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The Evolving Impact of Robots on Jobs*

Jong Hyun Chung, Auburn University

Yong Suk Lee, Stanford University

Abstract

We examine the impact of industrial robots on US labor markets between 2005 and 2016.

Analyzing the 5-year intervals within this period, we find that robot exposure reduces employment

in the earlier periods but augments employment in the more recent periods. Similarly, the effect of

robot exposure on the average wage is initially negative but gradually becomes positive in more

recent years. The evolving impact of robots is primarily driven by robot-intensive sectors,

consistent with robot deepening and the increasing adoption of collaborative robots. We also find

evidence of spillover effects on industries outside of manufacturing.

JEL Codes: J23, O30

Keywords: robots, automation, employment, jobs, wages, labor

*WethankChiaraFratto,PascualRestrepo,RyanDecker,HyunJuJung,DanielWilmoth,andseminarparticipantsatSeoul

NationalUniversity,KoreaUniversity,theUrbanEconomicsAssociationAnnualMeetings,theAIEA-NBERConference,the

InternationalSchumpeterSocietyConference,andtheKoreaDevelopmentInstitute.LeethankstheStanfordCyberPolicy

CenterandtheKAISTCenterforIndustrialFutureStrategyforsupportingthisresearch.

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1. Introduction

The popular concern that robots will displace workers by performing tasks previously done by

humans has recently received empirical support (Acemoglu and Restrepo 2020, Bessen et al. 2019,

Graetz and Michaels 2018). But new technologies can also increase productivity and ultimately

increase the number of jobs. Indeed, other recent papers have found positive relationships between

robot adoption and labor (Acemoglu et al. 2020, Humlum 2019, Koch et al. 2019, Dixen et al.

2019). While robots can destroy jobs by performing tasks previously done by humans, robots can

also augment human labor. The evidence at this point is inconclusive. Moreover, the impact of

robots on jobs is unlikely to be the same over time and across countries. Robot technologies,

business strategies, firm organizations, labor unions, and regulations vary over time and across

countries, and these factors could influence whether robots ultimately displace or augment jobs.

In this paper, we examine how the impact of industrial robots on jobs has changed over time in the

US. By fixing our analysis to the US, we maintain institutional and demographic factors, such as

regulations, labor unions, workforce age distribution, immigrant labor, etc. relatively stable and

focus on aspects of robot technology that could affect productivity.1

The degree to which robots displace workers can change over time depending on how the

range of tasks performed by robots changes. The degree to which robots augment workers can

evolve as well, but through various channels that increase labor productivity. Productivity can

increase via robot deepening, where robots become more productive in performing the tasks they

1 Economists around the world have examined the impact of robots on jobs in other countries. Below is a list of some

of these papers. Acemoglu et al. (2020) examine France, Humlum (2019) examines Denmark, Koch et al. (2019)

examine Spain, Dixen et al. (2019) examine Canada, Eggleston et al. (2020) and Adachi et al. (2020) examine Japan,

Cheng et al. (2019) examine China, and Lee and Lee (2020) examine South Korea.

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were designed to perform (Acemoglu and Restrepo 2019). For instance, newer vintages of welding

robots may weld faster with fewer errors and better integrate with the overall production line.

Second, productivity can increase via robots that directly augment human workers. Collaborative

robots differ from traditional stand-alone industrial robots in that they directly interact with human

workers to support human workers’ strength and precision for certain movements. Collaborative

robots are currently mostly adopted in the automotive and electronics sectors and are expected to

be increasingly in demand in the near future (McKinsey 2019).2 Finally, firms may reap the

benefits of robot technologies only after they make complementary investments and organizational

changes (Brynjolffson et al. 2017), as was the case with electrification (David 1990) and

information technology (Bresnahan et al. 2002). Hence, the productivity benefits from robots may

appear in the data several years after firms adopt robots. The above three channels highlight the

productivity benefits within the robot adopting sector, but additionally, there could be spillover

effects on other industries. Other sectors can benefit from the cost reduction in intermediate goods

and services that happen in the automating sector. Also, there could be a “reinstatement effect”,

where robots create new types of jobs after some period, similar to how automobiles and computers

created new jobs that did not exist before (Acemoglu and Restrepo 2019, Autor and Salomons

2018).

For these reasons, the impact of robots on jobs could change over time, initially from

displacement and later to augmentation, that is, if the productivity benefits from robots grow

sufficiently. We study this possibility by examining the impact of industrial robot exposure on the

2 In bio-medicine and health services, human augmenting robots such as, exoskeletons, prosthetics, human-robot

interfaces that can be worn or imbedded in humans to supplement their physical capabilities are increasingly being

adopted as well (Bogue 2009, Lancet 2019, Eggleston et al. 2020).

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jobs in the US commuting zones over different periods across industries. We construct a measure

of industrial robot exposure for each commuting zone similar to Acemoglu and Restrepo (2020)

and examine the change in employment and average wages over all 5-year periods between 2005

and 2016 using the US Census data. We find that the impact of robot exposure on employment

evolves from displacement to augmentation. Robot exposure decreases regional employment in

the earlier periods but increases regional employment in the more recent periods. Similarly, the

impact of robot exposure on the average wage is negative in the earlier periods but gradually

increases to turn positive in the more recent period. The evolving impact of industrial robots on

jobs is primarily driven by manufacturing, the sector that predominantly adopts industrial robots.

When we examine the impact of industry-specific robot exposure on regional industry-specific

employment and wages, we find that the evolving impact is driven by the automotive robots, the

industry with leading robot adoption in the US, but also find similar evidence from electronics and

chemicals robots, the other two sectors intensively adopting robots. These sectors account for

about 90% of the industrial robot adoption in the US.

The evolving impact of robots on jobs that we find, especially in the robot adopting sectors,

is consistent with robot deepening, i.e., the development of robot technology and quality, and the

increasing adoption of collaborative robots, which perform physically challenging tasks and allow

human workers to focus on other tasks. We also find evidence of spillover effects to other

industries within manufacturing as well as non-manufacturing.

We contribute to the literature that examines the impact of industrial robots on labor. Our

paper is closely related to Acemoglu and Restrepo (2020) which find that industrial robots displace

jobs in the US. However, they examine an earlier time period, primarily between 1990 and 2007.

