DPRIETI Discussion Paper Series 20-E-051

Robots and Employment: Evidence from Japan, 1978-2017

ADACHI, Daisuke
Yale University

KAWAGUCHI, Daiji
RIETISAITO, Yukiko Umeno
RIETI

The Research Institute of Economy, Trade and Industry

https://www.rieti.go.jp/en/

RIETI Discussion Paper Series 20-E-051
May 2020

Robots and Employment:
Evidence from Japan, 1978-20171
ADACHI, Daisuke（Yale University）
KAWAGUCHI, Daiji（University of Tokyo, IZA and RIETI）
SAITO, Yukiko U.（Waseda University and RIETI)
Abstract
We study the impacts of industrial robots on employment in Japan, the country with the longest
tradition of robot adoption. We employ a novel data set of robot shipments by destination industry androbot application (specified task) in quantity and unit values. These features allow us to use an
identification strategy leveraging the heterogeneous application of robots across industries andheterogeneous price changes across applications. For example, the price drop of the welding robot
relative to the assembling robot induced faster adoption of robots in the automobile industry that
intensively uses welding processes than in the electric machine industry that intensively uses
assembling processes. Our industrial-level and commuting zone-level analyses both indicate that the
decline of robot prices increased the number of robots as well as employment, suggesting that robots
and labor are gross complementary in the production process. We compare our estimates with thosereported by existing studies and propose a mechanism that explains apparent differences between the
results.
Keywords: Automation, Industrial robot, Application of robots, Employment, Factor demand elasticity.
JEL classification: J23, J24, R23, O33, R11.
The RIETI Discussion Paper Series aims at widely disseminating research results in the form ofprofessional papers, with the goal of stimulating lively discussion. The views expressed in the papers aresolely those of the author(s), and neither represent those of the organization(s) to which the author(s)belong(s) nor the Research Institute of Economy, Trade and Industry.
This study is conducted as a part of the Project “Dynamics of Inter-organizational Network and Firm Lifecycle''undertaken at the Research Institute of Economy, Trade and Industry (RIETI) and the Project ”Research on the task
models to cooperate with the human and new technology: Evaluating the impacts on labor market'' undertaken at the
Research Institute of Science and Technology for Society of the Japan Science and Technology Agency. This study
utilizes the micro data of “the Census of Manufacture'' and “the Basic Survey on Overseas Business Activities''
which are conducted by the Ministry of Economy, Trade and Industry (METI), “the Economic Census for Business
Activity'', “the Employment Status Survey'' and “the Population Census'' which are conducted by the Ministry ofInternal Affairs and Communications (MIC) and “the Basic Survey of Wage Structure'' which is conducted by the
Ministry of Labor, Health and Welfare. The authors are grateful for helpful comments and suggestions by Discussion
Paper seminar participants at NUS, Stanford, RIETI, and Yale. The authors thank Shuhei Kainuma for outstanding
research assistance and Taiyo Fukai for providing commuting zone information.
1

1

Introduction

How does automation affect employment? Academic researchers, policymakers, and journalists
alike all discuss this topic (Brynjolfsson and McAfee, 2014, Ford, 2015, Frey and Osborne, 2017,
OECD, 2019). An example of automation is the use of industrial robots (robots hereafter).1
This paper analyzes the effect of robot penetration on employment in Japan, relying on a novel
identification strategy enabled by a newly digitized data set of robot shipments by destinationindustry and robot application (task, defined further below) in quantity and sales values.
To estimate the impact of robot penetration on employment, we exploit the variation in robot
adoption due to heterogeneous changes in effective robot prices that differ by the characteristics ofrobot adopters. As our methodological benchmark, Acemoglu and Restrepo (2017) and Dauth etal. (2018) proxied the supply shock of robot to a domestic industry by the robot penetration of thesame industry in other developed economies. The presumption is that the industry-level penetrationof robots is similar within these economies, because both economies face the same industrial shockto the robot supply, such as innovation in a new robot model that is demanded by both countries.
We propose a new identification strategy based on robot applications.
Robot applications are the tasks in the production process that the introduced robots are supposed
to do, such as welding and parts assembling. This study substantiates the source of variation ofrobot adoption across industries by directly appealing to the differences in robot applications in
the production process across industries. Exploiting this inter-industry technological variation, weconstruct a robot price index that each industry faces in each year by averaging the robot price byapplication, weighted by industry-level application shares of robots. We use this industry-yearlevel robot price index to estimate the impacts of robot price on robot adoption and employment.
Subsequently, we estimate the impact of robot adoption on employment, using the price index as the
instrumental variable (IV) for robot adoption. The strengths of our methods in comparison to theliterature are as follows. First, our method does not require countries of comparison. Second, theregression coefficient reveals gross factor (robot and labor) demand elasticities with respect to robot
prices, which are directly interpretable. Furthermore, our method has an additional benefit: The
construction of different robot price indices across industries enables us to examine the experienceof Japan, which has no comparable foreign counterparts, because robots were adopted in the
1Other examples include the use of information and communication technologies (ICT, Autor et al., 2003, among
others) and artificial intelligence (e.g., Agrawal et al., 2018). Research on automation in non-industrial contexts is also
emerging. Png (2019), for example, finds positive welfare effects on routine cashier workers at supermarkets.

1

production process as early as the late 1970s, about 20 years earlier than other developed countries.
We implement our identification strategy employing a new data set featuring a long panel(1978-2017), quantity and sales values, and disaggregation by destination industry and robot
applications. Robot applications are the tasks in the production process that the introduced robots
are supposed to perform, such as welding and parts assembling. We first demonstrate that various
industries use robots in different production processes by showing that the composition of robot
applications is substantially different across destination industries. For instance, welding robots
and assembling robots consist of 46.3 percent and 7.1 percent of total shipments to the automobile
industry, respectively, whereas the values for the electronics industry are 3.0 percent and 76.2
percent, respectively, in the years 1978-82. We next show that the unit price of welding robots fell
consistently and substantially between 1978 and 2017, whereas the price drop of other robots was
not as striking. Exploiting the heterogeneity of robot application intensity across industries and thedifferent price trends by robot application, we construct the aggregate robot price index by industry
that changed differentially across industries. We take this price variation across industries as thesource of identification.
We find that robots are complementary to employment at both at the industry and region
levels. At the industry level, we show that a 1 percent decrease in robot price increased robot
adoption by 1.54 percent. Perhaps more surprisingly, we also find that a 1 percent decrease inrobot price increased employment by 0.44 percent. This finding implies that robots and labor are
gross complements. Therefore, taken together, our two-stage least squares estimates suggest that a
1 percent robot increase caused by price reduction increased employment by 0.28 percent.
To show the credibility of our identification assumption, we conduct robustness checks bydropping large robot-adopting industries and using different price measures. Robot prices may bedirectly affected by large adopters. For instance, large adopters, such as the automobile industry,
may exert scale effects and reduce the prices of robots they purchase. In this case, prices areendogenous for these adopters. To deal with the concern, we drop these large industries and showthat the results are qualitatively the same. To deal with further concerns about using prices asa direct shifter of our IV, we also report the results with alternative IVs based on measures ofleave-one-out prices and prices demanded by foreign purchasers. The results are also robust tothese considerations.
We move to the commuting-zone (CZ) level analysis to better compare our results with existing
estimates in the literature. By constructing shift-share measures of robot exposure, we conduct a2

two-stage least square estimation resembling the one by Acemoglu and Restrepo (2017) but withour cost-based instrumental variable. Our result indicates that one robot unit per 1,000 workers
increased employment by 2.2 percent, corroborating the finding that the robots and labor are
gross complements. This contrasts with the finding by Acemoglu and Restrepo (2017), whose
corresponding estimate was -1.6 percent. The difference of the results is not surprising, given the
difference of the country and the time period covered. Particularly, considering the export-oriented
nature of the automobile and electric machine sectors of Japan, robot adoption and its cost-reducing
effect have contributed to the expansion of exports and increased the labor demand. This scaleeffect may well have dominated the substitution effect of robots for labor.
The CZ-level analysis enables us to conduct further analyses regarding spillovers from the
manufacturing sector to the non-manufacturing sector, as advocated by Moretti (2010). First,
we find that the employment of non-manufacturing sectors neither increased nor decreased uponrobot adoption. This fact shows that within-region-across-industry reallocation from service tomanufacturing did not happen, suggesting that an across-region reallocation of workers (migrationor lack thereof) happened. In other words, in the context of the non-increasing population in Japan,
robots might work like a magnet that keeps workers from leaving for other regions. Second, we
find that although the total employment increased when robots were adopted, the hours worked
per worker decreased. This finding suggests that robots may have worked as work-sharing andtime-saving technological changes. In turn, this implies that the hourly-wage effect might be evenmore positive, which we confirmed in our data.
Relative to the previous literature, for the first time, we propose and implement a robot-cost based
identification strategy to estimate the causal effect of robot penetration on labor demand, drawing
on newly digitized data covering the long period of 1978-2017 in Japan and the heterogeneoustechnology of robot use across industries. There is a growing empirical literature studying theimpacts of robots on employment (Acemoglu and Restrepo, 2017; Dauth et al., 2018; Graetz andMichaels, 2018; Artuc et al., 2018; Bessen et al., 2019; Koch et al., 2019; Humlum, 2019). The
literature is finding mixed evidence from different contexts. Among them, Graetz and Michaels(2018) reported that robot penetration increased labor productivity as well as wages based on an
Organisation for Economic Co-operation and Development (OECD) country-industry level analysis.
Acemoglu and Restrepo (2017) analyzed US regional labor markets and concluded that robot
penetration into a local labor market reduces the employment-to-population ratio and the earningsof all workers regardless of skill levels, implying that all workers lose from robot penetration. In

3contrast, Dauth et al. (2018) analyzed German regional labor markets to find that the penetration ofrobots decreased employment in the manufacturing sector but increased employment in the service
sector, suggesting the coexistence of losers and winners in local economies. These conflicting
empirical results from two major economies, in addition to the theoretical discussion by Berg et al.
(2018) and Caselli and Manning (2019) that technological progress should benefit some workers
under fairly standard assumptions, warrant further empirical study on other major economies.
The existing literature on the impact of robots on employment does not use robot cost as a robot
demand shifter, because robot cost measures are not generally available. For example, the widelyused data set compiled by the International Federation of Robotics does not record the sales values
at a fine disaggregated level. Thus the unit value per robot is unknown. Graetz and Michaels(2018) is an exception, in that it reports the robot price taken from a survey of a subset of robot
producers and shows that the cost reduction was a critical factor behind robot adoption. They did
not, however, use the price information in their formal regression analysis.
There is also an emerging literature that uses automation data with firm-level observations tostudy the margins of adjustment (Bessen et al., 2019, Koch et al., 2019). These papers typicallyare not aimed at studying a particular machine, but instead, an abstract “automation cost or
technology.” In contrast, we study the role of a particular well-defined machine, the robot, whichmakes a quantitative interpretation possible as opposed to a qualitative one.
The use of equipment prices to estimate the elasticity of substitution between production factors
is not new.2 Our paper may be thought of as a contribution to this literature in that it examines aparticular type of capital, robots, that is expected to automate the production process and substitutefor labor. Recent examples from this literature include Karabarbounis and Neiman (2014), whoestimated capital-labor elasticity of substitution using cross-country and time-series variations tostudy drivers behind recent declines in labor shares in national income. Drawing on their arguments,
our finding that robots and labor may be gross complements implies that caution should be taken
in interpreting that the recent development in robot technology contributes to a decrease in labor
shares.
This paper proceeds as follows. Section 2 reviews the necessary background for this paper.
This includes the standard shift-share IV-based identification method, cross-country robot adoption
trends, and robotics application classifications. Section 3 describes our datasets, with particularemphasis on the critical raw trends of our newly digitized dataset on robot adoption. Section 42See Antras (2004) for a list of papers.

4and 5 are the primary analysis sections of this paper, showing two different and complementary
empirical strategies of industry-level and commuting-zone-level analyses, and their results. Section
6 discusses the efficiency adjustment over the long-run robot adoption and ensures the robustnessof our results when those adjustments are accounted for. Section 7 concludes.

2

Robot Penetration

As background for our research, we overview a standard identification approach using shift-share
instruments, raw trends of robot stock units by country, and the description of robots and robot
applications.
By showing cross-country raw trends, we not only convey the significance of our context as theusers of robots, but also show why a new identification strategy is needed. Namely, since Japan has
a very distinct trajectory in introducing robots for industrial production, it is hard to find similarcountries of comparison that would be necessary to construct the shift-share-type instrumental
variable prevailing in the literature.
We then move on to describe robots and robot applications. There are two reasons for providing
this description. First, it gives readers an understanding of the technological context of our research.
Second, by so doing, we introduce a core idea behind our novel identification strategy–an industrial
variation in the intensity of different tasks, or applications of robots. Throughout the paper, we use
the terms tasks and applications interchangeably.

