New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

New ESRI Working Paper No.65

The Effect of AI on Wages in Japan Using Computable General
Equilibrium Model
Toshiaki Shinozaki, Shigehiro Ihara, Naoki Saito, Shiro Takeda
June 2022

Economic and Social Research InstituteCabinet Office
Tokyo, JapanThe views expressed in “New ESRI Working Paper” are those of the authors and not those of
the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
(Contact us: https://form.cao.go.jp/esri/en_opinion-0002.html)

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

The Effect of AI on Wages in Japan Using Computable General Equilibrium Model†

Toshiaki SHINOZAKI‡, Shigehiro IHARA§, Naoki SAITO**, Shiro TAKEDA††

Abstract:
We quantitatively analyzed the effect of AI deployment to wage inequality with a 32sector CGE model. In our model, labor force was divided into AI-exposed labor and nonAI-exposed labor using Webb (2020)’s AI exposure rate. We examined the effect on wage
inequality by increasing AI capital from 3% of physical capital to 6% of physical capital.
The results are as follows, (i) wage inequality decreases with an increase of AI capital if
AI is not so complmentary to human labor, (ii) wage inequality in top 5 and bottom 5
industry’s average wage decreases most if AI is substitutive in high-income industries
and AI is complementary to low-income industries, and (iii) wage inequality in Gini
coefficient decreases most if AI is substitutive to human labor.

†

I would like to thank for Koichi Kume, Norimichi Ukita, Isamu Yamamoto and Daiji Kawaguchi
for their insightful comments and suggestions. The views expressed in this paper are those of theauthors and do not necessarily represent the views of the institutions with which they are affiliated‡ESRI§Reconstruction Agency**Japan Science and Technology Agency††Kyoto Sangyo University
1

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

1. INTRODUCTION
Artificial Intelligence (AI) technology is now rapidly being developed and deployed insociety. Some of the tasks of human labor are considered to be replaced by AI. The effect ofAI on human labor differs depending on the role of AI in that occupation, replacement of sometasks, or replace of occupation as a whole. Occupation-based analysis using machine learning
by Frey and Osbourne (2013) predicts 47 percent of occupations can be replaced by AI, whiletask-based analysis in the same manner as Arntz, Gregory, and Zierahn (2016) predicts only 7
percent of jobs will be replaced by AI.
In this paper, we analyzed the effect of AI exposure on human labor and wages using acomputable general equilibrium model (CGE model) and AI exposure rate by occupation
calculated by Webb (2020). AI exposure rate, which is a measure of the “exposure” ofoccupations in AI technology, calculation follows occ1990dd classifications１. We convert thatoccupation classification into Japanese JIP data containing 108 industries and 7 categories ofoccupations. For each industry by category, AI exposure rate is assigned and number of AIexposed human labor is calculated. In our CGE model, AI is considered to be substitute orcomplement to AI-exposed human labor and the composite of AI and AI-exposed human labor
is used as a production factor in the model in the same war as non-AI-exposed human laborand capital.
What occupation will be exposed to AI most? Webb (2020) points out “in contrast to
software and robots, AI is directed at high-skilled tasks.” That is, Webb’s AI exposure rate has
a positive relationship with wage. It implies that if AI is a substitute of AI-exposed humanlabor, average wage in those industries that use AI-exposed human labor intensively decreases
as an increase of AI input and that of those industries that do not use AI-exposed human labor
increases or at least not decrease so much compared with high AI exposed industries. Under
this assumption, wage inequality is expected to decrease mainly because a decrease in wages
in high-AI-exposed occupations, which are usually high wage. However, if AI is１

The occ1990dd occupation classification is U.S. Census occupation codes of occupations for the 1980,
1990, and 2000 Census.
2New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

