DP2024-29

Did COVID-19 Deteriorate Mismatch in theJapanese Labor Market?
Yu d a i H I G A S H I
Masaru SASAKI

August 29, 2024

Did COVID-19 Deteriorate Mismatch in theJapanese Labor Market?*
Yudai Higashi†

Masaru Sasaki‡
August 29, 2024

Abstract: This study explores how the COVID-19 pandemic deteriorated the mismatchin the Japanese labor market. First, we focus on the differences in job flows by occupationand employment type, which differ according to the risk of infection. We then estimate
the mismatch indices for distinct labor markets clustered by occupations that are more
and less vulnerable to the pandemic using the method developed by Şahin et al. (2014).
We find that the pandemic induced a mismatch for full-time workers in occupations witha high risk of infection, those in which it is easy to work remotely, and those in which itis particularly difficult to work remotely, as well as for part-time workers in occupations
in which it is easy to work remotely.
JEL Classification Codes: J61, J62, J63
Keywords: Mismatch, O-NET data, COVID-19, Labor market tightness*

The authors would like to thank the Japanese Ministry of Health, Labour and Welfare (MHLW)
and the Japan Institute for Labour Policy and Training (JILPT) for the data used in this research. The
authors are also grateful to Derick Blaauw, Laszlo Goerke, the members of the COVID-19 andJapanese-style O-NET Data Research Group, and participants at the Asian and Australasian Societyof Labour Economics 2022 Conference and the 96th International Atlantic Economic Conference for
their valuable comments. This study was conducted under the COVID-19 and Japanese-style O-NET
Data Project at the JILPT. The views expressed in this study are those of the authors and are notthose of the JILPT. An earlier version of this paper was written in Japanese and included in JILPTDocument Series No. 256. This study was supported by JSPS KAKENHI Grant Number
JP22K13391. All remaining errors are our own.
†
Faculty of Economics, Kyoto Sangyo University, Kyoto, Japan; Research Institute for Economics
and Business Administration, Kobe University, Kobe, Japan. Corresponding author, email:yhigashi@cc.kyoto-su.ac.jp‡
Graduate School of Economics, Osaka University, Osaka, Japan; IZA, Germany.
11.

Introduction
In many countries, the effect of the COVID-19 pandemic on the labor market has

been heterogeneous across dimensions such as industry, occupation, and worker attributes(e.g., Adams-Prassl et al. 2020; Crossley et al. 2021; Koebel and Pohler 2020). This
heterogeneity is associated with job characteristics, including the intensity of face-to-faceinteractions (Avdiu and Nayyar 2020) and the feasibility of remote work (Dingel andNeiman 2020). Consequently, firms in sectors vulnerable to COVID-19 madeemployment adjustments by firing workers or requesting them to stay at home. Some
workers chose to leave work until the pandemic was controlled. However, others decided
to change jobs, leading to an increase in the number of job seekers with specific skills forjobs vulnerable to COVID-19.
Previous studies have shown that the COVID-19 pandemic changed job searchbehaviors. For example, job seekers in Sweden changed their search direction to
occupations less affected by the pandemic (Hensvik et al. 2021). In the Netherlands,
although the unemployed searched less during the recession caused by the pandemic thanduring other recessions, those who faced work situations that were exceptionally affectedby the pandemic searched more intensely (Balgová et al. 2022). In the UK, as COVID19 spread, some workers changed their search direction to expanding occupations,whereas others, especially non-employed and less-educated workers, searched for
declining occupations (Carrillo-Tudela et al. 2023). Such evidence suggests that during
the pandemic, occupations with a low risk of infection were relatively preferable, but
occupations in which workers were required to have close contact with one another were
avoided.
However, it is difficult for workers to quickly transfer to an occupation with a low
2risk of infection because obtaining the necessary skills for a specific occupation is costly.
Carrillo-Tudela et al. (2023) show that, although some workers from declining
occupations preferred to search for jobs in expanding occupations, they were less likely
to succeed in transferring their occupations. This suggests that those who were once
employed in occupations vulnerable to infection had no choice but to search for similar
occupations.
Simultaneously, firms vulnerable to COVID-19 decreased their demand for labor,leading to a decrease in job vacancies. For example, after the pandemic hit, vacancies in
the leisure, hospitality, and non-essential retail sectors sharply decreased, whereas thosein the essential retail sectors were hardly affected (Forsythe et al. 2020). Furthermore,vacancies in care and nursing increased (Arthur 2021). Firms that allowed theiremployees to work remotely even before the pandemic did not need to hold back on hiring
new workers during the early stages of the pandemic (Fukui et al. 2020). Therefore, job
matches were less likely to be fulfilled in an occupational labor market vulnerable to
COVID-19, where there were many job seekers but fewer vacancies.
This misalignment between job seekers and vacancies is considered to have widenedin some occupational labor markets but narrowed in others because of exposure to thepandemic. Although examining how the mismatch between job seekers and vacancies has
evolved over time from the viewpoint of dynamic labor market policymaking is
worthwhile, little is known about the effects of the COVID-19 pandemic on mismatch.
Among the few studies on the subject, Pizzinelli and Shibata (2023) reveal that although
the extent of mismatch across industries in the US and the UK rose sharply immediately
after the start of the pandemic, it recovered to previous levels within a few quarters,suggesting that the pandemic had a limited effect on the job matching process in these3

countries.
This study examines whether the COVID-19 pandemic deteriorated mismatch in theJapanese labor market. We adopt a twofold approach to answer this question, using theadministrative data of public employment services and Japanese-style O-NET data. First,
we focus on the variants in terms of measures relevant to matching in the Japanese labormarket (i.e., labor market tightness and the job finding rate). Second, we estimate themismatch across occupations. The method we use to calculate the mismatch index,
developed by Şahin et al. (2014), involves calculating the counterfactual distribution ofmatches across distinct labor markets chosen by the social planner to maximize thenumber of matches in the entire labor market. The extent of the mismatch is measured as
the difference between the actual and counterfactual distributions of the matches. Thisdifference indicates the number of matches that would not have been lost if jobs had been
allocated efficiently across distinct labor markets. This method allows us to measuremismatch indices, given that distinct labor markets are heterogeneous with respect tomatching efficiency. Pizzinelli and Shibata (2023), who investigate the effect of COVID19 on mismatch in the US and UK, follow this method.
In Japan, Kawata (2019) and Shibata (2020) use this method and confirm that amismatch across occupations exists using the same administrative data we use butcovering periods before the pandemic. Kawakami (2021), using the same administrative
data from July 2017 to July 2020, reveals that in the early stages of the pandemic, themismatch that already existed in Japan deteriorated due to the COVID-19 pandemic.
Additionally, utilizing data on the characteristics of occupations from the Japanese-style
O-NET, the author also finds that during the early days of the pandemic, there was anexcess labor supply in occupations in which workers were required to work in close4contact with others. This finding is consistent with previous studies that show the adverse
effects of COVID-19 to be heterogeneous in Japan with respect to individual andoccupational characteristics.1
This study extends the length of the administrative data on public employment
services to March 2023. In addition to using longer data, two features of this study
distinguish it from previous literature and address a gap. First, we distinguish between
occupations in which workers were more vulnerable to COVID-19. The Japanese-style
O-NET dataset covers job characteristics of occupations and directly asks respondents
engaging in each occupation about “whether or not workers are in close contact withothers,” “whether the risk of infection is high or low,” and “whether or not workers areallowed to work remotely.” Merging this dataset with administrative data by occupational
code allows us to examine the differences in job flows and the extent of mismatch indistinct labor markets clustered by occupations at risk of infection and the availability of
remote work. This approach is more direct than that used in the literature measuring
occupations’ vulnerability to COVID-19 in terms of the risk of infection and remote work
feasibility based on job features in typical situations (e.g., Dingel and Neiman 2020).
Second, we focus on the effects of the pandemic on job flows and mismatch by
employment type (full-time versus part-time), as well as the extent of vulnerability to
COVID-19. We assume that labor markets segregated by employment type are

1

In Japan, women, part-time workers, and workers belonging to the restaurant and bar sectors weremore likely to be absent from work during the early days of the pandemic crisis (Fukai et al. 2021)
and to be unemployed or out of the labor force by December 2020 (Fukai 2022). Workers in firmsthat the local government requested to temporarily suspend their business because workers engaged
in frequent contact with others (Hoshi et al. 2022) and those who were not allowed to work a flexible
schedule or remotely were also adversely affected by the pandemic (Kikuchi et al. 2021). The yearon-year increasing rate of the number of unemployed was similar in the second half of 2020 to thatof the Great Recession, whereas the year-on-year decreasing rate of new hires in the restaurant andbar sectors was higher in the second half of 2020 than during the Great Recession (Kawata 2021).
5

independent of one another because workers do not typically alternate between these two
employment types.
The two main findings are summarized as follows. First, the labor market worsened
after March 2020 when the national government requested that all schools temporarilyclose because of an increase in the number of COVID-19 cases. We assume that the
pandemic began this month in Japan. Both labor market tightness and job finding rate
decreased. We observe the same patterns of job flow regardless of employment type (fulltime or part-time) and occupation type (low or high risk of infection, and easy or difficult
to work remotely). Labor market tightness gradually recovered in early 2021,
approximately one year after the start of the pandemic. However, the job finding rate did
not recover but remained at a lower level, suggesting that a mismatch occurred.
Second, we find that the mismatch rapidly increased after March 2020 for full-timeworkers in occupations with a high risk of infection, those in which it is easy to work
remotely, those in which it is particularly difficult to work remotely, and part-timeworkers in occupations in which it is easy to work remotely. High-risk and difficult-towork-remotely occupations can reflect those requiring face-to-face contact that were
adversely affected by the pandemic. Easy-to-work-remotely occupations may have been
preferred by workers during the pandemic. Thus, we consider that the mismatch in thesetypes of occupations increased due to the pandemic.
The remainder of this paper is organized as follows. Section 2 provides details of thedata we utilize. Section 3 describes the trends in the variables of interest, such as labormarket tightness and the job finding rate. Section 4 presents the trends in mismatch.
Finally, Section 5 presents concluding remarks.

6

2.

Data
This section introduces the two datasets and explains their merging. We then explain

which occupations are defined as low and high risk of infection and which are easy anddifficult to work remotely.

2.1. Merging the two datasets
We begin by explaining the “Employment Referrals for General Workers (Report on
Employment Service)” (ERGW/RES), which comprises administrative data on public
employment services released by the Japanese Ministry of Health, Labour and Welfare(MHLW). The data include the number of job seekers, vacancies, and new hires registeredat each local public employment security office nationwide.2 These variables enable us
to measure labor market tightness, job finding rates, and mismatch indices. We use
monthly panel data aggregated at the small-classified occupation level based on the
MHLW occupational classification (revised in 2011). The advantage of using the
ERGW/RES dataset based on the small-classified occupations rather than the larger
occupational classifications used by previous studies measuring mismatch indices forJapan through the method developed by Şahin et al. (2014) is that merging the datasetwith the Japanese-style O-NET, containing one level smaller occupational classification,
can be more precise.3 The variables are separately available by employment type, namely
full-time and part-time.4 New graduates and vacancies targeting them are not included in2