We find, to the best of our knowledge for the first time, that the impact of industrial robots on jobs

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evolves from displacement to augmentation. Recent papers have found positive relationships

between industrial robots and jobs. (Acemoglu et al. 2020, Humlum 2019, Koch et al. 2019, Dixen

et al. 2019). However, these studies examine different countries and industries with different

industrial characteristics and labor regulations and use different identification strategies. We adopt

the same research design and data used in Acemoglu and Restrepo (2020) and show that the impact

of robots on local employment has evolved from displacement to augmentation. As newer vintages

of robots become more productive in performing tasks, robots that directly augment labor become

increasingly developed, and business strategy and production processes reorganize to more

efficiently use these robots, robotics could contribute to employment in the near future.

Our paper is also related to the literature that examines the impact of automation

technologies more generally. Though most of the literature has examined manufacturing, robots

are increasingly being adopted in the service sectors. Eggleston et al. (2020) show that wearable

robots that directly aid caregivers or mobility robots that help residents are increasingly being used

in nursing homes and find that robot adoption complements nurse employment. Our paper also

relates to the expanding literature that examines the impact of artificial intelligence on the labor

market. Several papers have focused on creating indirect ways to measure tasks where AI can be

used and predict which occupations could be substituted by AI more generally (Webb 2020,

Brynjolfsson et al. 2018, Felten et al. 2018). Other papers have examined the impact of AI on labor

in specific sectors, such as finance and banking (Grennan and Michaely 2019, Choi et al. 2020).

In addition to the recent wave of papers that examine robotics and artificial intelligence, our

findings also contribute to the long-standing literature that examines the productivity and labor

market consequences of technology adoption (David 1990, Bresnahan and Tratjenberg 1995,

Brynjolffson and Hitt 2000).

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The paper proceeds as follows. In the next section, we discuss the data and the empirical

strategy. Section 3 presents the results and Section 4 concludes.

2. Data and Empirical Framework

2.1 Robot exposure by US commuting zones

We use the country-industry-year level shipments of industrial robots data provided by the

International Federation of Robotics (IFR), which has been used in several papers (Graetz and

Michaels 2018; Acemoglu and Restrepo 2019; Dauth et al. 2017) to construct robot exposure

measures. The IFR data are generally available from 1993 with varying degrees of coverage across

countries. For the US, however, industry-level data start in 2004. The degree of industry

aggregation varies across sectors. Within manufacturing, most subcategories are available at the

2-digit level, although some are grouped together (e.g. 10, 11, and 12, which are food, beverage,

and tobacco, are grouped together). For some subcategories (e.g. automotive), 3- or 4-digit level

detail is available. We partition the industries into 19 industry groups. These groups are used

throughout the entire analysis.

The IFR data have some shortcomings. Each year, some number of robot shipments are

categorized as “unspecified”. We reallocate these shipments across other sectors weighted

proportionately to the sector shipment. The redistribution assumes that whether a robot shipment

is classified as unspecified is independent of its actual industry. The failure of this assumption

would introduce measurement errors to our robot exposure measures; however, the instrument

variable strategy we use mitigates the concern from such measurement errors.

Another important caveat is that the IFR reports only the aggregate robot shipments to

North America until 2010. The individual shipments to the US, Canada, and Mexico become

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available starting 2011. We impute the US shipments from 2005 to 2010 by scaling the North

American shipments by the estimated US share. From 2011 to 2017, the US shipments share in

North American shows a downward trend, falling from 85% in 2011 to below 80% in 2017. To

account for this trend, we estimate the US share in earlier periods assuming the logit of the share

is linear in time.

We impute the stock of operational robots based on the annual shipment data.3 Following

Graetz and Michaels (2018), we use the perpetual inventory method with a five percent

depreciation rate to calculate the robot stocks.4

We scale the robot stocks by the number of workers in each country-industry from the EU

KLEMS data, which provide annual employment by sector for EU member countries along with

the US and Japan. Appendix Figure 1 presents the industry-level robots per worker in the US for

2005, 2010, and 2016. Robot adoption is concentrated in the automotive sector followed by the

electronics sector and has been increasing dramatically during this period. Automotive and

electronics are the two leading sectors accounting for nearly 90% of robot stocks in the US.

3AlthoughtheIFRdataalsoreportthestocks,thesereportedvaluesaretheIFR’sownestimationbasedontheshipments.

AccordingtotheIFR’spublication,WorldRoboticsReport,“Data on the annual shipments (sales) of robots is generally

more accurate than data on the robot stock. […] When calculating the operational stock, it is assumed that the average

service life is 12 years and that there is an immediate withdrawal of the robots after 12 years. Where countries actually

do surveys of the robot stock or have routines for their own calculation of operational stock, for instance in Japan,

then those figures are naturally used here as the operational stock of robots.”

4 Specifically, we use a depreciation rate of five percent and use the reported 1993 stock level as the initial value so

that, 𝑅!,#$ = (1 − 𝛿)𝑅!,#%&$ + 𝐼!,#$ , where 𝛿 = 0.05, 𝐼!,#$ is the deliveries for each country 𝐶 in industry 𝑖 at year 𝑡, and

operational stock in 1993, 𝑅!,&''($ , is given for each country-industry.

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We construct the change in commuting zone-industry-level exposure to robots between 𝑡1

and 𝑡2 as

ΔExposure!,#,(%&,%') = 𝑙!,#'))) 0*),*+,-+*),*.

,-

,),+///,- 1 (1)

where 𝑅𝑖,𝑡𝑈𝑆 is the US robots stock for industry 𝑖 in year 𝑡, 𝐿𝑖,2000

𝑈𝑆 is the number of people employed

in industry 𝑖 in 2000, and 𝑙!,#2000 is the commuting zone 𝑐 share of industry 𝑖 employment in 2000:

𝑙𝑐,𝑖2000 ≡ 𝐿𝑐,𝑖

2000

∑ 𝐿𝑐,𝑖2000

𝑐 (2)

where 𝐿𝑐,𝑖2000 is the number of people employed in industry 𝑖 in commuting zone 𝑐. We use the

2000 census 5% sample public use micro area (PUMA) level data from IPUMS to construct the

commuting zone share of each industry employment.5

Finally, we construct the commuting zone level exposure to robots between 𝑡1 and 𝑡2 as

∆Exposure𝑐,(𝑡1,𝑡2) = ∑ ∆Exposure𝑐,𝑖,(𝑡1,𝑡2)𝑖 (3)

which aggregates the industry-commuting zone level robot exposure measure across all industries

within each commuting zone.

