ISSN (Print) 0473-453X

Discussion Paper No. 1193

ISSN (Online) 2435-0982

PREFERENCE FOR YOUNG WORKERSIN MID-CAREER RECRUITING
USING ONLINE ADS FOR SALES JOBS:EVIDENCE FROM JAPANMirka Zvedelikova

October 2022

The Institute of Social and Economic Research
Osaka University
6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan

Preference for Young Workers in Mid-career Recruiting Using Online Ads for Sales Jobs: Evidence
from Japan†
Mirka Zvedelikova*
October 13, 2022

Abstract
This study uses an original dataset of online mid-career job ads for full-time sales jobs collected from
July 2018 to December 2019 to examine the use of explicit and implied age limits on job applicantsand characteristics of companies that set them. Although Japanese law prohibits age discriminationin employment, several exemptions are allowed, such as hiring young workers without prior work
experience on regular contracts. Companies can set an age limit, require job-related experience, or
search broadly; however, they can also express their age preference in other ways. In the sample,
24% of ads included explicit age limits generally capped at 35 years, 22% set experience
requirements, and nearly all contained some form of implied age preference. Companies with higher
capital, those with fewer employees, older companies and those located in urban centers tend to set
requirements on applicants. Further, companies that are domestic, with fewer employees, in urban
centers and companies using probation periods for new hires are more likely to set age limits.
Furthermore, the role of company market power is considered a factor allowing companies tovoluntarily limit the pool of applicants in a tight labor market. Companies that set age or experience
requirements appear not to engage in wage competition related to labor market tightness.
Companies that do not set either requirement partially respond to increased wage expectations
related to population age while reducing labor costs by increasing working hours covered by thebaseline salary.
Keywords: Mid-career recruiting; Age discrimination; Job ads; Monopsony; JapanJEL Classification: J42, J63, J71†

I want to express my sincere gratitude to Hideo Akabayashi and Sōichi Ōta for their patient guidance andconstructive criticism. I also want to thank the participants of conferences and seminars I had the opportunity
to present at for their valuable comments. No conflict of interest to declare.
*
Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka, 567-0047, JapanE-mail: zvedelikova@iser.osaka-u.ac.jp. https://orcid.org/0000-0002-3085-7502.

1. Introduction
The aging population and the surplus of vacancies over jobseekers have been some of the persistentfeatures of the Japanese labor market. Yet, looking at job ads aimed at mid-career jobseekers,
finding explicit age limits for applicants is not uncommon. In many countries, restricting applicant
eligibility based on age would be considered illegal discrimination. Although Japanese law prohibits
age discrimination in employment, it allows for a few loosely defined exceptions, such as hiring
young people on long-term contracts while treating them as fresh graduates. Preferential hiring of
fresh graduates is another longstanding feature of the Japanese labor market and is not legallyconsidered discriminatory under the relevant legislation. Exploiting this regulatory exemption allows
to use job ads to directly analyze what type of companies set age limits in recruiting without theneed to prove the presence of discriminatory practices first. In most cases, research on employment
discrimination must rely on less direct methods, such as experiments or company surveys. As setting
age limits on applicants is legal in Japan under the circumstances examined in this paper and as theword discrimination generally has a negative connotation, the term age targeting is used instead.
This paper examines an original sample of job ads for sales jobs collected from Doda, an online job
ad site aimed at mid-career jobseekers, over an 18-month period from July 2018 to December 2019prior to the outbreak of COVID-19. In general, the sales job title allows for the seamless applicationof young-age-related regulatory exemption. An explicit age limit was present in 24.4% of the 3,557
ads collected. However, an explicit age limit is not the only way to convey a preference for applicants’ages. Several types of indirect age targeting were identified, such as emphasizing how welcoming
the company is to young workers in the job ad text or using age in the model salary. Depending on
the definition, up to 96% of the ads in the sample used indirect age targeting.
The current paper describes the company characteristics associated with age targeting practices. A
theoretical framework of employee search based on a model by Kuhn and Shen (2013) is proposed,
hypothesizing that companies that can conduct more expensive search select targeted searchstrategy. The empirical results in this paper are generally consistent with the theoretical predictions.
Furthermore, monopsony is proposed and tested as the underlying condition allowing companies totarget age directly.
Voluntarily limiting the pool of potential applicants in a tight labor market may appear
counterintuitive on the part of employers, and it may also have serious social consequences.
Regardless of contract type, the job change rate in Japan in 2018 was 10% with significant
differences based on age and gender. Although 17.5% of males under the age of 19 changed jobs,
this rate dropped to 7.7% for males aged 35–39 and 4.7% for those aged 45–49. For females, the jobchange rates were higher, driven up by a higher ratio of part-timers who changed jobs more
frequently, peaking in the 25–29 age group at 18.3% and decreasing to 13.1% and 11.3% in the 35–
39 and 45–49 age groups, respectively (Ministry of Health, Labour and Welfare of Japan (MHLW),
2019d). Job advertisements are a crucial source of information for employees considering a jobchange and for individuals considering entering the labor market. Age restrictions in job
advertisements, particularly easily accessible online job ads hosted on heavily advertised commercial
sites, might discourage older workers from changing jobs, thereby contributing to their continued
employment in unfavorable working conditions. Moreover, workers who entered the labor market
during the 1990s economic downturn, known as the “employment ice age,” had limited access tostable jobs and related stable income and social benefits, limiting their ability to start families andplan for retirement. These workers are now middle aged, and despite the high number of vacancies,
they may no longer be eligible. People who dropped out of the labor force during their productive
years, for example, raising children or becoming caregivers, might also find it difficult to return totheir employment of choice. Furthermore, although the elderly population is growing and living
longer and healthier lives, the elderly may have limited employment options while the pension
system is struggling.
2This study makes two major contributions. First, although ample research on age limits in recruiting
exists in Japan, to the best of the author’s knowledge, this study is the first to use the richinformation found in online job ads to directly analyze the characteristics of companies in Japan thatpractice both direct and indirect age targeting in mid-career recruiting. Second, this study verifies
whether monopsony in the labor market is a factor allowing companies to target employee search.