In the original data, the number of “job seekers,” “vacancies,” and “new hires” are labeled as“monthly active applicants,” “monthly active job openings,” and “persons who found employment,”
respectively.
3
To calculate the mismatch indices for Japan using the method of Şahin et al. (2014), previous
studies that used the ERGW/RES dataset aggregate at occupations of large-classification (Kawata
2019; Shibata 2020) and middle-classification (Shibata 2020; Kawakami 2021).
4
In the original data, “full-time workers” and “part-time workers” are labeled as “regular workers
7the dataset. The dataset covers 369 occupations from June 2016 to March 2023, crossing
the threshold of March 2020 when school closures were ordered owing to the spread of
COVID-19 in Japan.5
Vacancies aggregated into small-classified occupations and employment types were
calculated based on the information of firms registered with the local public employment
security office. Job seekers registered their desired conditions, including occupation andemployment type, when they registered themselves as job seekers. Based on thisinformation, not only the number of vacancies but also the number of job seekers isavailable at the small-classified occupation level by employment type. Note that job
seekers who wrote about their desired occupation in the middle classification or who did
not write about their desired occupation in the application form are not included in thedataset.
A potential concern is whether the data on public employment services (ERGW/RES)
are representative of the Japanese labor market. According to the 2016–2022 Survey on
Employment Trends conducted by the MHLW, 14–18% of workers were hired by
employers through public employment services. Other major channels were
advertisements (approximately 30%), personal connections (approximately 20%),
schools (approximately 7%), and private employment referral services (approximately5%). Although the coverage of public employment services data is limited, we consider
its use to be the best way to capture the job matching process in Japan because no
excluding part-timers” and “regular part-timers,” respectively. According to the definition of the
MHLW, “regular workers” are those whose employment contract stipulates no employment periods,
or a four-month or longer period of employment. “Part-timers” indicates workers whose usual
weekly working hours are less than other workers in the same establishment. See the website of the
MHLW in detail (https://www.mhlw.go.jp/toukei/list/114-1_yougo.html (in Japanese) (accessed on
November 6, 2023)).
5
We do not use data after March 2023 because the occupational classification was changed in April
2023; thus, it is difficult to consistently connect with the data before and after this month.
8

comprehensive data on the number of job seekers, vacancies, and new hires for otherchannels are available. However, the data characteristics must be considered. Job seekers
registered in public employment security offices tend to have disadvantageous
characteristics, such as low education and longer unemployment periods, and experiencea decrease in their wages after they find jobs through this service, suggesting that public
employment services play a role in the safety net (JILPT 2015; Kodama et al. 2004).
Although the data on public employment services do not necessarily reflect the Japaneselabor market, we consider that the results from these data can have significant
implications for employment policy since the workers and firms in these data seem to be
the most vulnerable to negative shocks such as COVID-19, indicating that they are theprime policy targets.
The Japanese-style O-NET released by the Japanese Institute for Labour Policy andTraining (JILPT) quantifies the characteristics of each occupation.6 A large-scale survey
was conducted in which registered workers engaged in each occupation were subjectively
asked about occupational characteristics, such as the skills and knowledge necessary to
perform tasks.7 Workers responded to each question on a multiple-point scale, and theaverage score was calculated for each occupation. We use a version based on a survey
administered from January 19 to February 15, 2021, which added information identifying
which occupations were at risk of infection and which allowed workers to work remotely.
To explore the characteristics of labor markets clustered by occupation at a high or
low risk of infection and those allowing remote work, we merge the two datasets. Notethat this is possible because although the occupations in the Japanese-style O-NET follow
their own classification, they provide the corresponding MHLW occupational
6
7

The Japanese-style O-NET dataset is labeled as “job tag.”
See JILPT (2021) for details.
9classification (revised in 2011) code at the sub-small classification level, which is one
level smaller than the small classification. The version of the Japanese-style O-NET
dataset that we use covers 497 occupations, 75 of which are omitted because of a lack ofnecessary information.8 Some occupations in the Japanese-style O-NET dataset share
MHLW occupational codes. To merge the two datasets based on the MHLW occupationalclassification, we convert the values of the variables in the Japanese-style O-NET dataset
to the averages of the MHLW classification-based occupations. Finally, 422 occupationsin the Japanese-style O-NET dataset are consolidated into 228. These occupations are
merged into the ERGW/RES using occupational codes. As ERGW/RES cover 369
occupations, 141 occupations are deleted.9
There are two noteworthy points. First, the version of the Japanese-style O-NET data
used in this study contain information on the occupational characteristics of infection risk
and remote work availability collected from January 19 to February 15, 2021. We assume
that the occupation characteristics remain unchanged over a long period and apply theoccupational characteristics as of 2021 to any period from June 2016 to March 2023.
Second, Japanese-style O-NET data do not distinguish occupational characteristics by
employment type; therefore, we assume that there are no differences in occupational
characteristics between full-time and part-time workers.
As mentioned previously, 141 of the 369 occupations are deleted from the
ERGW/RES data because of a lack of information on occupational characteristics inJapanese-style O-NET data, leading to concerns about the problem caused by sample

8

The MHLW occupational code corresponding to the O-NET occupational code is based on “Input
Data for the occupational information network of Japan version 2.01,” released by the JILPT,
downloaded from the websites of the Japanese-style O-NET(http://shigoto.mhlw.go.jp/User/download) on August 2, 2021.
9
See Figure OA.1 in the Online Appendix for the method to merge the two datasets in detail.
10selection bias. Table OA.1 in the Online Appendix shows the differences in three variables(number of job seekers, vacancies, and new hires by employment type) between the 228
occupations with O-NET data and the remaining 141 occupations without O-NET data.
We find that the averages of these variables are 6–13 times larger in the 228-occupationgroup than in the deleted 141-occupation group. These results indicate that the deleted
141-occupation group is far smaller in scale than the overall labor market. Additionally,
Figure OA.2 in the Online Appendix shows that the trends of labor market tightness andthe job finding rate for all 369 occupations and 228 occupations with O-NET data arevery similar, whereas the trends of these variables for 144 occupations without O-NET
data largely differ from those for all 369 occupations. Therefore, we interpret that the
problem caused by sample selection bias is minor.

2.2. Which occupations are vulnerable to COVID-19?
This subsection presents two indices for identifying the occupations vulnerable to
COVID-19. We explain two methods to distinguish between the two groups ofoccupations: whether the risk of infection is high or low in the workplace and how often
workers are allowed to work remotely. Table OA.2 in the Online Appendix presents thegroups under which each occupation is classified.
We begin with the first method of distinguishing between workers at high and low
risk of infection. We compute the average of the scores from the two questions to measure
the extent of infection risk. The first question is “How frequently do you leave yourself
vulnerable to infectious diseases in your workplace?” Respondents answered thisquestion on a five-point scale: (1) once a year or not at all, (2) once a year or more, (3)once a month or more, (4) once a week or more, and (5) almost every day. This score11

indicates how seriously COVID-19 is endangering worker health in the workplace(infectious disease risk). The second question is “How closely do you and your colleagues
generally contact each other in your workplace?” and answered on a five-point scale: (1)no contact with others or far away from others by 30 meters or more; (2) work with others
but maintain a physical distance of 5 meters or more; (3) not close to or do not reachothers when one extends an arm; (4) close to and reach others when one extends an arm;and (5) very close (shoulder-to-shoulder). We also use the second question because close
contact (i.e., physical proximity to others) increases the risk of catching the virus, which
is airborne and carried by droplets from infected individuals.10
We compute the arithmetic average of the two scores as a synthetic risk index to
identify which occupations were at high or low risk of infection in the workplace. 11
Figure 1 shows the distributions of the indices for infectious disease risk and physicalproximity to others, as well as the distribution of the synthetic risk index. When thesynthetic risk index of an occupation is high, workers are more likely to be infected in theworkplace. We employ two thresholds to distinguish between occupations at high and low
risk of infection–the 50th or 75th percentile of the synthetic index–and define occupations
above these thresholds as high risk.
[Insert Figure 1 about here]
The second index measures the frequency with which workers are allowed to work
remotely. The Japanese-style O-NET additionally asked respondents the following

10

See WHO website, “Coronavirus disease (COVID-19): How is it transmitted?”(https://www.who.int/news-room/questions-and-answers/item/coronavirus-disease-covid-19-how-isittransmitted#:~:text=Current%20evidence%20suggests%20that%20the,%2C%20speak%2C%20sing%20or%20breathe.)11
Because the two scores are within a range from 1 to 5, the synthetic index is also within a range
from 1 to 5.
12

question: “How often do you think workers engaged in the same job as you were allowed
to work remotely during the state of emergency (April–May 2020)? It should be notedthat we would like you to respond to whether remote work is available not for you but forother workers in the same jobs.” Respondents answered on a six-point scale: (1) usually,no; (2) 20% of days of duty and below; (3) 20% or more but below 40% of days of duty;(4) 40% or more but below 60% of days of duty; (5) 60% or more but below 80% of daysof duty; and (6) 80% of days of duty and more.
The Japanese-style O-NET contains the proportion of respondents to this question
based on the six choices above.12 To create a summarized index of the availability of
remote work, we calculate the sum of the fraction of respondents weighted by the medianrange of the percentage of remote work availability for each choice, as shown in Figure
2.13,14 This figure shows that the availability of remote work is low for most occupations.
We define an occupation as difficult to work remotely if the magnitude of the availabilityof remote work, in order from the largest (i.e., from easy to difficult), is over the 50th or
75th percentile and as easy to work remotely otherwise.
[Insert Figure 2 about here]
Figure 3 plots the relationship between the synthetic index of risk of infection andthe index of availability of remote work. The correlation coefficient is -0.47, indicatingthat the two indices exhibit a negative correlation, but its magnitude is not large.
Occupations with a higher availability of remote work tend to face lower risks of infection.
However, occupations with a lower risk of infection do not always exhibit higher

12

Figure OA.3 shows these distribution.
The median of each choice is: (1) = 0%; (2) = 10%; (3) = 30%; (4) = 50%; (5) = 70%; (6) = 90%.
14
We also try another method to create the index of the availability of remote work. The Online
Appendix contains the definition of this alternative index and some descriptive and estimation
results based on it.
13

13

availability of remote work. This tendency suggests that the two different indices cancapture different aspects of vulnerability to the COVID-19 pandemic.
[Insert Figure 3 about here]

3.

Labor market tightness and job finding rate
This section presents the monthly moving average trends in labor market tightness(ratio of vacancies to job seekers) and job finding rates (ratio of new hires to job seekers)
from May 2017 to March 2023, covering the period before and after the pandemic.15
Panels (a) and (b) of Figure 4 display the trends in labor market tightness and job
finding rates by synthetic risk index (low versus high risk of infection) using two different
thresholds (the 50th and 75th percentiles), respectively. These panels show the trends forfull-time workers. Note that the extent of labor market tightness is measured on the left
vertical line, and that of the job finding rate is measured on the right vertical line.
[Insert Figure 4 about here]
First, labor market tightness decreased after the pandemic emerged, regardless ofwhether the infection risk was high or low, leading to a decrease in the job finding rate
for workers. However, labor market tightness increased in early 2021 and almost
recovered in March 2023. At the same time, although the job finding rate also stopped
decreasing in 2021, it did not recover and remained low, suggesting that a persistentmismatch across occupations occurred. Second, labor market tightness was always
greater than one over the period, implying that the demand for labor (the number ofvacancies) always exceeded the supply of labor (the number of job seekers). This reveals

15

The data are available from June 2016 but the figures plots from May 2017 because the moving
average is defined as the average of numbers obtained from the current month and the past 11
months (i.e., the 12-month backward moving averages).
14that the Japanese labor market for full-time workers suffered from a chronic labor
shortage even after negative shocks such as the COVID-19 pandemic.
Panels (c) and (d) for part-time workers show trends similar to those for full-timeworkers. That is, labor market tightness and the job finding rate decreased after the
pandemic. These decreases in labor market tightness and job finding rates for high-risk
occupations are steeper than those for low-risk occupations. However, labor market
tightness showed an upward trend in early 2021, whereas the job finding rate continued
to decrease.
We now examine these trends using the index of the availability of remote work.
Panels (a) and (b) of Figure 5 display the moving average trends in labor market tightness
and the job finding rate using the two thresholds (50th and 75th percentiles) to distinguishwhether remote work is difficult for full-time workers. The trends in panel (a) are similar
to those in panel (b); regardless of the thresholds, labor market tightness decreased forboth easy- and difficult-to-work-remotely occupations since the pandemic, and the job
finding rates also decreased. However, labor market tightness showed an upward trend inearly 2021, while the job finding rates stopped decreasing sharply and remained low.
[Insert Figure 5 about here]
Panels (c) and (d) for part-time workers show that for both easy- and difficult-towork-remotely occupations, labor market tightness decreased since the pandemic. Theyshow an upward trend in early 2021. The job finding rates decreased over the sample
period, except for easy-to-work-remotely occupations in panel (c), between early 2020and mid-2021. These opposite trends, namely, an increase in labor market tightness anda decrease in the job finding rates after the pandemic, suggest that the mismatch arose
due to the pandemic.
15

4.

