standard errors are clustered at the firm-level. * p < 0.1; **p < 0.05; *** p < 0.01

56
1

Table A.19: Country-year fixed effects and country-level clustering
Dependent variable

Auto95
Domestic + Foreign

Low-skill wage
High-skill wage
GDP gap
Labor productivity

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.8852**(0.8028)-2.4820***(0.7416)0.0623***(0.0239)

2.1429***(0.7524)-1.9117(1.3292)
0.0620**(0.0242)-1.2851(1.2933)

3.0411***(1.1398)-1.7526***(0.3511)0.0646***(0.0216)

3.4891***(1.2222)-3.5161**(1.5767)
0.0044(0.0445)

4.3023**(1.9288)-2.4740**(1.0274)0.0016(0.0397)-1.7494(1.6920)

3.7989**(1.6359)-3.3526***(1.2889)0.0044(0.0439)3.6420***(1.3319)-3.7549**(1.5240)0.0031(0.0456)

4.3362**(2.0053)-2.8325***(0.9297)0.0001(0.0407)-1.5475(1.6342)3.8663**(1.5920)-3.6398***(1.2942)0.0031(0.0452)

GDP per capita-0.1511***(0.0383)0.6549***(0.0532)
1.4782***(0.1276)-1.2259***(0.1690)-0.1506***(0.0382)
0.6556***(0.0530)
1.4762***(0.1317)-1.2020***(0.1690)-2.8260(1.7682)-0.1541***(0.0401)
0.6555***(0.0543)
1.4715***(0.1188)-1.2436***(0.1633)

Fixed effects

F + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

50070
3338

Stock automation
Stock other
Spillovers automation
Spillovers other-0.1522***(0.0371)0.6494***(0.0559)
1.4396***(0.1230)-1.2377***(0.1997)-0.1523***(0.0370)0.6471***(0.0570)
1.4128***(0.1585)-1.2268***(0.2111)-0.5289(1.3544)-0.1526***(0.0373)
0.6490***(0.0563)1.4355***(0.1243)-1.2436***(0.1934)-0.1530***(0.0370)0.6496***(0.0555)
1.4380***(0.1243)-1.2252***(0.2002)-0.1532***(0.0370)0.6475***(0.0567)
1.4161***(0.1574)-1.2141***(0.2126)-0.3829(1.2045)-0.1533***(0.0371)0.6493***(0.0559)
1.4357***(0.1254)-1.2300***(0.1941)

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson
regressions fixed-effects (HHG). All regressions include firm and country-year fixed effects. All regressions with stock variables include a dummy
for no stock and no spillover. In columns (4)-(6) domestic (resp. foreign) low-skill wages are interacted with the share of domestic (resp. foreign)
low-skill wages in total low-skill wages computed at the beginning of the sample, and similarly for high-skill wages, GDP per capita and VA per employee. In columns (7)-(9), they are interacted with the average shares over the sample period instead. In columns (4)-(9), domestic (resp. foreign)
GDP gap is interacted with the domestic (resp. foreign) weight. In columns (1)-(3), there is no such interactions. Standard errors are clustered at
the country-level * p < 0.1; ** p < 0.05; *** p < 0.01

Table A.20: Country-year fixed effects and auto90
Dependent variable

Auto901

Domestic + Foreign
Low-skill wage
High-skill wage
GDP gap
Labor productivity

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.3896*(0.8386)-1.5576*(0.8304)
0.0387(0.0270)

1.4107(0.8937)-1.5109(0.9212)0.0387(0.0270)-0.1045(1.1919)2.2798**(1.0390)-1.0014(0.8793)0.0405(0.0269)2.6344**(1.1574)-3.0164**(1.2101)-0.0044(0.0361)

3.1221**(1.3170)-2.3531*(1.3149)-0.0060(0.0361)-1.0847(1.2059)

3.2536**(1.3955)-2.6864**(1.2787)-0.0042(0.0360)

2.7215**(1.1927)-3.1666**(1.2485)-0.0053(0.0361)

3.1094**(1.3384)-2.6147*(1.3342)-0.0070(0.0362)-0.8988(1.1768)

3.2428**(1.4122)-2.8915**(1.2984)-0.0053(0.0361)

GDP per capita-0.0537(0.0405)
0.5846***(0.0510)1.7794***(0.5417)-1.5492***(0.4359)-0.0536(0.0406)
0.5847***(0.0509)
1.7789***(0.5421)-1.5469***(0.4375)-2.1599(1.4800)-0.0556(0.0404)
0.5845***(0.0508)
1.7682***(0.5434)-1.5563***(0.4366)

Fixed effects

F + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

73485
4899

73485
4899

73485
4899

73485
4899

73485
4899

73485
4899

73485
4899

73485
4899

73485
4899

Stock automation
Stock other
Spillovers automation
Spillovers other-0.0572(0.0405)
0.5802***(0.0508)
1.7676***(0.5367)-1.5439***(0.4321)-0.0576(0.0405)
0.5794***(0.0507)1.7438***(0.5388)-1.5316***(0.4320)-1.0595(1.4139)-0.0577(0.0405)
0.5792***(0.0506)
1.7562***(0.5381)-1.5527***(0.4315)-0.0577(0.0405)
0.5802***(0.0508)
1.7652***(0.5357)-1.5350***(0.4305)-0.0580(0.0404)
0.5796***(0.0507)1.7459***(0.5388)-1.5238***(0.4314)-0.8978(1.3541)-0.0581(0.0405)
0.5795***(0.0506)
1.7563***(0.5370)-1.5431***(0.4298)

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson
regressions fixed-effects (HHG). All regressions include firm and country-year fixed effects. All regressions with stock variables include a dummy
for no stock and no spillover. In columns (4)-(6) domestic (resp. foreign) low-skill wages are interacted with the share of domestic (resp. foreign)
low-skill wages in total low-skill wages computed at the beginning of the sample, and similarly for high-skill wages, GDP per capita and VA per employee. In columns (7)-(9), they are interacted with the average shares over the sample period instead. In columns (4)-(9), domestic (resp. foreign)
GDP gap is interacted with the domestic (resp. foreign) weight. In columns (1)-(3), there is no such interactions. Standard errors are clustered at
the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

57
1

Table A.21: Country-year dummies interacted with the domestic weight
Dependent variable

Auto95
Domestic + Foreign

Low-skill wage
High-skill wage
GDP gap
Labor productivity

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

1.8108(1.1242)-2.7802**(1.1391)0.0053(0.0436)2.3860*(1.2486)-2.0793*(1.2117)-0.0020(0.0444)-1.2255(0.9351)2.2889*(1.3755)-2.5647**(1.1867)
0.0021(0.0445)2.0881*(1.1178)-2.7271**(1.1229)0.0086(0.0440)2.6237**(1.2557)-2.1941*(1.2359)
0.0037(0.0448)-0.9968(0.9758)

2.9819**(1.3805)-2.3615**(1.1984)0.0046(0.0445)

2.1664*(1.1418)-2.9054**(1.1471)
0.0075(0.0441)2.6391**(1.2624)-2.4236*(1.2481)0.0028(0.0449)-0.9151(0.9585)2.9695**(1.3847)-2.5943**(1.2101)
0.0039(0.0447)

GDP per capita-0.1531***(0.0523)0.6433***(0.0605)
1.1705***(0.4154)-0.9536***(0.3302)-0.1525***(0.0521)0.6417***(0.0603)1.2209***(0.4139)-0.9457***(0.3305)-0.7515(1.2918)-0.1531***(0.0522)0.6429***(0.0603)1.2079***(0.4199)-0.9736***(0.3319)

Fixed effects

F + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

50085
3339

50085
3339

50085
3339

50085
3339

50085
3339

50085
3339

50085
3339

50085
3339

50085
3339

Stock automation
Stock other
Spillovers automation
Spillovers other-0.1518***(0.0522)0.6420***(0.0607)1.0883**(0.4241)-0.9431***(0.3315)-0.1514***(0.0520)0.6407***(0.0606)
1.1219***(0.4227)-0.9441***(0.3310)-1.3618(1.3924)-0.1523***(0.0521)0.6412***(0.0603)
1.1442***(0.4283)-0.9801***(0.3333)-0.1519***(0.0522)0.6422***(0.0607)
1.1121***(0.4191)-0.9379***(0.3315)-0.1515***(0.0520)0.6409***(0.0606)
1.1484***(0.4183)-0.9386***(0.3315)-1.2168(1.3560)-0.1525***(0.0520)0.6415***(0.0603)
1.1663***(0.4241)-0.9719***(0.3335)

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects (HHG). All regressions include firm and country-year fixed effects. Country-year fixed effects are interacting with thecountries’ weights. All regressions with stock variables include a dummy for no stock and no spillover. In columns (4)-(6) domestic (resp. foreign)
low-skill wages are interacted with the share of domestic (resp. foreign) low-skill wages in total low-skill wages computed at the beginning of thesample, and similarly for high-skill wages, GDP per capita and VA per employee. In columns (7)-(9), they are interacted with the average shares
over the sample period instead. In columns (4)-(9), domestic (resp. foreign) GDP gap is interacted with the domestic (resp. foreign) weight. In
columns (1)-(3), there is no such interactions. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