complementary to AI-exposed human labor, the average wage of “all” industries increases andwe miss the quantitative impact of AI to income inequality. Note that the mechanism of AI
deployment to mitigate or aggravate income inequality by industry is unique from a view ofprevious studies.２ Still we have no consensus about the role of AI deployment yet.
To assess the impact of AI deployment in the CGE model, we set three scenarios ofrelationships between AI and AI-exposed human labor; Case 1 is substitutive, Case 2 iscomplementary, Case 3 is complementary to bottom 5 industries and substitutive to top 5
industries. We set up the Case 3 by following implication of previous studies in the below
which mention negative impact on high-income occupations and positive effect on lowincome occupations.
Our simulation results show that as for the ratio between top 5 industries’ average wage
and bottom 5 industries’ average wage, Case 1 lowers income inequality more than Case 2 andCase 3 tops to improve income inequality. As for the Gini coefficient, Case 1 tops to improveincome inequality and Case 3 shows almost the same effect as Case 1 while Case 2 merely
works to improve income inequality. Poor results of Case 2, to improve income inequality, canbe explained by a high AI capital accumulation for middle-AI-exposed ratio industries, which
tend to have more physical capital. Average wage of those industries rises and consequently
income inequality was not fixed well.
We begin by reviewing recent development of assessment of AI on labor. Frey and Osborne(2013) built a model and calculated the probability of job automation for 702 occupations andthen classified occupations based on these probabilities to estimate the expected impact ofautomation on the US labor market by using 70 original hand-labelled occupations as training
data. Their estimates suggest that 47 percent of jobs in the US were at a "high-risk" (of morethan 70 percent) of being replaced by computerization within the next one or two decades.
One other feature of their calculation is that replacement of human labor by AI starts from
２

Higuchi (1991) reports “wages by industry are affected by current profit, product price, productionlevel and globalization.” By Ota (2010), wage premium by industry can also be affected by deregulation.
Ota (2010) also points out that wage premium by industry has not changed so much up to 2006, after ashrink in the first half of 1990s in Japan.
3New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model“low skilled labor,” that is, low-wage workers.
Based on the approach by Frey and Osborne (2013), Arntz, Gregory, and Zierahn (2016)
estimated the risk of automation and digitalization for jobs while taking the heterogeneity ofworkers’ “tasks” within occupations into account. They found that, when examined at the task
level, an average of 9 percent of jobs across the 21 OECD countries are automatable. Even
though automatability and digitalization are unlikely to destroy large number of jobs in thepaper, low qualified workers will likely bear the brunt of the adjustment costs as automatabilityof their jobs is higher compared with highly qualified workers.
Acemoglu et al. (2021) made a regression analysis about AI-related vacancies over 20102018 and found no discernible impact of AI exposure on employment or wages at theoccupation or industry level, implying that AI is currently substituting for humans in a subset
of tasks but it is not yet having detectable aggregate labor market consequences ３.
Watanabe et al. (2021) made micro-level analysis about the role of AI on worker’s
productivity in the same occupation, suggesting AI is complementary to human labor and willraise productivity. They found that AI improves drivers’ productivity by 5% on average andits gain is concentrated on low-skilled drivers while almost zero gains on high-skilled drivers.
Webb (2020) developed a new method to predict the impacts of technology by using theoverlap between the text of job task descriptions and the text of patents to construct a measureof the exposure of tasks to automation. Webb found that, in contrast to software and robots, AI
is directed at high-skilled tasks. Under the assumption that historical patterns of long-run
substitution will continue, Webb estimated that AI will reduce 90:10 inequality, but will notaffect the top 1%.
The remainder of this study is organized as follows. Section 2 introduces the dataset we use
in our analysis, including how we converted Webb’s AI exposure rate for US occupation into
Ja. In addition, it provides descriptive statistics and describes the relationships among

３

Contrary to Acemoglu et al. (2021), Grenan and Michaely (2020) reports “analysts with portfolios thatare more exposed to AI are more likely to reallocate efforts to soft skills, shift coverage towards low AI
stocks, and even leave the profession.”4New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Modelvariables. Section 3 presents a CGE model whose factors are AI-exposed human labor, nonAI-exposed human labor, physical capital, and AI. Section 4 presents our model simulation
results. Finally, Section 5 concludes and presents remarks on possible future extensions.

2. DATAThis section introduces the data we use in our CGE analysis, describes the conversion of
US occupational AI exposure by Webb (2020) to that of Japan, and provides basic descriptive
statistics.

2.1.US AI exposure rate
Here the way of AI exposure rate calculation method by Webb (2020) is briefly explained.
Webb (2020) used Google Patents Public Data and O*NET database of occupations and tasks
for job descriptions. On the patent side, Webb chose the set of patents corresponding to aparticular technology. For each pair, Webb (2020) calculated how often that pair, or ones
similar to it, occurs in the list of all pairs. For occupation, verb-noun pairs are collected andthe relative frequency of similar pairs in patent titles are assigned. To get a single overall scoreof occupations, an average of all the verb-noun pairs mentioned in the task descriptions of theoccupation, weighted by the “importance” of the task to the occupation.
Relative frequency is calculated as 𝑟𝑓𝑐𝑡 = ∑𝑟𝑓𝑐𝑡

𝑡𝑐∈𝐶𝑡 𝑓𝑐, where technology 𝑡, verb-noun pair 𝑐.