2.2 Empirical Framework

We use the following equation to examine the impact of robot exposure on the local labor

market:

∆𝑦!,(%&,%') = 𝛽∆Exposure!,(%&,%') + 𝐗𝐜,𝐭𝟏 ∙ 𝛅 + 𝜀!,%& (5)

5 The census response report industry even when the person is unemployed or not in the labor force. We only consider

employed persons. The population is summed within each industry-PUMA cell. The data is then aggregated to the

industry-commuting zone level. To map PUMAs or Countries to Commuting Zones, we use the crosswalks provided

by David Dorn. There are 741 distinct commuting zones.

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The above equation represents a first difference regression where the first differencing is between

𝑡1 and 𝑡2. The time periods we focus on are the 5-year intervals between 2005 and 2016. The

dependent variable ∆𝑦!,(%&,%') is the change in the employment to population ratio or log average

weekly wages for commuting zone (CZ) c between 𝑡1 and 𝑡2 . The main regressor

∆Exposure𝑐,(𝑡1,𝑡2) is the exposure of robots per worker between years 𝑡1 and 𝑡2. The control

variable vector 𝐗𝐜,𝐭𝟏 includes CZ level variables at the initial year. We control for the census

division fixed effects, demographic characteristics of the commuting zone (log population, share

of females, share of population 65 years or older, shares of whites, blacks, Hispanics, and Asians,

and shares of population with high school graduates, some college education, bachelor or associate

degree, master or doctoral degree, and professional degree), industry shares of the commuting zone

(the shares of employment in manufacturing, construction, and the durable sector, and the female

share in manufacturing), and the Chinese import exposure, share of routine jobs, and task

offshorability measures.6

We also examine variations of equation (5) where the outcome variable is ∆𝑦!,#,(%&,%'), the

commuting zone-industry-level changes in employment or wage. In this case, we are examining

the responses of the employment and wages in six broad industries,: manufacturing (MAN), retail

and wholesale (RW), construction, utilities, and transportation (CUT), finance, insurance, and real

estate (FIRE), services (SER), and professional services (PRO).

Additionally, we consider specifications in which the robot exposure variable is

∆Exposure𝑐,𝑖,(𝑡1,𝑡2) , i.e., the change in commuting zone exposure to industry-specific robots.

6WefollowAutor,Dorn,andHanson(2013)toconstructtheChineseimportexposure.Toconstructtheexposuremeasures

formorerecentyears,weusetheBACItradedataandCBPemploymentdatacleanedbyEckertetal.(2020).Shareofroutine

jobsandtaskoffshorabilitymeasuresarefromAutorandDorn(2013).

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Specifically, we examine the impact of robot exposure from the automotive, electronics, and

chemicals sectors on own industry effects, as well as spillover effects to other sectors within

manufacturing, and sectors outside of manufacturing.

2.3 Instrumental variable

The main coefficient of interest is 𝛽 in equation (5), but the OLS estimates are likely biased

since robot adoption is likely correlated with local demand shocks that are in turn correlated with

the local employment and wages. Hence, we employ an instrumental variable strategy, in which

we instrument the US exposure to robots with the instrumental variable constructed using robot

exposure to European countries. The instrumental variable isolates out the change in the US robot

adoption due to technological shock to filter the changes due to confounding demand-side factors.

As in Acemoglu and Restrepo (2020), we use robot exposure to five European countries, Denmark,

Finland, France, Italy, and Sweden, and construct the instrumental variable at the US CZ level:

∆Exposure!,(%&,%')9: = &;∑ 0∑ 𝑙!,#&<=) 0

*),*+4 +*),*.

4

,),+///4 1# 1>∈9 (6)

Here, 𝐸 is the set of the five European countries, 𝑅𝑖,𝑡𝑒 is the industry 𝑖 robots stock in country 𝑒 at

year 𝑡, and 𝐿𝑖,2000𝑒 is the number of country 𝑒 people employed in industry 𝑖 in 2000. The first stage

regression then takes the form

∆Exposure𝑐,(𝑡1,𝑡2) = 𝜋∆Exposure𝑐,(𝑡1,𝑡2)𝐸𝑈 +𝑿𝒄,𝒕𝟏 ⋅ 𝜸+ 𝜈𝑐,𝑡1 (7)

The validity of the instrument hinges on the assumption that the robot adoptions in the

European countries are not correlated with the local demand shocks in the US. Acemoglu and

Restrepo (2020) present various evidence that indicates that potential confounding factors, such as

common shocks to industries, do not explain their results. Since we adopt the same instrumental

variable strategy and mostly the same data sources as Acemoglu and Restrepo (2020), we do not

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replicate the various robustness checks they perform in our empirical analysis, but rather show that

our results are similar to their results when we examine the same time period.

3. Results

3.1 The evolving impact of robot exposure on local employment and wages

Figure 1 presents the impact of robot exposure on the employment to population ratio and the log

of average weekly earnings at the commuting zone level over the different time intervals.

Appendix Table 2 presents the results from the regressions that correspond to Figure 1 and some

additional results for the whole sample period, 2005 to 2016, and for 2000 to 2007. We examine

the 2000 to 2007 period to compare our results with Acemoglu and Restrepo (2020) which we

discuss in the robustness section. Figure 1 illustrates both the OLS and 2SLS coefficient estimates

from the 5-year first differenced regressions, which include the full set of control variables. Figure

1A indicates that robot exposure decreased local employment to population ratio in the earlier

periods but in the more recent years increased local employment to population ratio. Figure 1B

illustrates the results for log average weekly earnings. The coefficient estimates are negative in the

earlier periods, gradually increases (i.e., becomes less negative), and then turns positive, though

not as significant, in the most recent period. This could be due to the wage changes being slower

compared to employment changes. remain in the same area, which would have generated excess

labor.

Figure 2 presents the results when we separate out the outcome variables by industry.

Specifically, we examine employment to population ratio and log weekly wages in six industry

groups, i.e., manufacturing (MAN), retail and wholesale (RW), construction, utilities and

transportation (CUT), finance, insurance and real estate (FIRE), services (SER), and professional

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services (PRO). Appendix Tables 3 and 4 present the regression results. Most sectors exhibit a

pattern that shows a negative impact decreasing in magnitude and in cases becoming positive. The

sector that clearly exhibits the evolving impact of robots on jobs from displacement to

augmentation is manufacturing, the sector that is adopting the industrial robots. For manufacturing,

the impact of robot exposure on wages is initially negative in the earlier periods and gradually

increases (becomes less negative in more recent periods) and statistically not different from zero.

Figure 2 suggests that the evolving effect of industrial robots we found in Figure 1 is driven by the

own industry displacement effects and productivity gains within manufacturing, but also spillovers

to other industries.