2. Related literature
The economic literature on discrimination, pioneered by Becker (1957), is extensive. However,
because nearly all OECD countries have passed laws prohibiting employment discrimination,
including age discrimination (OECD, 2019), explicit discriminatory practices in the workplace areuncommon in developed countries. Empirical studies must thus rely on indirect methods, typically
including experiments or correspondence studies, such as sending out fictitious resumes that differ
only in the aspect of interest and comparing call-back rates (Bertrand and Mullainathan, 2004). The
primary goal of these studies is to identify discrimination. Using data with a clear preference for
specific types of workers allows for skipping this step and focusing directly on determinants ofcompany behavior. Arrowsmith and McGoldrick (1993) examined explicit and inferred age limits in
job advertisements published in newspapers and magazines in the United Kingdom during 1981–
1991 prior to the passage of anti-age discrimination legislation. Kuhn and Shen (2013) studiedcompanies’ preferences for gender, age, height, and beauty of applicants using job ads from a
Chinese online job ad site in a country where employment discrimination is mostly unregulated. In a
subsequent study Helleseter et al. (2020) expanded their dataset to include data from Mexico. Burnet al. (2021) discovered that age stereotypes in job ads sometimes predicted age discrimination by
combining the mining of US job ads for ageist language with correspondence studies.
Turning to Japan, appreciation for young age in employment, lifetime employment for regular
employees, and the corresponding low job mobility are some of the defining features of theJapanese postwar labor market. This system is dependent on companies’ ability to hire young
workers, particularly fresh graduates. Ono (2010) demonstrated that lifetime employment was apersistent practice, particularly among highly educated male regular employees in large corporations
and the public sector. Because preferential hiring of young workers is a long-standing practice inJapan, it has been extensively researched, with most studies depending on company surveys.
Company surveys are useful because they provide information on the company’s internal
operations; however, they tend to suffer from low response rates and response bias, particularly
regarding sensitive topics.
Using job ads to study age targeting in Japan before the introduction of anti-age discrimination
legislation, Kitaura (2003) found that only 1.6% of job ads registered with the public employment
office in September 2001 excluded age restrictions on applicants. Tokunaga (2008) examined job ads
from the 1985, 1995, and 2005 issues of a women-oriented job ad magazine and reported thataround 97% of ads contained age limits at all three time points, with the maximum eligible age
increasing over time. Okunishi (2008) discovered that younger companies and those that did notpractice on-the-job training hired more mid-career workers, whereas larger companies expanded
mid-career recruitment in the three years preceding the survey. Similarly, Ōta and Yasuda (2010)concluded that companies with well-developed training programs and high levels of firm-specificknowledge hired more fresh graduates than they did mid-career workers, thereby indicating apreference for younger workers. Finally, Kambayashi and Ōta (2010) used company survey data andfound that companies with more employees and companies with higher quit rates tended to search
for more workers. They further found differences in age limits in recruiting between industries andoccupations.

3

Next, reviewing the monopsony in labor market literature, Manning (2020) observed that the idea ofimperfect labor markets has been growing in popularity and was starting to gain traction among
policymakers. Sokolova and Sorensen (2020) conducted the first meta-analysis of labor market
monopsony literature and concluded that the evidence for monopsonistic competition was strong
with a significant impact on wage markdowns while also highlighting the lack of empirical research
examining potentially discriminatory wage gaps. The traditional approach towards analyzing
company monopsony power was to use matched employer–employee data to compare workers’
marginal productivity to wages (Scully, 1974) or estimate the (inverse) labor supply elasticity(Sullivan, 1989). One of the more recent approaches to the subject looks at the elasticity ofrecruitment. Dal Bó et al. (2013) used data from a Mexican government experiment to attract public
servants to marginalized municipalities, wherein two different wage offers were randomly assigned
across recruitment localities to conclude that higher wages attracted better applicants and helped
close the recruitment gap in less desirable municipalities. Using job ads as a tool to assess labormarket monopsony, Azar et al. (2020) used online job ads to examine the association between labormarket concentration and posted wages in the US and observed lower posted wages in markets withfewer firms. Although both these papers had access to applicant information—data this study does
not have—an approach primarily inspired by Azar et al. (2020) is used to evaluate the indicators ofmonopsony power in Japan and their relevance to age targeting in mid-career recruitment.
To the best of the author’s knowledge, this paper is the first to investigate the company
characteristics of companies in Japan that use age targeting in recruiting mid-career workers throughonline job ads and to link age targeting practices with company monopsony power.