By this relative frequency, AI exposure is calculated as follows.
∑𝑘∈𝐾 [𝜔𝑘,𝑖 ∙∑𝑐∈𝑆 𝑟𝑓𝑐𝑡 ]𝐴𝐼 𝐸𝑥𝑝𝑜𝑠𝑢𝑟𝑒𝑖,𝑡 = ∑

𝑖

𝑘

.

𝑘∈𝐾𝑖 [𝜔𝑘,𝑖 ∙|{𝑐:𝑐∈𝑆𝑘 }|]In the above equation, 𝐾𝑖 is the set of tasks in the occupation 𝑖,𝑆𝑘 is the set of the verb-noun
pairs extracted from task 𝑘 ∈ 𝐾𝑖 .𝜔𝑘,𝑖 , the weight of task 𝑘 in occupation 𝑖, is an average ofthe frequency, importance, and relevance of task 𝑘 to occupation 𝑖 , as specified in the
O*NET database, with weights scaled to sum to one.

2.2. Conversion Webb’s US AI exposure rate to Japanese occupation classification

5

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Webb’s US AI exposure rate has 341 occupations and it is converted into Japanese
occupations in line with the Japan Industrial Productivity Database (JIP) code. JIP code has
108 industries and seven categories of occupations, technicians, managers, office workers,
sales workers, service personnel, and production workers for each industry. That is, we have756 categories of occupations in JIP and it implies that Webb’s US AI exposure rate cannot
have one-to-one correspondence with JIP for all of the occupations.
We tried to match occupations by US AI exposure rate with occupations by industry of JIP
in three ways: (i) one-to-one conversion, (ii) group-to-one conversion, (iii) group-to-groupconversion, which are prioritized in this order.
As an example of (i) one-to-one conversion, we can use “Financial managers” in O*NET
assigned to financial industry’s “administrative occupation worker” in JIP. As an example of(ii) group-to-one conversion, we use a group of occupations such as “Tailors, dressmakers,
and seamstresses,” “Winding and twisting textile and apparel operatives,” etc. corresponds totextile industry’s craftsman and manufacturing and construction workers.” Webb’s AI
exposure rate is weighted sum by the number of each occupation in the US. As for (iii) groupto group conversion, I assigned the group of workers in O*NET to professions in several
industries in JIP since I could not find out one-to-one or group-to-one correspondence in some
professions between O*NET and JIP. For example, “professional and technical workers:
manufacturing average,” which is a weighted sum of occupations in Table 2, are assigned to“professional and technical workers” of textile industry, and pulp paper and wood products in
JIP.
JIP, for all occupations, assigns the proportion of workers, 𝑂𝑐𝑐𝑖,𝑗 industry 𝑖 andoccupation𝑗, is summed to be one, ∑𝑗 𝑂𝑐𝑐𝑖,𝑗 = 1. Let the AI exposure rate for industry 𝑖 andoccupation𝑗 be 𝐴𝐼_𝑟𝑎𝑡𝑒𝑖,𝑗 . AI exposure rate for industry 𝑖 can be calculated as 𝐴𝐼_𝑟𝑎𝑡𝑒 =∑𝑗 𝐴𝐼_𝑟𝑎𝑡𝑒𝑖,𝑗 ∗ 𝑂𝑐𝑐𝑖,𝑗 .

2.3. Relationship between AI Exposure Rate and Wages
Before we discuss the model, let us discuss the relationship between AI exposure rate and6New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Modelwages. In previous studies, Frey and Osbourne (2013) discussed that negative relationshipbetween wages and educational attainment with an occupation’s probability ofcomputerization. Arnz, Gregory and Zierahan (2016) also pointed out that automatibility of
low-qualified workers is highly likely to be higher than that of high qualified workers and theymight bear the adjustment cost of automatibility. Contrary to preceding papers, Webb (2020)
found high-skilled tasks could be replaced by AI, while software or robots could replace lowskilled tasks.
Figure 1 shows the result of conversion to JIP. High-wage industry corresponds to high-AI
exposure rate. This result is in line with Webb (2020). Note that we cannot see the relationshipbetween the wage of executives and their AI exposure rate directly since this is industry-level
aggregation.
Table 3 shows occupational average wage and AI exposure rate. High-skilled occupations,such as professional and technical workers, administrative workers and clerical workers, arehighly exposed to AI, while low-skilled occupations, such as sales workers, service workers
and craftsman and manufacturing and construction workers, are not. The reason of low-AIexposure rate for low-skilled occupation is that tasks of those occupations are composed ofmore physical tasks than high skilled occupations and AI cannot be substitutable to those tasks.
3. THE MODEL
In this section we explain the model for simulations. The structure of the model is basedon Saito, Kato and Takeda (2017). In our model, AI capital and a composite AI-exposed labor
are added. Our model is a small open economy for Japan with 32 sectors４. All markets are
considered to be perfectly competitive and all agents act as price takers.
As for production, firms are assumed to have production function with constant returns toscale. Appendix 1 shows the model structure including production function, which is a multistage CES function. Note that E_KL, E_LA, E_AI in Appendix 1 are elasticity of substitutionof each stage. Firms use intermediate inputs, capital stock, and two types of labor force, AI４