3.2. Own industry effects and spillover effects

We next examine more specifically the own industry effects and the spillover effects. We

first examine the impact of manufacturing robot exposure on jobs in manufacturing and jobs

outside of manufacturing in Figure 3. Appendix Tables 5 and 6 present the corresponding

regression results. Given that robot adoption primarily occurs within manufacturing, the top panels

in Figure 3 are nearly identical to that of the manufacturing sector results in Figure 2. We confirm

the strong own-industry effects within manufacturing both in terms of displacement and

augmentation, but we also confirm the spillover effects to non-manufacturing (bottom panels of

Figure 3). Compared to the patterns in manufacturing, the non-manufacturing sectors exhibit a

one- or two-year lag in the transition from displacement to augmentation. The impact of

manufacturing robots on the non-manufacturing industries are likely the consequence of aggregate

demand effects. Job displacement and wage reduction in the manufacturing sector depress the

overall local economy, thereby creating negative spillover effects to other sectors, e.g.,

construction and the service sectors. When manufacturing is rebounding the upward push in

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demand spills over to other sectors, but the addition of jobs in other sectors tends to be slower

relative to job losses during the downturn.

We also examine whether the impact of industry-specific robots spills over within

manufacturing. We focus on the three industries—automotive, electronics, and chemicals—that

adopt robots most intensively. Figure 4A examines the impact of exposure to automotive robots

on jobs within automotive and all other manufacturing jobs excluding automotive. Similarly,

Figure 4B examines the impact of exposure to electronic robots on electronic jobs and non-

electronic manufacturing jobs, and Figure 4C examines the impact of exposure to chemical robots

on chemical jobs and non-chemical manufacturing jobs. Appendix Tables 7 and 8 present the

regression results. The own industry effect within automotive is significant and evolves from

displacement to augmentation. However, the impact of automotive robot adoption on

manufacturing jobs outside of automotive is varied. For example, the impact of robot adoption in

the automotive sector on the other manufacturing sectors is positive and significant in the earlier

periods but then gradually decreases in magnitude. This may reflect the movement of labor from

automotive to other manufacturing industries when jobs are lost from robots. Similar patterns are

revealed for electronics and chemicals robots. However, in the more recent periods, the own

industry job gains from robot exposure seem to spur increased labor demand in other

manufacturing sectors as well. Such a varied effect could depend on the degree to which each

industry is linked to each other within manufacturing, via supply chain effects.7

7For example, the impact of automotive robots on machinery jobs exhibit a similar pattern where the estimate is negative and

gradually turns positive over time. This may be a consequence of the automotive and machinery industries being closely connected

through supply chains. On the other hand, the impact of robot adoption in the automotive sector on the electronics sector is positive

and significant in the earlier periods but then gradually decreases in magnitude. A similar pattern holds for non-metal and paper

manufacturing jobs.

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3.3. Magnitudes

The IV estimates we find in the previous sections measure the impact of one additional

robot per thousand workers on the employment to population ratio and the log of the local average

wage of a commuting zone relative to other areas. The estimates in Figure 1 (or Appendix Table

2) imply that an increase of one robot per thousand workers decreased employment to working-

age population ratio by 3.1 percentage points and wages by 6.8 percent between 2005 and 2010,

and decreased employment to population ratio by 1.2 percentage points and wages by 2.5 percent

between 2006 and 2011. These estimates imply that one industrial robot reduced employment by

about 46 workers between 2005 and 2010 and by 19 workers between 2006 and 2011.8 On the

other hand, in the more recent periods one robot per thousand workers increased employment to

population ratio by 0.78 to 0.96 percentage points. The estimates imply that one industrial added

about 13 to 15 jobs in the more recent years. The positive impact of robots on jobs has been

consistent in the more recent years and if similar patterns persist, robots may eventually increase

jobs on net in the longer period.

3.4. Robustness

We next examine the robustness of our main results, i.e., the results in Figure 1 (Appendix

Table 2). We first examine the results for the periods between 2000 and 2007 and compare our

results with that of Acemoglu and Restrepo (2020, hereafter AR). The AR estimate is presented in

Appendix Table 2 column (9). Our 2SLS estimate for employment to population ratio is -1.436.

However, due to the different way we measure the robot stocks, our measure is not directly

8Between2005 and2010one robot per thousand workers decreased employment to working-age population ratio by 3.06

percentage points. For every EUKLEMS workers in 2000 (which was the basis for robot exposure measure) there was about 1.49

working-age population in 2005. This translates to 1 robot reducing 45.6 jobs (-0.0306×1.49×1000).

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comparable to AR. Using IFR-reported values will overstate the stock growth compared to the

stock measures discounted using the perpetual inventory method, so the estimated effects would

be smaller. In the specification closer to AR (using IFR-reported stocks and 1990 as the base year),

the coefficient value is -0.844, which is more similar to AR’s estimate of -0.623. In terms of the

wage, we predict a 3.73% decline in wage, while AR predict a 1.19% decline in wage. In the more

comparable specification, where we use reported robot stock measures, we predict a 2.23%

decline. We note that our wage regression is at the commuting zone level and AR run wage

regressions at the commuting zone-demographic cell level. AR presents extensive robustness

checks related to the sample, data, and identification strategy. Since we use the same empirical

specification as AR and find consistent results with AR, we do not present the set of extensive

robustness checks done in AR. Instead, we present a few key sensitivity tests related to the

construction of the robot stock measures in Figure 5 and Appendix Table 9.

In Figure 5A and Panels A and D of Appendix Table 9, we examine results when we use

the North American robot stock measures. IFR reports only the aggregate robot shipments to North

America until 2010 and individual shipments to the US, Canada, and Mexico become available

only starting in 2011. We impute the US shipments from 2005 to 2010 by scaling the North

American shipments by the estimated US share, but AR use North American robot stock measures.

The results are similar to results using our imputed US shipment measures.

The industrial robot stock data reported by the IFR adds up the number of industrial robots

but does not account for depreciation or quality of robots degrading. Hence, we chose to depreciate

robot stock annually at 5% using the perpetual inventory method. We check whether to depreciate

or not, or the rate of depreciation affects our main findings in Figure 1. Figure 5B and Panels C

and F of Appendix Table 9 present results when we depreciate robot stock at a higher rate, i.e.,

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10% annually, and Figure 5C and Panels B and E of Appendix Table 9 present results when we

use the robot stock data as reported by the IFR without any adjustments for depreciation. Both

figures present similar patterns as Figure 1. The 2005-2010 results in Figure 5C is relatively large

both in terms of magnitudes and standard errors, due to the instrument being weak, but the

estimates for other periods are more similar. Overall, Figure 5 indicates that the evolving impact

of robots on jobs persist regardless of how the robot stock measure is constructed.