3. Institutional setting
In Japan, discrimination in employment is governed by the Employment Measures Act (EMA),
originally implemented in 1966. Age as a basis for discrimination was first referenced in the 2001
EMA revision, which stipulated duty to endeavor to provide equal opportunity to prospective
workers regardless of age. The 2007 EMA amendment changed this duty to endeavor into an
obligation with six exemptions. Every job ad stipulating age limit in the sample used in this study
cited the following exemption:“Recruit only new graduates who are youth, or below certain ages, in order to givethem the opportunity to develop and improve their occupational abilities over a long
period of service.” (Sakuraba, 2009)This exemption shields the traditional recruiting process aimed at recent graduates from age
discrimination lawsuits. Companies may set upper age limits when hiring youth on indefinite
contracts; however, they may not require any work-related experience, licenses, or qualifications
obtained only during the course of employment, thereby effectively treating applicants as fresh
graduates. Employers who want to hire workers on a regular, non-fixed-term, contracts have three
options: set a specific age limit for applicants, set experience requirements, or set neither. The
exemption does not define “youth” or “below certain ages”; however, official explanatory materials
frequently mention the age of 35 and in some cases 45 (MHLW, 2016, 2019a). Furthermore, because
indirect age targeting is not addressed, employers are free to express their preference in other ways
and are thus, in practice, permitted to require applicants to have both work experience and be of a
specific age.
Exemptions for artistic reasons, hiring for jobs with statutory age limits, setting upper age limits tocomply with mandatory retirement age, specifying the age range when a given age category isunderrepresented in the company, and hiring seniors or other persons whose employment isencouraged by government policies are among the remaining exceptions. Under the current version
4

of the law, no other exemptions are specified, such as setting age limits to facilitate smooth businesswith customers of specific age groups or setting age limits to maintain an in-company pay structure.

4. Data
4.1. Data collection
The data for this study were gathered over an 18-month period from July 2018 to December 2019from the Japanese online job ad site Doda (https://doda.jp/), a website aimed at mid-career
jobseekers. Doda, founded in 2007, claimed to have approximately 4,100,000 registered users as ofMarch 31st, 2018, with this number growing to 5,450,000 by March 31st, 2020. (Persol, 2018, 2020).
Doda does not reveal the age breakdown of all registered users, only those who have recently
registered. From October 2017 to March 2018, 19.5% of users registered during this period were
under the age of 25, 30.9% were aged 25–29 years, 18.7% were 30–34 years, 11.6% were 35–39
years, and 19.4% were 40 and older. As previously registered users age, the average age of all Doda
users is likely higher.
Doda was chosen as the data source for the following three reasons. First, Doda is a general job
board that, in contrast to some of its primary competitors, does not operate sister sites aimed at a
specific subset of jobseekers based on age, job title, or industry. Second, Doda claimed to host themost job ads among popular Japanese online job ad sites at the start of the sample collection period.
Finally, the structure of the job ad itself is appropriate for this study because it provides employers
with substantial space and is thus a rich source of information. Doda-affiliated companies draft theads, thereby resulting in highly standardized language and ad structure while ensuring the adcomplies with applicable legal requirements. Except for job ads for entities without registered capital,such as religious institutions or municipalities announcing job fairs, all new complete ads, ads withboth company press page and job ad page, for full-time positions uploaded to the sales (eigyō, 営業)
category were collected. After excluding job ads that lacked information that could not be obtained
elsewhere, the final sample comprises 3,557 ads from 1,341 companies.
Doda allows businesses using its services to select from various ad formats. Ads with a dedicatedcompany PR page and a dedicated job-posting page, as collected for this study, appear preferentiallyin both the overall list of ads regardless of posting date and the criteria-based search using filters.
Doda categorizes job postings into 15 categories based on job title. This study chose the sales
category because sales jobs can generally be transitioned into without specific education, licenses,
or other qualifications, thereby allowing for compliance with the young-age-related EMA exemption.
The sales category was the largest at the beginning of the sample collection period. Additionally,
Doda divides postings into three categories based on employment contract type: regular employee(seiki shain, 正規社員), contract employee (keiyaku shain, 契約社員), and other (sono ta, その他).
Approximately 95% of the ads posted in the sales category during the sample collection period
advertised regular contract jobs.

4.2. Dataset
The complete sample comprises 3,557 ads from 1,341 companies; however, this sample contains
reposts. Companies posting multiple ads often use a mix of targeting strategies for an otherwise
identical ad. An ad is considered a repost if it matches a previously posted ad by the same company
in all its job characteristics and search strategy attributes. Jobseekers are assumed to recognize arepost and always reject it if the terms are undesirable but are assumed to consider an ad thatmeets their criteria regardless of their possibly having rejected another ad by the same company.
This study thus adopts an ad-based approach instead of a company-based approach, excluding5