Mismatch
This section presents the transitions in the mismatch between workers and firms

across distinct labor markets segmented by small-classified occupations over time. The
mismatch indices developed by Şahin et al. (2014) are calculated. Their techniquemeasures the fraction of actual matches to optimal matches; that is, it is the fraction ofmatches that would have been fulfilled if workers had searched for jobs in ideal distinct
labor markets. Their model assumes that a social planner who maximizes the number ofmarket-wide matches determines the optimal allocation of job seekers across distinct
labor markets. An innovation of their technique is that the optimal number of matches iscalculated by accounting for the heterogeneity of matching efficiencies across distinct
labor markets. Appendix A presents the definition and calculation method of the mismatch
index in detail.
From the sample, we delete data on occupations in which the number of new hires iszero for one or more periods because we cannot take the log of its value, which isnecessary to calculate the mismatch indices. 16 Therefore, the number of occupations
decreases from 228 to 207 in the sample of both employment types (i.e., aggregation of
full-time and part-time workers), 202 in the sample of full-time workers, and 156 in thesample of part-time workers.17 This exclusion of occupations is not expected to reduce
the representativeness of the sample, as occupations with zero new hires for one or more
periods across all public employment offices are relatively minor in Japan. Indeed, Figure
OA.4 in the Online Appendix shows that the trends in labor market tightness and the job

16

See Appendix A in detail.
Occupations utilized to calculate the mismatch indices are listed in Table OA.2 in the Online
Appendix.
17

16

finding rate for occupations included in the sample used to estimate the mismatch indices
are quite similar to those in the full sample until the previous section. In contrast, thetrends for occupations excluded from the sample differ markedly from those in the fullsample.18
Subsection 4.1 first shows the mismatch indices nationwide. Additionally, as in
Section 3, subsection 4.2 measures the mismatch indices across occupations according to
the risk of infection. That is, we compare the mismatch across occupations in the labormarket with low and high risks of infection. In other words, we assume that the mismatchoccurs across occupations with a similar level of risk of infection but not acrossoccupations with a largely different risk level. We consider a case in which there is little
labor mobility across occupations with largely different risks of infection because there
are broad variations in their job characteristics. For example, “food and drink service
workers” and “Japanese Inn, hotel, and transportation customer service workers” areclassified as high-risk occupations based on the 75th percentile threshold. “Telephone
receptionists” is classified as a high-risk occupation based on the 50th percentile threshold.
These workers may be able to change their occupations to “building cleaning workers,”
a low-risk occupation based on both the 50th and 75th percentile thresholds. However,many low-risk occupations seem to require high skills or qualifications of workers, suchas “accountants business clerks,” “software creators,” and “electrical, electronic,
telecommunications technicians.” In subsection 4.3, the same procedure is used for
occupational groups based on the availability of remote work. We also assume little labor

18

Labor market tightness and job finding rate can be calculated for the excluded sample because
these variables are calculated by aggregating all occupations in this sample. Additionally, even if thenumber of new hires is zero for some occupations in some periods, it is not zero for otheroccupations simultaneously in this sample. As a result, the total number of new hires for the entire
excluded sample is not zero across all periods.
17mobility across the largely different availabilities of remote work. For example, “foodand drink service workers” and “building cleaning workers” are classified as difficult-towork-remotely occupations based on the 75th percentile threshold. “Japanese Inn, hotel,
and transportation customer service workers” is also classified as a difficult-to-workremotely occupation based on the 50th percentile threshold. These workers can change
their occupations to “telephone receptionists,” an easy-to-work-remotely occupation
based on the 50th percentile threshold. However, similar to low-risk occupations, manyeasy-to-work-remotely occupations seem to require high skills or qualifications fromworkers.
Finally, subsections 4.4 and 4.5 focus on the mismatch across different risks ofinfection and across different availabilities of remote work, respectively. These
approaches allow us to measure the mismatch associated with labor mobility across theseoccupational attributes.

4.1. Overall mismatch
Before showing the mismatch by occupational group, we begin with Figure 6, whichdisplays the trends in the overall mismatch across occupations, using an aggregatedsample of full- and part-time workers. Panel (a) presents the mismatch index. Because
the mismatch index fluctuates seasonally, we also show its moving average. 19 The
mismatch index shows an overall upward trend. This trend accelerated immediately after
the pandemic, and the extent of the mismatch remained high until early 2023.
[Insert Figure 6 about here]

19

The data are available from June 2016, but the figures plot the original series and moving average
series of mismatch indices from July 2016 and June 2017, respectively, because we took a oneperiod lag to calculate the indices. See Appendix A in detail.
18

Panel (b) shows actual, counterfactual, and mismatch unemployment rates.
According to Şahin et al. (2014), the counterfactual unemployment rate, derived from themismatch index, is the optimal unemployment rate in the absence of a mismatch.20 The
mismatch unemployment rate is the actual unemployment rate minus the counterfactual
unemployment rate. This panel shows that actual and counterfactual unemployment rates
increased after the onset of the pandemic. Consequently, the mismatch unemployment
rate remained almost constant before and after the pandemic. This suggests that themismatch indeed increased due to the pandemic but hardly resulted in an increase in theunemployment rate. We consider that the increase in the unemployment rate after the
pandemic mainly came from labor demand shortages.
The labor market should be segmented into full-time and part-time workers. Figure
7 shows the trends in the mismatch indices according to employment type. In panel (a),we find that, although the variation in the mismatch index for full-time workers is larger
than the overall trends (panel (a) of Table 6), their trends are very similar. In contrast, themismatch index for part-time workers in panel (b) did not follow an upward trend but wasalmost constant before the pandemic. After the pandemic, the mismatch index for parttime workers increased. Overall, the pandemic increased the mismatch across occupations
for both full-time and part-time workers. Note that we do not calculate the mismatch
unemployment rate by employment type because no employment type can be attributed
to the unemployed.
[Insert Figure 7 about here]

4.2. Occupational mismatch by high versus low infection risk occupation groups
20

See Appendix A for the method of calculation of counterfactual unemployment rate, using themismatch indices, in detail.
19

Next, we focus on exactly where the mismatch found in the previous subsection
occurred within the labor market by dividing the labor market into high- and low-risk
groups. As mentioned in the previous subsection, the overall mismatch increased but
hardly induced an increase in the mismatch unemployment rate. Nevertheless, we explore
the mismatch indices in detail because workers in some parts of the labor market, namely
those in occupations that are vulnerable to the COVID-19 pandemic, might suffer from alarge mismatch even if the overall mismatch unemployment rate does not seem to change.
Panels (a) and (b) of Figure 8 illustrate the trends in the mismatch indices using thesynthetic risk index for both employment types (i.e., aggregation of full- and part-timeworkers). Panels (a) and (b) present the results of the 50th and 75th percentiles of thesynthetic risk index as thresholds for occupational groups with a high risk of infection,
respectively. We find similar results for both panels with different thresholds. Before thepandemic, the mismatch indices remained almost constant or increased slightly over time,
regardless of the risk of infection. Immediately after March 2020, when the pandemic
began in Japan, the mismatch indices sharply increased for high-risk occupations,whereas those for low-risk occupations gradually increased over the sample period.
[Insert Figure 8 about here]
Panels (c) and (d) of Figure 8 present trends in the mismatch indices for full-timeworkers. We obtain results similar to those in panels (a) and (b) for both employment
types. After March 2020, the mismatch indices increased sharply in high-risk occupations,whereas the rate of increase in the mismatch indices for low-risk occupations was trivial.
The mismatch across high-risk occupations due to the pandemic was gradually reconciled,
but the level remained higher than before the pandemic. The mismatch indices began toincrease gradually from mid-2020 for low-risk occupations and remained high until early202023.
Panels (e) and (f) of Figure 8 present trends in the mismatch indices for part-timeworkers. Panel (e), using the 50th percentile as the threshold, shows that the mismatch
index in low-risk occupations gradually increased from 2020 to 2022, although it
temporarily decreased immediately after March 2020. The extent of change in themismatch was small. For high-risk occupations, the mismatch index increased after
March 2020; however, this increase was small. This trend did not continue, and themismatch index decreased by 2021. When we employ the 75th percentile as the threshold,as shown in panel (f), the mismatch index in low-risk occupations increased from 2020
to mid-2021, although to a lesser extent. By contrast, the mismatch index in high-risk
occupations was almost constant throughout the sample period. Thus, for part-timeworkers, the mismatch due to the pandemic was small.

4.3. Occupational mismatch by easy- versus difficult-to-work-remotely occupationgroupsThis subsection presents the trends in mismatch indices by occupational group basedon the availability of remote work. In Figure 9, panels (a) and (b), which use the 50th and75th percentiles, respectively, as thresholds to distinguish between the availability of
remote work, present the results for both employment types (i.e., the aggregation of fulltime and part-time workers). Easy-to-work-remotely occupations exhibited a steadyincrease in the mismatch indices after the pandemic. For difficult-to-work-remotely
occupations, the mismatch indices increased slightly after the pandemic using the 50th
percentile threshold. When we employ the 75th percentile threshold, the mismatch acrossdifficult-to-work-remotely occupations increased sharply immediately after the pandemic
21

emerged. This extent of mismatch gradually decreased after mid-2020 but remained at ahigher level than before the pandemic. This result suggests that a mismatch in the labormarket for easy-to-work-remotely occupations arose because of the pandemic andcontinued to worsen. The mismatch due to the pandemic also increased in the labormarket of occupations in which it is particularly difficult to work remotely (i.e., difficultto-work-remotely occupations based on the 75 percentile threshold).
[Insert Figure 9 about here]
Panels (c) and (d) of Figure 9 display the trends in the mismatch indices for full-timeworkers using the 50th and 75th percentiles as thresholds, respectively. We obtain results
similar to those in panels (a) and (b) for both employment types. We find that themismatch increased for easy-to-work-remotely occupations and particularly difficult-towork-remotely occupations.
Finally, panels (e) and (f) of Figure 9 present the trends in the mismatch indices forpart-time workers using the 50th and 75th percentiles as thresholds, respectively. For bothpanels, the mismatch across easy-to-work-remotely occupations increased after March
2020, but to a small extent. In contrast, the mismatch across difficult-to-work-remotely
occupations did not change much over time, even during the pandemic. Thus, we consider
that the COVID-19 pandemic did not cause a severe mismatch in the part-time labormarket, which was segmented by the availability of remote work.

4.4. Mismatch across occupational clusters with different infection risks
Subsections 4.2 and 4.3 evaluate the mismatch across small-classified occupations,assuming that the labor market was segmented by the risk of infection and the availabilityof remote work, respectively. These analyses enable us to identify the part of the labor22market affected by the mismatch. However, some job seekers may have searched for jobs
in different labor markets due to the pandemic. Some high-risk occupations may have
moved to low-risk occupations and vice versa. The same is true for mobility between
easy- and difficult-to-work-remotely occupations. The following subsections describe themismatch related to these types of mobility.
In this subsection, occupational clusters are defined on the basis of a synthetic risk
index. As defined in Section 3, each occupation has a synthetic risk index score. We list
the occupations in order of their scores and divide them into eight clusters. In other words,
we reaggregate the small-classified occupations into eight clusters that are attributed to
the risk of infection.21 Furthermore, we compare the mismatch indices across clusters in
the high- and low-risk labor markets using the 50th and 75th percentiles as thresholds. In
this case, we assume that job seekers do not move to largely different labor markets interms of the risk of infection but move to nearby markets. To measure mismatch indices,
there must be at least two clusters. We adopt eight clusters because when the 75th
percentile is employed as the threshold, the number of clusters in low- and high-risk
occupations is six and two (i.e., the necessary minimum number of occupations),
respectively.22
Panel (a) of Figure 10 illustrates the mismatch index across the eight occupational
clusters with different levels of infection risk for both employment types. After March
2020, although the extent of the change in the mismatch index was small, the mismatchdecreased, suggesting that labor mobility across occupational clusters with different risks
was promoted. This result differs from that shown in Figure 6, which indicates an increase

21

Table OA.2 in the Online Appendix presents eight occupational clusters.
When the 50th percentile is employed as the threshold, the number of clusters for both low- andhigh-risk occupations is four.
22

23

in mismatch across small-classified occupations after the pandemic. However, a decreasein the mismatch index hardly affects the mismatch unemployment rate, as shown in panel(b) of Figure 10.23[Insert Figure 10 about here]
Panel (a) of Figure 11 for full-time workers shows results similar to those for both
employment types; that is, the mismatch index decreased after the pandemic began. For
part-time workers, panel (b) shows that the mismatch index remained almost constantover time.
[Insert Figure 11 about here]
Overall, especially for full-time workers, we found conflicting results between thedefinitions of distinct labor markets; that is, the mismatch across small-classified
occupations increased, whereas it decreased across occupational clusters with different
risks. One possibility for the unstable results is that a mismatch occurred in some fractionsof the labor market, while it did not occur in other fractions if most workers did notfrequently move to largely different types of occupations, such as the risk of infection.
This is because low-risk occupations are considered to require higher skills or
qualifications than high-risk occupations, as mentioned at the beginning of this section.
To detect where the mismatch occurred in the labor market, we compare the mismatchindices between the labor markets of low- and high-risk occupations.
Panels (a) and (b) of Figure 12 present the mismatch indices of low- and high-risk
occupations for both employment types. Using the 50th percentile as the threshold, panel(a) shows that the mismatch for low-risk occupations decreased after March 2020, while