4. Conclusion

Whether robots will displace or create jobs is hotly debated. We find that the answer is

nuanced and that the impact of robots on jobs has changed over time. In this paper, we examined

the impact of industrial robot exposure on local employment and average earnings in the US

commuting zones between 2005 and 2016. Looking at the 5-year intervals within this period, we

find that the impact of robot exposure on employment has been evolving. Robot exposure reduces

employment in the earlier periods but augments employment in the more recent periods. Average

wages decrease with robot exposure in the earlier periods, but the effect gradually rebounds and

becomes non-negative in the more recent periods. The evolving impact of industrial robots is

primarily driven by sectors that intensively adopt industrial robots, i.e., manufacturing, and

automotive within manufacturing. The impact of robots on employment and wages spills over to

the other sectors, both within manufacturing and outside of manufacturing, especially to the service

sectors. The delayed productivity gains that occur primarily in the robot adopting sectors is

consistent with robot deepening and the increasing adoption of collaborative robots. Firms may be

adapting and reorganizing to better make use of robots as well. We find evidence of spillover

17

effects on other industries within manufacturing as well as non-manufacturing, which suggests

that spillover can happen via input-output linkages as well as aggregate demand effects.

Though the employment rebound in the more recent periods could be due to new

occupations being created, the so-called reinstatement effect, this seems unlikely at this point.

Employment rebound from industrial robots is concentrated in the robot adopting sectors, i.e., the

manufacturing industries, where there has not been much evidence of new occupation creations.

Autor and Salomons (2019) document the evolution of new work between 2000 and 2015 and find

that new work is primarily organized around the professional, health, and service sectors, and that

the share of new work in sectors like construction, transportation, and production actually declined

during this period.

How robotics will affect future labor remains to be seen. This paper has shown that the

view of robots taking over human jobs may be overly pessimistic. Robot technology and

businesses evolve, thereby resulting in productivity improvements from robots, as well as

increased employment and wages. Many businesses across different industries today are just

beginning or in the midst of adopting robots. We predict that the impact of robots on jobs will

continue to evolve. Scholars are still in the process of understanding the consequences and there

are still many remaining questions. Further research on identifying and quantifying the different

channels by which robots can affect jobs, across different industries and countries, would help

shed further light on how robots and human labor will interact in the future.

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Figure 1. Impact of robots on employment and wages – 5 years differences, all industries

Notes: the dots represent the coefficient estimate on robot exposure for the respective years using the fully specified regression specifications. The horizontal lines represent the 95% confidence intervals.

22

Figure 2. Impact of robots on employment and wages – 5 years differences by industry

Notes: The bars represent the coefficient estimate on robot exposure for the respective years using the fully specified regression specifications. The horizontal lines represent the 95% confidence intervals. The industries are manufacturing (MAN), retail and wholesale (RW), construction, utilities and transportation (CUT), finance, insurance and real estate (FIRE), services (SER), and professional services (PRO).

23

Figure 3. Impact of manufacturing robots on industry employment and wages – 5 years differences by industry

Notes: The bars represent the coefficient estimate on robot exposure for the respective years using the fully specified regression specifications. The horizontal lines represent the 95% confidence intervals. The industries are manufacturing (MAN), retail and wholesale (RW), construction, utilities and transportation (CUT), finance, insurance and real estate (FIRE), services (SER), and professional services (PRO).

24

Figure 4. Impact of industry specific robots on own industry and other sectors within manufacturing

A. Impact of automotive robots on automotive jobs and non-automotive manufacturing jobs

B. Impact of electronics robots on electronics jobs and non-electronics manufacturing jobs

25

C. Impact of chemical robots on chemical jobs and non-chemical manufacturing jobs

26

Figure 5. Robustness results using different robot stock calculations

A. Robot stocks for North America

B. Stock calculated with the depreciation rate of 10%

27

C. IFR-reported stocks

28

Appendix Figure 1. Robot exposure by industry and year

29

Appendix Table 1. Summary statistics

Level Change 2005 2011 2005-2010 2011-2016 Mean S.D. Mean S.D. Mean S.D. Mean S.D. A. Employment to population ratio All 49.42 5.89 45.34 6.78 -4.09 2.73 1.75 2.70 Manufacturing 7.98 3.87 6.43 3.21 -1.65 1.61 0.12 1.32 Retail & wholesale 11.97 1.77 10.96 1.71 -0.94 1.39 0.45 1.57 Finance, insurance & real estate 2.75 1.21 2.55 1.07 -0.17 0.67 -0.00 0.70 Construction, utility & transportation 7.77 1.64 6.35 1.53 -1.43 1.30 0.50 1.21 Services 13.68 2.53 13.94 2.87 0.25 1.57 0.82 1.74 Professional services 1.52 0.81 1.46 0.80 -0.06 0.47 0.11 0.49 Other 3.74 2.99 3.65 2.75 -0.09 0.93 -0.26 1.04

B. Log weekly wage All 6.38 0.15 6.53 0.14 0.14 0.06 0.13 0.07 Manufacturing 6.54 0.22 6.69 0.22 0.16 0.17 0.14 0.15 Retail and wholesale 6.12 0.17 6.22 0.15 0.10 0.14 0.12 0.14 Finance, insurance, and real estate 6.51 0.27 6.67 0.27 0.15 0.26 0.22 0.29 Construction, utilities, and transportation 6.46 0.15 6.65 0.14 0.17 0.13 0.13 0.14 Service 6.34 0.15 6.48 0.14 0.13 0.10 0.13 0.11 Professional service 6.54 0.36 6.75 0.36 0.21 0.44 0.20 0.37 Other 6.40 0.25 6.59 0.23 0.16 0.14 0.12 0.15

C. Exposure changes Robot exposure (US) 0.26 0.22 0.69 0.62 Robot exposure (IV) 0.37 0.23 0.65 0.63 Chinese import exposure 1.15 3.82 -0.37 6.21

D. Commuting zone characteristics Log population 11.56 1.64 11.63 1.65 Population share of… …females 0.51 0.01 0.50 0.01 …65 years or older 0.14 0.03 0.15 0.03 …White 0.84 0.13 0.84 0.12 …Black 0.08 0.12 0.08 0.12 …Asians 0.01 0.02 0.01 0.02 …Hispanic 0.09 0.14 0.11 0.15 …foreign-born 0.05 0.05 0.06 0.05 Employment share of… …manufacturing 0.16 0.07 0.14 0.06 …nondurable manufacturing 0.06 0.03 0.06 0.03 …construction 0.09 0.03 0.07 0.02 …females in manufacturing 0.29 0.07 0.28 0.07 Population share with… …less than high school diploma 0.35 0.06 0.32 0.05 …high school diploma 0.27 0.04 0.26 0.04 …some college education 0.23 0.04 0.26 0.03 …bachelor’s or professional degree 0.11 0.03 0.11 0.04 …master’s or doctoral degree 0.04 0.02 0.04 0.02 Share of routine occupations 0.36 0.11 0.18 0.06 Offshorability index -0.02 0.19 -0.58 0.32

Notes: The unit of observation is 1990 US commuting zone, excluding Alaska and Hawaii. The number of observations is N = 722.