Originally, JIP data and corresponding IO table of Japan have 108 sectors.
7

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

exposed labor and non-AI-exposed labor, explained in the previous section. Note that mining
sector uses specific factor, that is natural resource, and mining sector’s production function
tree is different in this point.
AI and AI-exposed labor are first aggregated into a labor composite, then composite of AIexposed labor and non-AI-exposed labor are aggregated into labor composite. Finally, laborcomposite and capital are aggregated into a primary factor composite. The output isdetermined using a fixed coefficient aggregation of the primary factor composite and otherintermediate inputs, that is Leontief production function.
The value of the elasticity of substitution is provided in Table 4. Therefore, our model is astandard CGE model, while labor is divided into two types, AI-exposed labor and non-AIexposed labor, and AI is introduced in the production function. The products are allocated toforeign and domestic markets through a constant elasticity of transformation (CET) function.
As for the demand side, we assume a representative household to maximize its utility
with CES utility function. The household earns its income by providing production factors tofirms and uses its income for consumption and saving. Saving rate is assumed to be constant.
Savings are used for investment and accumulated to capital stock.
As for trade, Japan is assumed to be a small country and the Armington assumption is used.
Under the Armington assumption, domestically produced goods and imported goods areimperfect substitutes. Domestic and imported goods are aggregated through a CES function.
In this model, the current account is equal to trade balance minus remittance by AI capital
holders. We see the effect of remittance mainly on consumption by with and without
remittance. Exchange rate, the price of foreign currency, is determined such that the current
account is equal to the benchmark value.
Note that our CGE model comes from the dataset of 2010 Input-Output Table of Japan, in
which AI is not so much included as 2022. In that sense, this study has limits in functions andparameters of which AI has different values from physical capital or traditional software
investment.

8

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

4. SIMULATIONS AND THEIR RESULTS
In this section, we simulated the effect of AI deployment to average wage by industry using
the CGE model explained in the last section. Three scenarios are set by the value of elasticity
of substitution of AI and AI-exposed labor. The effect on income inequality is measured by aGini coefficient and the ratio between top five industries’ average wage and bottom fiveindustries’ average wage.

4.1. Simulation Scenario
We assume initial AI deployment is 3 percent to physical capital５ and the model is solved
with that AI capital then AI capital is increased by 10 percent point up to be doubled andbecome 6 percent of physical capital and the rise of AI capital effect on wage and wage gap
by industry is calculated. Let us call this benchmark equilibrium. Note that each scenario,
explained below, has its own benchmark equilibrium.
Next, we set elasticity of substitution between AI and AI-exposed labor from 5, AI-exposedlabor being strongly substitute to AI, to 0.8, AI-exposed labor being complement to AI andanalyzed the effect on wage and wage gap by industry. Let the former be Case 1 and the latterbe Case 2. In the Case 1 assumption, AI’s complementarity of human labor is from Kanazawa
et al. (2021), “AI improves drivers’ productivity by shortening the time to search for customers
by 5% on average.” Case 2 assumption, AI’s substitute of human labor, is from Frey and
Osbourne (2013), “According to our estimates, around 47 percent of total US employment isin the high-risk category.”
Finally, we analyzed the mixture of AI’s complementary effect and substitute effect at thesame time by letting the AI and AI-exposed labor’s elasticity of substitution of low-income
industry, whose wages are lower than average, be 0.8 and that of high-income industry, whose
wages are higher than average, be 5. For middle-income industries, we set elasticity of

５

In the model, the share of AI to physical capital of real estate and petroleum and coal products are set

to be 0.01 since its level of physical capital is large.
9New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

substitution to be 3. This Case is called Case 3６. We set this scenario because preceding papers
indicate that AI has different effects on wages by the nature of tasks, and same for occupations.
Kanazawa et al. (2021) estimated high-productivity gains for low-skilled taxi drivers７. On theother hand, there are some researches which say AI deployment is being done in high wage
industry such as Grenan and Michaely (2020). We discuss the simulation result of Case 3 only
in inequality.