23

The mismatch unemployment was negative in a few months, although this seems theoreticallyinconsistent. This is probably because the sources of data for the mismatch indices and thecounterfactual unemployment rate are different. See also Appendix A in detail.
24

that for high-risk occupations remained almost constant over time. When we employ the75th percentile as the threshold, as shown in panel (b), the mismatch for low-risk
occupations decreased, whereas that for high-risk occupations increased immediately
after March 2020. The mismatch for high-risk occupations was gradually eliminated, but
it took approximately three years to recover to the pre-pandemic level. These results
suggest that a mismatch due to the pandemic occurred in the riskiest occupational group.
[Insert Figure 12 about here]
For full-time workers, panels (c) and (d) provide suggestions similar to those for both
employment types. The mismatch index for both low- and high-risk occupations
decreased when the threshold was at the 50th percentile (panel (c)). Using the 75th
percentile as the threshold, the mismatch for high-risk occupations increased immediately
after March 2020 (panel (d)), suggesting that the pandemic caused a mismatch in theriskiest occupational group.
Panels (e) and (f) of Figure 12 show the mismatch indices for part-time workers.
Using the 50th percentile as the threshold, panel (e) shows that the mismatch between
low- and high-risk occupations decreased and increased after March 2020, respectively.
However, these results are reversed when the 75th percentile is employed as the threshold,as shown in panel (f). The two relatively lower-risk occupational clusters among the four
occupational clusters in the high-risk labor market under the 50th percentile threshold
move to the low-risk labor market under the 75th percentile threshold. The mismatch
between these two clusters and the four existing clusters in the low-risk labor market
below the 50th percentile threshold may be large. However, in both panels, the extent of
the change in the mismatch indices was small for both low- and high-risk occupations.
Altogether, when we separately measure the mismatch indices by high- and low25

risk occupations, we find that the mismatch across small-classification occupations (Table8) and occupational clusters with different infection risks (Table 12) leads to similar
evidence, although there are some different results. First, the extent of the mismatch inhigh-risk occupations for full-time workers increased after March 2020, suggesting thatlabor mobility in this market was discouraged due to the COVID-19 pandemic. High-risk
occupations are in sectors requiring face-to-face contact. Such sectors were implicitly
forced to suspend their businesses or shorten their working hours through voluntary banson leaving their homes, resulting in a mismatch. Second, for part-time workers, the extent
of the mismatch due to the pandemic was limited.

4.5. Mismatch across occupational clusters with different availability of remote
work
Next, we define occupational clusters based on the availability of remote work in amanner similar to that described in the previous subsection. Specifically, occupations are
divided into eight clusters after they are listed in order of the availability of remote work.
In Figure 13, for both employment types, panel (a) presents the mismatch index
across the eight occupational clusters with different levels of remote work availability,
whereas panel (b) displays the mismatch unemployment rate calculated by the mismatch
index. The mismatch index slightly decreased immediately after March 2020 but hardly
affected the mismatch unemployment rate. These results are similar to those using
occupational clusters defined on the basis of a synthetic risk index (panels (a) and (b) of
Figure 10).
[Insert Figure 13 about here]
Panel (a) of Figure 14 shows that the mismatch index for full-time workers decreased26

after March 2020, although the extent of this change was small. By contrast, for part-timeworkers in panel (b), the extent of the mismatch was constant before the pandemic but
increased after March 2020.
[Insert Figure 14 about here]
As in the previous subsections, Figure 15 measures the mismatch indices by labormarket of easy- and difficult-to-work-remotely occupations. Panels (a) and (b) present
the mismatch indices for both employment types, using the 50th and 75th percentiles as
thresholds, respectively. For easy-to-work-remotely occupations, the mismatch increased
after March 2020, regardless of the threshold. The mismatch for difficult-to-workremotely occupations decreased after March 2020 using the 50th percentile threshold.
Using the 75th percentile as the threshold, the mismatch index remained almost
unchanged. Similar trends are confirmed for full-time workers (panels (c) and (d)),
although the extent of the change in the mismatch indices is small.
For part-time workers (panel (e)), using the 50th percentile as the threshold, the result
is similar to that for full-time workers; that is, the mismatch across occupational clusters
for easy- and difficult-to-work-remotely occupations increased and decreased,
respectively, after the pandemic. Using the 75th percentile as the threshold (panel (f)), themismatch for easy-to-work-remotely occupations still increased after March 2020, butthat for difficult-to-work-remotely occupations remained almost constant, regardless ofthe pandemic. These results suggest that the mismatch due to the pandemic is larger inthe labor market, especially for easy-to-work-remotely occupations for part-time workers.
[Insert Figure 15 about here]
Consequently, based on the results of the mismatch across small-classified
occupations (Figure 9) and across eight occupational clusters with different availabilities27

of remote work (Figure 15), we consider the effects of the COVID-19 pandemic on thelabor markets of easy- and difficult-to-work-remotely occupations as follows. First, theextent of mismatch in easy-to-work-remotely occupations increased among full-timeworkers after March 2020. For part-time workers, this result is confirmed for easy-towork-remotely occupations when we focus on the mismatch across the occupational
clusters. No results indicate that the mismatch indices decreased for part-time workers.
These results suggest that the COVID-19 pandemic discouraged labor mobility in easyto-work-remotely occupations. During the pandemic, workers may have preferred easyto-work-remotely occupations, resulting in less mobility across occupations. This result
does not conflict with previous results that the mismatch for full-time workers in highrisk occupations increased because our dataset indicates that some easy-to-work-remotely
occupations are classified as high-risk (see Figure 3 in Section 2).
Second, for full-time workers, the mismatch in particularly difficult-to-workremotely occupations increased due to the pandemic, according to the mismatch indicesacross small-classified occupations. This result is consistent with the increasing mismatchin the labor market for full-time workers in high-risk occupations.

5.

Concluding remarks
This study explored how the labor market in Japan was adversely affected by the

COVID-19 pandemic that began in March 2020, leading to temporary school closures.
First, we focused on the differences in job flows of various occupations according to theextent of vulnerability to the pandemic. Second, using the method developed by Şahin etal. (2014), we estimated the mismatch indices for distinct labor markets clustered byoccupations more and less vulnerable to the pandemic.
28

We employed two datasets: administrative data on public employment services from
the MHLW and Japanese-style O-NET data released by the JILPT. The Japanese-style ONET dataset directly asked respondents about “whether or not workers are in close contact
with others,” “whether the risk of infection is high or low,” and “whether or not workers
are allowed to work remotely.” This allowed us to identify the occupations that werevulnerable to COVID-19. By merging the two datasets by occupational code, we explored
how the pandemic affected the Japanese labor market depending on the extent of infection
risk and the availability of remote work.
We derive the following two main findings. First, labor market conditions worsened
after March 2020, the starting point of the pandemic. Labor market tightness, and in turn,the job finding rate, decreased for both full-time and part-time workers, regardless ofwhether their occupation types were vulnerable to infection. Labor market tightness
gradually recovered in early 2021, while the job finding rate remained low. This suggeststhat a mismatch arose due to the pandemic.
Second, according to the mismatch indices, after March 2020, the mismatch
increased for full-time workers in high-risk occupations. Similar trends were observed forfull-time workers in occupations in which it was easy to work remotely. Additionally,some results indicated that the mismatch increased for full-time workers in occupations
where it was particularly difficult to work remotely. The mismatch for part-time workers
increased in easy-to-work-remotely occupations. We believe that high-risk andparticularly difficult-to-work-remotely occupations in sectors requiring face-to-facecontact were seriously affected by the pandemic, resulting in a mismatch in the labormarket. Additionally, job seekers may have preferred easier remote work opportunities,
resulting in a mismatch.
29

Finally, for future research, we discuss whether the Employment Adjustment Subsidy(EAS), a public grant aimed at protecting employment, could have influenced themismatch during the pandemic. To protect employment in firms adversely affected by
COVID-19, the Japanese government implemented a special measure to increase the EAS
from April 2020 to March 2023. 24 Such a generous EAS might have mitigated themismatch by reducing the number of unemployed job seekers. In contrast, it could haveworsened the mismatch by making it more difficult for vacancies to be filled by suitablejob seekers from a reduced unemployment pool due to the generous EAS, and vice versa.
Whether the extent of the mismatch found in this study was mitigated or deteriorated by
the generous EAS seems to be a significant issue in revealing the impact of theemployment policy on labor mobility during the pandemic.

Appendix A. Mismatch index
This appendix describes the definition and calculation method of the mismatch index
developed by Şahin et al. (2014). First, we briefly outline the theoretical framework of
their study. Let the labor market be segmented by sector. Assuming that the labor market
is segmented by occupation, we label the sectors as occupations. In each frictional labormarket segmented by 𝐼 occupations, the number of new hires, namely the number ofmatches between job seekers and vacancies, is assumed to be given by the following
matching function:ℎ!" = Φ" 𝜙! 𝑚(𝑢!" , 𝑣!" ),

24

(𝐴1)

See the MHLW website for the normal EAS program(https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/koyou_roudou/koyou/kyufukin/pageL07_202005
15.html, in Japanese) and for the special EAS program to the COVID-19(https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/koyou_roudou/koyou/kyufukin/pageL07.html, inJapanese).
30

where ℎ!" denotes the number of new hires in occupation 𝑖 during period 𝑡; 𝑢!" and𝑣!" are the number of job seekers and of vacancies in occupation 𝑖 at the beginning of
period 𝑡, respectively; and 𝑚(∙) is the strictly increasing concave function in 𝑢!" and𝑣!" and is homogeneous of degree one. Φ" 𝜙! represents the matching efficiency, whereΦ" and 𝜙! are the time-specific and the time-invariant occupation-specific components,
respectively.25,26 The matching efficiencies and vacancies that vary across occupational
labor markets are presented. The matching function determines the number of new hiresℎ!" once the number of job seekers 𝑢!" is allocated to the labor market.
Here, a social planner is assumed to allocate job seekers across occupational labormarkets at no cost to maximize the number of new hires nationwide, given the matchingefficiencies and number of vacancies. For all occupational labor markets 𝑖 and 𝑗, thecondition for maximizing is:𝑣%"
𝑣!"
𝜙! 𝑚#! 3 ∗ 4 = 𝜙% 𝑚#% 3 ∗ 4 ,
𝑢!"
𝑢%"

(𝐴2)

where 𝑚#! (∙) is a derivative of 𝑚(∙) with respect to 𝑢! and is written as a function oflabor market tightness (i.e., the ratio of vacancies to job seekers) because 𝑚(∙) follows∗
a homogeneous degree of one. Consequently, 𝑢!"is the social planner’s optimal

allocation of job seekers.
Next, we describe the definition of the mismatch index. Here, the matching functionof equation (A1) is assumed to follow the Cobb-Douglas specification:'(&ℎ!" = Φ" 𝜙! 𝑣!"& 𝑢!",(𝐴3)

where 𝛼 ∈ (0,1) is a parameter common across occupational labor markets. We obtain∗
the optimal nationwide number of new hires ℎ!"by aggregating equation (A3) for each

25

Practically, we take a one-period lag for 𝑢!" and 𝑣!" in the dataset.
Şahin et al. (2014) assume that 𝜙 follows 𝜙!" , namely an idiosyncratic sectoral time effect. In
this study, 𝜙 is assumed to follow 𝜙! , namely the time-invariant occupational effect.
26

31

occupational labor market, as follows:)∗ '(&𝑣!" & 𝑢!"∗& '(&ℎ" = Φ" 𝑣" 𝑢" :; 𝜙! < = 3 4 > ,
𝑣"
𝑢"
!*'

(𝐴4)

where 𝑢! and 𝑣! are the aggregate numbers of job seekers and vacancies, respectively.
Equation (A2) gives:'

𝜙% & 𝑣%"𝑣!"∗ = < =∗ .
𝑢!"
𝜙! 𝑢%"(𝐴5)

Substituting equation (A5) into equation (A4), the optimal number of new hires is given
by
)

'
𝑣!"
ℎ"∗ = 𝜙B" Φ" 𝑣"& 𝑢"'(& , 𝑤ℎ𝑒𝑟𝑒 𝜙B" = :; 𝜙!& < =>
𝑣"
!*'

&

.