duplicates of the previously posted ads while keeping multiple ads from companies not considered
reposts.
After excluding reposts, the reduced sample consists of 2,679 unique ads by 1,341 companies, with
790 of these companies posting a single unique ad, corresponding to 29.5% of ads and 58.9% ofcompanies in this sample. The company with the most ads posted 173 ads, including reposts, with 34of these ads identified as unique. The 10 most active companies posted 504 ads in total, 214 ofwhich were unique, constituting 14.2% of the full and 8.0% of the reduced sample. Although the adbased approach shrinks this gap, the most active companies are overrepresented.
Table A1 in Appendix 1 contains the descriptive statistics for both samples. Using the reduced
sample, the average registered capital of a company posting an advertisement was 13.5 billion yen,with a median of 63.3 million yen. With a mean of 215 employees, the average number of
employees was 3,302. The average company posting an ad was founded in 1992, whereas themedian year of founding was 1999. Larger, older companies tended to post a variety of ads, pushing
the sample means higher. Furthermore, a publicly traded companies posted 14.1% of the ads, andsubsidiaries of one posted 19.2% of the ads. Domestic Japanese businesses posted 94.7% of the ads
and 69.4% of ads were posted by a company claiming to be based in Tokyo or Osaka. A probation
period was required in 66.0% of the ads. If the posting company’s information was not included in
the ad, it could be found elsewhere, such as on the company’s website or in job ads on other job adsites. In all cases, the ad included information about the industry wherein the company operates.
For this paper, two types of age targeting are defined: direct and indirect. Companies that impose an
explicit age limit on applicants citing EMA’s young-age-related exemption are considered to engage
in direct age targeting. Direct age targeting was present in 23.7% of unique ads, whereas job
experience was required in 25.0% of ads. In terms of maximum eligible age, 3.7% of ads in the
reduced sample accepted only applicants aged 30 or younger, 15.7% of ads accepted only applicants
aged 35 or younger, and 20.9% of ads set the maximum eligible age to 40 or younger. Figure 1
depicts the maximum eligible age distribution in a sample of ads with age restrictions. In 39.0% of
these ads the cut-off age was 35, with the lowest limiting age being 25 and the highest being 49.

Figure 1: Age limit by cut-off age
Following that, indirect age targeting is defined as specifying a preference for young workers in the
ad in ways other than explicitly setting an age limit. Within the highly standardized structure of theads, six patterns were identified, five in writing and one visual. The six types were summarized into
two variables: Age index 1, ranging from 0 to 6, and Age index 2, ranging from 0 to 5. Variable Age
index 2 excludes visual-based age targeting. The average number of indirect age targeting types in6unique ads was 3.1 for Age index 1 and 2.4 for Age Index 2. A detailed discussion on the types ofindirect age targeting is provided in Appendix 2.
All job ads collected contained a monthly salary offer upon completion of the probation period, with
48.3% of ads including overtime pay in the offer. The minimum number of overtime hours includedin these ads was 10, the maximum 80 and the average stood at 38.2 hours. When salary offer waspresented as a range, the lower bound was used, and for ads advertising vacancies in multiple
locations, the salary offer corresponding to the reported company location was recorded. To
describe the salary offer, two variables were created. First, the hourly wage was computed based on
the average number of regular monthly contracted hours.1 The hourly wage was then weighted by
the minimum wage in the company’s reported location’s prefecture. The hourly wage is calculated in
2 ways: not reflecting the number of overtime hours included (variable Hourly wage ratio) andincluding overtime hours in contracted hours (variable Hourly wage ratio with overtime).
𝑠𝑎𝑙𝑎𝑟𝑦 𝑜𝑓𝑓𝑒𝑟
165
𝐻𝑜𝑢𝑟𝑙𝑦 𝑤𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜 =𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑤𝑎𝑔𝑒
𝑠𝑎𝑙𝑎𝑟𝑦 𝑜𝑓𝑓𝑒𝑟
165+
𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 ℎ𝑜𝑢𝑟𝑠
𝐻𝑜𝑢𝑟𝑙𝑦 𝑤𝑎𝑔𝑒 𝑟𝑎𝑡𝑖𝑜 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 =𝑚𝑖𝑛𝑖𝑚𝑢𝑚 𝑤𝑎𝑔𝑒
Figure 2 depicts the distribution of Hourly wage ratio and Hourly wage ratio without overtime in the
reduced sample. The average hourly wage ratio was 1.55 and 1.41 for hourly wage ratio withovertime, with the minimum and maximum values for both variables being 0.62 and 3.77,
respectively. In some cases, the convoluted system of incentive pay pushed the hourly base rate
below the minimum hourly wage.

Figure 2: Hourly wage to minimum wage ratio
Finally, the local labor market, the definition of which is limited by data availability, is described by
two variables: vacancy ratio and population age. The vacancy ratio is the number of job openings perjobseeker, published monthly at the prefectural level. The value for the prefecture of company
reported location 1 month before the ad-posting month is used. The vacancy ratio ranged from 1.24
to 2.21, indicating that the job market is tight for all ads in both the full and redacted samples, with
1

The average number of standard hours in a regular contract in 2018 was 165 (MHLW, 2019b).

7

more jobs available than jobseekers. Following that, population age displays the average population
age in the city of the reported company location, as determined by the 2015 census. However,
although the data were matched where possible, the location reported by the company may not be
the same as the location of the job, particularly for job ads seeking to fill openings in multiple
company locations.