2.1

Standard Identification Method–Shift-share Instrument

Although we do not employ the standard shift-share identification method in this paper, it isworth mentioning it to highlight the features of our method. When estimating the effect of robots
on employment, one confronts a classical identification problem: Observed robot absorption is
endogenous to employment. Consider a standard model that attempts to identify the impact ofrobot adoption in commuting zone (CZ) c in year t on the employment of the industry in the year:ln (Lct ) = αc + αt + β ln (Rct ) + Xct0 γ + εct,

(1)

where Lct is the employment in CZ c in year t, Rct is the stock of employment in CZ c in year t,
and Xct is the set of time-varying control variables in CZ c in year t.
5

The challenge of estimating this equation is the endogeneity of Rct , as the unobserved productdemand shock to the CZ included in εct is correlated with Rct . For example, the depreciationof JPY disproportionately increases the product demand in Toyota city, where factories of Toyota
Motor Co. are located, which increases both the employment and robot stock of the area. A typical
workaround to this problem is to construct a region-level shift-share instrumental variable (IV) Zct .
Formally, the IV is defined as follows:
comparison countries

Zct ≡

Õ

lcit0

∆Rit

Lit

i

,

(2)

where c is a region (e.g., commuting zone), t is year, i is industry, lcit0 is base-year (t0 )comparison countries

industrial employment share in (c, t), Rit

is the operational robot stock in (i, t) ofcomparison countries, and Lit is total employment in (i, t). A researcher should use the stock value
in comparison countries rather than the country of interest, because the observed robot stocks inthe country of interest are subject to endogeneity problem; for example, the increase in the robot
stock may be due to a demand boom in (i, t), which raises the industrial labor demand at thesame time. In this case, the unobserved error term is positively correlated with the robot stock
variable. By using values from comparison countries and assuming that the industrial demands inthese countries are uncorrelated, the researcher can take an exogenous source of variation, such as
robotics technological growth.
It is not obvious which countries of comparison should be selected. An intuitive method is
to take countries that are similar on some measure. Acemoglu and Restrepo (2017), for example,
takes Denmark, Finland, France, Italy, and Sweden as comparison countries for the US, basedon the similarity of robot absorption trends, while Dauth et al. (2018) takes Spain, France, Italy,
the United Kingdom, Finland, Norway, and Sweden for Germany. To see potential comparison
countries, the next subsection reviews the trends of robot stock units in several countries. This will
demonstrate the difficulty in finding natural comparison countries in our research context.

2.2

Trends of Robot Stock Units

To grasp the cross-country trends of robot stock units, we leverage two data sources: the International Federation of Robotics and the Japan Robot Association (JARA). The IFR data are from acountry-industry-level panel of operational stock of robots for years 1993 to 2016. This panel has
been used intensively in the literature (Graetz and Michaels, 2018, Acemoglu and Restrepo, 2017,
6

Figure 1: Trends of Robot Stock Units, by Country

Note: Notes: Authors’ calculation based on JARA and IFR data. The JARA data show the application-aggregatedrobot shipment units from Japan to Japan between 1974 and 2017. We supplement the shipment during the 1974-77
period by type-aggregated units. We assume zero units before 1973. To calculate the stock units, we assume eight(8) year immediate withdrawal method to match the stock unit trend of Japan observed in the IFR. The IFR data showstock unit trends for selected countries and aggregated rest of the world (ROW) between 1993 and 2016.

among others). The JARA data are based on an establishment-level survey completed by Japaneserobot producers, which we will detail in Section 3.1. The JARA provides the statistics to IFR, andthus the Japanese series of the IFR is based on JARA statistics. Remarkably, the dataset is available
since 1978. Based on these datasets, we construct the trends of robot stock units for each country
in Figure 1.
In the figure, two colors are used to separate the data sources: one for JARA and the other for
IFR. Different line types indicate different countries. Note that the solid line represents the trendof Japan, and the trends for Japan overlap well between the IFR and JARA datasets. This is not
surprising, because the IFR creates its Japanese series based on JARA data. The two series do not
overlap perfectly due to minor adjustments. Several findings stand out from Figure 1. First, Japan
experienced a very different trajectory than other countries. There is a rapid increase in the 1980s,
and the trend has been stable or even decreased from the 1990s and onward. Second, other large
robot purchasers are Germany and other European countries, South Korea, the US, and recently,
China. All of these countries increased the stocks rapidly in 1990s and 2000s. Although there are
no data available for these countries before 1993, the relative novelty of the robotics technology

7

suggest that the stocks before 1993 would have not been more than those of 1993. Therefore, Japan
had a quite unique trend in introduction of robots.
Given that the standard regional shift-share IV (2) leverages the trends of similar comparison
countries, how to apply it in our context is not straightforward. To solve the identification problem,
we scrutinize robotics technology. In particular, we study the robot applications in the next
subsection. The application-level variation turns out to be an additional source of variation thathelps the identification in our context and with our dataset.

2.3

Robots and Robot Applications

Given the difficulty of finding countries of comparison, we employ a new identification strategy
that is based on industry-level variation in the intensities of robot applications. To understandwhat this entails, we begin with the definition of an industrial robot (ISO 8373): “an automaticallycontrolled, reprogrammable, multipurpose manipulator, programmable in three or more axes, which
can be either fixed in place or mobile for use in industrial automation applications.” Althoughwell-defined, the definition is fairly broad and thus has different functionalities depending on theparticular applications.
In fact, different robot applications are used in different industries with varying intensities.
To understand this point better, consider an example task of spot welding (SW) in a certain
manufacturing production process. SW combines two or more metal sheets together by applying
pressure and heat to the weld area (spot). The task may be conducted in several ways, including
using human labor or robots. Furthermore, different industries input the SW task differently. For
instance, automobile industry intensively conducts SW, because it has thousands of metal parts
that are combined together to make a car body. In contrast, in textile industries such tasks are
seldom needed. Therefore, if the automation proceeds in SW task-intensive industries, it is likelythat SW-application robots are adopted in such industries.
Another example of a robot application in our paper is surface mounting (SM). SM places
surface-mount devices (SMDs) onto a printed circuit board (PCB). Since PCBs are primary inputs
to a majority of electric machines, the electric machine industry uses SM robots intensively. Notethat robots deployed for SM are different in the type or structure from those used for spot welding(SW). SM requires quick and accurate movement along a horizontal dimension for mounting SMDs.
For this purpose, a typical structure of SM robots is called the Selective Compliance Assembly
Robot Arm (SCARA), which is well suited to horizontal movements. In contrast, SW requires
8intensive movements in any directions within three-dimensional spaces. This feature helps welding
the body sheets to assemble a complex automobile body shapes without losing an efficiency of
production. Therefore, SW robots are typically structured as articulated robots, which are equippedwith multiple joints (typically six) that enable smooth movements along any direction.
The difference of mechanical structures by robot applications creates price variation by robot
applications. For example, the technological progress of the articulated robot reduced the price of
welding robots (including SW robots) relatively faster than that of Assembly robots (including SM
robots) in recent decades, as we will see in the data section.
Finally, we discuss the reasons behind robot adoption for such applications. Adoption of robots
eventually depends on the productivity of robots relative to human labor, and the relative prices ofrobots and wages. For example, one of the first robot brands in Japan, the Kawasaki-Unimate 2000,
was reported to be quite efficient in SW tasks: “The unmanned production line capable of spot
welding 320 points per minute took over the work of 10 experienced welders. Including day andnight shifts, it saved the labor of 20 people and as a result, the use of such highly versatile robots
freed workers from welding, one of Japan’s so-called ‘3K’ (kitsui, or ‘hard’; kitanai, or ‘dirty’;and kiken, or ‘dangerous’) jobs.” (KHI, 2018) The time-saving nature of robots indicates that thehours worked for each worker may decrease more than the headcount of workers when producers
adopt robots. We consider this point thoroughly in Section 6. This enhanced productivity of robots
relative to human labor and the decrease of the relative price of robots to labor explains robot
adaptation.

3

Data

We combine several data sources; two of the most important ones are the robot variables from theJapan Robot Association (JARA) and employment variables from the Employment Status Survey(ESS) from the Japan Statistic Bureau. We also complement these sources with data by Comtradefrom the United Nations (UN), the Census of Manufacture, the Survey of Overseas Business
Activities, and the Commodity Distribution Survey from Japan’s government surveys. We will
detail these below.

9

3.1

JARA Robot Data

3.1.1

Data Source

Our primary data source for robot purchases is the Appendix tables to Survey Report on Company
Conditions of Manipulators and Robots (survey tables, henceforth), obtained from the Japan Robot
Association (JARA, henceforth). The JARA is a non-profit organization of robot-producing member
companies. As of October 2019, the number of full member companies is 53, and the associate
member companies count 194.3
JARA sends an annual questionnaire to member companies and publicizes the survey tables totheir member companies.4 The survey is available starting from 1974, and we focus on the years after
1978 in our main analysis, because the shipment disaggregation by application became available
from that year. The long period of coverage enables us to analyze the long-run and potentiallytime-varying effects of robots on labor markets. Furthermore, the sample period contains the year
in which Japan introduced the industrial robots most intensively, as we will see.
The JARA’s questionnaire asks for the unit and value of industrial robots that the firm shipby each application, type, structure, and destination industry. We focus on the disaggregation
by applications in our main analysis, as we discussed in detail in the previous section. Robot
types refer to a robot categorization based on the mechanical way in which robots work. Robot
structures refer to a categorization based on the more particular dimensions and directions along
which each joint of robots moves. Given the sophistication of robotic technologies, in 2004, theIFR and robot-producing companies agreed that the structure categorization should take over thetype categorization. The full lists of the application and type categories are given in Table 1.5
We work with 13 industry classifications (12 manufacturing and one “others” aggregates) thatare consistent with the other datasets we describe below. The list of industries are: Steel; Nonferrous metal; Metal products; General machine (includes robot producers); Electric machine;
Precision machine; Transport machine; Food, beverages, tobacco, feed; Pulp, paper, printing,publishing; Chemical, pharmaceuticals, cosmetics, etc.; Ceramics and earthwork products; Other
manufacturing; and Non-manufacturing.
3See https://www.jara.jp/about/index.html. (In Japanese. Accessed on October 22, 2019)4In 1996, the questionnaire was sent to 587 establishments and firms. Therefore, one establishment corresponds
to one firm. Among them, 445 answered (for a response rate of 76 percent). Among the answering establishments,
231 actually produced robots in the year of the survey. Further details regarding the coverage of data are discussed in
Section C.1.
5Each application is further discussed in Section A.1. The structure categorization is discussed in Section C.2.

10

Table 1: Classifications of Robots
Classification by Application
Classification by Types
Handling operations/Machine tending (HO/MT)
Manual manipulator
Welding and soldering (Welding)
Fixed sequence robot
Dispensing
Variable sequence robot
Classification
Processing
Playback robot
Assembling and disassembling (Assembling)
Numerical control robot
OthersSAL control robot
Available years
1978-2017
1974-2000
Note: Authors’ aggregations based on consistently available classifications in JARA data for different years. "SAL
control robot" stands for Sensory-, Adaptive-, or Learning-control robot.

To do the long-run analysis, we construct a time-consistent aggregation of applications, types,
and structures. Interestingly, there is no clear pattern of robot sophistication between the different
applications while the robot types evolved as the technological frontier of robot-producing industry
expanded, as we will see when we discuss the descriptive statistics. Therefore, the application-basedclassification is relatively stable for each destination industry. This feature provides a justificationfor our focus on applications. In particular, since we take a weighted average of each category’s
cost to construct industry-level robot cost measures, it is desirable that the weight is invariant anddifferent across industries. Furthermore, the application-based classification is available for a more
extended period, 1978-2017. For these reasons, our primary analysis hinges on the classification
by applications.
The dimensions of the summary tables differ by years. The application-destination industry
cross tables are available between 1978 and 2017. The details of the availability are discussed in
Section C.2.
3.1.2

Raw Trends

We overview some raw aggregate trends of the JARA data to highlight three novelties of our data: thelength of the sample period, the availability of the unit value measures, and the availability of data
by applications (and types up to 2000). To smooth the year-level volatility, we average observations
with five-year bins by taking five-year observations prior to each year (e.g., the observation in 1982
is the simple average of those from 1978 to 1982). Further discussions and descriptive statistics
are in Section C.3.
We first describe who buys robots from Japan. Figure 2 shows the industry decomposition11

of the aggregate sales of robots. The left panel 2a shows the decomposition into export anddomestic sales, while the right panel 2b shows the disaggregation of domestic sales into destinationindustries. From Figure 2a, one can see that the growth of robot adoption within Japan expanded
during 1980s, while the trend is stagnant afterward. Then starting around 1990s, the export trend
expanded rapidly. This finding corroborates the trend from IFR data shown in Figure 1. Although
the structural break before and after 1990 is an interesting phenomenon per se, our focus is on thedomestic adoption trend and its industrial variation.
Figure 2b tells us that, within such domestic sales, electric machine and transportation machine(including automobiles) industries are significant purchasers of robots. These two industries
represent 68.2 percent of the domestic absorption in 2017. This feature of the data leads ourempirical strategy and indicate cautions in the identifications at the same time. Namely, our ideaof the empirical strategy is driven by the salient differences in applications between the big buyers.
At the same time, since these buyers are large, they might have market powers that affect observed
unit values. We argue the first point in the next paragraph and address the concern in detail when
discussing the empirical results.
At the same time, we also note that robot adoption was widespread in all manufacturingindustries during the 1980s and was not concentrated in particular industries like electric andtransportation machines. We confirm this point in a different slice of raw trends in Section C.3.
Next, Figure 3a shows the application-expenditure shares for the large robot purchasers, electricmachines, and transportation machinery (including automobiles). We also show the share trend
for the other aggregated domestic industries. We highlight two empirical regularities. First, there
is significant across-industry variation. The electric machine industry, for example, intensively
purchased robots for assembling and disassembling, while the transportation machinery industry
bought a significant amount of robots for welding and soldering. In particular, welding robots
consisted of 46.3 percent of the total shipments to the transportation machinery industry in years
1978-82, whereas assembling robots consisted of 7.1 percent. In contrast, welding robots consistedof 3.0 percent of the total shipments to the electric machine industry, whereas assembling robots
consisted of 76.2 percent during the same period. Second, within-industry patterns of expenditure
shares are fairly stable.6 These points suggest that industries engage in different tasks for which
they demand robots. Therefore, the initial composition of applications and the different unit-value
6The only exception is the decrease in assembling robots and the increase of other robots shipped for the electricmachinery industry. The reason is due to the classification. In the electric machine industry, the assembling process
was integrated with the measuring and testing process and thus these robots became classified as other robots.