Finally, we obtain the following mismatch index, which measures the fraction of actual
new hires to optimal new hires as a counterfactual:)ℎ"𝜙! 𝑣!" & 𝑢!" '(&ℳ+" = 1 − ∗ = 1 − ; 3 4 < = < =.
ℎ"𝑢"
𝜙B" 𝑣"(𝐴6)!*'

The range of values that this index can take is between zero and one. The magnitude ofthis index represents the fraction of matches lost due to misallocation of job seekers. A
significant feature of this index is that it accounts for the heterogeneity of matchingefficiencies across occupational labor markets. When the matching efficiencies are
identical across occupational labor markets, this mismatch index equals the conventional
mismatch index proposed by Jackman and Roper (1987). This conventional index
underestimates mismatch because it ignores the heterogeneity of matching efficiencies(Kawata 2019).
To calculate the mismatch index in equation (A6), we can use the data on “persons
32who found employment,” “active applicants,” and “active job openings” for ℎ!" , 𝑢!" , and𝑣!" , respectively. These variables are obtained from the ERGW/RES provided by the
MHLW. We obtain the matching efficiencies Φ" and 𝜙! by estimating the matching
function. By dividing both sides of equation (A3) by 𝑢!" , log-linearizing, and adding anerror term 𝜀!" , we obtain the following regression model.
ln 𝑓!" = 𝛾 , 𝑡𝑟𝑒𝑛𝑑" + ln 𝜙! + 𝛼 ln 𝜃!" + 𝜀!" ,(𝐴7)

where 𝑓!" ≡ ℎ!" /𝑢!" is the job finding rate; 𝜃!" ≡ 𝑣!" /𝑢!" is labor market tightness; and𝑡𝑟𝑒𝑛𝑑" is a vector of two elements for a quadratic time trend that captures Φ" . 27
Following Kawata (2019), who estimates the mismatch index of Şahin et al. (2014) using
the same Japanese data we use, we can obtain 𝜙! by estimating equation (A7) as a fixed
effect model. Note that occupations that contain zero new hires for one or more periods
are omitted from the sample because they cannot be logged. Following Şahin et al. (2014),
we also assume the parameter 𝛼 = 0.5 for all occupational and both full-time and parttime workers’ labor markets when we calculate the mismatch index of equation (A6). To
estimate equation (A7), we utilize data from June 2016 to December 2019, before the
COVID-19 pandemic.
Next, using the mismatch indices, Şahin et al. (2014) also propose a method tomeasure the counterfactual unemployment rate, 𝑢"∗ , which the social planner’s optimal
allocation excludes mismatch in the economy. Equation (A6) can induce the optimal job
finding rate 𝑓"∗ , as follows:&𝑓"∗ = 𝑓"1
𝑢"3 ∗4 ,
1 − ℳ+" 𝑢"

where 𝑓" is the actual job finding rate in the economy. Given the initial counterfactual
unemployment rate 𝑢-∗ , the counterfactual unemployment rate is obtained in the27

We do not employ cubic or quartic time trends because they are omitted from the regressions.
33

following transition form:∗𝑢".'= 𝑠" + (1 − 𝑠" − 𝑓"∗ )𝑢"∗ ,where 𝑠" denotes the separation rate. We obtain the actual unemployment rate 𝑢" , fromthe Labor Force Survey (LFS) conducted by the Statistics Bureau of the Ministry ofInternal Affairs and Communications. 28 We assume that the initial counterfactual
unemployment rate equals 𝑢-∗ the actual unemployment rate. The actual separation rate𝑠" and actual job finding rate 𝑓" are calculated using the worker flow data in the
LFS.29,30 Finally, the gap between the actual and optimal unemployment rates (𝑢" − 𝑢"∗ )indicates the mismatch unemployment rate.

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to min-ei shokugyo shokaigyo ni kansuru chosa (Survey of transition of job searchmethod and private employment service).” JILPT shiryo series, No. 159 (in Japanese).
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38

Figure 1: Distribution of Risk IndicesDensity.4 .6 .8
.2
0

0

.2

Density.4 .6 .8

1

Physical proximity to others

1

Infectious disease risk

1

2

3
Score

4

5

1

2

3
Score

4

5

0

.5

Density1

1.5

Synthetic risk index

1

2

3
Score

4

5

Notes: The “synthetic risk index” is the arithmetic mean of “infection disease risk” and “physicalproximity.” Vertical lines in the panel for the synthetic risk index indicate, from left to right, 50
and 75 percentile points, respectively.

39

0

2

4

Density
6

8

10

12

Figure 2: Distribution of Availability of Remote Work

0

10

20

30
40
50
60
70
Availability of remote work (%)

80

90

100

Notes: Availability of remote work is the sum of the proportion of respondents weighted by the
median of the range of percentages of remote work availability for each choice. The median ofeach choice is: (1) = 0%; (2) = 10%; (3) = 30%; (4) = 50%; (5) = 70%; (6) = 90%. Vertical lines
indicate, from right to left (from the largest), 50 and 75 percentile points, respectively.

40

0

10

Availability of remote work (%)
20
30
40
50
60

70

80

Figure 3: Scatter Plot of Synthetic Risk Index versus Availability of Remote Work1

2

3
Synthetic risk index

45Notes: The solid red and dashed green vertical lines indicate 50 and 75 percentile pointsof the synthetic risk index, respectively. The solid red and dashed green horizontal lines
indicate 50 and 75 percentile points of the availability of remote work, respectively.

41

Figure 4: Trends in Labor Market Tightness and Job Finding Rate by Low- andHigh-risk Occupational Group(a) Full-Time Workers (the 50th percentile (b) Full-Time Workers (the 75th percentileof the synthetic risk index)
of the synthetic risk index)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.1
.08

2

.06
.04

.5

Job finding rate

Job finding rate

2.5

.12

3

.14

3.5

4

.16

High risk

1.5

Labor market tightness

.14
.12
.1
.08

Job finding rate

.06
.04

Labor market tightness

Low risk1

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

4
3.5
3
2.5
2
1.5
1
.5

Labor market tightness

Synthetic risk index (Threshold = 75 pctl); Full-time

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1High risk.16

Low risk

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1Synthetic risk index (Threshold = 50 pctl); Full-time

Labor market tightness

Job finding rate(c) Part-Time Workers (the 50th percentile (d) Part-Time Workers (the 75th percentileof the synthetic risk index)
of the synthetic risk index)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.1
.08
.06
.04

.5

Job finding rate

Job finding rate

2.5

.12

3

.14

3.5

4

.16

High risk

2

Labor market tightness

.14
.12
.1
.08

Job finding rate

.06
.04

Labor market tightness

Low risk

1.5

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

4
3.5
3
2.5
2
1.5
1
.5

Labor market tightness

Synthetic risk index (Threshold = 75 pctl); Part-time1

High risk.16

Low risk

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1Synthetic risk index (Threshold = 50 pctl); Part-time

Labor market tightness

Job finding rate

Notes: Job finding rate = new hires/job seekers. Labor market tightness = vacancies/job seekers.
The 12-month backward moving averages of the mean values of the occupational groups areplotted. The vertical line indicates March 2020.

42

Figure 5: Trends in Labor Market Tightness and Job Finding Rate by Easy- andDifficult-to-Work-Remotely Occupational Group(a) Full-Time Workers (the 50th percentile (b) Full-Time Workers (the 75th percentileof the availability of remote work)of the availability of remote work)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.1
.08

2

.06
.04

.5

Job finding rate

Job finding rate

2.5

.12

3

.14

3.5

4

.16

Difficult-to-work-remotely

1.5

Labor market tightness

.14
.12
.1
.08

Job finding rate

.06
.04

Labor market tightness

Easy-to-work-remotely

1

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

4
3.5
3
2.5
2
1.5
1
.5

Labor market tightness

Availability of remote work (Threshold = 75 pctl); Full-time

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Difficult-to-work-remotely.16

Easy-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = 50 pctl); Full-time

Labor market tightness

Job finding rate(c) Part-Time Workers (the 50th percentile (d) Part-Time Workers (the 75th percentileof the availability of remote work)of the availability of remote work)
Easy-to-work-remotely

Difficult-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.16

4

4

.14
.12

3

.1

2.5

.08

2

.06

1.5
.5

.04

1

.1
.08
.06
.04

Job finding rate

.12

Labor market tightness

3.5

.14

3.5
3
2.5
2
1.5
1

Labor market tightness.5

Labor market tightness

Job finding rate

Job finding rate

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = 75 pctl); Part-time

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Difficult-to-work-remotely.16

Easy-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = 50 pctl); Part-time

Labor market tightness

Job finding rate

Notes: Job finding rate = new hires/job seekers. Labor market tightness = vacancies/job seekers.
The 12-month backward moving averages of the mean values of the occupational groups areplotted. The vertical line indicates March 2020.

43

Figure 6: Mismatch across Occupations(a) Mismatch index(b) Mismatch unemployment rate

Original series

Actual, u (%)
Mismatch, u-u* (ppts.)

Moving-average

Notes: The vertical line indicates March 2020.

44

Counterfactual, u* (%)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

.08

0

.1

1

2

Mismatch index
.12
.14
.16

.18

3

Whole; Both emp. types

Figure 7: Mismatch across Occupations for Full-time and Part-time Workers(a) Full-time workers(b) Part-time workers

Mismatch index
.12
.14
.16
.1

Moving-average

Original series

Notes: The vertical line indicates March 2020.

45

Moving-average

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7.08

Original series

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7.08.1

Mismatch index
.12
.14
.16

.18

Whole; Part-time.18

Whole; Full-timeFigure 8: Mismatch across Occupations by Low- and High-risk Occupational Group(a) Both employment types (the 50th(b) Both employment types (the 75th
percentile of the synthetic risk index)
percentile of the synthetic risk index)

Low risk (OS)High risk (OS)Low risk (MA)High risk (MA)Low risk (OS)High risk (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7.05.05

Mismatch index
.1
.15
.2

Mismatch index
.1
.15
.2

.25

Synthetic risk index (Threshold = 75 pctl); Both emp. types.25

Synthetic risk index (Threshold = 50 pctl); Both emp. types

Low risk (MA)High risk (MA)(c) Full-Time Workers (the 50th percentile (d) Full-Time Workers (the 75th percentileof the synthetic risk index)
of the synthetic risk index)
Synthetic risk index (Threshold = 75 pctl); Full-time

Low risk (MA)High risk (MA)Low risk (OS)High risk (OS)(e) Part-Time Workers (the 50th percentileof the synthetic risk index)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m7.05

Low risk (OS)High risk (OS)2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7.05

Mismatch index
.1
.15
.2

Mismatch index
.1
.15
.2

.25

.25

Synthetic risk index (Threshold = 50 pctl); Full-time

Low risk (MA)High risk (MA)(f) Part-Time Workers (the 75th percentileof the synthetic risk index)

Low risk (OS)High risk (OS)Low risk (MA)High risk (MA)Low risk (OS)High risk (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7.05.05

Mismatch index
.1
.15
.2

Mismatch index
.1
.15
.2

.25

Synthetic risk index (Threshold = 75 pctl); Part-time.25

Synthetic risk index (Threshold = 50 pctl); Part-time

Low risk (MA)High risk (MA)

Notes: OS and MA are the original series and 12-month backward moving averages, respectively.
The vertical line indicates March 2020.

46

Figure 9: Mismatch across Occupations by Easy- and Difficult-to-Work-Remotely
Occupational Group(a) Both employment types (the 50th(b) Both employment types (the 75th
percentile of the synthetic risk index)
percentile of the synthetic risk index)

Easy (OS)Difficult (OS)Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

Mismatch index.02 .04 .06 .08 .1 .12 .14 .16

Availability of remote work (Threshold = 75 pctl); Both emp. types

Mismatch index.02 .04 .06 .08 .1 .12 .14 .16

Availability of remote work (Threshold = 50 pctl); Both emp. types

Easy (MA)
Difficult (MA)(c) Full-Time Workers (the 50th percentile (d) Full-Time Workers (the 75th percentileof the synthetic risk index)
of the synthetic risk index)
Availability of remote work (Threshold = 75 pctl); Full-time

Easy (OS)Difficult (OS)Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)(e) Part-Time Workers (the 50th percentileof the synthetic risk index)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

Mismatch index.02 .04 .06 .08 .1 .12 .14 .16

Mismatch index.02 .04 .06 .08 .1 .12 .14 .16

Availability of remote work (Threshold = 50 pctl); Full-time

Easy (MA)
Difficult (MA)(f) Part-Time Workers (the 75th percentileof the synthetic risk index)

Easy (OS)Difficult (OS)Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

Mismatch index.02 .04 .06 .08 .1 .12 .14 .16

Availability of remote work (Threshold = 75 pctl); Part-time

Mismatch index.02 .04 .06 .08 .1 .12 .14 .16

Availability of remote work (Threshold = 50 pctl); Part-time

Easy (MA)
Difficult (MA)

Notes: OS and MA are the original series and 12-month backward moving averages, respectively.
The vertical line indicates March 2020.
47

Figure 10: Mismatch across Occupational Clusters with Different Infection Risks(a) Mismatch index(b) Mismatch unemployment rate

Original series

3

Actual, u (%)
Mismatch, u-u* (ppts.)