4.3. RepresentativenessA company with an opening to be filled with a mid-career employee is faced with a choice of a
recruiting channel, such as the public employment office, referrals, or online job ad sites. Although
the selection likely depends on the job itself, the jobseekers the company wants to reach, and thecorresponding costs, the popularity of online job ad sites in Japan is growing, evidenced by theincreasing number of registered Doda users. Kambayashi and Ota (2010) found that vacancies
advertised using a job ad attracted more applicants while pointing out that these vacancies
could be for less specialized jobs, which would be expected to attract more applicants thanhighly specialized positions. Since 2010, Internet usage and overall digitalization of all aspects ofsociety further increased and the number of online job ad sites in Japan with it. It is likely that
accordingly, the choice of the online job ad site channel for a company looking to fill a sales, nota highly specialized, position became more widespread. However, as this study only examines
the online job ad site recruiting channel, its conclusions can only be viewed in the appropriate
context.
In terms of the data’s main limitations, because the sample was collected from a single source, forregular contract jobs in a single job title category only, it is not representative of the Japanese midcareer job market. In 2018, 37.8% of workers in Japan were in non-regular jobs (Statistics Bureau ofJapan, 2019). Another limitation of this sample is that it is based on self-reported data that may not
be consistent across companies, such as the inclusion or exclusion of non-regular employees fromthe total number of employees. Furthermore, companies that do not set age limits in their job ads
may engage in age discrimination later in the recruiting process, particularly if they choose to set
experience requirements while preferring to hire young applicants. Another limitation of this study isthat there is some subjectivity in evaluating ad content, particularly the visual aspects, as discussed
in Appendix 2.
Furthermore, if the job on offer does not appeal to older jobseekers, companies may not need to set
age limits. Table 3 shows the average monthly salary offer in the reduced sample broken down byage, experience, and educational requirements. Panel A describes the salary offer in its current form,
and Panel B reports it cleared of overtime using the following formula:
𝑆𝑎𝑙𝑎𝑟𝑦 𝑜𝑓𝑓𝑒𝑟 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 = 𝑠𝑎𝑙𝑎𝑟𝑦 𝑜𝑓𝑓𝑒𝑟 ∗165.
165 + 𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 ℎ𝑜𝑢𝑟𝑠

Table 4 shows the average monthly salary in Japan in Japanese yen by the age group and contract
type in Panel A and the average first job starting monthly salary by educational level in Panel B toassess the data’s representativeness.

8

TABLE 3: Average monthly salary offer – sample (unique ads)
A. OVERTIME NOT CONSIDERED
AGE/EXPERIENCEREQUIREMENTEDUCATIONAll
No requirement
High school
Vocational, 2-year college
University

NONE(n = 1375)

AGE LIMIT(n = 634)

EXPERIENCE(n = 670)

238,245239,819
231,073
229,432
245,664

233,827
241,018
215,465
221,853
231,813

263,923
258,314
247,936
265,613285,513NONE(n = 1375)

AGE LIMIT(n = 634)

EXPERIENCE(n = 670)

215,954
217,519
206,104
218,264
231,824

209,907
213,665
195,274
214,092
227,310243,081
232,663
235,565
258,429
268,792

B. CLEARED OF OVERTIME
AGE/EXPERIENCEREQUIREMENTEDUCATIONAll
No requirement
High school
Vocational, 2-year college
University

TABLE 4: Average monthly salary - population
A. BY AGE GROUP AND CONTRACT TYPE

Under 19
20 – 24
25 – 29
30 – 34
35 – 39
40 – 4445 – 49
50 – 54
55 – 59
60 – 64
65 – 69Over 70
Average

2018
REGULAR
OTHER
179,200
165,500
213,200
182,100
245,700
198,200
282,400
204,900
313,300
207,700
342,100
205,600
372,800
206,100
400,000
204,300
400,200
206,200
316,700
236,500
283,300
208,200
281,000
199,500
323,900
209,400

2019
REGULAR
OTHER
180,200
168,000
214,600
180,800
249,500
198,900
284,800
204,700
317,100
207,600
344,400
208,200
368,900
208,100
398,600
206,600
396,300
205,500
325,100
237,900
286,500
216,500
274,700
195,800
325,400
211,200

MHLW (2019b, 2020b)

B. FIRST JOB BY EDUCATIONAL LEVELHigh school
Vocational school
Bachelor's degree
Master's degree

2018
165,100
181,400
206,700
238,700

2019
167,400
183,900
210,200
238,900

MHLW (2019e)9Comparing Tables 3 and 4, the offered salary in job ads on Doda is likely to attract predominantly
young regular workers, especially when overtime hours are taken into account, and non-regular
workers of all ages. The average offered salary in ads with an age limit and requiring university
education is for master’s degree holders lower than the average first job salary. Age restrictions may
thus be intended to discourage primarily non-regular workers from applying. Even job
advertisements with the most stringent requirements, such as experience and a university degree,
offer a lower salary than that a regular employee in the 35–39 age group earns on average. However,the salary offer included in job ads likely functions as a reference for salary negotiations, with theactual starting salary being higher. Nevertheless, the jobs posted in the sales category on Doda
generally seem to be aimed at younger workers, specifically those below the age of 30 in the case ofregular workers.