30

Appendix Table 2. Impact of robots on employment to population ratio and weekly wage (1) (2) (3) (4) (5) (6) (7) (8) (9)

2005 to 2010

2006 to 2011

2007 to 2012

2008 to 2013

2009 to 2014

2010 to 2015

2011 to 2016

2005 to 2016

2000 to 2007

A. Change in employment to population ratio - OLS estimates Robot exposure

-1.888*** -1.349* -0.272 1.090** 1.222*** 0.968*** 0.902*** 0.231* -0.848** (0.429) (0.504) (0.335) (0.365) (0.253) (0.109) (0.101) (0.112) (0.248)

N 722 722 722 722 722 722 722 722 722 R2 0.492 0.456 0.388 0.324 0.279 0.266 0.286 0.521 0.698

B. Change in employment to population ratio - 2SLS estimates Robot exposure

-3.060** -1.207* -0.563 1.048** 0.964*** 0.896*** 0.779*** 0.0273 -1.436*** (0.939) (0.596) (0.546) (0.370) (0.220) (0.164) (0.118) (0.151) (0.162)

N 722 722 722 722 722 722 722 722 722 F 24.30 72.17 218.6 461.1 315.1 238.9 561.8 297.3 234.1

C. Change in log average weekly wage - OLS estimates Robot exposure

-0.0556*** -0.0327*** -0.0111 -0.0207* 0.0146 -0.00128 0.0105* -0.00623 -0.0234*** (0.0124) (0.00912) (0.0113) (0.00882) (0.00770) (0.00587) (0.00395) (0.00376) (0.00586)

N 722 722 722 722 722 722 722 722 722 R2 0.350 0.348 0.343 0.278 0.306 0.223 0.197 0.369 0.508

D. Change in log average weekly wage - 2SLS estimates Robot exposure

-0.0680*** -0.0247 -0.0159 -0.0133 0.0174* -0.00462 0.00528 -0.00906 -0.0373*** (0.0174) (0.0174) (0.0107) (0.00931) (0.00693) (0.00620) (0.00504) (0.00500) (0.00696)

N 722 722 722 722 722 722 722 722 722 F 24.30 72.17 218.6 461.1 315.1 238.9 561.8 297.3 234.1 Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. *** p<0.01, ** p<0.05, * p<0.1

31

Appendix Table 3. Change in employment to population ratios by industry

Change in employment to population ratio (1) (2) (3) (4) (5) (6) (7) 2005 to 2010 2006 to 2011 2007 to 2012 2008 to 2013 2009 to 2014 2010 to 2015 2011 to 2016 A. Manufacturing Robot exposure -0.923* -0.0957 -0.248 0.546** 0.972*** 0.454*** 0.608***

(0.418) (0.274) (0.220) (0.177) (0.141) (0.122) (0.0994)

B. Retail and wholesale Robot exposure 0.0347 -0.999* -0.518* -0.416* -0.0476 -0.0628 0.00939

(0.626) (0.391) (0.247) (0.175) (0.0894) (0.0860) (0.0694)

C. Construction, utilities, and transportation Robot exposure -0.671* 0.622** 0.00882 0.386 0.00680 0.251** 0.00895

(0.316) (0.233) (0.238) (0.205) (0.117) (0.0819) (0.0822)

D. Finance, insurance, and real estate Robot exposure -0.140 -0.402* -0.160 0.375*** 0.0497 -0.148** 0.0660

(0.247) (0.200) (0.162) (0.111) (0.0731) (0.0549) (0.0839)

E. Service Robot exposure -0.482 0.0795 0.0662 0.214 -0.0325 0.335** 0.152

(0.360) (0.281) (0.297) (0.277) (0.104) (0.120) (0.0918)

F. Professional service Robot exposure -0.796*** -0.471*** 0.125 -0.0446 -0.0125 0.0757** 0.0462

(0.216) (0.125) (0.0906) (0.0748) (0.0599) (0.0292) (0.0353) N 722 722 722 722 722 722 722 F 24.30 72.17 218.6 461.1 315.1 238.9 561.8 Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. Number of observations is 722. *** p<0.01, ** p<0.05, * p<0.1

32

Appendix Table 4. Change in log weekly wages by industry Change in log weekly wage (1) (2) (3) (4) (5) (6) (7) 2005 to 2010 2006 to 2011 2007 to 2012 2008 to 2013 2009 to 2014 2010 to 2015 2011 to 2016 A. Manufacturing Robot exposure -0.0788 -0.0727* -0.0186 -0.00927 0.00157 0.00466 -0.00792

(0.0407) (0.0355) (0.0272) (0.0157) (0.0106) (0.0111) (0.00952)

B. Retail and wholesale Robot exposure -0.0560 -0.0881** -0.0797*** -0.0418* -0.00619 -0.0316* 0.00710

(0.0434) (0.0336) (0.0225) (0.0193) (0.0160) (0.0145) (0.0104)

C. Construction, utilities, and transportation Robot exposure 0.0412 0.0496 -0.0144 -0.0224 0.0553*** -0.00675 0.0128

(0.0477) (0.0355) (0.0217) (0.0172) (0.0135) (0.0152) (0.0105)

D. Finance, insurance, and real estate Robot exposure 0.0286 -0.0867 0.00507 -0.0235 0.0170 0.00712 0.0253*

(0.0639) (0.0540) (0.0284) (0.0296) (0.0193) (0.0178) (0.0114)

E. Service Robot exposure 0.00240 0.0279 0.00797 -0.00124 0.0112 -0.0117 -0.00669

(0.0367) (0.0266) (0.0233) (0.0150) (0.0122) (0.00944) (0.00847)