12

Figure 2: Industry Decomposition(a) Export and Domestic Sales(b) Within Domestic Sales

Note: Authors’ calculation based on JARA data. The left panel compares the sales of exports (black) and aggregateddomestic industries (dark gray). The right panel shows the decomposition of aggregated domestic industries into three
categories: electric machine (Elec. mach.), transportation machine (Transp. mach.), and aggregated other domestic
industries (Others).

evolution afterward had differential effects on robot purchases across industries. We formalize thisidea in the following sections.
To see how the unit values evolved, Figure 3b shows the trends of the unit values for eachapplication, aggregated across industries. The trends vary across applications. For example, robots
for welding and soldering show the stable decline in the unit value for a broad set of industries.
This suggests that the production technology for welding-soldering robots grew consistently overthe sample period. More importantly, combined with the fact found in Panel (a), the decrease of theunit value of welding-soldering robots was differentially enjoyed by the transportation machineryindustry, since the industry uses the welding-soldering robot intensively. These observations
suggest that the heterogeneous reduction of robot-application price generates differential impacts
across industries because of the heterogeneous composition of applications across industries.
The rapid drop of the unit price of welding robot is the key for identification and it is worth
while to document the potential reasons why the welding robots became cheaper. The majority of
welding robots is classified as the playback robot, a robot that repeats the same sequence of motions
in all its operations, as a robot type. Figure 4a tabulates the relationships between the robot type
and the robot application between 1982-1991 and the table shows that almost all of the welding

13

Figure 3: Application Trends(a) Expenditure Shares by Industries(b) Unit-Value Trends by Applications

Note: Authors’ calculation based on JARA data. The left panel shows the application-expenditure shares for the threeindustry aggregates: electric machine (Elec. mach.), transportation machine (Transp. mach.), and aggregated otherdomestic industries (Others). The right panel shows the aggregated unit-value trends for each application. The y-axis
is scaled by the natural logarithm. The application list is discussed in the main text and shown in Table 1.

robot consists of the playback robot.7 Figure 4b shows evolution of the unit price by robot types
and we see that the unit price of the playback robot monotonically decreased. This monotonic
price decline of the playback robot makes sharp contrast to the price trends of other robot types
and reduced the price of welding robots. As a caveat we note that the price trends presented here
do not adjust for the quality of robot and the price increase of intelligence robot may well reflect
the quality improvement. We will discuss the issues related to quality adjustment in Section 6.
There are technological reasons behind the robot price decline concentrated in the welding
robot or the playback robot. We reviewed the articles of Nekkei Telecom data base, a portal website
collecting articles whose sources include domestic and international newspapers and magazines,
between 1975 and 1985 and found two important technological developments during the period: theadoption of numerical control technology and the substitution of hydraulic actuator by electronic
motor actuator (Nikkei, 1982, 1984). These two developments presumably made the welding robot(or the playback robot) cheaper and gave Japanese manufacturers cost advantage.
In Section C.3, we examine the unit-value trends for each industry to find a relatively small
variation across destination industries within an application. This suggests that the robot prices are
not solely driven by demand shocks of particular industries, but by the technology for producing
7The type-application cross-tabulation tables are only available between 1982 and 1991. Further details of the
JARA dataset is discussed in Appendix C.

14

Figure 4: Robot Type Trends, 1982-1991(a) Expenditure Shares by Robot Type(b) Unit-Value Trends by Robot Type

Note: Authors’ calculation based on JARA data. The left panel shows the type-expenditure shares for the five robot
type aggregates: fixed sequence robot, variable sequence robot, playback robot, numerical control robot, intelligent
robot. The right panel shows the aggregated unit-value trends for each robot type.

robots. Section C.3 further shows detailed descriptive statistics of JARA data and discuss the pretrend before 1978, the distribution by robot types instead of applications, and some raw correlation
between prices and quantities for each application.

3.2

Other Data

For the employment-side variables, we take the Employment Status Survey (ESS) administered by
the Ministry of Internal Affairs and Communications (MIC). The ESS has conducted the survey asof October 1979 and in every five years that end with digit 2 or 7 since 1982. It samples roughly
one million persons who live in Japan and are age 15 or above, which is roughly one percent of thispopulation in Japan.
We obtain the regional and industrial employment and population variables for each demographic group. In particular, the ESS asks for basic demographic information, such as physicaladdress, age, gender, education attainment, and employment status. For workers, we also obtain theindustry classification of the workplace, hours worked, and income. For details of these variables,
see Section F.
Our main datasets are made from the JARA and the ESS. We relate robot adoption taken fromthe JARA to employment and other labor-market variables from the ESS. In addition to these, we
15

supplement the main dataset with the Census of Manufacture (CoM), the Basic Survey on Overseas
Business Activities (BSOBA), and the Japan Industrial Database (JIP). These are used mainly toconstruct control variables. The details are provided in Section 4.3.
The CoM, which is conducted by Japan’s Ministry of Economy, Trade and Industry (METI),
is an annual survey of manufacturing establishments in Japan. Depending on the year, it takes
the universe of establishments. The strength of the dataset is the fine industry coding employed.
The ESS surveys the 4-digit Japan Standard Industry Classification (JSIC) for each establishment.
Furthermore, the survey asks each respondent for their major products and product codes. Thus,
for each establishment, we can observe the intensity of production for each product. This allows
us to obtain a fairly accurate measure of employment in robot production. We report some of theresults with the employment variable net of such robot-producing workers, as it helps clarify themechanism. We will come back to this issue when we discuss the regression results. See Section
G.1 for details.
The BSOBA, which is also administered by the METI, is an annual survey of the universeof Japanese multinational enterprises (MNEs). For all MNEs, all children and grandchildren
subsidiaries must be reported. For each of these headquarters and subsidiaries, information on
basic variables, including financial variables from balance sheets, are recorded. This dataset
enables us to measure the offshore sales variables for each industry and headquarters location.
Since offshoring was a concurrent phenomenon that changed the labor demand in Japan (see, forexample, Hummels et al., 2014), we control it with variables constructed from the BSOBA. Section
G.2 provides the details.
The JIP, which is administered by Japan’s Research Institute of Economy, Trade and Industry(RIETI), releases long-run industrial data for the Japanese economy starting from 1970, assembled
from several sources of administrative data (Fukao et al., 2008). The industrial data contain
KLEMS variables and variables regarding trade. We use the gross export and import variables tofurther control the labor-market impacts of offshoring, trade, and technological changes. Detailed
discussions about constructing the variables based on the JIP are relegated to Section G.3.

4

Industry-level Analysis

We first analyze the robot impacts at the industry level. By so doing, we discuss the effect withminimal data transformation and explore a proper empirical specification. We then move on

16

to a region-level analysis in the next section, which aims to identify regional implications andprovide better comparison to the literature. We find qualitatively similar results between these two
approaches.
The similar findings are appealing, given the recent development of econometric theories.
For instance, Borusyak et al. (2018) discuss the identification assumptions behind the shift-share
instrumental variables (SSIVs) based on exogeneity of the "shift" component and show that theidentification variation is at industry level (and time level). Adao et al. (2019) shows that in thepresence of cross-regional correlations, the standard SSIV estimators over-reject the null of noeffects. Our industrial results indicate that our findings are robust to these concerns, because ourfindings are robust to the use of industrial variations.

4.1

Robot Aggregation

In this section, we describe the method for constructing the cost indicators for each industry. We
will employ the cost indicator as an exogenous shifter of the robot introduction to each industry.
We begin by constructing stock measures of robots. Recall that the JARA data have only theflow shipment variable, whereas we are concerned about the stock of robots and the impact on
employment. To construct the robot stock, we follow the immediate withdrawal method, which isalso applied by the IFR (IFR, 2018). In particular, we aggregate the robot of a particular applicationfor 12 years to obtain the robotâĂŹs capital stock of the robot. The assumption is that the robot
begins to perform the capital service immediately upon purchase and depreciates completely in 12
years. The choice of 12 years is within the range of conventions. We discuss the robustness toother choices of stock measure construction in the robustness section below.
Suppose each industry i produces with labor and robots as primary factors. We assume that the
robots are aggregated across applications a and perform service for production in each industry. We
then consider the substitutability between labor and robots. Motivated by the observation that theexpenditure shares are roughly constant across applications for major industries, such as the electricmachine and transportation machine industries (Figure C.3), the robot aggregation is assumed to
be Cobb-Douglas:
Rit =

Ö

(Rait )ιai ,awhere R denotes the quantity of the robots, i indicates industry, t indicates the year, and a indicatesthe robot application. The aggregation weight ιai is assumed to be constant over time. Furthermore,

17

given that the variation of trends of unit values are similar across industries within applications(Figure C.4), we assume that the robot market for each application is competitive and sets the same
price rat for all buyer industries i. Section H.1 discusses the case without this assumption. Thenthe price indicator for robots in industry i is given by
rit =

Ö

(rat )ιai .

aTo measure rat in the data, we simply sum the values and quantities across all industries tocalculate the average unit value for each application and year. Formally,Í A
iv
rat = Í ait ,i Rait(3)

A is the sales value of application a to industry i in year t. Furthermore, given the idea
where vaitthat the initial expenditure share is the exogenous source of variation of price changes, we obtain
ιai by the expenditure share on application a for each industry i in year 1982, our earliest year ofÍ AA
analysis. Formally, ιai = vai,1982/ a vai,1982.

4.2

Effect of Robot Adoption on Employment

We consider the following regression specification:ln (Lit ) = αi + αt + β ln (Rit ) + Xit0 γ + εit,(4)

where Lit is employment of industry i in year t, Rit is the stock of robots in industry i in year t
and Xit is the vector of control variables explained in Subsection 4.3. Our coefficient of interest
is β, the gross elasticity of robot adoption on the employment at industry level. To capture therobot production shock that is not correlated to error term εit , we instrument ln (Rit ) with ln (rit ), ameasure of exogenous change in robot price for each industry in each year. The coefficient of interest
does not have a direct structural interpretation, but it makes possible a high-level interpretation of
the employment impacts of robots.
The first-stage and reduced-form equations of the two-stage least square (2SLS) estimation of
the model (4) are:18

ln (Rit ) = αiFS + αtFS + β FS ln (rit ) + Xit γ FS + εitFS,(5)ln (Lit ) = αiRF + αtRF + β RF ln (rit ) + Xit γ RF + εitRF,(6)

respectively, where Xit ’s are control variables described in Subsection 4.3. If the robot producers’ technological innovation drove the cost reduction, then our cost measures and robot purchasein quantity would be correlated negatively; thus β FS is expected to be negative in (5). The reduced form (6), in contrast, expresses the relationship between the robot price and employment,
conditional on fixed effects and control variables.
We assume that the variation in rit is exogenous to demand industries. Note that the i-levelvariation comes from the initial share ιai = sait0 across industries. Our identification assumption isthus that the initial applications shares are uncorrelated with unobserved labor-market factors after
conditioning on fixed effects and control variables. In other words, the endogeneity of rat does
not give industry i-level variation (Goldsmith-Pinkham et al., 2018), which alleviates the concern
about using application prices as shifters of the instrument. We also conduct a robustness checkusing several price measures that are less endogenous to robot adopters, such as leave-one-out robot
prices and export robot prices in the following section.
Interpreting rit as an exogenous price indicator of robots, the first-stage and reduced-form
regressions also have an interpretation relevant to the conditional gross own-price elasticity andthe conditional gross cross-price elasticity. In this sense, one may view the first-stage (FS) andreduced-form (RF) regressions as giving “low-level” interpretations. Note that standard factor
demand theory does not make a particular restriction on these elasticities, because they are a mixof the substitution effect and scale effect (Cahuc et al., 2014).
Note that all the elasticity estimates in equations (4), (5) and (6) are gross concepts. Namely,
they contain both the displacement effect, keeping the scale of output constant, and the scaleeffect, due to the productivity gain made possible by robot adoption. As we show in Section
B, the displacement effect is mechanically negative, while the productivity effect is positive if
robot adoption improves the overall productivity and the output demand is not perfectly inelastic.
Although it would be informative if one could separate the gross estimates into each mechanism,
there are few methods for such a purpose to date.8 For this purpose, we would need to have an
8Dekle (2020) sets up a general equilibrium model of a closed economy and suggests the decomposition of the total
effect into the displacement effect, the scale effect, and the general equilibrium income effect.

19

exogenous determinant of production scale apart from the exogenous source of robot price variation.
For instance, a standard SSIV method by Acemoglu and Restrepo (2017) also estimates the grosseffect of robots. The gross effect, however, is still informative for policy makers that are concerned
about the total labor market effects of robot adoption. Therefore, we stick to our estimation method
and leave addressing this problem as a future work.

4.3

Control Variables

We discuss potential confounders and how we deal with them. First and most importantly, recall
that we control for industry-specific time-fixed characteristics and aggregate shocks common acrossindustries. As further possible industry- and time-varying explanations for industrial employmentchanges, we consider demographics, globalization, and technology, as follows.
First, demographic dynamics change the labor-supply conditions that may correlate with robot
adoption incentives for producers and employment at the same time. In particular, in Japan, labor
shortages due to aging and low population growth rates have been an acute problem that partly
pushes firms to adopt robots (Acemoglu and Restrepo, 2018b). This may bias the 2SLS estimates
downward. To alleviate this concern, we control for detailed demographic variables, including
education (high-school/4-year university graduate shares), sex ratio (female share), and the age
distribution (age under 35/over 50 shares). All of these variables are taken from the ESS.
Second, concurrent globalization alters both the labor demand and robot adoptions. Given thecomplexity of modern manufacturing production, it is likely that easier access to foreign markets
of both outputs and inputs may alter the incentive to adopt new technology and workers (see, forinstance, Fort et al., 2018). Depending on the substitutability (e.g., foreign cheap workers replaces
the necessity of adopting robots and labor domestically) or complementarity (e.g., high availabilityof foreign cheap energies enhance the efficiency of robots), the bias may go upward or downward.
To alleviate this concern, we control for offshoring, import competitions, and outsourcing. In
particular, we take the total import values of each sector from the JIP database. This variable
controls for the role of import competition (e.g., Autor et al., 2013) and outsourcing (e.g., Hummels
et al., 2014). From the BSOBA data, we take the total gross sales value for each industry. Thisvariable controls the changes in labor demand due to global sourcing (e.g., Antras et al., 2017) or
export platforms (e.g., Arkolakis et al., 2018).
Third and finally, technological changes other than robots, such as increases in ICT adoptions(Autor et al., 2003), may also alter the labor demand and robot adoptions simultaneously. In fact,
20

robots need to be programmed rather than human-operated; as such, robots and ICT adoptions arecomplementary. Since our interest is the direct impact of robot-based automation on employment,
we control for other technological progress. We do so by using intangible capital stock values fromthe JIP database. In fact, all explanatory variables (i.e., robot, globalization, and technology) are
positively correlated. These variables are explained in detail in Section G.3.