5. Theoretical framework
The model proposed in this paper is based on Kuhn and Shen’s (2013) non-sequential search modelof discrimination in job ads. A company with an opening chooses the type of candidate to invite toapply in both the original model and the version proposed in this paper based on perceived
productivity and corresponding search costs. Companies can choose to search broadly, with norequirements, or narrowly, with conditions imposed on applicants. In the work of Kuhn and Shen
(2013), the option was gender-based, with preference for one automatically disqualifying preference
for the other. Although the choice between age and experience requirements is binary in this paper,
the underlying preferences are not mutually exclusive..
First, let’s assume that companies operate in a monopsonic local labor market. Manning (2003,2011) proposed that wage-setting power of companies stems from the existence of search frictions,such as the costs associated with job change or reservation wages, factors incorporated into thefollowing model. In a monopolistic setting, worker discrimination can be consistent with company
profit maximization.
The number of job separations company 𝑚 faces, 𝑠 (𝑛), is assumed to be a non-decreasing functionof the number of employees 𝑛:𝑠 (𝑛) ≥ 0.
For a company to maintain its size, each quit is turned into an opening, the number of openings isthus a non-decreasing function of the number of employees. This setting is consistent with
Kambayashi and Ota (2009), who found that companies with more regular employees andcompanies with higher quit rates post more mid-career job ads in Japan.
Further, the total search cost ∑ 𝑆𝐶 company 𝑚 is willing to spend searching for workers to fill all
their openings is at maximum a fixed portion 𝑡, 0 < 𝑡 < 1, of their assets 𝑐 :𝑆𝐶 ≤ 𝑡𝑐 .
Turning to the supply side, each jobseeker 𝑖 is assumed to possess several characteristics:
1)
2)
3)
4)
5)

productivity 𝑥 defined as 𝑥 = 𝑥̄ + 𝜀 , where 𝑥̄ is the average productivity for theadvertised job after undergoing relevant training and 𝜀 is 𝑖’s deviation from 𝑥̄
reservation wage 𝑊
experience 𝑒
age 𝑦location 𝑙 the jobseeker is willing to work in.
10

These attributes have the following probability distribution functions:
1)
2)
3)
4)

productivity distribution function 𝐹(𝑥)reservation wage distribution function 𝐺(𝑊)experience distribution function 𝐻(𝑒)age distribution function 𝐽(𝑦).

The probability 𝑃 of an applicant being suitable for an opening with an offered wage 𝑊, productivity
requirement 𝑥̄ , experience requirement 𝑒 and age requirement 𝑦 by a company located in location 𝑘is
𝑃 = 1 − 𝐹(𝑥̄ ) 𝐺(𝑊) 1 − 𝐻(𝑒) 𝐽(𝑦)and the number of applicants 𝐴 for this opening is
𝐴 = 𝑃𝐿
where 𝐿 is the total number of available jobseekers in a location 𝑘. As such, 𝐴 is an increasing
function of 𝐿, 𝑊, 𝑦 and is decreasing in 𝑥̄ and 𝑒.
Introducing the attractivity coefficient 𝜇, 0 ≤ 𝜇 ≤ 1, which reflects attractivity of the company, thenumber of arrivals is modified to the following:𝐴 = 𝜇 𝑃𝐿 .
Companies searching broadly see a higher number of suitable applicants arriving
𝐴 = 𝜇 𝑃 𝐿 , 𝑃 = 1 − 𝐹(𝑥̄ ) 𝐺(𝑊)than companies searching narrowly do, targeting either age or experience with the corresponding
number of applicants 𝐴 and 𝐴
𝐴 = 𝜇 𝑃 𝐿 , 𝑃 = (1 − 𝐹(𝑥̄ ))𝐺(𝑊)𝐽(𝑦)𝐴 = 𝜇 𝑃 𝐿 , 𝑃 = (1 − 𝐹(𝑥̄ ))𝐺(𝑊)(1 − 𝐻(𝑒)).
Broad search is associated with search cost 𝑆𝐶(𝐴 ), targeted search results in search cost 𝑆𝐶(𝐴 ) or𝑆𝐶(𝐴 ). Search costs are decreasing in the number of applicants 𝐴, 𝑆𝐶′(𝐴) < 0, reflecting theadditional costs of targeted search, such as the costs needed to reach jobseekers with specificcharacteristics or the smaller pool of applicants and thus likely longer time the opening goes unfilled.
Hence search costs decrease with higher wages, higher age limits, a larger jobseeker population, anda higher attractivity coefficient, and increase with tighter productivity or experience requirements.
Costs incurred during the subsequent stages of the hiring process, such as application processing orinterviews, are not considered.
For a single opening, company 𝑚 chooses the type of applicant to invite based on maximizing thefollowing profit function 𝜋:𝜋 = 𝑚𝑎𝑥 𝐸 𝑅 𝑈 ; 𝐷− 𝐷 𝑊 + 𝑆𝐶(𝐴 ) + 𝑇𝐶

𝑤ℎ𝑒𝑟𝑒 𝑈 = 𝑥 + 𝐷 𝑥 , 𝑗 ∈ (𝐵, 𝑌, 𝐸), 𝑥 = 0
𝑠. 𝑡. 𝐴 ≥ 1
𝑆𝐶(𝐴 ) ≤ 𝑡𝑐
where 𝑅(𝑈 ) is a revenue function, 𝑅′(𝑈 ) > 0 and 𝑅 (𝑈 ) < 0. The variable of choice is 𝐷 , asearch type indicator equal to 1 if the strategy is chosen and 0 otherwise, 𝑗 ∈ (𝐵, 𝑌, 𝐸), where 𝐵
stands for broad search, 𝑌 for age-targeted search and 𝐸 for experience-targeted search. The term
𝑥 is a company's perceived bonus in productivity associated with a given type of worker. A
11company’s perceived young-age productivity bonus 𝑥 can stem both from taste-based or statistical
discrimination, 𝑥 is company’s perceived productivity bonus from relevant experience and 𝑥 = 0.
𝑇𝐶 is the cost of training allocated to a worker hired through the respective type of search, where
𝑇𝐶 > 𝑇𝐶 > 𝑇𝐶 . 𝑊 is the wage associated with the productivity level 𝑥̄ and 𝑊 < 𝑊 < 𝑊 .
A company facing 𝑞 number of openings 𝑣 chooses a mix of search strategies to jointly maximize
profit 𝛱, subject to constraints on the minimum number of wanted applicants and the maximum
affordable search costs.
𝛱 = 𝑚𝑎𝑥

𝐸 𝑅 𝑈 ;𝐷𝑠. 𝑡.