158

Table A.22: All machinery innovations
Dependent variable

MachineryDomestic + Foreign

Low-skill wage
High-skill wage
GDP gap
Labor productivity

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

0.4615(0.5070)-0.0290(0.5224)-0.0211**(0.0086)

0.5068(0.5585)0.0429(0.4950)-0.0209**(0.0084)-0.1676(0.6030)

1.7568***(0.5336)
1.2020**(0.5625)-0.0219**(0.0086)

0.1366(0.6721)-0.0638(0.7663)0.0080(0.0153)0.1813(0.7338)
0.0273(0.8065)
0.0080(0.0153)-0.1986(0.9082)

1.1875(0.8614)0.5109(0.7838)0.0100(0.0153)-0.0553(1.0951)-0.2290(1.2089)0.0228(0.0235)

0.4116(1.2570)0.3470(1.1884)
0.0221(0.0235)-0.9600(0.9293)0.9712(1.2306)
0.2678(1.2571)
0.0239(0.0234)

GDP per capita
Stock machinery
Stock other
Spillovers machinery
Spillovers otherForeign(1)0.3337***(0.0352)
0.2443***(0.0441)2.7148***(0.4332)-2.4318***(0.5096)0.3339***(0.0352)
0.2444***(0.0442)2.7216***(0.4335)-2.4309***(0.5110)-3.5955***(0.6309)
0.3337***(0.0341)0.2416***(0.0426)
1.9670***(0.4422)-1.8095***(0.5022)0.3400***(0.0341)0.2456***(0.0424)
1.1095**(0.4652)-1.2125**(0.4859)

0.3401***(0.0342)0.2458***(0.0423)
1.1138**(0.4692)-1.2136**(0.4867)-2.3745**(1.1561)
0.3386***(0.0340)0.2454***(0.0420)
1.0117**(0.4640)-1.1326**(0.4857)

0.3379***(0.0345)0.2449***(0.0413)1.0863**(0.4638)-1.1784**(0.4802)0.3372***(0.0346)
0.2442***(0.0414)
1.1211**(0.4666)-1.2191**(0.4839)-1.6536(1.0728)0.3370***(0.0345)0.2435***(0.0413)
1.0510**(0.4644)-1.1742**(0.4801)

Fixed effects

F+Y

F+Y

F+Y

F + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

16029010686

16029010686

16029010686

16029010686

16029010686

16029010686

16029010686

16029010686

16029010686

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG). In columns (1)-(3), the regressions include firm and year fixed effects. In columns (4)-(9), the regressions include firm and country-year fixed effects. All regressions with stock variables include a dummy for no stock and no spillover. In
columns (7)-(9) domestic (resp. foreign) low-skill wages are interacted with the share of domestic (resp. foreign) low-skill wages in total lowskill wages computed at the beginning of the sample, and similarly for high-skill wages, GDP per capita and VA per employee. In columns(7)-(9), domestic (resp. foreign) GDP gap is interacted with the domestic (resp. foreign) weight. In columns (1)-(6), there is no such interactions. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

159

Table A.23: Predicted wages
Dependent Variable

Auto95
joint ρ, averageseparate ρ, averageseparate ρ, t+4

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

1.6899**(0.8152)-1.7960**(0.8440)0.0162(0.0143)

1.7039**(0.8167)-1.7638**(0.8440)0.0164(0.0143)-0.1433***(0.0509)0.6405***(0.0602)0.5049*(0.3036)-0.1842(0.2281)1.4318*(0.8087)-2.7068**(1.2652)
0.0119(0.0139)1.4848(1.1824)-0.1451***(0.0517)0.6371***(0.0604)
0.7348**(0.3702)-0.4488(0.3182)1.7803**(0.8314)-1.7874**(0.8283)0.0144(0.0142)-0.1430***(0.0509)0.6407***(0.0601)
0.4848(0.3049)-0.1674(0.2278)

1.4899*(0.8107)-2.8597*(1.4860)
0.0163(0.0143)1.6234(1.7208)-0.1446***(0.0514)0.6379***(0.0603)0.6209*(0.3445)-0.3214(0.2866)

1.7557**(0.8286)-1.7838**(0.8196)0.0144(0.0142)-0.1433***(0.0509)0.6408***(0.0601)
0.4847(0.3045)-0.1628(0.2276)

1.4813*(0.8080)-2.9855**(1.5046)
0.0161(0.0143)1.7353(1.7310)-0.1451***(0.0514)0.6380***(0.0603)
0.6321*(0.3449)-0.3290(0.2877)-0.1431***(0.0510)0.6405***(0.0601)
0.5097*(0.3044)-0.1899(0.2282)

1.4313*(0.8137)-2.7378**(1.2776)0.0117(0.0139)
1.5467(1.2247)-0.1450***(0.0517)0.6371***(0.0604)
0.7364**(0.3679)-0.4498(0.3152)

Fixed effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

Low-skill wage
High-skill wage
GDP gap
Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers otherjoint ρ, t+4

Note: Marginal effects; Standard errors in parentheses. Estimation is by conditional Poisson regressions fixed-effects (HHG). The wage
variables and labor productivity are predicted at time t-2. Columns (1) to (4) predict wages and labor productivity with an AR(1) processwith country-specific trends and with the same auto-regression coefficient across countries. Columns (5) to (8) use different auto-regression
coefficients across countries. In columns (1), (2), (5) and (6) the wages and labor productivity are the average of the predicted values between years t+2 and t+7. In columns (3), (4), (7) and (8), they are the predicted values for year t+4. All regressions with stock variables(resp. spillover variables) include a dummy for no stock (resp. no spillover). Standard errors are clustered at the firm-level. * p < 0.1; **p < 0.05; *** p < 0.01

Table A.24: Minimum wage1

Dependent variable

Auto95
Domestic + Foreign

Minimum wage
High-skill wage
GDP gap
Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers otherForeign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

1.5230**(0.6865)-1.2239*(0.7166)
0.0235(0.0151)

1.5171**(0.6628)-1.2358(0.8701)
0.0235(0.0150)
0.0246(0.9249)-0.1446***(0.0513)0.6374***(0.0596)0.6462*(0.3397)-0.3559(0.2535)

1.4636(0.9127)-3.0712***(1.0907)0.0562(0.0347)

1.8773(1.2125)-2.8017**(1.4072)-0.0232(0.0513)-0.1563***(0.0530)
0.6549***(0.0607)1.4198***(0.4939)-1.2744***(0.3795)

1.8401(1.2411)-2.9368(1.8000)-0.0232(0.0514)0.1730(1.4426)-0.1564***(0.0531)0.6552***(0.0607)1.4215***(0.4966)-1.2764***(0.3821)1.8331*(1.1117)-2.7456**(1.3157)-0.0238(0.0512)-0.1548***(0.0522)0.6569***(0.0597)1.4309***(0.4958)-1.1991***(0.3854)

1.5601(0.9566)-2.6564**(1.1667)
0.0563(0.0347)-0.7554(1.4016)-0.1544***(0.0523)0.6572***(0.0597)
1.4270***(0.4967)-1.1837***(0.3864)-0.1562***(0.0530)
0.6540***(0.0607)1.4157***(0.4929)-1.2806***(0.3787)

1.7770(1.1271)-3.0195*(1.7324)-0.0236(0.0513)0.3355(1.3849)-0.1565***(0.0531)0.6547***(0.0606)
1.4192***(0.4955)-1.2846***(0.3810)-0.1445***(0.0513)0.6374***(0.0596)0.6456*(0.3363)-0.3546(0.2408)

Fixed effects

F

F

F + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

50070
3338

50070
3338

50040
3336

50040
3336

48765
3251

48765
3251

48795
3254

48795
3254

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects (HHG). Columns (1)-(2) include firm fixed effects. Columns (3)-(8) include firm and countryyear fixed effects. All regressions with stock variables include a dummy for no stock and no spillover. In columns (5)-(6) domestic(resp. foreign) minimum wages are interacted with the share of domestic (resp. foreign) minimum wages in total minimum wages computed at the beginning of the sample, and similarly for high-skill wages and VA per employee. In columns (7)-(8), they are interactedwith the average shares over the sample period instead. In columns (5)-(8), domestic (resp. foreign) GDP gap is interacted with thedomestic (resp. foreign) weight. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

60

1Table A.25: Minimum wage with country level clustering
Dependent variable

Auto95
Domestic + Foreign

Minimum wage
High-skill wage
GDP gap
Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers otherForeign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

1.5230**(0.5926)-1.2239**(0.5538)
0.0235***(0.0046)

1.5171***(0.4580)-1.2358(1.0144)0.0235***(0.0047)
0.0246(0.9997)-0.1446***(0.0390)0.6374***(0.0513)0.6462***(0.2225)-0.3559(0.2362)

1.4636**(0.6148)-3.0712***(0.5048)0.0562***(0.0209)

1.8773***(0.4685)-2.8017***(1.0073)-0.0232(0.0246)-0.1563***(0.0392)
0.6549***(0.0572)
1.4198***(0.1192)-1.2744***(0.1956)

1.8401***(0.6459)-2.9368***(0.8003)-0.0232(0.0245)0.1730(1.3091)-0.1564***(0.0402)0.6552***(0.0595)
1.4215***(0.1309)-1.2764***(0.2102)

1.8331***(0.4272)-2.7456***(0.9635)-0.0238(0.0238)-0.1548***(0.0403)
0.6569***(0.0563)1.4309***(0.1139)-1.1991***(0.1684)