4.4

Main Results

With the industry-level aggregate robot service measure and cost indicators, we proceed to theanalysis of the effect of robots on employment at the industry level. Several past studies focus onregion-level analysis. We conduct a industry-level analysis for two reasons. First, our result is not
driven solely by the increased observations by constructing the local-level outcomes by allocating
the industry-level variables with fewer observations (Borusyak et al., 2018; Adao et al., 2019).
Second, we devote the section to discuss the preferred specifications in several dimensions. We
then stick to the preferred specification in the CZ-level analysis. Other specification results can befound in the Appendix.
Table 2 shows the first-stage result of regression (5). Four columns show results with alternative
sets of control variables. Column 1 controls only industry and year fixed effects, column 2 adds
demographic controls, column 3 globalization, and column 4 technology. The sample size is 104,
interacting 13 industries and eight 8-year periods (quinquennially from 1982 to 2017). We report
the first-stage IV F-statistics, indicating that those in the specifications from columns 2 to 4 exceed
10, a conventional value for checking weak instruments. In all specifications, we find consistently
negative and significant estimates. This result is not surprising, given that the price reductions
made by different application compositions drive increases in demand. Our preferred specificationis column 4, because it includes all potential confounders discussed in Section 4.3 and has thehighest first stage F-statistics among the four specifications. According to column 4, controlling for
fixed effects and other factors, a one-percent decline in robot prices drive a 1.54 percent increasein robot adoption.
Table 3 shows the reduced-form result of regression (6). Each of the four columns controls fora
different set of control variables, as in Table 2. Again, in all specifications, we find consistently
negative and significant estimates, though specification in column 3 shows statistical significance atonly the 10 percent level. To clarify, the negative coefficient implies that the fall of the effective robot
price induced employment growth, implying that robots and employment are gross complements.
21

Table 2: Industry-level, First Stage
Dependent variable:ln(Rit )ln(ritZ )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

−1.413∗∗∗(0.469)−1.852∗∗∗(0.557)−1.322∗∗∗(0.400)−1.542∗∗∗(0.377)

X
X

X
X
X

X
X
X
X

9.1
104
0.971

11.047104
0.979

10.912
104
0.984

X
X
X
X
X
16.703104
0.986

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot cost measure and log robot stock measure across industries and years. All columns
control the industry and year fixed effects. All regressions are weighted by purchase values of robots in each year.
The standard errors are shown in the parenthesis. Columns 1 shows the result without other control variables.
Column 2 includes the demography controls. Demography controls include share of high school graduates, share of
4-year university graduates, share of female workers, share of workers under age of 35, and share of workers above
age of 50 from ESS. Column 3 includes the logarithm import values from JIP database and logarithm offshoring value
added from SOBA. Column 4 includes logarithm stock value measures for ICT capital, innovation capital,
competition capital from the JIP database. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

Our preferred estimate from column 4 indicates that a one percent decrease in robot price increases
employment by 0.44 percent.
Table 4 shows the 2SLS result of regression (4). Four columns control different sets of control
variables. Given the results in Tables 2 and 3, it is not surprising that we find positive andsignificant point estimates in the four specifications. This confirms that robots and employment aregross complementary. Again, our preferred specification (column 4) indicates that a one-percent
increase in robot adoption, caused by a drop in the robot prices, increases employment by 0.28
percent.

22

Table 3: Industry-level, Reduced Form
Dependent variable:ln(Lit )ln(ritZ )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

−0.853∗∗∗(0.130)−0.465∗∗∗(0.144)−0.272∗(0.151)−0.437∗∗(0.171)

X
X

X
X
X

X
X
X
X

9.1
104
0.975

11.047104
0.984

10.912
104
0.985

X
X
X
X
X
16.703104
0.987

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot cost measure and log employment measure across industries and years. The
employment measure includes the employment of robot-producing plants. All columns control the industry and year
fixed effects. All regressions are weighted by purchase values of robots in each year. The standard errors are shown in
the parenthesis. Columns 1 shows the result without other control variables. Column 2 includes the demography
controls. Demography controls include share of high school graduates, share of 4-year university graduates, share offemale workers, share of workers under age of 35, and share of workers above age of 50 from ESS. Column 3 includes
the logarithm import values from JIP database and logarithm offshoring value added from SOBA. Column 4 includes
logarithm stock value measures for ICT capital, innovation capital, competition capital from the JIP database. ∗ p<0.1;∗∗ p<0.05; ∗∗∗ p<0.01.

Validating Identification Since the novelty of this paper is the new identification method basedon robot prices, we argue that our choice of robot price IV is appropriate. In this section, we
discuss two robustness exercises: other price measures and dropping big robot buyers. The firstrobustness check partly addresses the concern about the endogenous initial industry share of robot
applications, as we will discuss.
First, we consider other price measures that do not depend on the own industries’ robot prices.
Although in the previous section we explained that the source of identification comes from initial
application share variations for each industry, our IV does depend on the actual aggregate trends ofprices of robots. If the initial application share is endogenous, however, the endogeneity of actual
price trends may add bias to our estimates (Goldsmith-Pinkham et al., 2018). To alleviate this23

Table 4: Industry-level, 2SLS
Dependent variable:ln(Lit )ln(Rit )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

0.716∗∗∗(0.154)0.251∗∗∗(0.071)0.206∗(0.105)0.283∗∗(0.108)

X
X

X
X
X

X
X
X
X

9.1
104
0.941

11.047104
0.986

10.912
104
0.988

X
X
X
X
X
16.703104
0.988

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot stock measure and log employment across industries and years, with the instrument oflog robot cost measure. The employment measure includes the employment of robot-producing plants. All columns
control the industry and year fixed effects. All regressions are weighted by purchase values of robots in each year.
The standard errors are shown in the parenthesis. Columns 1 shows the result without other control variables.
Column 2 includes the demography controls. Demography controls include share of high school graduates, share of
4-year university graduates, share of female workers, share of workers under age of 35, and share of workers above
age of 50 from ESS. Column 3 includes the logarithm import values from JIP database and logarithm offshoring value
added from SOBA. Column 4 includes logarithm stock value measures for ICT capital, innovation capital,
competition capital from the JIP database. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

concern, we consider the following two alternative price measures. The first measure leaves out theown industry from the calculation of robot prices. Namely, we construct the following robot price:ÍA
i 0 ,i vai 0 t
LOOrait = Í.
i 0 ,i Rai 0 t(7)Then we construct the industrial price index based on equation (4.1). This measure is supposed totake the variation that is external to each industry.
Since we have 12 manufacturing industries, however, the leave-one-out price measure may
contain feedback bias (dropping one industry in turn affects the price of own industry). To address

24

this further concern, we consider the second measure using export price as follows:
EXP
rait
=

A
va,exp,tRa,exp,t.

(8)Then we aggregate to the industrial price index by equation (4.1). This measure is supposed totake the variation that is further external to each domestic industry, while the concern is that therelevance of IV may be attenuated, because it takes different prices than the actual price eachdomestic industry faces. We report the results based on these prices in Table 5. Column 1 shows
the baseline estimates (thus the same as Table 4, column 4). Columns 2 and 3 are based on leaveone-out prices (7) and export prices (8), respectively. We find that the estimates are robust to theseprice measures. Therefore, the endogeneity of the actual price measure poses minimal threat to ouridentification strategy. Furthermore, the robust findings also support our identification idea, theexogenous initial application shares. This is because of the discussion in Goldsmith-Pinkham et al.
(2018)–if initial application shares are exogenous, the choice of price measures merely changes theweighting of the Generalized Method of Moments (GMM), affecting the precision of the estimates
but not the consistency. This prediction is consistent with our robust findings in Table 5.
Second, we drop large robot buyer industries. The endogeneity of robot prices may be most
severe to big robot buyers, such as the transportation industry and electrics, because they may
have market power and affect robot prices (recall Figure C.3). If their output demand surges, it
may enhance both robot and labor demands. The increase in robot demand decreases prices due
to market power. In this case, there is a spurious negative correlation between robot price andlabor. While this concern is already addressed by using an arguably exogenous price series in
the above analysis, to address this concern in a more direct way, we drop the two large industries,
transportation and electrics. The resulting sample is made from smaller industries, whose robot
prices are thus less endogenous to robot demand-side shocks. Table 6 shows the 2SLS estimatesof these regressions. In column 1, we show the baseline estimate. Columns 2-4 show the droppingof each of the large buyers: Column 2 drops electrics, column 3 drops transportation, and column
4 drops both. We find that the positive results are consistent across columns. The finding suggeststhat the concern about endogeneity due to market powers of demand industries is minimal.

25

Table 5: Industry-level, 2SLS, Different Price Measurement
Dependent variable:ln(Lit )ln(Rit )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Price Measurement
Observations
R2

(1)

(2)

(3)

0.283∗∗(0.108)0.303∗∗∗(0.101)0.318∗∗(0.139)X
X
X
X
X
Industry Aggregate
104
0.988

X
X
X
X
X
Leave-one-out
104
0.987

X
X
X
X
X
Export104
0.987

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot stock measure and log employment across industries and years, with the instrument oflog robot cost measure. The employment measure includes the employment of robot-producing plants. All columns
control the industry and year fixed effects, demography controls, the logarithm import values from JIP database andlogarithm offshoring value added from SOBA, logarithm stock value measures for ICT capital, innovation capital, andcompetition capital from the JIP database. Demography controls include share of high school graduates, share of
4-year university graduates, share of female workers, share of workers under age of 35, and share of workers above
age of 50 from ESS. All regressions are weighted by purchase values of robots in each year. The standard errors areshown in the parenthesis. Column 1 shows the result with the instrumenal variable of industry-aggregate robot prices(benchmark). Column 2 shows the result with the instrumenal variable of leave-one-out robot prices. Column 3shows the result with both the instrumenal variable of export robot prices. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

4.5

Heterogeneity

How does our aggregate finding mask heterogeneous impacts to different groups of workers? To
see this, we consider our main empirical specification (4) with several subgroups of workers as
outcome variables. Table 7 shows the results. In column 1, we show our baseline estimate (the sameas Table 4, column 4). We then consider the impacts of robots on the employment of high-school
graduates (column 2), four-year university graduates (column 3), female workers (column 4), young
workers (less than or equal to 35 years old, column 5), and elder workers (more than or equal to 50
years old, column 6). Perhaps surprisingly, our finding of the positive labor-market implications ofrobots is robust to all of these subgroups. Moreover, the sizes of the estimated coefficients do not
26

Table 6: Industry-level, 2SLS, Dropping Major Industries
Dependent variable:ln(Lit )
(1)

(2)

(3)

(4)

ln(Rit )0.283∗∗(0.108)0.283∗∗(0.118)0.579∗∗(0.270)0.351∗∗(0.146)

Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Industries
Observations
R2

X
X
X
X
X
All
104
0.988

X
X
X
X
X
Net Elec.
96
0.989

X
X
X
X
X
Net Transp.
96
0.979

X
X
X
X
X
Net E&T
88
0.988

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot stock measure and log employment across industries and years, with the instrument oflog robot cost measure. The employment measure includes the employment of robot-producing plants. All columns
control the industry and year fixed effects, demography controls, the logarithm import values from JIP database andlogarithm offshoring value added from SOBA, logarithm stock value measures for ICT capital, innovation capital, andcompetition capital from the JIP database. Demography controls include share of high school graduates, share of
4-year university graduates, share of female workers, share of workers under age of 35, and share of workers above ageof 50 from ESS. All regressions are weighted by purchase values of robots in each year. The standard errors are shownin the parenthesis. Column 1 shows the result with the full sample (benchmark). Column 2 and 3 show the resultswith the electronic machine industry and transportation machine industry dropped, respectively. Column 4 shows theresult with both the electronic and transporation machine inustry dropped together. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

differ substantially across demographic groups.