4.2. Simulation Results
First, we see the results of Cases 1 and 2. Simulation results of wage change and average
wage by industry are shown in Figures 2 and 3. In Case 1, average wage of high-wage industry
decreases, while the average wage of high-wage industry increases in Case 2. In particular,
there is a large difference in impact on wages in middle-income industries, that is negative
impact in Case 1 and positive impact in Case 2. The results reflect the relatively high AI
exposure rate of middle-income industries in Figure 1. The impact on wages in middle-income
industries affects income inequality by the Gini coefficient. Note that we observe a
productivity gain, in terms of per capita GDP, by 0.9 percent with an increase of AI capital by
3 percentage points. As is the case of physical capital, capital accumulation leads a
productivity gain in our model.
The changes of wage and number of employees are shown in Figures 4 and 5. In both Case
1 and Case 2, the number of employees decreases as average wage decreases. In the CGE
model, since there is no unemployment, all workers are employed and the number of
employees increases even though the average wage of some sectors decreases.
We compare the change of wages and AI exposure rate in Figures 6 and 7. Average wage
in high AI exposure rate industry decreases in Figure 6 while average wage in high AI exposure
rate increases in Figure 7. The wage increase in middle AI exposure rate industry is large in

６

All elasticity of substitution values is shown in Table 4.
In our model, taxi driver is classified into transport sector, TRS in Appendix 3. However, taxi driver’s
income is not high enough.
７

10

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 7.
Figure 8 shows the ratio between the top five industries’ average wage and bottom fiveindustries’ average wage. It is intuitive that Case 1, the high substitution case, lowers income
inequality by AI capital accumulation since high-income industries’ AI exposure rates arehigher and its average wage decreases more as in Figure 2. On the other hand, Case 2, thecomplementary case, does not change the ratio since average wage in both low-income
industries and high-income industries increased as in Figure 3. In Case 3, average wage
decreases for top 5 industries, whose AI-exposed rate is high, while average wage increases
for bottom 5 industries. Then the ratio decreases most and income inequality is corrected.
The Gini coefficient in Figure 9 also shows Case 1 has lowered income inequality muchwhile Case 2 does not affect income inequality so much. The result of Case 2 can be explainedby AI capital accumulation for middle AI-exposed ratio industries, which tend to have more
physical capital as in Figure 10, and that high AI capital raised wages of those middle-income
industry. Consequently, all industry wages increased in Case 2 and income inequality does notchange so much. In Case 3, since E_AI is set 3 for middle-income industries and average wage
for those industries decrease, the simulation result of Gini coefficient is almost the same as
Case 1.

4.3. Sensitivity Analyses
Since our simulation depends much on the value of E_AI, elasticity of substitution between
AI and AI-exposed labor, then we did sensitivity analyses on different values of E_AI.
Table 5 shows that for wage inequality in top 5 and bottom 5 industry’s average wage, wage
inequality widens with high complementary E_AI value, 0.6 while income inequality shrinks
with high substitutive value, 7. This result is in line with our main results.
The result of Gini Coefficient is in the same direction. Inequality widens with highcomplementary E_AI value, 0.6 while income inequality shrinks with high substitutive value,
7.
From those two values, the simulation results’, whose assumption of E_AI has changed,
11New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Modelquantative impact is in line with our main simulation results. It follows that the analyses in theprevious sections have a certain level of robustness.