1.5601***(0.4905)-2.6564**(1.2687)
0.0563***(0.0210)-0.7554(1.4283)-0.1544***(0.0400)
0.6572***(0.0561)
1.4270***(0.1151)-1.1837***(0.1736)-0.1562***(0.0391)0.6540***(0.0569)
1.4157***(0.1182)-1.2806***(0.1920)1.7770***(0.5790)-3.0195***(0.8786)-0.0236(0.0235)
0.3355(1.3969)-0.1565***(0.0404)
0.6547***(0.0598)
1.4192***(0.1314)-1.2846***(0.2072)-0.1445***(0.0385)0.6374***(0.0514)0.6456***(0.2076)-0.3546(0.2214)

Fixed effects

F

F

F + CYF + CYF + CYF + CYF + CYF + CY

Observations
Firms

50070
3338

50070
3338

50040
3336

50040
3336

48765
3251

48765
3251

48795
3254

48795
3254

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects (HHG). Columns (1)-(2) include firm fixed effects. Columns (3)-(8) include firm and countryyear fixed effects. All regressions with stock variables include a dummy for no stock and no spillover. In columns (5)-(6) domestic(resp. foreign) minimum wages are interacted with the share of domestic (resp. foreign) minimum wages in total minimum wages computed at the beginning of the sample, and similarly for high-skill wages and VA per employee. In columns (7)-(8), they are interactedwith the average shares over the sample period instead. In columns (5)-(8), domestic (resp. foreign) GDP gap is interacted with thedomestic (resp. foreign) weight. Standard errors are clustered at the country-level * p < 0.1; ** p < 0.05; *** p < 0.01

1

Table A.26: Correlation matrix

Low-skill wage
Middle-skill wage
High-skill wage
GDP gap
GDP per capita
Labor productivity

Low-skill wage

Middle-skill wage

High-skill wage

GDP gap

GDP per capita

Labor productivity

10.94010.6009-0.0660
0.69720.6678.
10.7469-0.02390.79740.7340.
.
1
0.04820.72770.7724

.
.

.
.
.

.
.
.
.
.
1

1
-0.0117
0.1980Note: Correlation of residuals for the auto95 sample controlling for year and firm fixed effects.

61

10.6519

Table A.27: Skill premium
Dependent variable

Auto95
Domestic + Foreign

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

1.9423**(0.7552)
0.0263*(0.0157)

2.1995**(0.9170)0.0627*(0.0343)-0.1505***(0.0530)
0.6546***(0.0603)
1.4755***(0.4968)-1.2535***(0.3717)2.0520**(0.9049)
0.0620*(0.0343)-1.0613(1.1591)-0.1507***(0.0528)0.6556***(0.0602)
1.4769***(0.5004)-1.2021***(0.3824)

3.5089***(1.2083)0.0049(0.0526)-0.1448***(0.0509)0.6407***(0.0599)
0.5783*(0.3153)-0.2349(0.2129)

2.0420***(0.7607)
0.0268*(0.0157)
0.7026(0.7035)-0.1456***(0.0510)0.6402***(0.0601)
0.5783*(0.3114)-0.3132(0.2328)-0.1522***(0.0526)
0.6495***(0.0602)1.4397***(0.4868)-1.2387***(0.3669)

3.4205***(1.1960)-0.0017(0.0496)-0.2814(0.7369)-0.1524***(0.0525)0.6480***(0.0602)1.4346***(0.4892)-1.2253***(0.3755)

Fixed effects

F+Y

F+Y

F + CYF + CYF + CYF + CY

Observations
Firms

50115
3341

50115
3341

50070
3338

50070
3338

50070
3338

50070
3338

Low-skill / High-skill wage
GDP gap
Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers other

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods.
Estimation is by conditional Poisson regressions fixed-effects (HHG). Columns (1)-(2) include firm fixed effects
and year dummies. Columns (3)-(6) include firm and country-year fixed effects. All regressions with stock variables (resp. spillover variables) include a dummy for no stock (resp. no spillover). Columns (5)-(6) use the log
difference between foreign low-skill wages interacted with the share of foreign low-skill wages in total low-skill
wages at the beginning of the sample and foreign high-skill wages similarly interacted; GDP gap and VA per employee are also their interacted foreign components. Standard errors are clustered at the firm-level. * p < 0.1; **p < 0.05; *** p < 0.01

Table A.28: Including middle-skill wages
1
Dependent Variable

Auto95
(1)

(2)

(3)

(4)

2.3617**(1.0085)-1.7189*(0.9218)

3.8985***(1.3667)-2.2614(1.6773)-0.9608(0.8867)

5.1140***(1.5892)-4.2997**(1.6815)-0.1454***(0.0508)0.6458***(0.0598)
0.4733(0.2891)-0.3173(0.2254)-0.1404***(0.0508)
0.6394***(0.0598)
0.4518(0.3140)-0.1874(0.2208)

Fixed effects

F+Y

Observations
Firms

50115
3341

Low-skill wage
Middle-skill wage
High-skill wage

4.7035***(1.4991)-3.9194**(1.6096)

GDP gap

(5)

(6)

(7)
5.0971***(1.5759)-4.0739**(1.6090)0.0288*(0.0153)2.4746**(1.0411)-1.8154*(0.9485)0.0216(0.0151)

4.2760***(1.4222)-2.5516(1.6819)-1.0225(0.8960)
0.0304*(0.0157)-0.1457***(0.0509)
0.6436***(0.0600)
0.5330*(0.3097)-0.3100(0.2265)-0.1460***(0.0509)
0.6456***(0.0599)0.5007*(0.2885)-0.3478(0.2247)-0.1405***(0.0509)
0.6389***(0.0600)
0.4692(0.3143)-0.2013(0.2197)

F+Y

F+Y

F+Y

50115
3341

50115
3341

50115
3341

Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers other

(8)

(9)

-0.1464***(0.0510)0.6433***(0.0601)
0.5657*(0.3105)-0.3416(0.2257)

0.0292*(0.0154)-0.3258(0.8572)-0.1455***(0.0509)
0.6455***(0.0599)
0.4998*(0.2891)-0.3281(0.2315)

2.3236**(1.0573)-1.9749**(0.9982)
0.0216(0.0151)0.4545(0.8858)-0.1415***(0.0510)0.6387***(0.0600)
0.4846(0.3155)-0.2315(0.2279)

4.2398***(1.4510)-2.5526(1.6825)-1.0756(0.9602)0.0303*(0.0157)
0.1316(0.9310)-0.1466***(0.0511)0.6432***(0.0601)
0.5694*(0.3120)-0.3492(0.2325)

F+Y

F+Y

F+Y

F+Y

F+Y

50115
3341

50115
3341

50115
3341

50115
3341

50115
3341

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects (HHG). All regressions include firm fixed effects and year dummies. All regressions with stock variables include adummy for no stock and no spillover. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

62

1

Table A.29: Wages and deflators

Dependent variable

Auto95

SectorDeflator

Manufacturing
Manufacturing PPI,
conversion in 2005

Total

US manufacturing PPI
conversion every year

GDP deflator
conversion in 1995

Manufacturing PPI
conversion in 1995

US Manufacturing PPI
conversion every year

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

2.7140***(0.8686)-1.7475**(0.7943)0.0285*(0.0158)

1.9084***(0.6949)-2.4692***(0.7517)0.0153(0.0146)-0.1446***(0.0506)
0.6485***(0.0596)0.4874*(0.2862)-0.2943(0.2399)

3.9769***(1.2666)-2.3907**(0.9545)0.0440**(0.0172)
2.6369**(1.2281)-0.1474***(0.0509)
0.6455***(0.0596)
0.5675**(0.2879)-0.4228*(0.2510)1.4172**(0.7192)-2.0329***(0.7025)0.0158(0.0152)-0.1454***(0.0510)0.6403***(0.0600)
0.6503*(0.3451)-0.3447(0.2220)2.7044***(1.0238)-1.9409**(0.9578)
0.0262(0.0161)-0.4125(0.7779)-0.1446***(0.0511)0.6405***(0.0599)
0.6444*(0.3456)-0.3310(0.2219)

4.1859***(1.3286)-1.3163(0.8454)0.0431**(0.0171)-0.1501***(0.0510)0.6391***(0.0598)
0.8540**(0.3471)-0.4295*(0.2332)

2.1264**(0.8261)-2.2154***(0.7790)0.0149(0.0146)-0.5012(0.7122)-0.1493***(0.0510)0.6396***(0.0597)
0.8568**(0.3459)-0.4312*(0.2332)

2.5733***(0.9691)-2.1163**(0.9229)0.0254(0.0161)-0.1439***(0.0510)0.6392***(0.0600)
0.5795*(0.3073)-0.3314(0.2259)2.6338***(0.8933)-1.8694**(0.8603)
0.0283*(0.0158)0.3056(0.9422)-0.1444***(0.0511)
0.6390***(0.0601)
0.5887*(0.3093)-0.3499(0.2344)-0.1457***(0.0506)
0.6434***(0.0592)
0.6379**(0.3217)-0.2826(0.2403)

1.1137(0.8024)-2.3743**(0.9521)0.0148(0.0153)
0.6389(0.9348)-0.1462***(0.0508)
0.6424***(0.0592)
0.6536**(0.3275)-0.2962(0.2414)