Moving-average

Notes: The vertical line indicates March 2020.

48

Counterfactual, u* (%)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

.025

0

1

2

Mismatch index
.05
.075
.1

.125

Whole; Both emp. types

Figure 11: Mismatch across Occupational Clusters with Different Infection Risks
for Full-time and Part-time Workers(a) Full-time workers(b) Part-time workers

Mismatch index
.05
.075
.1

.125

Whole; Part-timeMoving-average

Original series

Notes: The vertical line indicates March 2020.

49

Moving-average

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7.025

Original series

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7.025

Mismatch index
.05
.075
.1

.125

Whole; Full-timeFigure 12: Mismatch across Occupational Clusters with Different Infection Risks by
Low- and High-risk Occupational Group(a) Both employment types (the 50th(b) Both employment types (the 75th
percentile of the synthetic risk index)
percentile of the synthetic risk index)

Low risk (MA)High risk (MA)Low risk (OS)High risk (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m70

Low risk (OS)High risk (OS)2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index.025 .05 .075 .1 .125 .15

Synthetic risk index (Threshold = 75 pctl); Both emp. types

Mismatch index.025 .05 .075 .1 .125 .15

Synthetic risk index (Threshold = 50 pctl); Both emp. types

Low risk (MA)High risk (MA)(c) Full-Time Workers (the 50th percentile (d) Full-Time Workers (the 75th percentileof the synthetic risk index)
of the synthetic risk index)

Low risk (MA)High risk (MA)Low risk (OS)High risk (OS)(e) Part-Time Workers (the 50th percentileof the synthetic risk index)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m70

Low risk (OS)High risk (OS)2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index.025 .05 .075 .1 .125 .15

Synthetic risk index (Threshold = 75 pctl); Full-time

Mismatch index.025 .05 .075 .1 .125 .15

Synthetic risk index (Threshold = 50 pctl); Full-time

Low risk (MA)High risk (MA)(f) Part-Time Workers (the 75th percentileof the synthetic risk index)

Low risk (MA)High risk (MA)Low risk (OS)High risk (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m70

Low risk (OS)High risk (OS)2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index.025 .05 .075 .1 .125 .15

Synthetic risk index (Threshold = 75 pctl); Part-time

Mismatch index.025 .05 .075 .1 .125 .15

Synthetic risk index (Threshold = 50 pctl); Part-time

Low risk (MA)High risk (MA)

Notes: OS and MA are the original series and 12-month backward moving averages, respectively.
Vertical line indicates March 2020.
50

Figure 13: Mismatch across Occupational Clusters with Different Availability of
Remote Work(a) Mismatch index(b) Mismatch unemployment rate

Original series

Actual, u (%)
Mismatch, u-u* (ppts.)

Moving-average

Notes: The vertical line indicates March 2020.

51

Counterfactual, u* (%)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7

.05

0

.075

1

2

Mismatch index.1 .125 .15 .175

.2

3

Whole; Both emp. types

Figure 14: Mismatch across Occupational Clusters with Different Availability of
Remote Work for Full-time and Part-time Workers(a) Full-time workers(b) Part-time workers

Mismatch index.1 .125 .15 .175
.075

Moving-average

Original series

Notes: The vertical line indicates March 2020.

52

Moving-average

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7.05

Original series

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m7.05.075

Mismatch index.1 .125 .15 .175

.2

Whole; Part-time.2

Whole; Full-timeFigure 15: Mismatch across Occupational Clusters with Different Availability of
Remote Work by Easy- and Difficult-to-Work-Remotely Occupational Group(a) Both employment types (the 50th(b) Both employment types (the 75th
percentile of the synthetic risk index)
percentile of the synthetic risk index)

Mismatch index
.05
.1
.15

Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m70Easy (OS)Difficult (OS)2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index
.05
.1
.15

.2

Availability of remote work (Threshold = 75 pctl); Both emp. types.2

Availability of remote work (Threshold = 50 pctl); Both emp. types

Easy (MA)
Difficult (MA)(c) Full-Time Workers (the 50th percentile (d) Full-Time Workers (the 75th percentileof the synthetic risk index)
of the synthetic risk index)

Mismatch index
.05
.1
.15

Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)(e) Part-Time Workers (the 50th percentileof the synthetic risk index)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m70Easy (OS)Difficult (OS)2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index
.05
.1
.15

.2

Availability of remote work (Threshold = 75 pctl); Full-time.2

Availability of remote work (Threshold = 50 pctl); Full-time

Easy (MA)
Difficult (MA)(f) Part-Time Workers (the 75th percentileof the synthetic risk index)

Mismatch index
.05
.1
.15

Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m70Easy (OS)Difficult (OS)2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index
.05
.1
.15

.2

Availability of remote work (Threshold = 75 pctl); Part-time.2

Availability of remote work (Threshold = 50 pctl); Part-time

Easy (MA)
Difficult (MA)

Notes: OS and MA are the original series and 12-month backward moving averages, respectively.
The vertical line indicates March 2020.
53

Online Appendix for“Did COVID-19 Deteriorate Mismatch in theJapanese Labor Market?”This Online Appendix provides supplemental tables and figures.
Table OA.1
This table shows the differences in three variables (number of job seekers, vacancies,and new hires by employment type) between 228 occupations with O-NET data, including
the sample for this study, and the remaining 141 occupations without O-NET data,
excluding from this study.
Table OA.2
This table shows the occupation list, indicating which occupations are classified as
low-risk, high-risk, easy-to-work-remotely, and difficult-to-work-remotely. It also
indicates the sample occupations used to measure the mismatch indices for both full-timeand part-time workers.
Table OA.3
This table shows the three alternative thresholds for distinguishing between easy- anddifficult-to-work-remotely occupations. The first threshold, labeled TW1, separates (1)from the remaining choices. If the proportion of (1) exceeds that of the sum of the rest of
the choices in an occupation, the occupation is categorized as “difficult-to-work-remotely,”
and workers engaging in the occupation are defined as those not allowed to work remotely.
Otherwise, we interpret that those workers engage in “easy-to-work remotely”
occupations. The second threshold (TW2) extends the definition of “difficult-to-workremotely.” TW2 distinguishes (1) and (2) from the rest of the choices. The third threshold(TW3) further extends the definition of “difficult-to-work-remotely,” namely (1), (2), and(3) versus (4), (5), and (6). We further narrow the standard for the availability of remote
work by changing the threshold from TW1 to TW3. According to TW1, 171 out of 228
occupations are defined as “difficult-to-work-remotely” occupations, but the rest of them1(57 occupations) are categorized as “easy-to-work-remotely.” According to TW2 andTW3, the number of “easy-to-work-remotely” occupations is smaller, suggesting thatfewer workers are allowed to work remotely.
Figure OA.1
This figure shows the method to construct the dataset.
Figure OA.2
This figure shows the comparison of trends of labor market tightness and job finding
rate between occupations with and without O-NET data. It also shows the trends for all
occupations in the Employment Referrals for General Workers (Report on Employment
Service) as a benchmark.
Figure OA.3
This figure shows the distribution of respondents by each choice about availabilityof remote work in the O-NET questionnaire.
Figure OA.4
This figure shows the comparison of trends in labor market tightness and job finding
rate between occupations included in and excluded from the sample for the mismatchindices. It also shows the trends for occupations in the full sample as a benchmark.
Figure OA.5
This figure shows trends in labor market tightness and job finding rate by easy- anddifficult-to-remote-work occupational group based on alternative definition, show inTable OA.3. The results are similar to those in Figure 5 in the main text.
Figure OA.6
This figure shows that trends in mismatch indices by easy- and difficult-to-remotework occupational group based on alternative definition, show in Table OA.3. The results
are similar to those in Figure 9 in the main text.

2

Table OA.1: Comparison of Mean Values of New Hires, Job seekers, and Vacancies
between Occupations Excluded from and Included in the Sample(1) With O-NET(Obs=18,696 (=228 occ
x 82 months))(2) Without O-NET(Obs=11,562 (=141 occ
x 82 months))

New hires

262.68

40.70

6.45

Active applications

3489.44498.91

6.99

Vacancies

5813.94647.308.98

New hires

183.34

20.63

8.89

Active applications

1942.31226.70

8.57

Vacancies

3481.57

266.22

13.08

Variable(1)/(2)

Panel A: Full-time

Panel B: Part-timeNotes: Occupations included in (excluded from) the sample represent those (not)included in the Japanese-style O-NET among occupations recorded in the Employment
Referrals for General Workers (Report on Employment Service). However, occupations
without necessary variables for the analyses are also excluded from the sample even if
they are in the Japanese-style O-NET.

3

Table OA.2: List of Occupations in the Sample
Occupational group
Availabiliy of remote

Availabiliy of remote work

work

(Alternative definition)

Synthetic risk index

Sample for mismatch indices

Occupational clusters

Synthetic Availabiliy
CodeOccupation

risk index

of remote(1‒5)

work (%)

Availability
Both emp.
50 pctl.

75 pctl.

50 pctl.

75 pctl.

TW1

TW2

TW3

types

Infectious of remote
Full-timePart-time

risk

work

21 Company officers

2.35

21.24 Low

Low

Easy

Easy

Difficult

Difficult

Difficult22

31 Company management staff

3.05

15.10 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

6

3

39 Other corporations, organization management staff

3.80

2.49 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

8

7

51 Researchers

2.44

35.01 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

2

1

61 Agriculture, forestry, and fishery engineers

2.23

6.92 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

1

5

71 Food engineers (development)

2.67

14.02 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

4

3

2.62

26.58 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

4

2

73 Machinery engineers (development)

2.51

32.52 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

3

1

77 Chemical engineers (development)

2.45

26.94 Low

Low

Easy

Easy

Easy

Difficult

Difficult2

2

81 Food engineers (except development)

2.49

12.94 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

3

2.50

42.67 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

Y

3

1

87 Chemistry engineers (except development)

2.21

16.76 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

1

3

91 Architectural engineers

2.65

23.10 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

4

2

92 Civil engineers

2.51

22.78 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

3

2

93 Surveyors

2.59

9.32 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

4

101 System consultants

1.96

64.90 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

1

1

102 System designers

2.28

46.82 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

1

1

103 Data processing project managers

2.24

58.73 Low

Low

Easy

Easy

Easy

Easy

Easy

1

1

104 Software creators

2.27

53.04 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

1

1

105 System operation managers

2.45

42.02 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

2

1

106 Communication network engineers

2.64

33.03 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

4

1

109 Other data processing and communication engineers

2.12

60.30 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

Y

1

1

119 Other engineers

2.66

26.71 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

4

2

121 Doctors

4.03

3.97 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult8

6122 Dental surgeons

4.50

0.00 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult88123 Veterinary surgeons