5. CONCLUSION
We quantitatively analyzed the effect of AI deployment to wage inequality. AI capital was
introduced into a CGE model with a 32-sector CGE model. Labor force was divided into AIexposed labor and non-AI-exposed labor. AI capital was combined with AI-exposed labor,
then the composite of AI capital and AI-exposed labor is combined with non-AI combinedlabor. Three scenarios are thus set: AI and AI-exposed labor are substitute (Case 1),complement (Case 2) and substitute in high average wage industry and complement in low
average wage industry (Case 3).
We examined the effect on wage inequality by increasing AI capital from 3% of physicalcapital to 6% of physical capital. The results are as follows, (i) wage inequality decreases withan increase of AI capital if substitution of AI and human labor is not so complmentary, (ii)wage inequality in top 5 and bottom 5 industry’s average wage decreases most in Case 3, and(iii) wage inequality in the Gini coefficient decreases most in Case 1.
A decrease in wage inequality by substitutive elasticity of AI is from average wage
reduction in relatively higher wage industries because their AI exposure rate is high. This result
is in line with Webb (2020). Wage inequality decreased some under complementary elasticityof AI. Our simulation results suggest that AI capital accumulation reduce income inequality insome extent. However, as in Table 5, if we set super high complementary subsutitution value
for E_AI, then wage inequality rises.
Our main results have some limitations. First, conversion of Webb’s AI exposure rate toJapanese occupation might not be so precise and accurate. Second, indicator of wage
inequality is calculated by industry aggregate average wage. There could be a wage differencein the same occupation and in the same industry. In a third, we expect AI deployment to start
uniformly and proportionate to industry physical capital. There can be some other assumption
to set AI capital deployment depending on industry level affinity to AI.
12

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

For future research, using exposure rate of robotics and IT that Webb (2020) calculated
can be used to compare the results of this paper. Webb (2020) estimated that the particularoccupation exposed to robots or software have resulted in declines in employment and wages
even though the occupations that are exposed to robots and software are different from thoseof AI. In addition, income outflow by AI capital developers and holders, who are sometimes
foreign companies, can also be discussed.

13

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

REFERENCES
Acemoglu, Daron, and David Autor. 2011. “Skills, Tasks and Technologies: Implications for
Employment and Earnings”. In Handbook of Labor Economics, Vol. 4B, edited by Orley
Ashenfelter, David Card, 1043–1171. Amsterdam: North-Holland.
Acemoglu, Daron, and Pascual Restrepo. 2018a. “The Race between Man and Machine:
Implications of Technology for Growth, Factor Shares, and Employment”. American
Economic Review 108(6): 1488–1542.
Acemoglu, Daron, and Pascual Restrepo. 2018b. “Artificial Intelligence, Automation andWork”, NBER Working Paper No. 24196. Cambridge, MA: National Bureau of Economic
Research.
Acemoglu, Daron, and Pascual Restrepo. 2020. “Robots and Jobs: Evidence from US Labor
Markets”. Journal of Political Economy 128(6): 2188–2244.
Acemoglu, Daron, David Autor, Jonathan Hazell and Pascual Restrepo. 2020. “AI and Jobs:
Evidence from Online Vacancies”. NBER Working Paper No. 28257. Cambridge, MA:
National Bureau of Economic Research.
Aghion, Phillipe, Céline Antonin and Simon Bunel. 2019. “Artificial Intelligence, growth and
Employment: The Role of Policy”. Economie et Statistique 510-512: 149–164.
Arntz, Melanie, Terry Gregory, and Ulrich Zierahn. 2016. “The Risk of Automation for Jobs
in OECD Countries: A Comparative Analysis”, OECD Social, Employment and Migration
Working Papers No. 189. Paris: OECD Publishing.
Autor, David H., and David Dorn. 2013. “The Growth of Low-Skill Service Jobs and thePolarization of the US Labor Market” The American Economic Review 103(5), 1553–1597.
Autor, David H., Frank Levy, and Richard J. Murnane. 2003. “The Skill Content of Recent
Technological Change: An Empirical Exploration”. The Quarterly Journal of Economics
118(4), 1279–1333.
Felton, Edward W., Manav Raj and Robert Seamans. 2018. “A Method to Link Advances in
Artificial Intelligence to Occupational Abilities”. AEA Papers and Preceedings 108: 1–4.
Felton, Edward W., Manav Raj and Robert Seamans. 2019. “The Occupational Impact of
14