Fixed Effect

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms
Clustering

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

50115
3341
Firm

Low-skill wage
High-skill wage
GDP gap
Labor productivity

63

Stock own
Stock other
Spillover own
Spillover other

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixedeffects (HHG). All regressions include firm fixed effects and year dummies. All regressions include a dummy for no stock and no spillover. Columns (1) to (6) are on
manufacturing wages and columns (7) to (10) on total wages. In columns (1) and (2), macroeconomic variables are deflated with the local manufacturing PPI andconverted in USD in 2005. In columns (3), (4), (9) and (10) they are converted in USD every year and deflated with the US manufacturing PPI. In columns (5) and(6), macroeconomic variables are deflated with the local GDP deflator and converted in USD in 1995. In columns (7) and (8), macroeconomic variables are deflatedwith the local manufacturing PPI and converted in USD in 1995. Standard errors are clustered at the firm-level * p < 0.1; ** p < 0.05; *** p < 0.01

1

Table A.30: Baseline regressions in 2000-20009 only
Dependent variable
Low-skill wage
High-skill wage

Auto95
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

2.6434***(0.7284)

2.4828**(0.9935)
0.2690(0.8835)

1.8409*(1.0261)-0.3563(0.8904)-0.4117***(0.0630)
0.6746***(0.0709)

1.9912*(1.0700)-0.5113(0.9219)-0.4100***(0.0631)0.6708***(0.0711)0.0243(0.0164)2.0772*(1.0718)-0.4602(0.9557)-0.4105***(0.0628)0.6725***(0.0714)0.0246(0.0162)-0.1968(0.9325)

2.4664**(1.2056)-0.2960(0.8762)-0.4050***(0.0635)
0.6687***(0.0708)0.0196(0.0157)

2.9215**(1.1447)-1.1540(0.9894)-0.4375***(0.0636)0.6881***(0.0744)0.0419**(0.0171)

2.6339**(1.1285)-1.4516(1.0716)-0.4398***(0.0639)0.6864***(0.0743)0.0437**(0.0174)
1.1082(0.9940)

4.4721***(1.4064)-0.7074(0.9416)-0.4335***(0.0639)0.6937***(0.0735)
0.0360**(0.0169)

Stock automation
Stock other
GDP gap
Labor productivity
GDP per capita
Spillovers automation-1.5031(1.1155)

Spillovers other

0.9119**(0.4167)-0.5948*(0.3577)

1.0198**(0.4249)-0.7380*(0.3820)-3.7815**(1.4968)
1.1483***(0.4267)-0.8383**(0.3731)

Fixed effects

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

F+Y

Observations
Firms

27110
2711

27110
2711

27110
2711

27110
2711

27110
2711

27110
2711

27110
2711

27110
2711

27110
2711

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG) from 2000 to 2009. All regressions include firm fixed effects and year dummies. All regressions withstock variables (resp. spillover variables) include a dummy for no stock (resp. no spillover). Standard errors are clustered at the firm-level. *p < 0.1; ** p < 0.05; *** p < 0.01

164

Table A.31: Nickell’s bias
Dependent Variable
Low-skill wage
High-skill wage
GDP gap
Labor productivity

Auto95
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

2.3903***(0.8004)-1.5544**(0.7840)0.0276*(0.0159)2.3926***(0.8227)-1.5510*(0.8704)
0.0276*(0.0158)-0.0084(0.9696)

2.1515***(0.7991)-0.9069(0.6129)
0.0266(0.0191)

2.2066***(0.8150)-0.5857(0.7453)
0.0278(0.0187)-0.7779(1.0755)
1.1818***(0.0238)0.0897***(0.0118)-0.0315(0.0689)0.0781(0.0748)2.0925**(0.9778)-2.4648**(0.9779)
0.0653*(0.0343)2.2884**(1.0886)-2.0312**(0.9708)
0.0651*(0.0342)-0.9781(1.5602)2.3955**(0.9713)-2.5627***(0.9338)
0.0752**(0.0353)

0.5230***(0.0439)
1.3383***(0.4669)-1.0318***(0.3544)

0.5237***(0.0440)
1.3373***(0.4676)-1.0139***(0.3558)

1.1912***(0.0243)0.0869***(0.0120)-0.0667(0.0784)
0.1163(0.0827)

2.9126***(1.0899)-1.2324(1.0583)
0.0761**(0.0353)-2.6421(1.6507)1.1870***(0.0235)0.0879***(0.0118)-0.0518(0.0767)
0.1013(0.0815)

Stock automation
0.5101***(0.0454)0.3519(0.2949)-0.0735(0.2127)0.5101***(0.0453)
0.3517(0.2977)-0.0730(0.2227)

1.1938***(0.0244)0.0895***(0.0120)0.0098(0.0746)
0.0219(0.0782)

Fixed effects

F+Y

F+Y

BGVR + Y

BGVR + Y

F + CYF + CYBGVR + CY

BGVR + CY

Observations
Firms

50115
3341

50115
3341

50115
3341

50115
3341

50070
3338

50070
3338

50070
3338

50070
3338

Stock other
Spillovers automation
Spillovers other

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG) in columns (1), (2), (5) and (6). In columns (3), (4), (7) and (8), estimation is done by Poisson
regressions where the firm fixed effects are replaced by the pre-sample mean, following Blundell, Griffith and Van Reenen (1999, BGVR).
Columns (1) to (4) include year fixed effects and columns (5) to (8) country-year fixed effects. All regressions with stock variables (resp.
spillover variables) include a dummy for no stock (resp. no spillover). Standard errors are clustered at the firm-level. * p < 0.1; ** p <0.05; *** p < 0.01

Table A.32: Other innovation indicators
1
Dependent Variable
Low-skill wage
High-skill wage
GDP gap
Labor productivity
Stock automation
Stock other
Spillovers automation
Spillovers otherObservations
Firms

Biadic (US, JP, EU)

Auto95

Triadic

At least one citation

Citations weighted

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

2.2776**(1.0383)-1.3409(0.9663)0.0397**(0.0191)

3.1886**(1.4150)-2.3417*(1.3640)0.0178(0.0289)-0.1468***(0.0557)
0.6457***(0.0635)
0.5736*(0.3140)-0.2978(0.2404)

2.1241**(0.8720)-1.7443**(0.8577)
0.0267*(0.0157)
0.3450(0.9171)-0.1474***(0.0559)
0.6456***(0.0635)
0.5845*(0.3151)-0.3187(0.2468)

1.7405*(1.0257)-1.8007*(0.9814)0.0368*(0.0190)-0.3665***(0.0772)0.6500***(0.0875)
0.7925(0.5469)-0.3499(0.4685)2.9795*(1.5827)-2.6759*(1.3768)
0.0172(0.0288)0.7272(1.6987)-0.3677***(0.0766)
0.6494***(0.0875)
0.7981(0.5451)-0.3742(0.4599)2.2198***(0.8341)-1.6034**(0.8099)
0.0269*(0.0158)-0.1683***(0.0597)
0.6342***(0.0662)
0.3839(0.4014)-0.5402**(0.2587)

2.0079*(1.0785)-1.7718*(1.0724)0.0390**(0.0191)0.9807(1.1988)-0.1699***(0.0598)
0.6333***(0.0663)
0.4064(0.4028)-0.5915**(0.2715)-0.2220***(0.0438)0.6805***(0.0688)
0.1427(0.2878)
0.1625(0.2595)

1.6520(1.1403)-1.9515**(0.9717)0.0366*(0.0190)0.3518(1.1755)-0.2223***(0.0438)0.6802***(0.0687)
0.1499(0.2858)
0.1429(0.2600)

40410
2694

40410
2694

26310
1754

26310
1754

47115
3141

47115
3141

50115
3341

50115
3341

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional
Poisson regressions fixed-effects (HHG). All regressions include firm fixed effects and year dummies. All regressions include a dummy for nostock and no spillover. Columns (1)-(2) consider biadic patents applied for in at least two countries among US, JP, EU. Columns (3)-(4)consider triadic patents (applied for in US, JP and EU). Column (5)-(6) consider biadic patents with at least one citation within 5 years
after publication. Column (7)-(8) consider biadic patents and add to each patent the number of citations within 5 years after publication
normalized by year and technological field. Standard errors are clustered at the firm-level. * p < 0.1; ** p < 0.05; *** p < 0.01

65
1

Table A.33: Industry-year fixed effects
Dependent variable

Auto95
Domestic + Foreign

Low-skill wage
High-skill wage
GDP gap
Labor productivity

Foreign

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

2.3157**(0.9890)-2.9978***(0.9457)
0.0709**(0.0323)2.5169**(1.1159)-2.5654**(1.0210)0.0707**(0.0323)-0.9736(1.7031)3.5773***(1.2188)-2.1617**(1.0263)
0.0731**(0.0322)4.1573***(1.3041)-4.3227***(1.2915)-0.0059(0.0470)

5.0264***(1.5426)-3.1470**(1.3761)-0.0083(0.0469)-1.9354(1.4734)

4.2013**(1.7227)-4.2974***(1.3321)-0.0059(0.0470)

4.3562***(1.3302)-4.5869***(1.3283)-0.0066(0.0471)