3.86

1.35 HighHigh

Difficult

Difficult

Difficult

Difficult

DifficultY88124 Pharmacists

3.91

5.25 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

8

6

131 Public health nurses

3.52

4.31 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

6

132 Midwives

4.45

1.70 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

133 Nurses (including assistant nurses)

4.35

3.68 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

8

6

141 Diagnostic radiographers

4.42

0.45 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

142 Clinical engineers

4.13

1.87 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

8

8

143 Clinical laboratory technicians

3.98

0.48 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

144 Physiotherapists

4.33

2.64 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

145 Occupational therapists

4.33

2.38 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

146 Certified orthoptists, speech therapists

4.25

2.42 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

147 Dental hygienists

4.09

1.87 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

148 Dental technicians

2.98

0.16 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

6

8

151 Nutritionists

3.39

6.37 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

5

152 Masseurs, chiropractors, acupuncturists, and moxacauterists

4.10

1.23 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

153 Judo-orthopedists

4.43

1.77 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

159 Health care workers not classified elsewhere

3.41

17.56 High

High

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

7

3

161 Welfare counseling guidance professionals

3.66

7.18 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

5

162 Welfare facility guidance professionals

3.94

3.40 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

163 Childcare workers

4.32

0.81 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

169 Other social welfare specialist professionals

3.85

6.24 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

8

5

173 Attorneys

2.43

27.19 Low

Low

Easy

Easy

Easy

Difficult

Difficult2

2

174 Patent attorneys

2.16

59.22 Low

Low

Easy

Easy

Easy

Easy

Easy

1

1

175 Judicial scriveners

2.54

15.76 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

3

3

179 Other legal workers

2.27

9.95 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

1

4

181 Certified public accountants

2.51

54.46 Low

Low

Easy

Easy

Easy

Easy

Easy

3

1

182 Licensed tax accountants

2.51

18.93 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

3

2

183 Certified social insurance and labor consultant

2.51

23.01 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

3

2

184 Finance and insurance professionals

2.45

46.04 Low

Low

Easy

Easy

Easy

Easy

Easy

2

1

189 Other management, finance, and insurance professionals

2.54

37.30 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

3

1

191 Kindergarten teachers

3.79

5.45 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

5

192 Elementary school teachers

3.79

5.43 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

5

193 Junior high school teachers

3.41

7.09 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

7

5

194 Senior high school teachers

3.33

17.42 High

High

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

3

196 Special needs education school teachers

3.74

11.77 High

High

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

7

3

199 Other teachers

3.16

15.52 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

6

3

211 Authors

1.80

75.44 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

1

1

212 Journalists

2.30

47.64 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

1

1

213 Editors

2.40

51.35 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

2

1

222 Painters, calligraphers

1.55

73.08 Low

Low

Easy

Easy

Easy

Easy

Easy

1

1

224 Designers

2.08

52.54 Low

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

Y

1

1

225 Photographers, film operators

2.84

5.05 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

6

234 Producers and directors

3.10

15.42 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

6

3

241 Librarians

3.10

2.99 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

6

7

242 Curators

2.73

25.47 High

Low

Easy

Easy

Easy

Difficult

DifficultY

5

2

243 Counselors (except medical and welfare facilities)

2.97

17.72 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

6

3

244 Private tutors

3.15

14.24 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

3

246 Communication equipment operators

2.74

17.09 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

5

3

249 Specialist professionals not classified elsewhere

2.52

39.30 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

Y

3

1

251 General affairs workers

2.57

28.85 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

3

2

252 Human affairs workers

2.63

34.12 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

Y

4

1

253 Planning clerical workers

2.45

37.28 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

Y

2

1

254 Reception and guidance clerical workers

3.16

4.94 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

6

255 Secretaries

2.49

36.03 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

Y

3

1

256 Telephone receptionists

2.79

14.54 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

3

72

82

Electrical, electronic, telecommunications engineers (except
communication network engineers) (development)

Electrical, electronic, telecommunications technicians (except
communication network engineers) (except development)

4

Y

Y

Y

Y

Y

Y

Y

Y

257 Comprehensive clerical workers

2.73

25.35 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

5

2

258 Medical and care clerical workers

3.70

4.82 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

6

259 Other general clerical workers

2.58

25.80 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

4

2

261 Cash disbursement and receipt clerks

3.02

19.20 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

6

2

262 Deposit counter clerks

3.07

2.84 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

6

7

263 Accountants business clerks

2.25

17.90 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

3

271 Production sites clerical workers

2.56

14.94 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

3

3

281 Sales clerks

2.72

25.94 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

5

2

289 Other sales clerical workers

3.20

19.14 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

2

299 Other outdoor service workers

2.24

7.20 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

4

301 Passenger / freight clerical workers

3.75

3.64 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

6

302 Transportation management clerical workers

2.94

6.71 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

5

312 Data entry device operators

2.24

20.99 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

2

321 Retailers, retail manager

3.29

8.13 High

High

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

4

323 Shop assistants

3.19

5.38 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

5

325 Home visit and mobile sales workers

2.45

14.38 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

3

333 Stock dealers, Stock brokers, finance brokers

2.26

36.53 Low

Low

Easy

Easy

Easy

Difficult

Difficult1

1

339 Other quasi-sales workers

2.91

7.03 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

5

5

343 Medicine sales workers

3.31

41.88 High

High

Easy

Easy

Easy

Easy

Difficult

Y

Y

7

1

344 Machinery sales workers (except communication equipment)

2.78

14.80 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

5

3

345 Communication and system sales workers

2.85

31.85 High

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

5

1

346 Finance and insurance sales workers

2.92

24.22 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

6

2

347 Real estate sales workers

3.07

17.79 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

6

3

349 Other sales workers

2.75

29.33 High

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

5

2

351 Housekeepers, home helpers

2.54

4.18 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

3

6

359 Other domestic support service workers

3.91

4.74 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

8

6

361 Care workers (medical and welfare facilities, etc.)

4.24

0.93 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

362 Home visiting care workers

4.27

2.37 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

371 Care assistants

4.41

0.80 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

379 Other healthcare service workers

3.85

1.02 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

8

381 Hairdressers

3.98

2.88 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

382 Beauticians

3.80

3.28 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

383 Cosmetic service workers (except beauticians)

3.41

8.49 High

High

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

4

385 Launderers

3.01

4.32 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

6

391 Cooks

2.98

1.49 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

6

8

392 Bartenders

3.37

2.88 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult7

7

401 Restaurateurs, restaurant managers

3.44

3.63 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

7

6

402 Japanese inn owners and managers

3.29

5.41 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

7

5

403 Food and drink service workers

3.38

1.98 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

7

8

3.39

5.83 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

5

3.48

3.41 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

7

7

2.42

3.88 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

6

414 Car park management personnel

2.78

2.72 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

5

7

421 Travel and tourist guides

3.40

10.97 High

High

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

4

423 Commodity hire workers

3.23

6.60 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

5

425 Undertakers, crematorium workers

3.27

4.68 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

7

6

426 Pet groomers

2.94

3.40 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

6

7

429 Service workers not classified elsewhere

3.96

3.29 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

8

7

431 Self-defense officials

2.58

10.18 Low

Low

Easy

Easy

Difficult

Difficult

Difficult44441 Police officers

3.73

4.82 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult76442 Maritime safety officials

2.75

7.32 High

Low

Easy

Easy

Difficult

Difficult

Difficult5

4451 Prison guards

3.54

1.60 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult78452 Firefighters

4.01

4.45 HighHigh

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

8

6

453 Security staff

3.03

0.75 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

6

8

459 Other public security workers not classified elsewhere

2.82

3.55 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

5

7

461 Crop farming workers

2.01

6.77 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

5

462 Livestock farm workers

2.38

2.00 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

2

7

463 Landscape gardeners, nursery workers

2.22

2.21 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

1

7

471 Forest nursery workers

2.01

5.00 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

6

481 Fishery workers

2.36

5.98 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

2

5

2.70

6.26 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

5

5

2.59

17.67 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

4

3

2.33

3.88 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

2

6

2.41

8.17 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

2

4

2.59

29.46 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

4

2

404

Japanese Inn, hotel, and transportation customer service
workers

406 Service workers in places of entertainment, etc.
411

497
501
505
507
508
512

Condominiums, apartment buildings, and lodging houses
management personnel

Metal sculpture and plating facility control and monitoring
workers
Chemical product manufacturing facility control andmonitoring workers
Spinning, weaving, apparel, and fiber product manufacturing
facility control and monitoring workers
Printing and bookbinding facility control and monitoring
workers
Rubber, plastic product manufacturing facility control andmonitoring workers
Electro-mechanical apparatus assembly facility control and

Y

2.75

22.23 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

5

2

523 Cast metal manufacturing workers

2.59

3.60 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

6

524 Forging workers

2.51

6.27 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

5

528 Numerical control metal machine tools workers

2.02

0.85 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

1

8

531 Metal press workers

2.46

3.07 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

3

7

532 Ironworkers, boilermakers

2.59

3.45 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

4

7

533 Sheet metal workers

2.32

0.72 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

1

8

536 Metal product manufacturing workers

2.38

6.29 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

5

537 Metal welding and fusion cutting workers

2.35

6.71 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

5

541 Chemical product manufacturing workers

2.50

18.39 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

2

542 Ceramic, earth, and stone product manufacturing workers

2.45

9.28 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

4

2.40

11.46 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

4

545 Bread and confectionery manufacturing workers

2.68

6.06 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

5

546 Tofu, konjak jelly, wheat-gluten bread manufacturing workers

2.43

13.23 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

3

547 Canned and bottled foods manufacturing workers

2.80

12.04 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

3

543

monitoring workers

Cereal polishing, flour milling, and seasoned foods
manufacturing workers

5548 Milk and milk products manufacturing workers

2.56

18.61 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

2

551 Meat products manufacturing workers

2.43

5.09 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

6

552 Fishery products manufacturing workers

2.49

10.47 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

4

553 Preserved foods manufacturing workers

2.44

8.70 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

4

554 Packed lunch products manufacturing workers

2.69

5.60 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

5

555 Vegetable pickle products manufacturing workers

2.57

3.80 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

6

556 Beverage and cigarette manufacturing workers

2.56

13.43 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

3

558 Apparel and fiber product manufacturing workers

2.17

16.37 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

3

561 Wooden product manufacturing workers

2.22

11.10 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

4

562 Pulp, paper, and paper product manufacturing workers

2.59

3.43 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

4

7

563 Printing and bookbinding workers

2.60

9.77 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

4

565 Plastic product manufacturing workers

2.43

12.51 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

3

2.31

21.61 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

2

2.61

18.77 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

2

3.04

28.02 High

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

Y

6

2

576 Semiconductor product manufacturing workers

2.48

9.18 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

3

4

584 Automobile assembly workers

2.51

10.68 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

4

586 Weighing and measuring appliance assembly workers

2.63

11.47 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

4

587 Photo-optic mechanical apparatus assembly workers

2.54

15.75 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

3

3

2.65

19.16 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

4

2

2.44

21.56 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

2

2.57

3.19 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

4

7

3.18

8.08 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

4

612 Metal processing, welding, fusion cutting workers

2.56

4.99 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

6

641 Painter2.38

0.90 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

2

8

642 Painter, signboard production worker

2.31

36.65 Low

Low

Easy

Easy

Easy

Easy

Difficult

Y

Y

1

1

643 Drafting technicians

2.55

32.74 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

Y

3

1

651 Electric train drivers

3.01

1.94 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult6

8661 Bus drivers

3.46

2.16 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

7

7

662 Passenger automobile drivers

3.79

3.18 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

7

7

663 Goods vehicle drivers

2.35

2.19 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

2

7

672 Navigation officers, navigators (except fishing boats), pilots

2.80

8.85 High

Low

Easy

Easy

Difficult

Difficult

Difficult5

4

673 Ships' chief engineers, engineers (except fishing boats)

2.71

9.58 High

Low

Easy

Easy

Difficult

Difficult

Difficult5

4674 Aircraft pilots

3.08

10.70 High

Low

Easy

Easy

Easy

Difficult

Difficult6

4681 Conductors

3.59

3.07 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult77683 Deckhands, Ships stokers

3.14

9.20 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

6

4

684 Forklift operators

2.17

6.80 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

1

5

689 Transport workers not classified elsewhere

2.69

43.74 High

Low

Easy

Easy

Easy

Easy

Easy

Y

Y

Y

5

1

691 Power plant and substation workers

2.39

15.21 Low

Low

Easy

Easy

Easy

Difficult

Difficult

Y

Y

2

3

692 Boiler operators

2.57

8.57 Low

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

4

4

695 Construction, well-drilling machinery operators

2.19

1.19 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

1

8

697 Building facility managers

2.86

3.23 HighLow

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

5

7

701 Molding box carpenters2.55

5.66 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

3

5

702 Scaffolding workers

2.98

8.37 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

6

4

703 Steel reinforcement workers

2.67

7.03 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

4

5

711 Carpenters2.63

1.73 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

4

8

712 Block and tile laying workers

2.38

1.60 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

2

8

714 Plasterers

2.29

2.40 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

1

7

716 Pipe laying workers

2.85

5.27 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

5

6

717 Interior finishers

2.66

4.54 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

4

6

718 Waterproofers

2.76

5.28 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

5

5

721 Power line hanging and laying workers

2.72

11.67 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

5

3

725 Other electric construction workers

2.96

5.69 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

5

731 Civil engineering workers

2.54

5.75 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

5

732 Railway line construction workers

2.86

8.09 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

5

4

752 Onboard and quayside cargo handlers

2.74

13.75 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

5

3

753 Land-based cargo handling and carrying workers

3.01

5.34 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

5

754 Warehouse workers

2.68

1.89 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

4

8

755 Delivery workers

2.76

3.84 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

6

756 Packing workers

2.43

4.00 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

2

6

761 Building cleaning workers

2.59

0.97 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

4

8

762 House cleaning workers

2.81

6.09 HighLow

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

5

764 Garbage and human waste treatment workers

3.29

0.72 HighHigh

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

7

8

765 Industrial waste treatment workers

2.76

7.31 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

5

4

769 Other cleaning workers

3.10

11.51 High

Low

Easy

Easy

Difficult

Difficult

Difficult

Y

Y

Y

6

4

771 Packaging workers

2.67

3.80 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

4

6

781 Sorters

2.50

3.18 Low

Low

Difficult

Difficult

Difficult

Difficult

Difficult

Y

Y

Y

3

7

782 Light-duty workers

2.53

4.96 Low

Low

Difficult

Easy

Difficult

Difficult

Difficult

Y

Y

Y

3

6

569
571
574

601
602

Other product manufacturing and processing workers (except
metal products)
General-purpose, manufacturing, and business-use
mechanical apparatus assembly workers
Electronic applied machinery and equipment assembly
workers

General-purpose mechanical apparatus maintenance andrepair workers
Electro-mechanical apparatus maintenance and repair
workers603 Automobile maintenance and repair workers
604

Transportation machinery maintenance and repair workers(except automobiles)

Y
Y
Y
Y

Notes: Occupation names are translated from Japanese into English by the authors referencing the Japan Standard Occupational Classification (Rev. 5th, December 2009)(https://www.soumu.go.jp/english/dgpp_ss/seido/shokgyou/co09-4a.htm), released by the Ministry of Internal Affairs and Communications, which is similar to the MHLW occupational classification. The value of occupational clusters
represents that the occupations belong to the corresponding clusters: 1 = [0, 12.5] pctl.; 2= (12.5, 25] pctl.; 3 = (25, 37.5] pctl. 4 = (37.5, 50] pctl.; 5= (50, 62.5] pctl.; 6 = (62.5, 75] pctl.; 7 = (75, 87.5] pctl.; 8 = (87.5, 100] pctl.