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Artificial Intelligence: Labor, Skills, and Porlarization”. Ewing Marion Kauffman
Foundation Research Paper series.
Frey, Carl Benedikt, and Michael A. Osborne. 2017. “The Future of Employment: How
Susceptible Are Jobs to Computerisation?” Technological Forecasting and Social Change
114(C), 254–280.
Grennan, Jilian, Roni Michaely. 2020. “Artificial Intelligence and High-Skilled Work:
Evidence from Analysts”. Swiss Finance Institute Research Paper Series n20-84.
Higuchi, Yoshio. 1991. Japanese Economy and Vocational Behavior. Tokyo: Toyo Keizai Inc.
International Labour Organization. 2008. International Standard Classification of
Occupations 2008.
Nedelkoska, Ljubica, and Glenda Quintini. 2018. “Automation, Skills Use and Training”,
OECD Social, Employment and Migration Working Papers No. 202. Paris: OECD
Publishing.
Ota, Kiyoshi. 2010. “Wage Difference: by Individuals, Firm-size and Industries”. In Labor
market and income distribution, edited by Yoshio Higuchi, 319–368. Tokyo: Keio
University Press Inc.
Saito, Muneyuki, Shinya Kato and Shiro Takeda. 2017. “Effects of Immigration in Japan: A
Computable General Equilibrium Assessment”.
Tinbergen, Jan. 1974. “Substitution of Graduate by Other Labor”. Kyklos 27(2): 217–226.
Tinbergen, Jan. 1975. Income Difference: Recent Research. Amsterdam: North-Holland.
Webb, Michael. 2020. “The Impact of Artificial Intelligence on the Labor Market”.

15New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Table 1:

Conversion of Webb (2020)’s AI exposed rate to JIP by industry and occupation

Classification
15 Textile

Occupation classification by JIP
Professional and technical workers

Occupations in O*NETNumber ofworkers in U.S.

Webb's AI
exposure AI exposure
rate
rate

Professiona and technical workers (manufacturing): Average

86

Administrative occupation workers: Average

60

Clerical workers: Average

60

Sales Workers: Average

36Service Workers: Average

29

Administrative occupation workers
Clerical workers
Sales workers
Service workers
Craftsman and manufacturing and construction workers
Tailors, dressmakers, and sewers
Winding and twisting textile and apparel operatives
Knitters, loopers, and toppers textile operatives
Textile cutting and dyeing machine operators
Textile sewing machine operators
Clothing pressing machine operators
Miscellanious textile machine operators
69 Finance

72,514
12,792
9,323
11,137205,365
45,42524,388

57
92
100
98
47
21
84

52

Professional and technical workers
Professiona and technical workers (non-manufacturing): Average

64

Administrative occupation workers
Financial managers

67

67

Clerical workers
Clerical workers: Average

60

Sales workers
Financial service sales occupations

64

64

Bank tellers

2424Service workers
Craftsman and manufacturing and construction workers
Crafts man and manufacturing and construction workers: Average

1629New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Table 2: Composition of Professional and Technical Workers (Manufacturing): Average
Number ofworkers in U.S.

Occupations in O*NET
Production supervisors or foremen
Metallurgical and materials engineers
Civil engineers
Electrical engineers
Industrial engineers
Mechanical engineers
Engineers and other professionals, n.e.c.
Operations and systems researchers and analysts
Designers
Engineering and science technicians
Drafters
Surveryors, cartographers, mapping scientists/techs
Biological technicians
Chemical technicians

1,101,858
44,872362,290360,764
218,636
267,666
557,823
273,519785,607496,318
188,068
116,280
56,88579,569Webb's AI
exposure AI exposure
rate
rate
96
100
85
87
84
75
90
83
77
91
81
84
84
23
86

Table 3: Aggregate AI Exposure Rate by Occupation
Occupations
Professional and technical workers
Administrative occupation workers
Clerical workers
Sales workers
Service workers
Craftsman and manufacturing and construction workers

17

AI exposure
rate
62.3
67.3
62.5
26.4
29.2
51.9

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Table 4: Values of Elasticity of substitution
Symbol Sectors
E_KL
AGR
MIN
FOO
TEX,PPW,CHM,PAC,CSC,IAS,NFM, MET, GMA, ELE, ICE, ELC, TRN, PRE,
OTH, CNS, EGW, WAW, COM, FAI, RES, TRS, CAB, PUB, EDR, MED, OPS, BSE,
PSE
ONS
COM, TRS
E_LA
E_AI
Scenario
Case 1for all industries
Case 2for all industries
Case 3
Top 5 industry
CHM, PAC, EGW, WAW, PUB
Bottom 5 industry
AGR, RES, MED, OPS, PSE
Middle income industry
MIN,FOO,TEX,PPW,CSC,IAS,NFM,MET,GMA,ELE,ICE,ELC,TRN,PRE,OTH,CNS,
COM,FAI,TRS,CAB,EDR,BSE

Value
0.24310.20001.12001.26001.40001.6800
2

5.0
0.8

5.0
0.8
3.0

Note: Values of E_KL and E_LA is from Saito et al. (2017). Abbreviation of industries is in
Appendix 2.