5.0852***(1.5430)-3.5601**(1.3944)-0.0093(0.0470)-1.6865(1.4339)4.3077**(1.7067)-4.6144***(1.3495)-0.0065(0.0471)

GDP per capita-0.1586***(0.0466)
0.6549***(0.0552)
1.3924***(0.4759)-1.0750***(0.3623)-0.1582***(0.0466)
0.6555***(0.0552)
1.3897***(0.4766)-1.0587***(0.3642)-3.1161*(1.7989)-0.1601***(0.0468)
0.6548***(0.0549)
1.3786***(0.4761)-1.0900***(0.3618)

Fixed effects

F + CY +IY

F + CY +IY

F + CY +IY

F + CY +IY

F + CY +IY

F + CY +IY

F + CY +IY

F + CY +IY

F + CY +IY

Observations
Firms

49890
3326

49890
3326

49890
3326

49890
3326

49890
3326

49890
3326

49890
3326

49890
3326

49890
3326

Stock automation
Stock other
Spillovers automation
Spillovers other-0.1607***(0.0463)0.6492***(0.0550)
1.3568***(0.4658)-1.0864***(0.3553)-0.1603***(0.0461)
0.6470***(0.0549)
1.3324***(0.4651)-1.0802***(0.3527)-0.0777(1.8617)-0.1607***(0.0464)0.6491***(0.0549)
1.3562***(0.4675)-1.0873***(0.3540)-0.1617***(0.0462)0.6497***(0.0549)
1.3558***(0.4653)-1.0740***(0.3538)-0.1615***(0.0460)
0.6478***(0.0548)
1.3371***(0.4651)-1.0670***(0.3520)0.0860(1.7683)-0.1617***(0.0463)0.6497***(0.0548)1.3563***(0.4665)-1.0730***(0.3527)

Note: Marginal effects; Standard errors in parentheses. The independent variables are lagged by two periods. Estimation is by conditional Poisson regressions fixed-effects(HHG). All regressions include firm, industry-year and country-year fixed effects. All regressions with stock variables (resp. spillover variables) include a dummy for nostock (resp. no spillover). Domestic (resp. foreign) low-skill wages are interacted with the share of domestic (resp. foreign) low-skill wages in total low-skill wages computedat the beginning of the sample, and similarly for high-skill wages, GDP per capita and VA per employee. Standard errors are clustered at the firm-level. * p < 0.1; **p < 0.05; *** p < 0.01

B

Appendix

B.1

Details on the classification of automation patents

B.1.1

List of keywords
1

For each technological category, we compute the following share of patents:41
1. Automat* patents. Share of patents which contain the words:(a) Automation or automatization;(b) or automat* at least 5 times;(c) or (automat* or autonomous) in the same sentence as (machine or manufacturing or machining or equipment or apparatus or operator or handling or“vehicle system” or welding or knitting or weaving or convey* or storage orstore or regulat* or manipulat* or arm or sensor or inspect* or warehouse)at least twice.
41

x* indicates any word which starts with x, for instance automat* corresponds to the words automatic, automatically, automate, automates, etc...

66

2. Labor patents. Share of patents which contain the words: laborious, labourious,
labor or labour.
3. Robot patents. Share of patents which contain the word robot* but not (surgicalor medical ).
4. Numerical control patents. Share of patents which contain the words:(a) CNC or “numerically controlled ” or “numeric control ” or “numerical control ”
or the same terms but with hyphens;(b) or NC in the same sentence with (machine or manufacturing or machining
or equipment or apparatus).
5. Computer aided design and manufacturing patents. Share of patents which containthe words:(a) “computer aided ”, “computer assisted ” or “computer supported ” or the sameterms with hyphens) in the same patent with (machine or manufacturing or
machining or equipment or apparatus);(b) or (CAD or (CAM and not “content addressable memory”)) in the samesentence with (machine or manufacturing or machining or equipment or apparatus).
6. Flexible manufacturing. Share of patents which contain the words: “flexible manufacturing”.
7. PLC patents. Share of patents which contain the words: “programmable logic
controller ” or (PLC and not (powerline or “power line”)).
8. 3D printing patents. Share of patents which contain the words: “3D print* ” or“additive manufacturing” or “additive layer manufacturing”.
9. Automation patents. Share of patents which satisfy any of the previous criteria.
We derived this exact list after experimenting extensively with variations around thosewords and looking at the resulting classification of technological codes and the associatedpatents. For instance, the thresholds (5 and 2) used in the definition of the shareof automat* patents where chosen so that the distribution of the share of automat*
patents is comparable to the distribution of the share of numerical control patents across67technological codes. Similarly, requiring that NC be in the same sentence as words suchas machine, ensures that NC is short fort numerical control instead of North Carolina.
Relative to the original list of technologies given in the SMT, we did not include
keywords related to information network, as these seem less related to the automationof the production process and the patents containing words such as “local area network”
do not appear related to automation. We also did not directly count all laser related
technologies as not all of these are related to automation—but we obtain patents related
to automation using laser technologies thanks to our other keywords.
B.1.2

Statistics on the classification

68

Table B.1: Summary statistics on the prevalence of keywords across technological codes in machinery
IPC/CPC 6 digit

69

Share

all

robot

Mean
S. d.
p25
p50
p75
p90
p95
p99

20.9
14.4
10.5
18
26.6
38.7
47.7
75

4.3
8.4
0.8
2
4.5
9.1
13.7
35.8

IPC4 + (G05 or G06)

automat* CNC
11.2
9.5
4.2
8.7
15.3
24.3
29.4
43.8

2.4
5.8
0
0.4
1.8
6.1
12.7
33.1

all

robot

53.2
19.3
40
54.3
63.8
77.9
85.6
90.1

15.4
17.7
6.7
10
16
36.4
44.3
82.9

IPC 4 pairs

automat* CNC
32.4
11
26.6
31.9
40.3
43.3
45.2
59.9

11.2
16.5
0.8
3
15.5
38.2
55.3
56.6

all

robot

18.5
16.3
7.7
13.6
23
36.8
51.8
84.5

4.5
10
0.6
1.8
4.2
8.9
14.5
60

automat* CNC
8.8
9.9
2.5
5.2
10.7
21.7
31
45.3

1.8
4.7
0
0.4
1.4
4.4
7.7
23.1

Note: This table computes summary statistics on the share of patents with any automation keywords, robot keywords, automat* keywords or CNC keywords foreach type of technological categories (6 digit codes, pairs of 4 digit codes and combinations of ipc4 codes with G05 or G06) within machinery with at least 100
patents.

Table B.1 gives summary statistics on the shares of patents containing certain keywords across technological codes in machinery. We look at the share of automation
keywords (“all” in the table) and then focus on the three main subcategories, namely
automat* patents, robot patents and numerical control (CNC) patents (defined above).
The 95th and 90th percentile for the share of automation patents in the distribution of
6 digit codes in machinery define the threshold used to categorize auto95 and auto90
patents. The distributions are quite similar for the C/IPC 6 digit codes and for pairsof IPC 4 digit codes (see also the histograms below). As expected, the distributions are
significantly shifted to the right for combinations of IPC 4 digit codes with G05 or G06.
The distributions of each subcategory are right-skewed particularly for 6 digit codes and4 digit pairs, and even more for the robot and CNC patents. The automat* keywords
are also more common as the mean share for automat* is significantly higher than forthe other keywords, however the difference narrows somewhat in the right tail: the 95th
percentile for 6 digit codes is 29.4% for the share of automat* patents and 13.7% and12.7% for the share of robot and CNC patents. In the right tail, the distribution of robot
patents and CNC patents are quite similar.
Figure B.1 gives the histograms of the prevalence of automation keywords for all
pairs of C/IPC 4 digit codes (panel a) and all pairs with at least one member in themachinery technological field (panel b). The histograms are very similar to those of
C/IPC 6 digit codes in Figure 1. Figure B.2 shows the histograms for all combinationsof IPC 4 digit codes with G05 or G06 (panel a), or when the IPC 4 code is in the relevant
technological field (panel b). Both distributions are considerably shifted to the right,
in line with expectations since G05 proxies for control and G06 for algorithmic, two
set of technologies which have been used heavily in automation. There are, however,much fewer combination of these types (in part because all histograms only considergroups with at least 100 patents), and accordingly few patents can be characterized as
automation innovations this way.
B.1.3

How are auto90 and auto95 patents identified?

Given that our classification procedure is relatively complex, we assess here which features dominate. To do so, we focus on the set of 15, 212, 134 biadic patent applicationsin 1997-2011 (corresponding to the 3, 187, 536 patent families which have patent applications in at least two countries), since this corresponds to the set on which we run
our main regressions. There are 310, 458 auto95 patent applications corresponding to70

(a) For all pairs of C/IPC 4 digit codes(b) For all pairs of C/IPC 4 digit codes
within machinery with 100 patents

Figure B.1: Histogram of the prevalence of automation keywords for C/IPC pairs of 4 digit
codes(a) For all combinations of IPC4 with G05 G06(b) For combinations of IPC4 in machinerywith G05 G06 and at least 100 patents

Figure B.2: Histogram of the prevalence of automation keywords for combinations of IPC 4
digit codes with G05 G06

71

Table B.2: Identification of automation technological categories(b) Auto patents and subcategories of automation innovations(a) Type of C/IPC codes identifying
auto90 and auto95 patents
Ipc codes / Patents

Auto90

Auto95

Matches ipc6
Matches ipc4 pair
Matches ipc4 - G05/G06 combination

78.2%
17.3%
47.7%

78.7%
24.3%
47.8%

Note: Share of innovations classified as automation innovation through ipc6 codes, ipc4 pairs or ipc4 - G05/G06 pairs.
Statistics computed on biadic patents from 1997-2011.