27

Table 7: Industry-level, 2SLS, Effects on Subgroups
Dependent variable:28ln(Lit )ln(LitHS )ln(LitCG )ln(LitFem )ln(LitU35 )ln(LitO50 )

(1)

(2)

(3)

(4)

(5)

(6)

ln(Rit )0.283∗∗(0.108)0.279∗∗(0.107)0.303∗∗(0.117)0.278∗∗(0.115)0.328∗∗∗(0.114)

0.398∗∗∗(0.125)

Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Group of Worker
Observations
R2

X
X
X
X
X
All
104
0.988

X
X
X
X
X
High School
104
0.987

X
X
X
X
X
4-year Univ.
104
0.989

X
X
X
X
X
Female
104
0.994

X
X
X
X
X
Age ≤ 35
104
0.988

X
X
X
X
X
Age ≥ 50104
0.986

Notes: Authors’ calculation based on JARA, ESS, CoM, SOBA and JIP data. The table presents estimates of the relationship between log robot stock
measure and log employment across industries and years, with the instrument of log robot cost measure. The employment measure includes theemployment of robot-producing plants. All columns control the industry and year fixed effects, demography controls, the logarithm import values from
JIP database and logarithm offshoring value added from SOBA, logarithm stock value measures for ICT capital, innovation capital, and competition
capital from the JIP database. Demography controls include share of high school graduates, share of 4-year university graduates, share of female workers,
share of workers under age of 35, and share of workers above age of 50 from ESS. All regressions are weighted by purchase values of robots in each year.
The standard errors are shown in the parenthesis. Column 1 shows the result with the outcome variable of all workers (benchmark). Column 2 and 3 show
the results with the outcome variables of high-school graduates and 4-year university graduates (and more), respectively. Column 4 shows the result withthe outcome variable of female workers. Columns 5 and 6 show the results with the outcome variable of workers with age equal or lower than 35, and withage higher than 50, respectively. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

4.6

Further Robustness Checks

We briefly discuss additional robustness checks. In particular, we consider (i) netting out of robotproducing workers, (ii) industry-specific robot prices, and (iii) alternative stock measures. All ofthese results suggest the validity of our main definitions above.
First, we net out robot-producing workers from employment. Although our primary interest is
the robots’ gross impact on employment, it may mask a heterogeneity of impacts. As Acemoglu
and Restrepo (2018c) clarifies, robot innovation is likely to increase the set of tasks that are input inproduction. One such example is clearly the robot-producing workers themselves. By measuring
the number of such workers, we may gain more insights from our positive estimates found in
Section 4.4. Section G.1 discusses in detail how to generate robot-producing worker employment
and regression results when robot producers are netted out of the outcome variable.
Second, we consider industry-specific robot prices rait ; in the main specification (3), the robot
prices are aggregated and not industry-specific. We also consider industry-specific rait , calculated
A /R . The use of this variable can be viewed as a robustness check that complements theby vaitait

main specification; the industry-specific prices are likely to reflect the actual prices to each industry.
We do not, however, consider it as a main specification. Recall the above discussion regarding theendogeneity concern. The industry-specific-price specification is more vulnerable to this concern,
because it varies across industries by definition. In fact, as shown in Section H.1, it is less robust to
the inclusion of control variables, suggesting a negative bias due to negative correlations between
industry-specific prices and the unobserved component of employment.
Third and finally, we check several measures of robot stocks. Recall that our data come withpurchase of robots, which is a flow quantity and monetary values. Robots are durable investment
goods, however, and thus the flow values may understate the actual robot stock active in production.
To construct the stock variable, our main specifications follow the literature and IFR suggestion
and aggregate the 12-year flows of robots, which we call the immediate withdrawal method (IWM).
As several authors argue, it is also natural to follow the standard perpetual inventory method (PIM)
that is also used in capital formation in National Accounts (Graetz and Michaels, 2018; Artuc et al.,2018). We thus create the robot stock variables based on different-year IWM and PIM with somedepreciation rates. When we use alternative measures, we find that the variation in the definitionof stock variables has minimal impacts on the regression results. We discuss further details andempirical results in Section E.
Note that the invariance of results with respect to alternative stock measures provides further
29

support for our empirical strategy. The invariance suggests that industry-level robot adoption
variation is more significant than time-level variations. Recall that, in our empirical specification,
the main identifying variation does not come from the one in application-year prices rat , but insteadfrom that of the initial application shares ιait0 . Since we take a stand that the latter variable is moreexogenous than the former, the invariance to stock measures indicates our method has a minimal
concern regarding endogeneity. Section E discusses the detailed construction of variables andregression results.

5

Region-level Analysis

Our industry-level analysis reveals that at the national level, robots and employment are grosscomplements in our context. In this section, we consider converting the data and regression analysis
to the region level. We do so for three purposes: (i) to discuss robots’ impacts on subnational andlocal economies, (ii) to make better comparisons to the literature, and (iii) to delve into spillover
effects.
In particular, as the definition of local labor markets, we employ Commuting Zones (CZs)developed by Adachi et al. (2020), who defined CZs following the method employed in, among
others, Tolbert and Sizer (1996).9 CZs made via this method have several strengths. First, they represent the local labor-market delineation better and contain more observations than administrative
divisions, such as prefectures. Second and more importantly, they partition the overall national area
in a mutually exclusive and exhaustive manner. This point is in contrast to city-level delineations,such as MSAs.10 This point is especially relevant in our study, because the impacts of robots may
not be constrained in urban areas, but also penetrate into rural ones. Rural areas may by overlooked
by non-exhaustive regional delineations, such as MSAs.
Based on the industrial robot adoption and price measures and region definitions, we employ
the shift-share method to construct a CZ-level robot exposure measure (Acemoglu and Restrepo,2017):∆Rct =

Õ
i

lcit

∆Rit
,
Lit

where c is commuting zones. Following Acemoglu and Restrepo (2017), we call this variable robot
9In particular, the hierarchical agglomerative clustering method with average linkages and dissimilarity measures
based on bilateral flows of commuters and populations.
10A famous regional classification based on city-level density inhabited districts (DIDs) in Japan is proposed andpopularized by Kanemoto and Tokuoka (2002).

30

exposures. To construct CZ-level IVs, we use t0 ≡ 1979 as the base year and similarly generate theshift-share measures but based on price changes:∆ ln (rct ) =Õ

lcit0 ∆ ln (rit ) .

iWith these region-level variables, we then study the following specification:
CZ∆Yct = αcCZ + αtCZ + βCZ ∆Rct + Xct γ CZ + εct,(9)where ∆Rct is instrumented by ∆ ln (rct ). Note that in the region-level specification, we measurechanges in robot adoption and prices. We do so for the following two purposes. First, the fixed effectin specification (9) controls the differential growths trends in each location. This is more flexible
than just controlling for level differences, and it is preferable when studying regional differences,
given that the various regions experience different trends in labor-market characteristics (e.g.,
Diamond, 2016). Second, it makes the specification more comparable to the ones in the literature,such as Acemoglu and Restrepo (2017). Note that our specification also allows differential trends
across CZs, because we access many (in particular, seven time-first differences) time periods.
Table 8 shows the first-stage results. Each column shows the results with a different set of control
variables: Column 1 controls only fixed effects, column 2 adds demographic controls, column 3
adds globalization, and column 4 adds technology. The results show a statistically significant andnegative correlation between robot price changes and robot exposures, both with and without thecovariates. The F test indicates that the use of our price measure passes the test of weak instruments.
From now on, we focus on our preferred specification with full controls, the one in column 4.
Table 9 shows the 2SLS results of specification (9).11 All columns report the preferred specification with the full set of our control variables. The standard deviation of robot exposure measures
is 5.25 at the raw level and 2.17 when residualized. For columns 1-6, the outcome variables arelog manufacturing employment (column 1), log total employment (column 2, baseline estimate),
log total population (column 3), (non-log) employment-to-population ratio (column 4, the main
outcome variable of Acemoglu and Restrepo, 2017), log non-manufacturing employment (column5), and log non-working population (column 6), respectively. First, as our primary interest, column
2 tells us that overall employment increased in CZs that were exposed to robots, confirming thefindings in Section 4, even in the first difference-based specification (9) with two-way fixed effects.
11Since the reduced-form results are redundant, they are not reported but are available upon request.

31

Table 8: CZ-level, First Stage
Dependent variable:
∆Rct
∆rctZ

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-statistics
Observations
R2

(1)

(2)

(3)

(4)

−30.074∗∗∗(1.864)−30.928∗∗∗(1.846)−27.330∗∗∗(1.763)−21.179∗∗∗(1.424)

X
X

X
X
X

X
X
X
X

260.305
1,466
0.938

280.586
1,466
0.941

240.268
1,466
0.949

X
X
X
X
X
221.249
1,466
0.971

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between shift-share logarithm robot prices and shift-share measures of changes in robot stock per
thousand workers. All regressions are weighted by initial-year population. The standard errors are shown in theparenthesis. Column 1 controls the industry and year fixed effects. Column 2 controls the demographic variables:
share of high school graduates, share of 4-year university graduates, share of female workers, share of workers underage of 35, and share of workers above age of 50 from ESS. Column 3 controls the logarithm import values from JIP
database and logarithm offshoring value added from SOBA as well as control variables in Column 2. Column 4(baseline specification) controls logarithm stock value measures for ICT capital, innovation capital, and competition
capital from the JIP database in addition to the controls in column 3. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

Perhaps surprisingly, column 1 shows that the impacts to manufacturing employment are evenstronger and positive. This may suggest that the scale effects of robot adoption rather than thesubstitution effects that drive the labor-market impacts.

32

Table 9: CZ-level, 2SLS
Dependent variable:

∆Rct

33

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Observations
R2∆ ln(LctM AN. )∆ ln(Lct )∆ ln(Pct )∆ PLctct∆ ln(LctSE R. )∆ ln(PctDE P )

(1)

(2)

(3)

(4)

(5)

(6)

4.658∗∗

2.203∗∗

2.003∗∗(2.062)(1.017)(0.929)0.133(0.230)1.101(1.036)1.617(1.103)

X
X
X
X
X
1,413
0.619

X
X
X
X
X
1,466
0.817

X
X
X
X
X
1,466
0.811

X
X
X
X
X
1,466
0.732

X
X
X
X
X
1,466
0.831

X
X
X
X
X
1,466
0.768

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of the relationship between shift-share measures ofchanges in robot stock per thousand workers and log difference of outcome variables multiplied by 100. All regressions control demographic variables,
globalization controls, and technology controls as well as the industry and year fixed effects. All regressions are weighted by initial-year population. The
standard errors are shown in the parenthesis. The demographic variables include share of high school graduates, share of 4-year university graduates,
share of female workers, share of workers under age of 35, and share of workers above age of 50 from ESS. The globalization controls contain thelogarithm import values from JIP database and logarithm offshoring value added from SOBA. The technology controls include logarithm stock value
measures for ICT capital, innovation capital, and competition capital from the JIP database. The outcome variables are manufacturing employment, total
SE R
employment (baseline), total population, (non-log) employment-to-population ratio, non-manufacturing employment (Lct), and non-working
DE
P
∗
∗∗
∗∗∗
population (Lct ) in columns 1-6, respectively. p<0.1; p<0.05; p<0.01.

Turning to columns 3 and 4, we discuss the relationship with the estimates obtained in theliterature and the mechanism. In column 4, we do not find a statistically significant effect on
the employment-population ratio. This can be understood from column 3 (and column 2): The
population positively responded to robot exposure. Thus, both employment (column 2) and population (column3) receive positive effects, cancelling the impacts to the ratio by increasing both thenumerator and denominator. This finding, however, is in contrast to part of the literature. In fact,using their similar-country shift-share IV strategy, Acemoglu and Restrepo (2017) found -0.388
with the standard error of 0.091 for column (4).12
Note that our findings in columns 2-4 may be regarded in consistent with the findings in “local
multiplier” literature (Moretti, 2010): An increase in demand for a group of people positively
spillovers to other group of people in a locality. Indeed, Moretti (2010) discusses the positive
spillover of local manufacturing labor demand on service labor demand, such as local restaurants,
and bars worker demands for manufacturing workers there. In contrast, our results indicate that theincrease in labor demand in general even has an implication for increasing the local population in
general, such as housekeepers for general employees.
To further compare our results with the literature and highlight the relevance of our price-based
instruments, we also conduct an analysis based on a similar country. We choose Germany as thecountry of comparison and take the robot adoption trends from the IFR. As Table I.1 shows, theregression results are imprecise. This is not surprising, given the discussion in Section 2: Even
compared with a close innovator of robots, Germany, the trend of robot adoptions in Japan has beenvery unique. Therefore, the conventional identification method in the literature is not applicable inour context. In contrast, our cost-based identification method is robust to contexts.
Finally, columns 5 and 6 show suggestive evidence on spillover effects. Column 5 takes
non-manufacturing employment (labeled as “L SE R ” as abbreviation for service), defined as total
employment (cf. column 2) net of manufacturing employment (cf. column 1) as the outcome
variable. Column 6 takes the non-working population (labeled as “P DE P ” as abbreviation for
dependent), defined as the total population (cf. column 4) net of employed population (cf. column2), as the outcome variable. These results show insignificant effects on non-manufacturing employment or non-employed population and suggest the following. First, we find little evidence of
spillover effects to demand for these groups of people. Second, given that there are negative effects
to demand for these groups, there is no evidence about reallocations within CZ across sectors. If
12See their Table 7, panel A, column 3. They also report the effect on changes in log employment in the Appendix.
The point coefficient is -1.656 with a standard error of 0.411.

34this was the case, the reallocation happened across CZs.13
So far, our main outcome variables have been regarding headcounts of people, such as employment and population. Given that robot technology is characterized by the time-saving nature ofroutine tasks (e.g., spot welding), it is possible that the impacts on hours worked may be different
from the headcount impacts. Furthermore, such hour effects may have implications for hourly
wages, which reflects the hourly productivity of workers. We explore these dimensions in Table 10.
In column 1, we show the baseline result (cf. Table 9, column 2), which takes log employee headcounts as the outcome variable. For columns 2 and 3, we take per-capita hours worked and hourly
wages as the outcome variables, respectively. Column 2 reveals that the average hours worked
decreased dramatically due to the adoption of robots. In contrast, and partly as a result of hourreducing effects, column 3 shows that hourly wages increased due to robot adoption. These findings
suggest that robots enable work-sharing or time-saving technological changes, which enhance thehour-unit productivity of employed workers.
Finally, we conduct the CZ-level analysis by subgroup of workers. We overview the takeaways,
as follows. Robot-exposed regions increased the employment of both educated (more than four-year
university) and non-educated (high-school graduates) workers, increased female workers more thanmale workers, and increased middle-aged workers (workers aged between 35 and 49) more thanthe other age groups of workers. Regarding wages and hours, robot-exposed regions increased
wages and decreased hours worked of workers with all educational backgrounds. The impacts ofrobot adoption were homogeneous as found by Acemoglu and Restrepo (2017) but in the opposite
direction; in Japan in 1978-2017, robot adoption improved working conditions of workers acrossthe board while it was opposite in the US in 1992-2007. Detailed regression results are reported in
Section I.

6

Discussion

In this section, we discuss the efficiency adjustment. This is motivated to address another interpretation of our primary findings in Sections 4 and 5. Note that our sample covers a very long
period, 1978-2017. Therefore, it is likely that the unit efficiency of robots grew over that period, as
we discussed in Section 2. This may cause time series-variation based bias, as follows. Suppose
the unit efficiency in later robots is higher than in earlier ones. This may have further set of tasks
13Since the first and second implications contrast their predicted sign of the effect (positive spillover effects andnegative reallocation effects), we acknowledge the possibility that both effects worked but canceled each other out.