F. Professional service Robot exposure -0.228* 0.0574 -0.0191 0.0108 0.00959 -0.0193 -0.0444*

(0.104) (0.0805) (0.0878) (0.0347) (0.0255) (0.0289) (0.0178) N 722 722 722 722 722 722 722 F 24.30 72.17 218.6 461.1 315.1 238.9 561.8 Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. Number of observations is 722. *** p<0.01, ** p<0.05, * p<0.1

33

Appendix Table 5. Impact of manufacturing robot exposure on employment to population ratio by industry

Change in employment to population ratio (1) (2) (3) (4) (5) (6) (7) 2005 to 2010 2006 to 2011 2007 to 2012 2008 to 2013 2009 to 2014 2010 to 2015 2011 to 2016 A. Manufacturing Robot exposure in manufacturing

-0.929* -0.0916 -0.243 0.541** 0.978*** 0.456*** 0.609*** (0.418) (0.276) (0.220) (0.175) (0.143) (0.122) (0.0994)

B. Non-manufacturing Robot exposure in manufacturing

-2.151 -1.084 -0.366 0.475 -0.0234 0.435* 0.173 (1.112) (0.579) (0.608) (0.460) (0.228) (0.200) (0.113)

C. Retail and wholesale Robot exposure in manufacturing

0.0244 -0.986* -0.516* -0.410* -0.0513 -0.0628 0.0104 (0.630) (0.389) (0.246) (0.175) (0.0886) (0.0862) (0.0691)

D. Construction, utilities, and transportation Robot exposure in manufacturing

-0.141 -0.408* -0.173 0.372*** 0.0455 -0.149** 0.0656 (0.247) (0.199) (0.165) (0.110) (0.0737) (0.0549) (0.0840)

E. Finance, insurance, and real estate Robot exposure in manufacturing

-0.660* 0.634** -0.00483 0.369 0.00283 0.249** 0.00961 (0.318) (0.233) (0.242) (0.213) (0.118) (0.0821) (0.0823)

F. Service Robot exposure in manufacturing

-0.495 0.0889 0.0615 0.201 -0.0264 0.332** 0.152 (0.369) (0.280) (0.301) (0.281) (0.104) (0.120) (0.0918)

G. Professional service Robot exposure in manufacturing

-0.798*** -0.465*** 0.123 -0.0440 -0.0161 0.0752* 0.0466 (0.218) (0.124) (0.0896) (0.0747) (0.0597) (0.0292) (0.0354)

N 722 722 722 722 722 722 722 F 23.90 70.66 213.7 460.9 315.9 238.8 559.6 Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. Number of observations is 722. *** p<0.01, ** p<0.05, * p<0.1

34

Appendix Table 6. Impact of manufacturing robot exposure on log weekly wages by industry Change in log weekly wage (1) (2) (3) (4) (5) (6) (7) 2005 to 2010 2006 to 2011 2007 to 2012 2008 to 2013 2009 to 2014 2010 to 2015 2011 to 2016 A. Manufacturing Robot exposure in manufacturing

-0.929* -0.0916 -0.243 0.541** 0.978*** 0.456*** 0.609*** (0.418) (0.276) (0.220) (0.175) (0.143) (0.122) (0.0994)

B. Non-manufacturing Robot exposure in manufacturing

-2.151 -1.084 -0.366 0.475 -0.0234 0.435* 0.173 (1.112) (0.579) (0.608) (0.460) (0.228) (0.200) (0.113)

C. Retail and wholesale Robot exposure in manufacturing

0.0244 -0.986* -0.516* -0.410* -0.0513 -0.0628 0.0104 (0.630) (0.389) (0.246) (0.175) (0.0886) (0.0862) (0.0691)

D. Construction, utilities, and transportation Robot exposure in manufacturing

-0.141 -0.408* -0.173 0.372*** 0.0455 -0.149** 0.0656 (0.247) (0.199) (0.165) (0.110) (0.0737) (0.0549) (0.0840)

E. Finance, insurance, and real estate Robot exposure in manufacturing

-0.660* 0.634** -0.00483 0.369 0.00283 0.249** 0.00961 (0.318) (0.233) (0.242) (0.213) (0.118) (0.0821) (0.0823)

F. Service Robot exposure in manufacturing

-0.495 0.0889 0.0615 0.201 -0.0264 0.332** 0.152 (0.369) (0.280) (0.301) (0.281) (0.104) (0.120) (0.0918)

G. Professional service Robot exposure in manufacturing

-0.798*** -0.465*** 0.123 -0.0440 -0.0161 0.0752* 0.0466 (0.218) (0.124) (0.0896) (0.0747) (0.0597) (0.0292) (0.0354)

N 722 722 722 722 722 722 722 F 23.90 70.66 213.7 460.9 315.9 238.8 559.6 Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. Number of observations is 722. *** p<0.01, ** p<0.05, * p<0.1

35

Appendix Table 7. Impact of industry specific robots on employment to population ratio within manufacturing

(1) (2) (3) (4) (5) (6) (7)

2005 to 2010

2006 to 2011

2007 to 2012

2008 to 2013

2009 to 2014

2010 to 2015

2011 to 2016

A. Impact of automotive robots on change in automotive employment to population ratio Automotive robot exposure

-1.941*** -1.129*** -0.768*** -0.269*** 0.431*** 0.298*** 0.361*** (0.0874) (0.0890) (0.0514) (0.0375) (0.0396) (0.0283) (0.0414)

N 646 626 657 655 655 674 622 F 419.3 458.8 474.1 599.4 561.2 767.5 653.6

B. Impact of automotive robots on change in manufacturing employment to population ratio, excluding automotive Automotive robot exposure

0.746*** 0.604*** 0.340* 0.713*** 0.462*** 0.0510 0.241** (0.149) (0.162) (0.166) (0.141) (0.100) (0.0790) (0.0843)

N 646 626 657 655 655 674 622 F 419.3 458.8 474.1 599.4 561.2 767.5 653.6

C. Impact of electronics robots on change in electronics employment to population ratio Electronics robot exposure

-2.384*** -0.670 -1.280** -0.222 -0.0578 -0.205 -1.037*** (0.575) (0.527) (0.478) (0.323) (0.430) (0.318) (0.177)

N 717 722 717 722 720 714 722 F 49.42 52.77 57.39 52.72 67.51 52.48 63.53

D. Impact of electronics robots on change in manufacturing employment to population ratio, excluding electronics Electronics robot exposure

4.781** 1.783 4.008** -2.333 -0.620 -0.704 1.077 (1.839) (1.288) (1.222) (1.213) (1.507) (1.164) (1.397)