Sources / Patents

Auto80

Auto90

Auto95

Auto80Automat*80
CNC80
Robot80
Auto90Automat*90
CNC90
Robot90
Auto95
Automat*95
CNC95
Robot95

100.0%
36.2%
5.0%
12.0%
62.4%
21.6%
2.2%
7.8%
35.8%
4.4%
1.6%
6.3%

100.0%
53.1%
8.0%
19.2%
100.0%
34.6%
3.6%
12.5%
57.3%
7.1%
2.5%
10.2%

100.0%
72.1%
13.2%
33.6%
100.0%
56.0%
6.3%
21.8%
100.0%
12.4%
4.4%
17.7%

Note: Share of auto95 (auto90 and auto80,
respectively) innovations which are also classified as automat*80/90/95, CNC80/90/95, androbot80/90/95 innovations. Statistics computed on
biadic patents from 1997-2011.

61, 788 patent families (and similarly 541, 693 auto90 patent applications corresponding
to 107, 237 patent families). Table B.2.a gives the share of biadic patents which are
identified through a C/IPC 6 digit code, a pair of 4 digit codes or a combination of 4
digit code with G05/G06 (the shares sum up to more than 100% since patents may be
identified as automation innovations in several ways). 6 digit codes appear to be themost relevant since they are enough to identify close to 80% of auto90 or auto95 patents
alone. Similarly, one may wonder which keywords are the most important in identifying
automation patents. To do that, we define robot95 (respectively CNC95 or autm95)
patents as patents which contain a technological group with 1a share of “robot” (respec1
tively CNC or automat*) keywords above the threshold used to define auto95 (namely
0.4766), therefore those patents are a subset of the auto95 patents. We define robot90,
CNC90, autm90, robot80, CNC80 and autm80 similarly. The other keywords are muchless common. Table B.2.b reports the share of auto95, auto90 and auto80 patents which
belong to each subcategory. “Automat*” appears to be the most important keywords
since 72% of auto95 patents are also automat*80 patents. “Robot” matters as well with33.6% of auto95 patents which are robot80. This is true particularly at the top of the
distribution since 17.7% of auto95 patents are also robot95 (more than autm95). CNC
does not matter too much: only 13% of auto95 patents are CNC80.

72

Table B.3: Confusion table for different classification periods

Confusion Matrix
Auto95 based on
the 1978-2017classification

Yes
No
Total

Auto95 based on the
1998-1997 classification

Auto95 based on the
1998-2017 classification

Auto95 based on the
1997-2011 classification

Yes

No

Yes

No

Yes

No

240,194
53,137
293,331

70,264
14,848,539
14,918,803

280,047
25,186305,233

30,411
14,876,490
14,906,901

262,972
26,368289,340

47,486
14,875,308
14,922,794

Total
310,458
14,901,676
15,212,134

Notes: The statistics are always computed on patents from 1997-2011.

B.1.4

Stability of the classification

To assess the stability of our classification, we redo exactly the same exercise but insteadof using EPO patents from 1978 to 2017, we restrict attention to EPO patents from thefirst half of the sample (1978-1997), the second half of the sample (1998-2017) and theperiod of our main regression analysis (1997-2011). We focus on the same set of biadic
patent applications in 1997-2011. Table B.3 shows confusion tables on the classificationof patents as auto95 according to each of the classification period. Regardless of the time
period used the number of automation patents stays roughly constant. In particular,
85% of the baseline auto95 patents are still auto95 if we run the classification over theyears 1997-2011. This common set of patents then represent 91% of all biadic patents
classified as auto95 patents when using the period 1997-2011 instead of the full sample.

B.2

Redoing ALM

1

In this Appendix, we provide details on the analysis conducted in section 2.6. We use
granted patents at the USPTO between 1970 and 1998. To assign patents to sectors, wefirst use Lybbert and Zolas (2014) who provide a concordance table between IPC codes atthe 4 digit level and NAICS 1997 6 digits industry codes (mostly in manufacturing). The
concordance table is probabilistic (so that each code is associated with a sector with a certain probability). In this exercise we are interested in matching patents with a sector ofuse and not the inventing sector (which is what is provided by the Eurostat concordancetable for instance). The Lybbert and Zolas concordance tables are derived by matchingpatents texts with industry descriptions, and as such they cannot a priori distinguishbetween sector of use and industry of manufacturing. We checked, however, that patents
associated with “textile and paper machines” for instance are associated with the textile
and paper sectors and not with the equipment sector (as is the case with the Eurostat
concordance table). We attribute patents to sectors fractionally in function of their IPC
codes. To assign patents to the consistent Census industry codes used by ALM, we first73

use a Census concordance table (https://www.census.gov/topics/employment/industryoccupation/guidance/code-lists.html) to go from NAICS 1997 to Census industry codes
1990, then we use the concordance table of ALM to get to the consistent Census industry codes of ALM. Finally, for each sector and each time period, we compute the sumsof automation patents and machinery patents and take the ratio to be our measure ofautomation intensity. We exclude sectors with less than 50 machinery patents (which is
why the number of sectors varies across time periods). We are left with 66 to 68 sectors,
with only 7 of them not in manufacturing.
The other variables are directly taken from ALM. We refer the reader to that paper
for a detailed explanation. The task measures are computed using the 1977 Dictionaryof Occupational Titles (DOT) which measure the tasks content of occupations. Occupations are then matched to industries using the Census Integrated Public Micro Samples
one percent extracts for 1960, 1970 and 1980 (IPUMS) and the CPS Merged OutgoingRotation Group files for 1980, 1990 and 1998 (MORG). The task change measure at theindustry level reflects changes in occupations holding the task content of each occupation constant, which ALM refer to as the extensive margin. Since tasks measures do nothave a natural scale, ALM converted them into percentile values corresponding to theirrank in the 1960 distribution of tasks across sectors, so that the employment-weighted
means of all tasks measure across sectors in 1960 is 50. Our analysis only uses manufacturing sectors and starts in 1970 but we kept the original ALM measure to facilitatecomparison. As in ALM, the dependent variable in Table 4 corresponds to 10 times theannualized change in industry’s tasks inputs to favor comparison across periods of different lengths. Computerization ∆Cj is measured as the annual change in the percentage
of industry workers using a computer at their jobs between 1984 and 1997 (estimatedfrom the October Current Population Survey supplements), multiplied by 10 to ensurethat all variables are over the same time length. For all regressions, observations areweighted by the employment share in each sector. In Table 4, the ratio of high-skill to
low-skill workers are measured as the ratio of college graduates (and more than college)
to high-school dropouts and graduates, taken from ALM—knowing that their data inturn come from IPUMS and MORG.
Table B.5 reproduces Table 4 but with the laxer auto90 measure. The results arevery similar—the only difference is that the coefficient on routine manual tasks is not
significant at the usual levels in the 90s.42
42

To interpret the effect of the automation variable, note that the means are 0.13, 0.15 and 0.14 in
the 70s, 80s and 90s, and the standard deviations are 0.10, 0.12 and 0.11 with the auto90 definition.

74

ind6090 Title
ind6090 Title
16
Ag production crops & livestock;201
Misc. petroleum and coal products
Ag services; Horticultural services
206
Household appliances; Radio, TV &communications equipment; Electric
30
Forestrymachinery, equipment & supplies, n.e.c., not31
Fishing, hunting and trapping
specified electrical machinery, equipment &40
Metal mining
supplies
41
Coal mining
42
Crude petroleum and natural gas extraction 211
Other rubber products, and plastics
footwear and belting + tires & inner tubes
50
Nonmetallic mining & quarrying, except fuel66
Construction
212
Misc. plastic products
100
Meat products
220
Leather tanning and finishing101
Dairy products
221
Footwear, except rubber and plastic
102
Canned and preserved fuits and vegetables 222
Leather products, except footwear
110
Gain mill products
230
Logging
111
Bakery products
231
Sawmills, planning mills, and millwork
112
Sugar and confectionary products
236
Railroad locomotives & equipment; Cycles& misc transporation equipment; Wood120
Beverage industries
buildings & mobile homes
121
Misc. food preparations, kindred products
130
Tobacco manufactures
241
Misc. wood products
132
Knitting mills
242
Furniture and fixtures
140
Dyeing and finishing textiles, except wool
246
Scientific and controlling instruments;
and knit goods
Optical and health service supplies
141
Floor coverings, except hard surfaces
250
Glass products
142
Yarn, thread, and fabric mills
251
Cement, concrete, gypsum & plasterproducts clay products
146
Primary aluminum and other primary metal 252
Structuralindustries
261
Pottery and related products
150
Misc. textile mill products
262
Misc. nonmetallic mineral & stone products
151
Apparel and accessories, except knit
270
Blast furnaces, steelworks, rolling andfinishing152
Misc. fabricated textile products
271
Iron and mills
stell foundaries
160
Pulp, paper, and paperboard mills
281
Cutlery, handtools, and other hardware
161
Misc. paper and pulp products
282
Fabricated structural metal products
162
Paperboard containers and boxes
346
Plastics, synthetics & resins; Soaps &cosmetics; Agricultural chemicals; Industrial
166
Screw machine products; Metal forgings && miscellaneous chemicals
stampings; Misc. fabricated metal products
172
Printing, publishing, and allied industries
351
Transportation equipment
except newspapers
360
Ship and boat building and repairing
176
Engine and turbines; Construction & material 362
Guided missiles, space vehicles, and parts,
handling machines; Metalworking machinery; 380
ordnance, andequipment
aircraft andparts
Photographic
andsupplies
Machinery, except electrical, n.e.c.; Not
381
Watches, clocks, and clockwork operated
specified machinery
devices391
Misc.
manufacturing industries and toys,
amusement
181
Drugs
460
Electric
light andand sporting
power goods
186
Electronic computing equipment; Office and 462
Eletric and gas, and other combinations
accounting machines
470
Water supply and irrigation
190
Paints, varnishes, and related products
471
Sanitary services
200
Petroleum refining
636
Grocery stores; Retail bakeries; Food
stores, n.e.c.