35

Table 10: CZ-level, 2SLS
Dependent variable:∆ ln(Lct )∆ ln(hct )∆ ln(wct )

(1)

(2)

(3)

∆Rct

2.213∗∗(1.000)−1.963∗∗∗(0.585)

4.117∗∗∗(0.913)

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Variable
Observations
R2

X
X
X
X
X
# Workers
1,453
0.818

X
X
X
X
X
Average Hours
1,453
0.827

X
X
X
X
X
Average Hourly Wage
1,453
0.948

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of the relationshipbetween shift-share measures of changes in robot stock per thousand workers and log difference of outcome variables
multiplied by 100. All regressions control demographic variables, globalization controls, and technology controls aswell as the industry and year fixed effects. All regressions are weighted by initial-year population. The standard errors
are shown in the parenthesis. The demographic variables include share of high school graduates, share of 4-year
university graduates, share of female workers, share of workers under age of 35, and share of workers above age of 50
from ESS. The globalization controls contain the logarithm import values from JIP database and logarithm offshoringvalue added from SOBA. The technology controls include logarithm stock value measures for ICT capital, innovation
capital, and competition capital from the JIP database. The outcome variables are total employment (baseline),average weekly hours, and average hourly wages in columns 1-3, respectively. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

displaced by robots from labor due to relative cost efficiency (Acemoglu and Restrepo, 2018c). In
the later years, observed robot adoption units are smaller (even when efficiency-unit robot unitsgrew), while at the same time, labor is displaced and employed less. This generates a spurious
positive correlation between robots and employment that is not caused by gross complements, butby (gross) substitution and unit-efficiency growth.
To alleviate this concern, we estimate robot unit efficiency. Following and extending Khandelwal
et al. (2013), we consider the following CES aggregation function for robots:
Rit =

Õ

σ−1

ιeai (λait Rait ) σ

a

36

σ
! σ−1

,

where Rait is the robot stock of application a in industry i in year t, ιeai is the expenditure shareparameter reflecting the applicability of application a in each industry i, λait is unobserved quality,and σ>0 is the elasticity of demand substitution. The cost-minimizing demand for robot applicationa is then solved as
σ−1 −σ σ−1
Rait = (ιeai )σ λait
rat rit Rit,

where rat is the robot price of application a in year t, and rit =Îa (rat )ιfai. Taking logs, we obtainln Rait = −σ ln rat + αai + αit + ηait,
where αai = σ ln (ιeai ), αit = ln Citσ−1 Rit and ηait = (σ − 1) ln (λait ). Regressing ln Rait on ln rat
and fixed effects αai and αit gives b
σ and the residual ηbait . Thus, we obtain the efficiency estimateλcηait /(bσ − 1)).
ait = exp (bSeveral discussions follow in terms of our model choice. First, we model quality as theapplication-augmenting shocks. We view this is as a natural interpretation in the case of robotics,
because robot quality may be conceptualized by the speed of task performance relative to that ofold types of robots or human hands. Second, our extended specification (6) nests the Cobb-Douglas
Î ιfaicase. Namely, with σ → 1 and λait = 1, we revert to our initial specification Rit = a rait
, withιeai = ιai . Third, relative to Khandelwal et al. (2013), we have an additional expenditure share term
ιai , because we have a clear pattern of applicability for each industry. Finally, compared to the
IFR’s quality adjustment, our treatment of the efficiency estimation is more systematic and basedon a standard demand theory. Recall that Graetz and Michaels (2018) also reports quality-adjusted
prices. In their data source, quality adjustment is not backed up based on a demand function.
Instead, the method is called a “production-cost mark-up” method, which is “subjective but with acertain amount of knowledge through experience” (IFR, 2006).
With the unit efficiency estimate, we augment the application-level robot quantity measurementfrom Rait to λcat Rait and construct the industrial robot stocks. Using such a measure of efficiencyaugmented robot stocks, we rerun the industry-level regression (4). Table 11 reports the 2SLS result.
The column structures remain the same as in Table 4. The first-stage IV F-statistic is somewhat
weaker, especially in the specification without control variables.14 The estimated coefficients are
14The first-stage regression results are reported in Section H.2. The reason for the weaker first stage in column 1of Table 11 than column 1 of Table 4 is mechanical. The quality adjustment factor λait is estimated by the residualof the regression of lnRait on ln rat and fixed effects, αai and αit . By this construction, the adjustment factor ln
λait
is orthogonal to the application price ln rat . The price aggregate, which is the weighted average of ln rat , is thus
orthogonal to the quality adjustment factor. Therefore, using the log of the quality-adjusted robot as the dependent

37

Table 11: Industry-level, 2SLS, Efficiency-adjusted Quantity
Dependent variable:ln(Lit )
eit )
ln( R

Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

2.587
(2.739)0.297∗∗∗(0.099)0.219∗(0.121)0.378∗∗(0.183)

X
X

X
X
X

X
X
X
X

0.855
104
−0.44014.131
104
0.981

8.195
104
0.986

X
X
X
X
X
5.860
104
0.980

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log efficiency-adjusted robot stock measure and log employment across industries and years,
with the instrument of log efficiency-adjusted robot cost measure. The employment measure includes the employmentof robot-producing plants. Efficiency adjustment is performed by the method described in the main text. All columns
control the industry and year fixed effects. All regressions are weighted by purchase values of robots in each year.
The standard errors are shown in the parenthesis. Columns 1 shows the result without other control variables.
Column 2 includes the demography controls. Demography controls include share of high school graduates, share of
4-year university graduates, share of female workers, share of workers under age of 35, and share of workers above
age of 50 from ESS. Column 3 includes the logarithm import values from JIP database and logarithm offshoring value
added from SOBA. Column 4 includes logarithm stock value measures for ICT capital, innovation capital,
competition capital from the JIP database. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

similar with and without quality adjustments of robots after conditioning on the control variables.
In our preferred specification of column 4, we still find a significant positive effect of robot adoption
on employment. Therefore, we conclude that our results are robust to the efficiency adjustment.
variable in the first stage is adding a factor orthogonal to the instrument variable to the dependent variable and reduces
the first-stage F-Statistics. Conditioning on the control variables mitigates this mechanical relationship.

38

7

Conclusion

How does automation affect labor demand? We study this question by focusing on a context with along tradition in robot adoption, Japan 1978-2017. We develop a cost-based identification method
for studying the impact of robots on employment, given the difficulty of applying a conventional
identification method, and a newly digitized data of robot adoption. The data are characterized bythree novelties: (i) a long panel covering 1978-2017 that suits the study of our context, (ii) adoption
units and total values that allow systematic calculation of robot unit costs, and (iii) disaggregationof these by robot applications. Armed with these unique features, our identification hinges on
industry-level application share variations during the initial period of robot adoption. We conduct a
industry-level analysis and a region-level analysis. The industry-level analysis suggests that robots
and labor are gross complements. Our preferred estimates mean that a one-percent decrease inrobot price increased labor demand by 0.44 percent, which implies that robots and labor are grosscomplements. This thus implies that a one percent increase in robot adoption made by the robot
price reduction increased employment by 0.28 percent. We show that these results are robust toother definitions of price measures and dropping major robot purchasers, indicating the validity of
our cost-based IV. The CZ-level shift-share regressions confirm this, which shows a clear distinction
from the findings in the literature and suggest across-region reallocations and time-saving technicalchanges.
We suggest a potential reason why we find a different result than the one in the literature: thedifferences in scale effects. To see this, note that our context might have been unique in strong
robot demands. This may be seen, for example, in a biography of a robot innovator, Kawasaki
Heavy Industry, as the incentive for producing robots: “Japan’s rapid economic growth increased
demands for automobiles and factories faced serious labor shortages” (KHI, 2018). If the demand
for the robot-intensive industry is strong due to either domestic or international demands, then robot
adoption may invoke the scale effect to the industry, mitigating the displacement effect found in,among others, Acemoglu and Restrepo (2017). We leave testing this hypothesis or other potential
explanations as a future work.

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Goldsmith-Pinkham, Paul, Isaac Sorkin, and Henry Swift, “Bartik instruments: What, when,
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43

Appendix
A

Detail in Robotics

A.1

Other Application Examples

Out of the application list Table 1, we list some of example tasks for each category. Handling
operations/Machine tending (or Handling) includes material handling, picking, and packaging.
Recent developments enable food industries to package foods automatically by robots. Welding
and soldering (Welding) includes several welding technologies, such as spot welding discussed in
the main text, and arc welding. Dispensing includes painting and plating. Processing includes
loading and unloading, polishing, and deburring. Assembling and disassembling (Assembling)includes surface mounting, as discussed in Section ??, and bonding. Finally, Others include robots
used for education and research and to clean rooms.

B

Net Elasticity of Factor Prices

The compensated (net) elasticity concepts cannot explain the positive elasticity findings. In fact, thenet elasticity of robot prices must be negative out of the standard profit-maximizing factor demand.
To simplify the notations, we drop all subscripts in this section. The Allen-Uzawa Elasticity of
Substitution (AUES) is
A
σCW
≡

DCW D
.
DC DW

We estimated the cross-price elasticity
C∂ ln L C ∂L
=
=
DCW ,
∂ ln CL ∂C DW
where the last equality holds by Shephard’s lemma. We haveC=

CR D CR D
=
= θR.
R
R D
R

44

(10)

Thus we have, with Shephard’s lemma R = DC ,∂ ln L1
D=
DCW θ R = σCW θ R .
∂ ln C DWR(11)

In contrast, we have the own-price elasticity
C∂ ln R=
DCC = σCC θ R∂ ln C DC
where the last equation by (10). By Euler’s formula we haveσCC =Thus we have

Equations (11) and (12) imply−σCW θ L.
θR∂ ln R= −σCW θ L .
∂ ln C(12)∂ ln Lθ R ∂ ln R=−.
∂ ln Cθ L ∂ ln C

Thus, the sign must be flipped.

C

Detail in the JARA

C.1

Coverage of the JARA

JARA data cover most robot producers in Japan. In 1996, 587 establishments were asked toanswer the survey, among which 445 answered, for a response rate of 76 percent. To show thecoverage trend of the JARA data in Japan’s robot production, we compare the aggregate trendwith government-based statistics. In particular, we employ Japan’s Census of Manufacture (CoM)
and Economic Census for Business Activity (ECBA), the latter of which was conducted jointly by
METI and MIC. From these data sources, we take the aggregated total sales of industrial robots
each year. The construction of aggregate statistics by CoM and ECBA is discussed in detail in
Appendix G.1. Figure C.1 shows the comparison of total shipment values taken from the JARA
and CoM/ECBA data. As one can see, overall, the two trends are parallel. In some years, the JARA
data even surpass the total shipment values observed in the CoM/ECBA data. Therefore, the JARA
covers most of the robot transactions measured in government statistics.
45

Figure C.1: Comparison of JARA and Census of Manufacture

Note: Authors’s calculation based on JARA and CoM/ECBA data. The trend for the JARA is the sum of shipment
value of any robot applications to any industries. The trend for the CoM is the sum of shipment value (net of VAT) of
all products categorized as industrial robots (available from 1983 to 2016). The CoM was not conducted in 2011 and2015. Instead, in these years, we employ data from the ECBA.

C.2

JARA Cross Tables

The cross tables we were able to access are as follows. The cross tables by application by buyer
industry are available between 1978 and 2017 and are the data source for our primary analysis. The
cross tables by types and buyer industry are also available, but only for the years between 1974 and2000. From 2001 to 2017, cross tables of robot structures and buyer industries are available. Thisis consistent with the statement of the IFR that since 2004, the robot classifications should only bedone in structures.15 For 1982-1991, we can also access the cross tables by application and types.
In our study, we leverage the heterogeneity in these robot classifications. Robots may be
categorized by several dimensions, such as applications, types, and structures. Applications are
the classification of robots due to the tasks (applications) that robots are expected to perform by
15See
https://www.ifr.org/downloads/press2018/SourcesandMethodsWRIndustrialRobots2018.
pdf. (Accessed on October 23, 2019)46

each user. Examples include Welding and soldering (WS) that are intensively needed tasks in theautomobile industry, and Assembling and disassembling (AD) that are intensive in the electricmachine industry. Types refer to the physical structure and features of robots. For instance, aplayback robot is a type of robot that remembers pre-specified moves and plays them back over andover again. Numerical control robots receive the input by programs and move without memory
based on the moves performed beforehand. Playback robots are relatively intensively used in
automobile industry, while numerical control robots tend to be in electric machine industry.
Starting in 2004, the IFR and major robot producers agreed that robots should not be classified
according to the above types but instead, by structures that represent the physical feature of robots.
In the JARA data, the type classification discontinues in 2000 and the structure classification begins
in the following year. The classifications are as follows: articulated robot, SCARA robot, polar
coordinate robot, cylindrical robot, cartesian robot, and parallel link robot.

C.3

Further Raw Trends of the JARA

Figure C.2 shows the trend of expenditure shares across industries in Japan taken from the JARA
data. The industries are sorted according to their value in 2017. As one can see, the distribution
is historically highly skewed to particular industries within manufacturing. In particular, in 2017,
the top 10 industries (industries above Chemical, pharmaceuticals, cosmetics, etc. in Figure C.2)
constitute more than 97 percent of the domestic expenditure. Furthermore, the electric machine
and automobile industries are noticeable significant purchasers. These two industries represent
68.2 percent of the absorption in Japan in 2017.

47

Figure C.2: Industry Shares

48
Note: Authors’ calculation based on JARA data. Industries are sorted by the total quantity in 2017.

We then show the raw trends of shipment quantity and unit values for each industry, aggregatedby all applications for the industry. To calculate unit values, we take the sales value and thecorresponding quantity units and divide the former measure by the latter. In addition to thedomestic adsorptions, we also show the trend of exports from now on for comparison. Figure C.3
shows the result. The left panel shows the quantity unit of robots, while the right shows the unit
values. First, note that our data spans 1978-2017. The coverage of our data allows us to study theearly period in which the quantity shipment grew rapidly in Japan. Our data show that the growth
did not concentrate in a particular industry, but instead occurred across a large set of industries.
As the other data source confirms, since 1990, shipments to domestic industries shrank in general,
while the export trends continued to grow at a somewhat slower pace than before. These constant
growth shares across domestic industries suggest that the robot penetration to domestic industries iscaused not only by demand shocks to a particular set of buyer industries, but by the overall growthof the robot-producing industry.
Second, in the right panel, we see that the unit-value trends for each industry are relatively
constant or slightly decreasing over time, with some exceptions. The constant or decreasing unit
prices would suggest that the robot producers become efficient and offer lower prices as time goeson. Note that these patterns hold for the two large buyers of industrial robots, electric machines,
and automobiles.

