The Evolving Impact of Robots on Jobs - Stanford Universityyongslee/robojobs.pdfemployment and...
Transcript of The Evolving Impact of Robots on Jobs - Stanford Universityyongslee/robojobs.pdfemployment and...
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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 +*),*.
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,),+///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
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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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References
Acemoglu, Daron, and Pascual Restrepo. 2020. “Robots and Jobs: Evidence from US Labor
Markets.” Journal of Political Economy 128(6): 2188-2244.
Acemoglu, Daron, Claire Lelarge, and Pascual Restrepo. 2020. “Competing with Robots: Firm
level Evidence from France.” NBER Working Paper No. 26738.
Acemoglu Daron, and Pascual Restrepo. 2019. Artificial intelligence, automation and work.
Published in The Economics of Artificial Intelligence: An Agenda. Edited by Ajay Agrawal,
Joshua Gans, and Avi Goldfarb, The University of Chicago Press, Chicago, 2019.
Adachi, Daisuke, Daiji Kawaguchi, and Yukiko Saito. 2020. “Robots and Employment:
Evidence from Japan, 1978-2017.” Mimeo.
Autor, David H. and David Dorn. 2013. “The Growth of Low-Skill Service Jobs and the
Polarization of the U.S. Labor Market.” American Economic Review 103(5): 1553-1597.
Autor, David, Anna Salomons. 2018. “Is Automation Labor-Displacing? Productivity, Growth,
Employment, and the Labor Share.” NBER Working Paper No. 24871.
Bessen James, Martin Goos, Anna Salomons, Wiljan van den Berge. 2019. “Automatic
Reaction- What Happens to Workers at Firms that Automate?” CPB Discussion Paper.
Bogue, Robert. 2009. “Exoskeletons and robotic prosthetics: a review of recent developments.”
Industrial Robot, 36(5): 421-427.
Bresnahan, Timothy, and Manuel Tratjenberg. 1995. “General Purpose Technologies “Engine of
Growth?”” Journal of Econometrics 65(1): 83-108.
Brynjolffson, Erik and Lorin M. Hitt. 2000. “Beyond Computation: Information Technology,
Organizational Transformation and Business Performance.” Journal of Economic
Perspective 14(4): 23-48.
19
Brynjolfsson, Erik, Daniel Rock and Chad Syverson. 2017. “Artificial Intelligence and the
Modern Productivity Paradox: A Clash of Expectations and Statistics.” NBER Working
Paper No. 24001.
Brynjolfsson, Erik, Tom Mitchell, and Daniel Rock. 2018. “What Can Machines Learn, and
What Does It Mean for Occupations and the Economy?” American Economic Review 108:
43-47.
Cheng, Hong, Ruixue Jia, Dandan Li, and Hongbin Li. 2019. “The Rise of Robots in China.”
Journal of Economic Perspectives 33(2): 71-88.
Choi, Jungho, Yeji Kee, Yong Suk Lee. 2020. “The Impact of Artificial Intelligence on
Productivity and Jobs: Evidence from Banking.” Mimeo.
David, Paul. 1990. “The Dynamo and the Computer: An Historical Perspective on the Modern
Productivity Paradox.” American Economic Review 80(2): 355-361.
Dixon, Jay, Bryan Hong, and Lynn Wu. 2020. “The Robot Revolution: Managerial and
Employment Consequences for Firms.” Mimeo.
Eckert, Fabian, Teresa C. Fort, Peter K. Schott, and Natalie J. Yang. 2020. “Imputing Missing
Values in the US Census Bureau's County Business Patterns.” NBER Working Paper No.
26632.
Eggleston, Karen, Toshi Iizuka, Yong Suk Lee. 2020. “Robots and Labor in the Service Sector:
Evidence from Nursing Homes.” Mimeo.
Felten, Edward W., Manav Raj, and Robert Seamans. 2018. "A Method to Link Advances in
Artificial Intelligence to Occupational Abilities." AEA Papers and Proceedings, 108: 54-57.
Gaulier, Guillaume, and Soledad Zigango. 2010. “BACI: International Trade Database at the
Product-Level. The 1994-2007 Version.” CEPII Working Paper No. 2010-23.
20
Graetz, Georg, and Guy Michaels. 2018. “Robots at Work.” Review of Economics and Statistics
100(5): 753-768.
Grennan, Jill, and Roni Michaely. 2019. “Artificial Intelligence and the Future of Work: Evidence
from Analysts.” Mimeo.
Humlum, Anders. 2019. “Robot Adoption and Labor Market Dynamics.” Mimeo.
Koch, Michael, Ilya Manuylov, and Marcel Smolka. 2019. “Robots and firms.” CESIfo Working
Papers 7608.
Lancet. 2019. “Human–robotic interfaces to shape the future of prosthetics.” EBioMedicine 46.
Lee, Changkeun, and Yong Suk Lee. 2020. “The Different Shades of Digitization in
Manufacturing: Automation vs. Smartification.” Mimeo.
McKinsey Global Institute. 2017. Artificial Intelligence, The Next Digital Frontier?
Ruggles, Steven, Sarah Flood, Ronald Goeken, Josiah Grover, Erin Meyer, Jose Pacas and
Matthew Sobek. 2020. IPUMS USA: Version 10.0 [Dataset]. Retrieved from
https://doi.org/10.18128/D010.V10.0.
Stehrer, Robert, Alexandra Bykova, Kirsten Jäger, Oliver Reiter, and Monika Schwarzhappel.
2019. “Industry-level Growth and Productivity Data with Special Focus on Intangible
Assets: Report on methodologies and data construction for the EU KLEMS Release 2019.”
Webb, Michael. 2020. “The Impact of Artificial Intelligence on the Labor Market.” Mimeo.
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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
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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