Table B.4: List of sectors in the ALM regressions

75

Table B.5: Changes in task intensity and skill ratio across sectors and automation (auto90)(1)
D Nonroutine
analytic(2)
D Nonroutineinteractive(3)
D Routine
cognitive(4)
D Routine
manual(5)
D Nonroutine
manual(6)
D H/LShare of automation
patents in machinery

0.82(3.51)

3.57(4.32)***-17.95***(4.22)***-10.60***(3.74)-0.89(5.13)**0.11**(0.05)D Computer use
1984 - 1997

-7.16
(5.71)-2.99(7.03)***-18.91***(6.86)-3.26(6.09)*14.86*(8.36)

0.08(0.09)

Intercept

0.92(1.00)*2.14*(1.23)***4.34***(1.20)***3.39***(1.07)-1.70(1.47)***0.04***(0.02)

0.02
-0.05

0.01
2.17

0.31
-0.90

0.12
1.49

0.05
0.42

0.08
0.07

Share of automation
patents in machinery*9.01*(5.41)**13.29**(6.23)***-25.37***(4.96)***-13.79***(4.28)**9.70**(4.72)***0.73***(0.19)D Computer use
1984 - 1997**24.75**(10.34)*22.95*(11.90)-13.41(9.49)-1.55(8.18)-5.37(9.02)

0.39(0.37)

Intercept*-3.15*(1.77)-1.21(2.03)**3.55**(1.62)

1.69(1.40)-2.39(1.54)-0.06(0.06)

R2
Weighted mean D

0.13
1.86

0.13
4.17

0.32
-2.22

0.14
-0.59

0.06
-1.74

0.21
0.11

Share of automation
patents in machinery**9.23**(4.57)*10.63*(6.22)***-13.47***(5.12)-6.24(4.19)

3.95(4.76)***0.42***(0.12)D Computer use
1984 - 1997***27.31***(8.27)**28.19**(11.25)***-25.09***(9.26)***-26.11***(7.58)

8.05(8.61)***0.73***(0.22)

Intercept**-2.93**(1.44)-1.93(1.96)

2.23(1.61)*2.41*(1.32)*-2.55*(1.50)**-0.08**(0.04)

0.20
2.45

0.14
3.79

0.20
-3.44

0.19
-2.36

0.03
-0.79

0.29
0.09

Panel A: 1970 - 80, n=67

2

R
Weighted mean D
Panel B: 1980 - 90, n=67

Panel C: 1990 - 98, n=67

R2
Weighted mean D

Standard errors are in parentheses. Colums (1) to (5) of Panels A to C each presents a separate OLS regression of ten times theannual change in industry-level task input between the endpoints of the indicated time interval (measured in centiles of the 1960 task
distribution) on the share of automation patents in machinery (defined with the 90th percentile threshold) and the annual percentage
point change in industry computer use during 1984 - 1997 as well as a constant. In Column (6), the dependent variable is the ratio of
high-skill (college graduates) to low-skill (high-school graduates and dropouts) workers. Estimates are weighted by mean industry shareof total employment in FTEs over the endpoints of the years used to form the dependent variable. * p<0.1; ** p<0.05; *** p<0.0176

Table B.6 reproduces the Table 5 of ALM by carrying the analysis of Table 4 for eacheducation groups over the time period 1980-1998 with the auto95 measure (the results
are very similar with auto90). The table shows that automation reduces the amount ofroutine tasks undertaken by high-school dropouts and high-school graduates. Following
ALM, Panel F computes the average effect of automation in tasks changes (from PanelA) and how much of this average effect can be explained by changes within educationalgroups (from Panels B to E). We find that changes within educational categories explaina significant share of the overall reduction in routine tasks but changes in educational
composition also play a role, in line with Column 6 of Table 4. In contrast, ALM foundthat nearly all of the decline in routine tasks due to computerization came from within
educational group changes.

B.3

Validating our weights approach

In this Appendix, we compare our firm-level weights to bilateral trade flows and showthat they are strongly correlated. The first step is to compute patent-based weights at thecountry level. For this exercise (and this exercise only), we define the domestic country
d of a firm based on the location of its headquarters (according to the country code of
its identifier in the Orbis database—for firms which we merged, we keep the countrycode of the largest entity by biadic machinery patents in 1997-2011). We compute theforeign weights for each firm i by excluding the domestic country. Therefore the foreign
weight for country c 6= d for firm i is given by ωi,c /(1 − ωi,d ) (recall that these weights
are computed based on patenting from 1970 to 1994). We then build the foreign patentbased weight in country c for country das a weighted average of the foreign weights incountry c of the firms from country d (each firm is weighted according to the number ofmachinery biadic patents in 1997-2011).
The second step is to build similar weights based on exports. To do that, we collectsectoral bilateral trade flow from UN Comtrade data between between 1995 and 2009 for
40 countries (Taiwan is not included in the data). To obtain trade flows in machinery,
we use a concordance table between 4 digit IPC codes and 2 or 3 digits NACE Rev 2
codes provided by Eurostat, this concordance table matches IPC codes to the industryof manufacturing. The concordance table assigns a unique industry to each IPC code.
Then, for each industry and each country, we compute the share of patents over theperiod 1995-2009 which are in machinery according to our definition.43 This gives us a
43

To do that we use a fractional approach: each patent is allocated NACE sectoral weights (and77

Table B.6: Changes in task intensity and skill ratio across sectors and automation (auto95)
by skill groups(1)
D Nonroutine
analytic(2)
D Nonroutineinteractive(3)
D Routine
cognitive(4)
D Routine
manual(5)
D Nonroutine
manualPanel A: Aggregated within-industry change
Share of automation
patents in machinery**9.53**(4.53)***17.97***(5.39)***-26.66***(4.83)***-17.09***(3.90)***12.57***(4.30)D Computer use
1984 - 1997***24.91***(6.36)***23.81***(7.56)***-17.75***(6.79)**-11.53**(5.48)

0.47(6.03)

Intercept**-2.36**(1.03)-1.01(1.22)*2.05*(1.10)*1.73*(0.89)**-2.37**(0.98)

0.26
2.05

0.27
3.88

0.39
-2.62

0.29
-1.29

0.12
-1.34

R2
Weighted mean D

Panel B: Within industry: High school dropouts
Share of automation
patents in machinery

2.41(7.89)

13.61(10.85)***-26.19***(6.94)-5.80(6.22)

4.56(6.35)D Computer use
1984 - 1997

11.70
(11.08)18.08(15.24)15.84(9.74)

8.68(8.73)-9.95(8.91)

Intercept**-4.47**(1.79)***-8.45***(2.47)

0.87(1.58)

0.55(1.41)

1.16(1.44)

0.02
-2.56

0.05
-4.73

0.19
1.20

0.02
1.39

0.02
0.04

R2
Weighted mean D

Panel C: Within industry: High school graduates
Share of automation
patents in machinery-7.08(5.47)

6.50(7.05)***-26.09***(5.64)***-13.43***(4.25)*9.62*(5.37)D Computer use
1984 - 1997

9.30
(7.69)-0.76(9.90)*-14.39*(7.92)-2.86(5.96)

6.71(7.54)

Intercept**-2.86**(1.24)

2.19(1.60)*2.25*(1.28)

0.00(0.97)-1.43(1.22)

0.04
-2.03

0.01
2.57

0.30
-1.88

0.14
-1.45

0.06
0.30

R2
Weighted mean D

Panel D: Within industry: Some College
Share of automation
patents in machinery-11.94(8.04)-7.49(7.31)-4.92(6.01)-5.92(5.72)*12.48*(6.56)D Computer use
1984 - 1997

7.05
(11.29)13.85(10.26)*-14.68*(8.44)*-14.11*(8.03)

9.14(9.20)

Intercept-1.10(1.83)

0.31(1.66)

0.38(1.37)*2.21*(1.30)*-2.74*(1.49)

R2
Weighted mean D

0.04
-0.97

0.04
1.78

0.06
-2.17

0.07
-0.33

0.07
-0.43

-6.54
(4.25)**-7.28**(3.59)*-11.58*(6.48)-7.70(7.74)***17.00***(6.03)**14.44**(6.00)*9.29*(5.06)-5.55(9.14)-7.69(10.91)