49

Figure C.3: Raw Trends by Industry(a) Quantity Units(b) Unit Values

50
Note: Authors’ calculation based on JARA data. Industries are sorted by the total quantity in 2017. Exports are not classified by industries in the survey.

With the JARA data, we are able to show the trends by industry by applications according toTable 1 in Figure C.4. Panel (a) shows the quantity and Panel (b) shows the unit value. From Panel(a), one can see that the shipment quantity increases for broad applications and broad industry during
1980s, while the increasing trend stops after the 1990s, confirming the trend we found in Figure C.3.
In contrast, there is significant within-application and across-industry variation. For example, theelectric machine industry intensively purchased robots for Assembling and disassembling, while
the Automobile industry bought a significant amount of robots for Welding and soldering. Thissuggests that industries have different tasks for which they demand robots.
Panel (b) of Figure C.4 reveals the following two features. First, it shows relatively small
within-application and across-industry variations. This suggests that the robot prices are not driven
solely by particular industries, but by the technology for producing robots. Second, there are
significant variations in trends across different applications. For example, robots for Welding andsoldering show a stable decline in the unit value for a broad set of industries. This suggests that the
production technology for Welding-soldering robots grew consistently over the sample period.

51

Figure C.4: Raw Trends by Industry and Applications(a) Quantity Units(b) Unit Values

52
Note: Authors’ calculation based on JARA data. Industries are sorted by the total quantity in 2017. Exports are not classified by industries in the survey.
The y-axis is log scale and lines reaching the x-axis imply zero quantity in level.

To further highlight the application compositions, we show the expenditure share of applications
for each industry in Figure C.5. One may see significant variations across industries. As we havediscussed, the Electric machine industry and the Automobile industry intensively buy robots for
Assembling and disassembling, and Welding and soldering, respectively. Furthermore, Figure C.5
reveals that the within-industry patterns of expenditure shares are stable with some exceptions inminor industries. This confirms that the expenditure share is driven by the feature of industries.

53

Figure C.5: Raw Trends by Industry and Applications

54
Note: Authors’ calculation based on JARA data. Industries are sorted by the total quantity in 2017. Exports are not classified by industries in the survey.

Figure C.6: Robot Trends Before and After 1978

Note: Authors’ calculation based on JARA data. The red dashed line indicates 1978, the initial year of our primary
analysis. Trends before 1977 are taken by aggregating type-buyer industry across tables.

55

Figure C.7: Raw Trends by Industry and Types(a) Quantity Units(b) Unit Values

56
Note: Authors’ calculation based on JARA data. Industries are sorted by the total quantity in 2017. Exports are not classified by industries in the survey.
The y-axis is log scale and lines reaching the x-axis imply zero quantity in level.

Table C.1: Pooled Regression
Dependent variable:
log stock units (thousand)
(2)

(3)

(4)

(5)

(6)

(7)

log unit value−0.728∗∗∗(0.126)−1.449∗∗∗(0.253)−0.344(0.426)0.200(0.301)−1.250∗∗∗(0.232)−0.081(0.318)−0.230(0.266)

SampleIndustry FE
Year FE
Application FE
Observations
R2

All

110, Handling

160, Welding

170, Dispensing

180, Processing

200, Assembling

900, Others

818
0.039

144
0.188

129
0.005

134
0.003

138
0.176

135
0.0005

138
0.005

57

(1)

Note:

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Log unit values are in the unit of current million JPY.

Table C.2: Industry-Fixed Effects
Dependent variable:
log stock units (thousand)
(2)

(3)

(4)

(5)

(6)

(7)

log unit value−0.370∗∗∗(0.098)−0.210(0.188)−0.739∗∗∗(0.188)−0.237(0.191)−0.135(0.153)−0.105(0.162)−0.101(0.176)

SampleIndustry FE
Year FE
Application FE
Observations
R2

All
X

110, HandlingX

160, WeldingX

170, DispensingX

180, ProcessingX

200, AssemblingX

900, Others
X

818
0.499

144
0.785

129
0.893

134
0.753

138
0.823

135
0.813

138
0.720

58

(1)

Note:

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Log unit values are in the unit of current million JPY.

Table C.3: Industry- and Year- Fixed Effects
Dependent variable:
log stock units (thousand)
(2)

(3)

(4)

(5)

(6)

(7)

log unit value−0.504∗∗∗(0.097)−0.388∗∗∗(0.139)−0.572∗∗∗(0.140)0.010(0.198)−0.333∗∗∗(0.123)−0.512∗∗∗(0.138)−0.333∗(0.183)SampleIndustry FE
Year FE
Application FE
Observations
R2

All
X
X

110, HandlingX
X

160, Welding
X
X

170, Dispensing
XX

180, Processing
X
X

200, Assembling
X
X

900, Others
X
X

818
0.545

144
0.912

129
0.950

134
0.828

138
0.903

135
0.897

138
0.770

59

(1)

Note:

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Log unit values are in the unit of current million JPY.

Table C.4: Industry-Year-Application Fixed Effects
Dependent variable:
log stock units (thousand)log unit value−0.245∗∗∗(0.086)

SampleIndustry FE
Year FE
Application FE
Observations
R2

All
X
X
X
818
0.659

Note:

D

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Robot Imports in Japan

Japan produces most of its robots domestically (Acemoglu and Restrepo, 2018b, among others).
To confirm this, we compare the import (from the rest of the world to Japan) and the domestic
sales measures of robots (from Japan to Japan). In particular, we visit Comtrade data, take HS
Code 847950 (Industrial Robots for Multiple Uses), and compare the trend with domestic shipment
trends from our main data source, the JARA, discussed in detail in the next section. Trade data for
the HS code are available only since 1996, while JARA data exist from 1978. Table D.1 shows
the result. We also calculate the shipment share by domestic producers. We obtained 97.9 percent
to 99.3 percent shares, depending on the year, between 1996 and 2017. Therefore, we interpretthat most of robot purchases in Japan have been domestic-sourced. In our paper, we focus only on
JARA data, from which we may exploit a rich set of information that is crucial for our analysis, as
we will discuss in detail.

E

Calculating Robot Stock

As we briefly discussed in Section 4.6, we assume alternative and flexible assumptions on the
robot stock calculation. The first set of assumptions is based on the immediate withdrawal method60

Figure D.1: Domestic Shipments and Import of Robots in Japan(a) Level Scale(b) Log-10 Scale

Note: Authors’ calculation based on Comtrade and JARA data. The Comtrade trends show the total import value(reported by importer, Japan) of HS Code 847950 (Industrial Robots for Multiple Uses). The JARA trends show
the total shipment from the domestic producers, aggregated by all applications and industries. The Comtrade data isdenominated by current USD, while the JARA data are denominated by current JPY. To convert the monetary values,
we use the FRED data, the annual current JPY-USD exchange rate.

(IWM). The IWM assumes the shipped robots are in use immediately after purchase and not in use
in a specified length of years. IFR follows this method with the withdrawal period of 12 years. To
better compare the results with the literature, our primary specification follows the stock definition
based on IWM with 12 years. The 12-year assumption is debatable, however, as IFR admits: “Thisassumption was investigated in an UNECE/IFR pilot study, carried out in 2000 among some major
robot companies ... This investigation suggested that an assumption of 12 years of average life
span might be too conservative and that the average life/ service life was closer to 15 years.” (IFR,2018). German and US tax authorities, in contrast, suggest the standard depreciation schedules be
shorter. Given these discussions, we consider three alternatives: 10, 12 (baseline), and 15 years ofdepreciations.
The second set of assumptions is based on the perpetual inventory method (PIM). The PIM is astandard method used when calculating capital stocks, adopted in National Accounts (OECD, 2009).
A key parameter in the method is depreciation rates. There is no systematic empirical study on thevalue. As one measure, following Artuc et al. (2018), we use an annual 10 percent depreciation
rate. As a more context-based estimate, we employ the result from Nomura and Momose (2008).
Based on disposal asset data in Japan (Survey on Capital Expenditures and Disposables), Nomura

61

Table E.1: Industry-level, 2SLS, Different Stock Measurement
Dependent variable:ln(Lit )
(1)

(2)

(3)

(4)

(5)

ln(Rit )0.255∗∗∗(0.096)

0.283∗∗(0.108)0.322∗∗(0.123)0.349∗∗∗(0.114)0.284∗∗∗(0.093)

Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Stock Measurement
Observations
R2

X
X
X
X
X
10 Years
104
0.987

X
X
X
X
X
12 Years
104
0.988

X
X
X
X
X
15 Years
104
0.987

X
X
X
X
X
δ = 0.1
104
0.988

X
X
X
X
X
δ = 0.18
104
0.988

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between shift-share measures of changes in robot stock per thousand workers and log difference ofoutcome variables multiplied by 100. All regressions control demographic variables, globalization controls, andtechnology controls as well as the industry and year fixed effects. All regressions are weighted by initial-year
population. The standard errors are shown in the parenthesis. The demographic variables include share of highschool graduates, share of 4-year university graduates, share of female workers, share of workers under age of 35, andshare of workers above age of 50 from ESS. The globalization controls contain the logarithm import values from JIP
database and logarithm offshoring value added from SOBA. The technology controls include logarithm stock value
measures for ICT capital, innovation capital, and competition capital from the JIP database. The outcome variables
are total employment (baseline), under age-35 employment, age 35-50 employment, and over age-50 employment incolumns 1-4, respectively.∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

and Momose (2008) estimated the depreciation rate of machinery, with the category of machineryand equipment, as 18 percent. Admitting that machinery is a broader category than industrial
robots, we employ 18 percent as a larger alternative than 10 percent.
Table E.1 shows the baseline regression result of specification (4) with these alternative stock
measures. Column 2 shows the main regression result based on a 12-year IWM. Columns 1 and 3
show the different-year based IWM, 10 years and 15 years, respectively. Columns 4 and 5 show
the results with a PIM with depreciation rates of 10 percent and 18 percent, respectively. The
regression coefficients are robust to alternative choices of stock measurement of robots.

62

F

Detail in ESS

Employment status is based on the usual employment status (the “usual method”). Any answer ofmostly worked, worked besides doing housework, worked besides attending school, and worked
besides doing housework and attending school are recorded as employed. For the education
attainment, we define four values that are consistent across surveys: less than high-school diploma(LHS), high-school diploma (HS), technical/vocational school diploma (TVS), four-year college
diploma or more (FC). For age, we define the five-year bins from age 15 up to age 79 and aggregate
the age groups over 80. The survey records the annual earnings, annual days worked, and weekly
hours worked in categories. Industry and occupations are encoded according to the Japan Standard
Industry Classifications (JSIC) and the Japan Standard Occupation Classifications (JSOC). We
convert these categorical variables into continuous variables using the mid-point of the range.
Taking the 2007 survey as an example, the mean annual earnings is 3,170,263 current JPY (27,330
current USD), and the mean hours worked is 1,516 hours.
Another set of representative labor statistics is from the Basic Survey of Wage Structure thatis based on the random sampling of payroll records for June. For the purpose of comparison, we
report the statistics of 2007. The mean of the estimated annual salary based on the June salary andbonus payment of the previous year is 3,858,233 yen, and the mean of the estimated annual hours
worked is 1,869 hours.

G

Details in Other Data

G.1

Census of Manufacture

The Census of Manufacture (CoM) annually surveys manufacturing establishments in Japan.16 The
CoM asks each establishment for its product-level shipment values. The product code for industrial
robots used in CoM exists since 1977. We take the CoM data since 1983 to 2016. The survey was
not conducted in 2011 and 2015, because a substituting government survey, the Economic Census
of Business Activity (ECBA), was conducted. We also take ECBA data to construct the complete
observations of Japan’s establishments that shipped robots in any years between 1983 and 2016.
16The survey was conducted for all establishments in years with last digit 0, 3, 5, or 8 until the Economic Census
of Business Activity (ECBA) started in 2011. In other years and after 2011, all establishments with the number of
employees more than three are surveyed.

63

The CoM and the ECBA treat the VAT in the following way. For CoM, before 2014, respondents
were forced to report the shipment value gross of VAT. Since 2016, they have been allowed to choose
to gross or net VAT. For the ECBA, both surveys allowed respondents to select. For consistency,
we net out the VAT from all data by the legislative VAT rate from the total sales value. The VAT
rate is 0 before 1988, 0.03 between 1989 and 1996, 0.05 between 1997 and 2013, and 0.08 since2014 and onward.
The primary purpose of using the CoM and the ECBA is to take robot-producers’ employment.
For this purpose, we follow the following steps. First, we calculate each establishment’s intensityof robot production by taking the share of robot sales among total sales. To take robot sales,
we aggregate shipment values and processing fees of all products under the 1976 Japan Standard
Industrial Code (JSIC) of 3498, 1984 and 1993 JSIC of 2998, 2002 JSIC of 2698 (“Industrial Robot
Manufacturing,” all above), and 2007 and 2013 JSIC of 2694 (“Robot Manufacturing”). Second,
assuming a proportional allocation of workers for dollar sales, we multiply total workers by robot
production intensity for each establishment. These steps generate robot-producing workers for eachestablishment, aggregating up to industry-level robot-producing workers.
Table G.1 shows the result of the specification (6) with employment, net of robot-producing
workers as an outcome variable. The column structure is the same as that of our main result Table
3. One should note the very close estimates to the ones in Table 3. This solely comes from thefact that the number of robot-producing workers is very small relative to the size of the Japanesemanufacturing industry. In fact, the share of robot-producing workers in manufacturing is between
0.001 and 0.004 percent throughout the sample period. Therefore, as far as the directly robotproducing workers are concerned, the reinstatement effect of automation is quite small (Acemoglu
and Restrepo, 2018a).