− 𝐷 𝑊 + 𝑆𝐶 (𝐴 ) + 𝑇𝐶𝐴

≥ 𝑠 (𝑛)𝑆𝐶 (𝐴 ) ≤ 𝑡𝑐
The dataset compiled for this study allows for a direct observation of the chosen search strategy 𝐷 .
Based on this model, several hypotheses are proposed:
1)Companies for which targeting is comparatively more expensive tend to target less.
This includes companies that can allocate less money to search activities, companies
experiencing more quits, or companies struggling to attract applicants, such as those inlocations with fewer jobseekers or companies with low attractivity coefficient.

2)Companies highly valuing young age tend to age target more.
These are companies with a high perceived productivity bonus of young age 𝑥 . Companies
with highly developed on-the-job training programs or those with an established rigid inner
structure might place more value on young age. Companies investing heavily in training
workers, which might be observed as utilization of a probation period, might want to gain
the benefits over a long period of employment. The impact of unobserved company inner
structure or culture might manifest as a difference between younger and older companies
or between domestic Japanese and foreign-owned companies.

3)

Companies with a higher degree of market power tend to search narrowly.
Market power allows companies to set wages lower than workers’ marginal product,
enabling them to cover additional costs stemming from targeted search. This is especiallyrelevant to age-targeted search, as the young-age-related perceived productivity bonus 𝑥
might not be realized as an actual productivity bonus.

Besides search type, companies reveal their preference for young age by employing indirect age
targeting practices. In ads that set experience requirements but are unable to set age limits
simultaneously, indirect age targeting may be used as a substitute for age limits. In ads with an age
limit, indirect age targeting may be used to target specific age groups more precisely.

6. Empirical strategy
The first part of the empirical analysis discussed in Section 7.1 examines the association between
company characteristics and targeted employee search. Narrow search dummy variable for unique12

job ad i is regressed on company characteristics and a set of controls using the following linear
probability model:
𝑁𝑎𝑟𝑟𝑜𝑤 𝑠𝑒𝑎𝑟𝑐ℎ = 𝛼 + 𝜷 ∗ 𝑪𝒐𝒎𝒑𝒂𝒏𝒚 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒊 + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜀(1)Where Narrow search takes five forms based on the type of targeting used: Age limit + experience
indicating narrow search in general, Experience for experience-targeted narrow search, Age limit forage-targeted narrow search, Under 40 for age-targeted search with the maximum eligible age of 40
and Under 35 for age-targeted search with the maximum eligible age of 35. The coefficient ofinterest is 𝛽, a vector of parameter estimates of variables describing company characteristics.
Controls is a vector of control variables always including month and industry dummy variables and a
combination of variables describing job characteristics, local job market or specifics of the selected
search strategy. For all models, the error term 𝜀 is clustered at a company level.
Additionally, Indirect age targeting categorical variable for unique job ad i is similarly regressed oncompany characteristics and a set of controls in a linear regression model:
𝐼𝑛𝑑𝑖𝑟𝑒𝑐𝑡 𝑎𝑔𝑒 𝑡𝑎𝑟𝑔𝑒𝑡𝑖𝑛𝑔= 𝛼 + 𝜷 ∗ 𝑪𝒐𝒎𝒑𝒂𝒏𝒚 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒊 + 𝛾 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝛿 ∗ 𝑁𝑎𝑟𝑟𝑜𝑤 𝑠𝑒𝑎𝑟𝑐ℎ+𝜀(2)including Narrow search in the form of age-targeted and experience-targeted search dummy
variables. Indirect age targeting takes the form of Age index 1 (0–6 range) and Age index 2 (0–5range). The error term 𝜀 is analogously clustered at the company level. Furthermore, models (1) and(2) for all outcomes were additionally estimated using probit (model (1)) and ordered logit (model(2)) to check the robustness of the results, producing generally qualitatively identical results withhigher statistical power.
Next, Section 7.2 provides the discussion of the role of monopsony power in enabling companies totarget search using an approach inspired by Azar et al. (2020). Companies that operate in acompetitive environment should theoretically raise wages in response to a tightening labor market,
whereas companies that exercise market power would be unaffected. Assessing the relationshipbetween local labor market conditions and the salary offer may thus reveal the type of market inwhich the company operates. Both the reduced and full datasets are analyzed to capitalize on the
dynamic nature of the labor market during the data collection period.
To examine the association between the salary offer and the local labor market conditions, Wage
offer variables Hourly wage ratio and Hourly wage ratio without overtime of a unique job ad i or a
job ad j are regressed by a linear regression model on variables describing the local labor market anda set of controls using the following model:
𝐿𝑜𝑔(𝑊𝑎𝑔𝑒 𝑜𝑓𝑓𝑒𝑟 )= 𝛼 + 𝜷 ∗ 𝒍𝒐𝒈(𝑽𝒂𝒄𝒂𝒏𝒄𝒚 𝒓𝒂𝒕𝒊𝒐𝒌 ) + 𝜸 ∗ 𝑷𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝒂𝒈𝒆𝒌 + 𝛿 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠 + 𝜇∗ 𝑁𝑎𝑟𝑟𝑜𝑤 𝑠𝑒𝑎𝑟𝑐ℎ + 𝜀(3)where 𝑘 ∈ (𝑖, 𝑗), using complete samples and subsamples based on the type of search used.
Hourly wage ratio and Hourly wage ratio without overtime depict the salary offer as an hourly wage
weighted by the local minimum wage, respectively, ignoring and accounting for the number ofovertime hours included in the salary offer. Vacancy ratio is the number of open job positions to thenumber of jobseekers in the prefecture of the company location 1 month before the posting month.
13