11.14(8.50)

Intercept-0.94(0.97)

0.17(0.82)-1.22(1.48)-0.14(1.77)***-5.35***(1.38)

R2
Weighted mean D

0.01
0.69

0.09
0.99

0.06
-2.93

0.03
-1.86

0.14
-2.40

Panel E: Within industry: College graduates
Share of automation
patents in machinery
D Computer use
1984 - 1997

Panel F: Decomposition of automation effects into within and between education group
Explained task D

0.73

1.38

-2.04

-1.31

0.96

Within educ groups (%)
Between educ groups (%)-63.96163.96

15.80
84.20

72.32
27.68

54.61
45.39

81.96
18.04

n in Panels A-D is 69 and in Panel E it is 68 consitent CIC industries. Standard errors are in parentheses. Each column ofpanels A - E presents a separate OLS regression of ten times the annual change in industry-level task input for the
relevant education group (measured in centiles of the 1960 task distribution) during 1980 - 1998 on the the share ofautomation patents in machinery (defined with the 95th percentile threshold) and the annual percentage point change inindustry computer use during 1984 - 1997 as well as a constant. Estimates are weighted by mean industry share of total
employment (in FTEs) in 1980 and 1998. The 'explained' component in Panel F is the within-industry change in the task
measure predicted by the share of automation patents in regression models in Panel A. * p<0.1; ** p<0.05; *** p<0.01

78

Log patents based weights
−9
−5
−1

−9
−5
−1
Log exports based weights

JP

CY
MT
−13

−9
−5
−1
Log exports based weights

−15

−10

−5
Log exports based weights(a) All countries

0

−13

−9
−5
−1
Log exports based weights

LT

SI CY

−10
−6
−2
Log exports based weights

IT
0

GB

−12

CZ
SK RO
BG CY
LVSI ID
LT MT
EE
−12

−8
−4
Log exports based weights

DE
US
FR
GB
ES
JP
AT
CH
NL
SE
CA
BRBE
DK
AU
GR
PT
RU
LU FI CN
MX
IN KR
IE
PL
HUTR
CZ
RO
BG
SK
SI

Log patents based weights−8−4

Log patents based weights
−8
−4

DE
US
FR
JP
AUES
ITNL
CA
SE
ATCHBE
BR DK
FI
IE
GR
PT
IN
LU CN
MX KR
TR
PL
HURU

−12

Log patents based weights
−9
−5
−1
−13

−15

Coefficient: 0.94

US DE
GB
JPNLES
IT
BE
CA
AT
SECH
DK
BR
AU
PT
GR
LU
FI
KR
CN
MX
IERU
IN
TR
HUPL
CZ
RO
SK
SI
BG
ID
LT
CY
LV
EE
MT

LV

−14

0

FR

DE US
FRGB
KR
IT
CA
NL CN
ES AU
CH
SE
AT BE
BR
DK
FIRU MXLU GR IN
PT
PL IE
HU TR
RO
ID
BG CZ
SK

Log patents based weights
−10
−6
−2

US
FR
GB
JPESIT
AT
NL
SECH
BE
CA DK
BR
AU
FI
CN
GR
LU
RU
KR
PT
MX
IN HUPL
IE
CZ
TR
SK
RO
BGSI
LVLT
EE ID

−14

EE
MT
−13

DE

−13

DE
FRGBJPCA
IT
ES
AU
NL
SECH
BE
BR
AT
MX
DK
KR
FI CN
GR
PT IE
LU
IN
HU PL TR
RU
CZ
SK RO
BG
ID
LV
LTSI CY−13

Log patents based weights−10−5

Log patents based weights
−9
−5
−1

0

US

0

LT
−12

MT
CY

−8
−4
Log exports based weights

0(b) Trade from the 6 largest countries

Figure B.3: Bilateral patent flows and trade flows in machinery. Panel (a) plots log patent based
weights, which are a weighted average of the destination country’s weights in the (foreign)patent portfolio of firms from the origin country, against export shares in machinery over theyears 1995-2009. The size of each circle represents the product of the GDP of both countries,
which is used as a weight in the regression. Panel (b) focuses on the weights from the listed
countries and observations are weighted by the GDP of the partner country.

machinery weight for each industry code and each country. We then multiply sectoral
trade flows (after having aggregated the original data to the NACE Rev 2 codes usedin the concordance table) by this weight to get bilateral trade in machinery. We thencompute the export share in machinery across destinations. We could compute trade
based weights for each year but here we report results based on 1996 only (there are a
few missing observations for 1995).
Figure B.3 plots the patent-based weights against the trade-based weights. Panel (b)focuses on a few origin countries while Panel (a) plots all countries together. We find astrong correlation between the two measures with a regression coefficient of 0.94 (whenobservations are weighted by the trade flow in 1996).
Another way to summarize how close the two distributions are is to compute what
low-skill wages would be according to either sets of weights. We do this in Figure B.4.
There for each country, we compute “foreign low-skill wages” as a weighted average of
foreign wages where the weights are either the patent-based weights or the trade-based
weights derived above. Foreign wages are deflated with the local PPI and converted in
USD in 1995 as in our main analysis. Panel (a) then reports foreign log low-skill wages
according to both types of weights in 1995-2009, we find that they are strongly correlated.
machinery weights) depending on the share of IPC codes associated with a NACE sector or machinery.

79Low−skill wages (patents based residuals)
−.05
0
.05

.1

3.4
Low−skill wages (patents based)
2.8
3
3.2
2.6

Coefficient: 0.56

2.4

−.1

Coefficient: 0.41
2.4

2.6

2.8
3
Low−skill wages (exports based)

3.2

3.4

(a) Low-skill wages.

−.1

−.05
0
.05
Low−skill wages (exports based residuals).1(b) Residualized low-skill wages

Figure B.4: Foreign low-skill wages for each country computed either with patent-based
weights or with trade-based weights. Wages are computed for the years 1995-2009.
Panel (a) plots log foreign low-skill wages using either patent-based weights or trade-based
weights. Panel (b) plots the residuals of foreign wages according to both methods controlling for country and year fixed effects. Observations are weighted by the number of biadic
machinery patents by firms from the the country over the years 1997-2011.

Panel (b), reports the same foreign log low-skill wages but taking away country and year
fixed effects. We find a regression coefficient of 0.56, when observations are weighted by
the number of machinery patent in the country over the 1997-2011 time period.
Overall, this exercise shows that there is tight relationship between our patent-based
weights and (future) trade flows, suggesting that we can use these patent-based weights
as proxies for firms’ markets exposure.

B.4

Macroeconomic variables

Our main source of macroeconomic variables is the World Input Output Database (WIOD)
from Timmer, Dietzenbacher, Los, Stehrer and de Cries (2015) which contains information on hourly wages (low-skill, middle-skill and high-skill) for the manufacturing sector
and the total economy from 1995 to 2009 for 40 countries. It further contains information on both GDP deflators and producer price indices both for manufacturing andfor the whole economy. Their data on skill is based on the 1997 International Standard
Classification of Education (ISCED) system, where category 1+2 denote low-skill (nohigh-school diploma in the US) 3+4 denote middle-skill (high-school but not completed
college) and 5+6 denotes high-skill (college and above). Switzerland is not included in
the WIOD database and we add data on skill-dependent wages, productivity growth andprice deflators manually using data obtained directly from Federal Statistical Office of80

Switzerland.
We supplement this data with data from UNSTAT on exchange rates and GDP(and add Taiwan separately from the Taiwanese Statistical office). We use this data tocalculate the GDP gap as the deviations of log GDP from HP-filtered log GDP using asmoothing parameter of 6.25.
The primary data source for the hourly minimum wage data is OECD Statistics. Not
all countries have government-imposed hourly minimum wages. Spain, for instance, hada monthly minimum wage of 728 euros in 2009. To convert this into hourly wage wenote that Spain has 14 monthly payments a year (+1 payments in December and July).
Further, workers have 6 weeks off and the standard work week is 38 hours. Consequently
we calculate the hourly minimum wages as monthly minimum wage×14/ [(52 − 6) × 38],
which in the case of 2009 is 5.83 euros per hour. We perform similar calculations, depending on individual work conditions, for other countries with minimum wages that are
not stated per hour: Belgium, Brazil, Israel, Mexico, Netherlands, Poland and Portugal.
For the US, we use data from FRED for state minimum wages and calculate thenation-level minimum wage as the weighted average of the state-by-state maximum ofstate minimum and federal minimum wages, where the weight is the manufacturing
employment in a given state.
Further, the UK did not have an official minimum wage until 1999. Correspondingly,
we follow Dickens, Machin and Manning (1999) and use the wage levels agreed upon by
local wage councils. These were in effect from 1909 until 1993. For, 1995-1998, the four
years in our sample where no official minimum wage existed, we use the nominal levelfrom 1993. We use the employment-weighted industry average across manufacturingindustries. Finally, Germany did not have a minimum wage during the time period westudy. Instead, we follow Dolado, Kramarz, Machin, Manning, Margolis and Teulings(1996) and use the collectively bargained minimum wages in manufacturing which effectively constitute law once they have been implemented. These data come from personalcorrespondence with the Sabine Lenz at the Statistical Agency of Germany.

81