G.2

Basic Survey on Overseas Business Activities

Basic Survey on Overseas Business Activities (BSOBA) is a firm-level census of Japanese multinational enterprises (MNEs) and their foreign subsidiaries. We take offshoring intensity measures by
aggregating operating revenues in foreign subsidiaries. We take subsidiaries’ industry codes whenallocating revenues to each industry.

64

Table G.1: Industry-level, Reduced Form, net of Robot Producing Workers
Dependent variable:ln(LitN RP )ln(ritZ )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

−0.853∗∗∗(0.130)−0.466∗∗∗(0.144)−0.274∗(0.151)−0.437∗∗(0.171)

X
X

X
X
X

X
X
X
X

9.1
104
0.975

11.047104
0.984

10.912
104
0.985

X
X
X
X
X
16.703104
0.987

Notes: Authors’ calculation based on JARA, ESS, CoM, SOBA and JIP data. The table presents estimates of therelationship between log robot cost measure and log robot stock measure across industries and years. The
employment measure excludes the employment of robot-producing plants. All columns control the industry and year
fixed effects. All regressions are weighted by purchase values of robots in each year. The standard errors are shown in
the parenthesis. Columns 1 shows the result without other control variables. Column 2 includes the demography
controls. Demography controls include share of high school graduates, share of 4-year university graduates, share offemale workers, share of workers under age of 35, and share of workers above age of 50 from ESS. Column 3 includes
the logarithm import values from JIP database and logarithm offshoring value added from SOBA. Column 4 includes
logarithm stock value measures for ICT capital, innovation capital, competition capital from the JIP database. ∗ p<0.1;∗∗ p<0.05; ∗∗∗ p<0.01.

G.3

Japan Industrial Productivity Database

Japan Industrial Productivity (JIP) database is a long-run industrial aggregate of several measures
starting in 1970. Among them, we use import and intangible capital. The intangible capital measure
is composed of following items: ICT assets (ordered and packaged software, own-developed
software), innovation assets (R and D expenditures, mineral exploration, copyright and trademark
right, other product/design/research development), and competition assets (brand capital, firmspecific human capital, expenditure for restructuring). Basic concepts of these variables follow
National Accounts. Detailed discussion is provided in Fukao et al. (2008).

65

H

Further Industry-level Results

H.1

Industry-specific Prices

In this section, we assume that the prices at which each industry purchases robots may differ. In
particular, we assume that the robot price of application a in industry i and year t is given byA
vait,
rait =Rait(13)

A is purchase value of application a in industry i and year t. Armed with these variables,
where vaitin Table H.1, we report the regression results of the specification (4) based on the price index (13).
The results are not robust across control variables. In particular, in our preferred specification ofcolumn 4, we find a positive but insignificant coefficient estimate.

H.2

First Stage for Efficiency-adjusted Regressions

I

Further CZ-level Analysis

I.1

Similar Country SSIV Result

Table I.1 shows the results of regression (9) with the standard geographic SSIV. Among the three
specifications (log changes in total employment in column 1, log changes in total population incolumn 2, and changes in employment-to-population ratio in column 3), the point estimates show
signs consistent with those of our main results in Table 9. They are not precise enough, however,to conclude the positive impacts of robots.

I.2

Heterogeneous Impacts

66

Table H.1: Industry-level, 2SLS, industry-specific prices
Dependent variable:ln(Lit )ln(Rit )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

0.429∗∗∗(0.079)0.199∗∗∗(0.061)0.103(0.101)

0.139(0.101)

X
X

X
X
X

X
X
X
X

27.262104
0.968

17.462104
0.987

12.754
104
0.987

X
X
X
X
X
15.404104
0.988

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot stock measure and log employment across industries and years, with the instrument oflog robot cost measure, based on equation (13). The employment measure includes the employment ofrobot-producing plants. All columns control the industry and year fixed effects. All regressions are weighted bypurchase values of robots in each year. The standard errors are shown in the parenthesis. Columns 1 shows the result
without other control variables. Column 2 includes the demography controls. Demography controls include share ofhigh school graduates, share of 4-year university graduates, share of female workers, share of workers under age of35, and share of workers above age of 50 from ESS. Column 3 includes the logarithm import values from JIP databaseand logarithm offshoring value added from SOBA. Column 4 includes logarithm stock value measures for ICT
capital, innovation capital, competition capital from the JIP database. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

67

Table H.2: Industry-level, First Stage, Efficiency-adjusted Quantity
Dependent variable:
eit )
ln( R
ln(ritZ )Industry FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
IV F-stasitic
Observations
R2

(1)

(2)

(3)

(4)

−0.325(0.352)−1.564∗∗∗(0.416)−1.244∗∗∗(0.435)−1.156∗∗(0.477)

X
X

X
X
X

X
X
X
X

0.855
104
0.949

14.131
104
0.969

8.195
104
0.973

X
X
X
X
X
5.860
104
0.977

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between log robot stock measure and log employment across industries and years, with the instrument oflog robot cost measure. The employment measure includes the employment of robot-producing plants. All columns
control the industry and year fixed effects. All regressions are weighted by purchase values of robots in each year.
The standard errors are shown in the parenthesis. Columns 1 shows the result without other control variables.
Column 2 includes the demography controls. Demography controls include share of high school graduates, share of
4-year university graduates, share of female workers, share of workers under age of 35, and share of workers above
age of 50 from ESS. Column 3 includes the logarithm import values from JIP database and logarithm offshoring value
added from SOBA. Column 4 includes logarithm stock value measures for ICT capital, innovation capital,
competition capital from the JIP database. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

68

Table I.1: Regressions with Exposure to Robots IV
Dependent variable:∆ ln(L)∆ ln(P)∆(L/P)

(1)

(2)

(3)

∆R

1.661
(1.479)0.835(1.287)0.472(0.386)

Controls
CZ and year FEs
Observations
R2

X
X
906
0.423

X
X
906
0.358

X
X
906
0.587

Notes: Authors’ calculation based on IFR, ESS, SOBA and JIP data. The table presents estimates of the relationshipbetween shift-share measures of changes in robot stock per thousand workers and log difference of outcome variables
multiplied by 100. The dependent variable is instrumented by the shift-share measure whose shift is taken from
German robot adoption trends and share is taken by the baseyear industrial employment share in each CZ. As
outcome variables, column 1 takes log total employment, column 2 takes log total population, and column 3 takes
employment-to-population ratio. All regressions are weighted by base-year populations in each CZ. Control variables
include demographic, industry, trade and capital controls in the base year. Demographic variables consists of CZ’s
female share and elderly (age 65 and above) share. Industry variables are CZ’s manufacturing and service employment
shares. Trade variable includes the import exposure from China as in Autor, Dorn, Hanson (2013). Capital control ismade from the information-technology capital in each industry from JIP database. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

69

Table I.2: CZ-level, 2SLS. Results by Education Groups
Dependent variable:

∆Rct

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Group
Observations
R2∆ ln(Lct )∆ ln(LctHS )CG )∆ ln(Lct

(1)

(2)

(3)

2.203∗∗

2.426∗∗(1.017)(1.200)3.320∗∗(1.616)

X
X
X
X
X
All
1,466
0.817

X
X
X
X
X
High School Grad.
1,466
0.841

X
X
X
X
X
4-year Univ. Grad.
1,439
0.759

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between shift-share measures of changes in robot stock per thousand workers and log difference ofoutcome variables multiplied by 100. All regressions control demographic variables, globalization controls, andtechnology controls as well as the industry and year fixed effects. All regressions are weighted by initial-year
population. The standard errors are shown in the parenthesis. The demographic variables include share of highschool graduates, share of 4-year university graduates, share of female workers, share of workers under age of 35, andshare of workers above age of 50 from ESS. The globalization controls contain the logarithm import values from JIP
database and logarithm offshoring value added from SOBA. The technology controls include logarithm stock value
measures for ICT capital, innovation capital, and competition capital from the JIP database. The outcome variables
are total employment (baseline), high-school graduate employment, and 4-year university graduate-or-more
employment in columns 1-3, respectively. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

70

Table I.3: CZ-level, 2SLS. Results by Sex Groups
Dependent variable:∆ ln(Lct )∆ ln(LctFemale )∆ ln(LctMale )

(1)

(2)

(3)

∆Rct

2.203∗∗(1.017)

3.112∗∗∗(1.122)

1.816∗(1.028)CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Group
Observations
R2

X
X
X
X
X
All
1,466
0.817

X
X
X
X
X
Female
1,466
0.802

X
X
X
X
X
Male
1,466
0.802

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of the relationshipbetween shift-share measures of changes in robot stock per thousand workers and log difference of outcome variables
multiplied by 100. All regressions control demographic variables, globalization controls, and technology controls aswell as the industry and year fixed effects. All regressions are weighted by initial-year population. The standard errors
are shown in the parenthesis. The demographic variables include share of high school graduates, share of 4-year
university graduates, share of female workers, share of workers under age of 35, and share of workers above age of 50
from ESS. The globalization controls contain the logarithm import values from JIP database and logarithm offshoringvalue added from SOBA. The technology controls include logarithm stock value measures for ICT capital, innovation
capital, and competition capital from the JIP database. The outcome variables are total employment (baseline),female employment, and male employment in columns 1-3, respectively. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

71

Table I.4: CZ-level, 2SLS. Results by Age Groups
Dependent variable:

∆Rct

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Group
Observations
R2∆ ln(Lct )∆ ln(Lcta≤34 )∆ ln(Lct35≤a≤49 )∆ ln(Lct50≤a )

(1)

(2)

(3)

(4)

(1.017)1.463(1.428)4.656∗∗∗(1.467)0.499(1.209)

X
X
X
X
X
All
1,466
0.817

X
X
X
X
X
Age ≤ 34
1,458
0.818

X
X
X
X
X
35 ≤ Age ≤ 49
1,465
0.804

X
X
X
X
X
50 ≤ Age
1,466
0.881

2.203∗∗

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between shift-share measures of changes in robot stock per thousand workers and log difference ofoutcome variables multiplied by 100. All regressions control demographic variables, globalization controls, andtechnology controls as well as the industry and year fixed effects. All regressions are weighted by initial-year
population. The standard errors are shown in the parenthesis. The demographic variables include share of highschool graduates, share of 4-year university graduates, share of female workers, share of workers under age of 35, andshare of workers above age of 50 from ESS. The globalization controls contain the logarithm import values from JIP
database and logarithm offshoring value added from SOBA. The technology controls include logarithm stock value
measures for ICT capital, innovation capital, and competition capital from the JIP database. The outcome variables
are total employment (baseline), under age-35 employment, age 35-50 employment, and over age-50 employment incolumns 1-4, respectively. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

72

Table I.5: CZ-level, 2SLS, Wage Effects By Education Level
Dependent variable:

∆Rct

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Education
Observations
R2∆ ln(wctMS )∆ ln(wctHS )∆ ln(wTCct )4U )∆ ln(wct

(1)

(2)

(3)

(4)

2.275∗(1.295)3.595∗∗∗(0.929)4.842∗∗∗(1.445)

3.213∗∗(1.613)X
X
X
X
X
Middle School
1,402
0.899

X
X
X
X
X
High School
1,402
0.951

X
X
X
X
X
Technical College
1,402
0.836

X
X
X
X
X
4-year Univ.
1,402
0.879

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between shift-share measures of changes in robot stock per thousand workers and log difference of hourly
wages multiplied by 100 for different education group of workers. All regressions control demographic variables,
globalization controls, and technology controls as well as the industry and year fixed effects. All regressions areweighted by initial-year population. The standard errors are shown in the parenthesis. The demographic variables
include share of high school graduates, share of 4-year university graduates, share of female workers, share ofworkers under age of 35, and share of workers above age of 50 from ESS. The globalization controls contain thelogarithm import values from JIP database and logarithm offshoring value added from SOBA. The technology
controls include logarithm stock value measures for ICT capital, innovation capital, and competition capital from theJIP database. The first column shows the result for the sample of middle-school graduates, the second for high-school
graduates, the third for technical and vocational college graduates, and the fourth for four-year university graduates ormore education. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

73

Table I.6: CZ-level, 2SLS, Wage Effects By Education Level
Dependent variable:

∆Rct

CZ FE
Year FE
Demographic Controls
Globalization Controls
Technology Controls
Education
Observations
R2∆ ln(hctMS )HS )∆ ln(hct∆ ln(hTCct )4U )∆ ln(hct

(1)

(2)

(3)

(4)

−2.160∗(1.117)−2.253∗∗∗(0.649)−3.336∗∗∗(1.017)−0.932(0.896)

X
X
X
X
X
Middle School
1,402
0.446

X
X
X
X
X
High School
1,402
0.780

X
X
X
X
X
Technical College
1,402
0.734

X
X
X
X
X
4-year Univ.
1,402
0.748

∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01

Notes: Authors’ calculation based on JARA, ESS, SOBA and JIP data. The table presents estimates of therelationship between shift-share measures of changes in robot stock per thousand workers and log difference of hours
worked multiplied by 100 for different education group of workers. All regressions control demographic variables,
globalization controls, and technology controls as well as the industry and year fixed effects. All regressions areweighted by initial-year population. The standard errors are shown in the parenthesis. The demographic variables
include share of high school graduates, share of 4-year university graduates, share of female workers, share ofworkers under age of 35, and share of workers above age of 50 from ESS. The globalization controls contain thelogarithm import values from JIP database and logarithm offshoring value added from SOBA. The technology
controls include logarithm stock value measures for ICT capital, innovation capital, and competition capital from theJIP database. The first column shows the result for the sample of middle-school graduates, the second for high-school
graduates, the third for technical and vocational college graduates, and the fourth for four-year university graduates ormore education. ∗ p<0.1; ∗∗ p<0.05; ∗∗∗ p<0.01.

74