N 717 722 717 722 720 714 722 F 49.42 52.77 57.39 52.72 67.51 52.48 63.53

E. Impact of chemicals robots on change in chemicals employment to population ratio Chemicals robot exposure

-3.143* -2.928* -4.947** -4.072*** -1.708*** -0.523 -2.252 (1.259) (1.418) (1.711) (1.045) (0.490) (1.286) (1.161)

N 711 708 706 707 717 714 716 F 174.5 155.3 159.0 138.7 143.7 174.7 169.7

F. Impact of chemicals robots on change in manufacturing employment to population ratio, excluding chemicals Chemicals robot exposure

3.543 10.38*** 5.961 4.010* -0.985 -1.456 -0.978 (2.578) (2.524) (3.127) (1.893) (1.851) (2.412) (2.211)

N 711 708 706 707 717 714 716 F 174.5 155.3 159.0 138.7 143.7 174.7 169.7

Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. Number of observations is 722. *** p<0.01, ** p<0.05, * p<0.1

36

Appendix Table 8. Impact of industry specific robots on log weekly wages within manufacturing

(1) (2) (3) (4) (5) (6) (7)

2005 to 2010

2006 to 2011

2007 to 2012

2008 to 2013

2009 to 2014

2010 to 2015

2011 to 2016

A. Impact of automotive robots on change in automotive log weekly wage Automotive robot exposure

0.0182 -0.0878 -0.137 0.143 0.0651 -0.0101 0.0143 (0.124) (0.121) (0.0900) (0.124) (0.0546) (0.0413) (0.0329)

N 597 577 607 608 599 632 578 F 412.2 439.5 452.6 614.8 548.5 761.3 639.4

B. Impact of automotive robots on change in manufacturing log weekly wage, excluding automotive Automotive robot exposure

-0.0385* -0.0506* -0.0437* -0.0268 -0.0213* 0.00957 -0.00994 (0.0185) (0.0206) (0.0173) (0.0141) (0.00943) (0.0142) (0.00815)

N 646 626 657 655 655 674 622 F 419.3 458.8 474.1 599.4 561.2 767.5 653.6

C. Impact of electronics robots on change in electronics log weekly wage Electronics robot exposure

0.0548 -0.380 -0.259 0.184 0.386 -0.426 0.408 (0.785) (0.502) (0.424) (0.464) (0.307) (0.426) (0.261)

N 661 674 670 681 668 674 691 F 49.01 53.01 56.18 52.85 66.80 52.28 63.06

D. Impact of electronics robots on change in manufacturing log weekly wage, excluding electronics Electronics robot exposure

0.154 0.108 -0.0212 -0.0304 -0.0228 -0.00670 -0.113 (0.136) (0.171) (0.197) (0.136) (0.145) (0.0909) (0.0929)

N 717 722 717 722 720 714 722 F 49.42 52.77 57.39 52.72 67.51 52.48 63.53

E. Impact of chemicals robots on change in chemicals log weekly wage Chemicals robot exposure

1.277 1.929 1.196 1.136 0.980 -0.370 -0.903 (0.721) (1.011) (0.919) (1.011) (0.856) (0.620) (0.602)

N 673 673 691 690 698 696 678 F 173.7 159.8 160.2 138.7 143.8 174.8 172.8

F. Impact of chemicals robots on change in manufacturing log weekly wage, excluding chemicals Chemicals robot exposure

-0.267 -0.159 0.0105 0.00146 -0.0596 0.724* -0.139 (0.351) (0.558) (0.644) (0.309) (0.294) (0.283) (0.378)

N 711 708 706 707 717 714 716 F 174.5 155.3 159.0 138.7 143.7 174.7 169.7

Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by commuting zones are in parentheses. Number of observations is 722. *** p<0.01, ** p<0.05, * p<0.1

37

Appendix Table 9. Robustness - Using different robot stock measures (1) (2) (3) (4) (5) (6) (7) 2005 to 2010 2006 to 2011 2007 to 2012 2008 to 2013 2009 to 2014 2010 to 2015 2011 to 2016

A. Change in employment to population ratio - Using North American robot stock measures Robot exposure

-2.538** -0.929* -0.418 0.753** 0.712*** 0.609*** 0.525*** (0.782) (0.459) (0.407) (0.262) (0.162) (0.112) (0.0795)

F 23.82 65.50 200.5 423.6 285.4 193.6 515.4

B. Change in employment to population ratio - Using IFR-reported robot stock measures Robot exposure

-18.46 -2.861* -1.224 1.908** 1.256*** 1.086*** 0.983*** (11.52) (1.404) (1.142) (0.729) (0.291) (0.200) (0.150)

F 2.811 125.2 222.1 306.1 365.9 303.4 563.7

C. Change in employment to population ratio - Discounting robot stock by 10% annually Robot exposure

-10.99*** -3.615* -1.297 1.997** 1.434*** 1.263*** 1.065*** (2.935) (1.761) (1.240) (0.731) (0.329) (0.232) (0.162)

F 60.56 129.4 266.1 453.3 348.7 275.8 565.1

E. Change in log weekly wages – Using North American robot stock measures Robot exposure

-0.0564*** -0.0190 -0.0118 -0.00954 0.0129* -0.00314 0.00356 (0.0145) (0.0134) (0.00797) (0.00668) (0.00510) (0.00422) (0.00340)

F 23.82 65.50 200.5 423.6 285.4 193.6 515.4

F. Change in log weekly wages - Using IFR-reported robot stock measures Robot exposure

-0.410 -0.0585 -0.0345 -0.0242 0.0227* -0.00560 0.00667 (0.286) (0.0440) (0.0235) (0.0170) (0.00911) (0.00751) (0.00636)

F 2.811 125.2 222.1 306.1 365.9 303.4 563.7

G. Change in log weekly wages - Discounting robot stock by 10% annually Robot exposure

-0.244*** -0.0739 -0.0365 -0.0253 0.0259* -0.00651 0.00722 (0.0602) (0.0537) (0.0248) (0.0178) (0.0104) (0.00874) (0.00689)

F 60.56 129.4 266.1 453.3 348.7 275.8 565.1 N 722 722 722 722 722 722 722

Notes: Each regression controls for Census Division fixed effects, demographic characteristics of the commuting zone (log population, the share of college-educated, share of high-school degree holders, and share of minorities), industry shares of the commuting zone, (the shares of employment in manufacturing, construction, and the durable sector, and the female share of manufacturing), and Chinese import exposure, share of routine jobs, and task offshorability measures. Standard errors clustered by