Table 5: Difference Between Benchmark and 100% increase of AI Capital
Wage inequality in top 5 and bottom 5 industry’s average wage
EOS_AI

0.6

0.0006 EOS_AI

3-0.0105

0.8-0.00215-0.01230.9-0.00167-0.01320.6

0.00020 EOS_AI

3-0.00116

0.8-0.00015

5-0.00138

0.9-0.00009

7-0.00149

Gini Coefficient
EOS_AI18

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 1: Average Wage by Industry and AI Exposure Rate
AI exposure rate
100

80

60

40

20

0
0.0

2.0

4.0

6.0

8.0

10.0

12.0

Average wage by industry (million JPY).

19

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 2: Change in Wages, E_AI = 5, Case 1

Change in wages (%)
0.5
0.0
0

2

4

6

8

10

12

-0.5
-1.0
-1.5
-2.0
-2.5

Average wage by industry (million JPY).

Figure 3: Change in Wages and Average Wage by Industry, E_AI = 0.8, Case 2

Change in wages (%)
2.5

2.0

1.5

1.0

0.5

0.0
0

2

4

6

8

10

12

Average wage by industry (million JPY).

20

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 4: Change in Wages and Number of Employees, E_AI = 5, Case 1
Change in wages (%)

2.0

0.0
-2.5

-2.0

-1.5

-1.0

-0.5

0.0

0.5
-2.0

-4.0

-6.0

-8.0

Change in number of employees (%).

Figure 5: Change in Wages and Number of Employees, E_AI = 0.8, Case 2
Change in wages (%)
4.0

2.0

0.0
0.0

0.5

1.0

1.5

2.0

2.5

-2.0

Change in number of employees (%).

21

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 6: Change in Wages and AI exposure rate, E_AI = 5, Case 1
Change in wages (%)
0.5
0.0
-0.5
-1.0
-1.5
-2.0
-2.5
0

20

40

60

80

100

AI exposure rate.

Figure 7: Change in Wages and AI exposure rate, E_AI = 5, Case 2
Change in Wages (%)
2.5

2.0

1.5

1.0

0.5

0.0
0

20

40

60

80

100

AI exposure rate.

22

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 8: Ratio Between Top 5 Industry Average Wage and Bottom 5 Industry Average Wage
with an Increase of AI
3.57
3.56
3.55
3.54
3.53
3.52

Case 1: EOS AI = 5

Case 2: EOS AI = 0.8

Case 3: EOS AI = Mixed by industry

Figure 9: Gini Coefficient with an Increase of AI
0.208

0.204

Case 1: EOS AI = 5

Case 2: EOS AI =0.8

Case 3: EOS AI = Mixed by industry

23

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Figure 10: AI Capital and AI Exposure Rate
AI capital (per capita, Million per capita)
1.4
1.2
1
0.8
0.6
0.4
0.2
0
0

20

40

60

80

100

AI exposure rate.

24

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Appendix 1: CGE Model Structure

PrivateGovernment

consumption(X1,…, Xn)

consumption(CG1,…,

Investment(INV1,…,

Intermediate(INT1,…, INTn)

INVn)
Export(X1,…, Xn)

Output(Y1,…, Yn)
Import(M1,…, Mn)

Domestic production（Leontief）

Composite production factor

Intermediate input

E_KL
Labor
E_LA
Composite

CapitalNon AI-exposed Labor

AI-exposed
E_AI
AI-exposed labor

AI capital

25

New ESRI Working Paper No.65
The Effect of AI on Wages in Japan Using Computable General Equilibrium Model

Appendix 2: Classification of 32 Industries
SymbolExplanation

SymbolExplanation

AGR

Agriculture, forestry and fishery

PRE

Precision instruments

MIN

MiningOTH

Other manufacturing products

FOO

Food products

CNS

Construction

TEX

Textile products

EGW

Electricity, gas and heat supply

PPW

Pulp, paper and wooden products

WAW

Water supply and waste

CHM

Chemical productsCOM

Commerce

PAC

Petroleum and coal products

FAI

Financial and insurance

CSC

Ceramic, stone and cray products

RES

Real estate

IAS

Iron and SteelTRS

Transport

NFM

Non-ferrous metal

CAB

Communication and broadcasting

MET

Metal Products

PUB

Public administration

GMA

General Machinery

EDR

Education and research

ELE

Electrical Macinery

MED

Medical service, health and social security, and nursing care

ICE

Information and communication equipment

OPS

Other public services

ELC

Electrical equipment

BSE

Business services

TRN

Transportation equipment

PSE

Personnel services

26