6

Table OA.3: Occupational Group Defined by Remote Work Availability Based on
Alternative Thresholds
Number of occupations

Occupational
group

Definition of difficult-to-

Easy-to-work-

Difficult-to-work-

definition

work-remotely occupation

remotely

remotely

57

171

28

200

14

214

TW1

TW2

TW3

If the fraction of respondents
is "(1)>(2)+(3)+(4)+(5)+(6)"If the fraction of respondents
is "(1)+(2)>(3)+(4)+(5)+(6)"If the fraction of respondents
is "(1)+(2)+(3)>(4)+(5)+(6)"Notes: The values in parentheses indicate choices in the questionnaire: (1) usually, no; (2) 20%
of days of duty and below; (3) 20% or more but below 40% of days of duty; (4) 40% or more
but below 60% of days of duty; (5) 60% or more but below 80% of days of duty; and (6) 80%
of days of duty and more.

7

Figure OA.1: Method to Construct the Dataset
Employment Referrals for
General Workers

Japanese-style O-NETMHLW occupationalclassification (rev. 2011) at subsmall-classification
O-NET

Major

occ.

occ. code score

Risk

…

Aggregation into smallclassification
Major

Risk

occ. code score…

MHLW occupationalclassification (rev. 2011) at
small-classification
Occ.

# of job

Ccodeseekers…

Dataset for analyses
Occ.

# of job

Ccodeseekers

…

Risk
score

1

001

xxx

001

xxx

001

yyy

001

yyy

xxx

2

002

xxx

002

xxx

002

yyy

002

yyy

xxx

3

002

xxx

004

xxx

003

yyy

004

yyy

xxx

4

003

007

xxx

004

yyy

007

yyy

xxx

5

004

xxx

008

xxx

005

yyy

008

yyy

xxx

6

004

xxx

228 occ.

006

yyy

007

yyy

008

yyy

009

yyy

010

yyy

7
8

007

9

007

10

008

xxx
xxx

497 occ. -> 422 occ.

369 occ.

Aggregate occupations based on “major
occupational classification code”• Variables are transformed into arithmeticalmean• Seven occupations lacking “major
occupational classification code” and 69
occupations without necessary variables forthe analyses are excluded from the sample(one occupation lacks both of them).

Merge the two data based on occupational
code (small-classification)• Occupations that have nocorresponding ones in the Japanesestyle O-NET are excluded from thesample8…

228 occ.

Dataset (occupational panel)• Scores in the Japanese-style O-NET areconstant over time; thus, different
periods of same occupation are
assigned to the same scores.

Without O-NET

With O-NET

Without O-NET

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.11

2

.09
.08
.07
.06

1

Labor market tightness
Job finding rate

Job finding rate

.1

1.8
1.6
1.4
1.2

.09
.08
.07
.06

Job finding rate

.1

Labor market tightness

.11

2
1.8
1.6
1.4
1.2

Labor market tightness

1

Labor market tightness (BM)Job finding rate (BM).12

Occupations with vs without O-NET; Part-timeWith O-NET.12

Occupations with vs without O-NET; Full-time

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1Figure OA.2: Trends in Labor Market Tightness and Job Finding Rate for
Occupations with and without O-NET Data(a) Full-time workers(b) Part-time workers

Labor market tightness (BM)Job finding rate (BM)Labor market tightness
Job finding rate

Notes: Job finding rate = new hires/job seekers. Labor market tightness = vacancies/job seekers.
The 12-month backward moving averages of the mean values of the occupational groups areplotted. The vertical line indicates March 2020. BM indicates the benchmark, including all
occupations in the Employment Referrals for General Workers (Report on Employment Service).

9

Figure OA.3: Distribution of Respondents by Each Choice about Availability of
Remote Work

5

Density
10

15

Availability = 305Density
10

15

Availability = 2

0

0

5

Density
10

15

Availability = 1

0
25
50
75
100
Fraction of respondents (%)

Availability = 4

Availability = 5

Availability = 6

0
25
50
75
100
Fraction of respondents (%)

Density
10
5
0

0

0

5

5

Density10

Density
10

15

15

0
25
50
75
100
Fraction of respondents (%)

15

0
25
50
75
100
Fraction of respondents (%)

0
25
50
75
100
Fraction of respondents (%)

0
25
50
75
100
Fraction of respondents (%)

Notes: The value of availability for each panel indicates a choice in the questionnaire: (1) usually,no; (2) 20% of days of duty and below; (3) 20% or more but below 40% of days of duty; (4) 40%
or more but below 60% of days of duty; (5) 60% or more but below 80% of days of duty; and (6)80% of days of duty and more.

10

Figure OA.4: Trends in Labor Market Tightness and Job Finding Rate for
Occupations Included and Excluded from the Sample for the Mismatch Indices(a) Both employment types(b) Full-time workers

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.11

2

.12

Excluded

Labor market tightness
Job finding rate

.1
.09
.08
.07
.06

Job finding rate

1.6
1.4
1.2

Labor market tightness

1.8

.11
.1
.09
.08

Job finding rate

.07
.06

Labor market tightness (BM)Job finding rate (BM)

Included

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

2
1.8
1.6
1.4
1.2
1

Labor market tightness

Occupations included in and excluded from the sample; Full-time

1

Excluded.12

Included

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Occupations included in and excluded from the sample; Both emp. types

Labor market tightness (BM)Job finding rate (BM)Labor market tightness
Job finding rate(c) Part-time workers
Excluded

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

.11
.09
.08
.07
.06

Job finding rate

.1

1.8
1.6
1.4
1.2
1

Labor market tightness2.12Included

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Occupations included in and excluded from the sample; Part-time

Labor market tightness (BM)Job finding rate (BM)Labor market tightness
Job finding rate

Notes: Job finding rate = new hires/job seekers. Labor market tightness = vacancies/job seekers.
The 12-month backward moving averages of the mean values of the occupational groups areplotted. The vertical line indicates March 2020. BM indicates the benchmark, including
occupations in the full sample merged with the Japanese-style O-NET. The full sample is divided
into the occupations included and excluded from the sample to estimate the mismatch indices.
The excluded occupations have zero new hires for one period or more. Labor market tightness
and job finding rate can be calculated for the excluded sample because these variables arecalculated by aggregating all occupations in this sample. Additionally, even if the number of newhires is zero for some occupations in some periods, it is not zero for other occupations
simultaneously in this sample. As a result, the total number of new hires for the entire excluded
sample is not zero across all periods.

11Figure OA.5: Trends in Labor Market Tightness and Job Finding Rate by Easy- and Difficult-to-Work-Remotely Occupational
Group Based on Alternative Definition

.08
.06
.04
.02

Job finding rate

1.5

Job finding rate

0

0

Labor market tightness

1

Labor market tightness

2

.1

.12

2.5

Difficult-to-work-remotely

.5

.06
.04
.02

Job finding rate

Job finding rate

.08

2
1.5
1

Labor market tightness

0

.5

.06
.04
.02

Job finding rate

.08

2
1.5
1

Labor market tightness.5

Labor market tightness

Easy-to-work-remotely

.1

.12

2.5

Difficult-to-work-remotely

.1

.12

2.5

Easy-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = TW3); Full-time

Difficult-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = TW2); Full-time

Easy-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = TW1); Full-time

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1(c) Full-Time Workers (TW3 as the thresholdof remote work availability)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1(b) Full-Time Workers (TW2 as the thresholdof remote work availability)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1(a) Full-Time Workers (TW1 as the thresholdof remote work availability)

Labor market tightness

Job finding rate

Labor market tightness

Job finding rate

.12
.06
.04
.02

Job finding rate

1.5
1

0

Labor market tightness

.5

.06
.04
.02

.08

2

.1

.12

2.5

Difficult-to-work-remotely0

Job finding rate

.08

2
1.5

Labor market tightness

1
.5

.06
.04
.02
0

Job finding rate

Job finding rate

.08

2
1.5
1

Labor market tightness.5

Labor market tightness

Easy-to-work-remotely

.1

.12

2.5

Difficult-to-work-remotely

.1

2.5

Easy-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = TW3); Part-time

Difficult-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = TW2); Part-time

Easy-to-work-remotely

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1

Availability of remote work (Threshold = TW1); Part-time

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1(f) Part-Time Workers (TW3 as the thresholdof remote work availability)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1(e) Part-Time Workers (TW2 as the thresholdof remote work availability)

2017m72018m1
2018m72019m1
2019m7
2020m1
2020m72021m12021m72022m12022m72023m1(d) Part-Time Workers (TW1 as the thresholdof remote work availability)

Labor market tightness

Job finding rate

Notes: Job finding rate = new hires/job seekers. Labor market tightness = vacancies/job seekers. The 12-month backward moving averages of the meanvalues of the occupational groups are plotted. The vertical line indicates March 2020.

12

Figure OA.6: Mismatch across Occupations by Easy- and Difficult-to-Work-Remotely Occupational Group Based on Alternative
Definition

Availability of remote work (Threshold = TW1); Both emp. types

Availability of remote work (Threshold = TW2); Both emp. types

Availability of remote work (Threshold = TW3); Both emp. types

Mismatch index.05.1

Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m7.15

13

Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)Easy (MA)
Difficult (MA)

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m7

2016m72023m12022m72022m12021m72021m1

2020m70Easy (OS)Difficult (OS)2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index
.05
.1

.15

Availability of remote work (Threshold = TW3); Full-time

Mismatch index
.05
.1

.15
Mismatch index.05.1

Easy (MA)
Difficult (MA)Easy (MA)
Difficult (MA)(f) Full-Time Workers (TW3 as the thresholdof remote work availability)

Availability of remote work (Threshold = TW2); Full-time

0

Easy (OS)Difficult (OS)2017m1

Easy (OS)Difficult (OS)(e) Full-Time Workers (TW2 as the thresholdof remote work availability)

Availability of remote work (Threshold = TW1); Full-time

2016m72023m12022m72022m12021m7

Easy (MA)
Difficult (MA)2017m1(d) Full-Time Workers (TW1 as the thresholdof remote work availability)

2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index.05.12023m12022m72022m12021m70Easy (OS)Difficult (OS)2021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index
.05
.1

.15

(c) Both employment types (TW3 as thethreshold of remote work availability).15(b) Both employment types (TW2 as thethreshold of remote work availability).15(a) Both employment types (TW1 as thethreshold of remote work availability)

(g) Part-Time Workers (TW1 as the thresholdof remote work availability)(h) Part-Time Workers (TW2 as the thresholdof remote work availability)
Availability of remote work (Threshold = TW2); Part-time

Availability of remote work (Threshold = TW3); Part-time

Mismatch index.05.1

Easy (OS)Difficult (OS)Easy (MA)
Difficult (MA)Easy (OS)Difficult (OS)Easy (MA)
Difficult (MA)

Notes: OS and MA are the original series and 12-month backward moving averages, respectively. The vertical line indicates March 2020.

14

2023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m12022m72022m12021m72021m1

2020m7

2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m72023m10

Mismatch index.05.1

Easy (MA)
Difficult (MA)

2022m72022m12021m72021m1

2020m70Easy (OS)Difficult (OS)2020m1

2019m72019m1

2018m72018m1

2017m72017m1

2016m70

Mismatch index
.05
.1

.15

.15

.15

Availability of remote work (Threshold = TW1); Part-time(i) Part-Time Workers (TW3 as the thresholdof remote work availability)