Population age represents the average population age in the city of company reported location.
Controls is a vector of control variables including month and industry dummy variables and a
combination of variables describing company, job, and search strategy characteristics. Equivalently
to equation (2), Narrow search is a vector of age-targeted and experience-targeted search dummy
variables. The error term 𝜀 is clustered at the company level for the sample of unique ads i (reduced
sample) and two-way clustered at the company level and a job ad level for the sample of all ads j(full sample).

7. Results and discussion
7.1 Company characteristics as determinants of narrow searchThis section examines the relationship between company characteristics and a narrow employee
search strategy. Table 5 displays the results of the narrow search model from equation (1) with avariable set of controls and an outcome variable Age limit in columns (1)-(3), and Experience incolumns (4)-(6). Columns (1) and (4) show the results of estimation with only the month and industry
controls. Without controlling for job and search characteristics, Japanese companies, companies inTokyo and Osaka, and companies that used a probation period were significantly more likely totarget age than their respective counterparts, a result that is most consistent with hypothesis 2 –appreciation for young-age channel. Companies targeting experience, on the other hand, were of
the type generally assumed to be able to afford more expensive search strategies, as proposed in
hypothesis 1; richer companies, companies with fewer quits, and older companies, which may havehigher recognition value than young companies. Furthermore, companies assumed to have a more
flexible internal structure, foreign-owned companies, and subsidiaries of publicly traded companies
were also significantly more likely to target experience. Companies may be inclined to target only
specific types of sales jobs; the inclusion of job-related controls in columns (2)-(3) and (5)-(6) filters
this effect out, weakening the significance of the effect of company characteristics. Columns (3) and(6) also show results with search-related controls that separate out the effect of search intensity,
such as the number of ads and reposts posted, the length of time the ad was set to be visible on thejob board, or the number of locations the company advertised for in an ad. Column (3) results showthat the company characteristics associated with age targeting survive the inclusion of control
variables, gaining significance. Controlling for search intensity in column (6), on the other hand,causes company characteristics associated with experience targeting to lose significance in general.
While the significance of the coefficient estimates varies with the controls, their direction and effectsizes remain largely stable. The specification used in columns (3) and (6), controlling for month,industry, job, and search strategy characteristics, thus revealing underlying preferences, is selected
for the remainder of this section. Results of models including company characteristics variables
separately without additional controls and with job and search-related controls are presented inAppendix 3.
Table 6 displays the results for all the introduced narrow search dummy variables (equation 1) andage indices (equation 2). Column (1) shows the results of an overall narrow search, columns (2) and(3) show the results of mutually exclusive types of narrow searches, and columns (4) and (5) show
the results of age targeting by eligibility cut-off ages (40, 35). Columns (6) and (7) show the
relationship between the company characteristics and the revealed preference for young age at the
top of the selected search type.
The results in Table 6 generally align with the proposed hypotheses. Companies with higher capitalare on a 1% level of significance more likely to choose an overall narrow search, however, the effecton experience and all types of age targeting is significant only marginally or non-significant. This
result suggests that while richer companies do not necessarily prefer young age or experience
specifically, they can undergo a more expensive search, even after controlling for search intensity.
14

Further, the effect of the number of employees is similarly consistent with hypothesis 1, as theincreasing number of employees, understood as more vacancies to fill, shows the expected opposite
trend to capital size. Bigger companies do not differ from smaller companies in terms of targeting
experience or showing a preference for young age indirectly but are highly significantly less likely tochoose an overall narrow search and target age at all thresholds. Experience is assumed to bring arealized productivity bonus, possibly offsetting higher search costs. Comparing the effect sizes acrosseligibility cut-off ages, 1% increase in the number of employees results in a 2.7% lower likelihood oftargeting age overall, but 3.5% and 3.2% lower probability of setting the maximum eligible age to 40
and 35, respectively. This finding suggests that larger companies are more likely to broaden eligibility
before abandoning age targeting. The impact of the foundation year follows a similar pattern as thenumber of employees. Younger companies are at a 5% level of significance less likely to practicenarrow search, with one year translating into a 0.17% lower likelihood, but they do not differ fromolder companies in terms of experience or direct age targeting, apart from not targeting jobseekers
aged 35–40. Younger companies, on the other hand, are highly significantly more likely to use moreindirect age targeting practices, displaying a strong preference for young age without acting on it,
possibly due to low attractivity caused by low name recognition or uncertainty about the future.
Analyzing the role of company ownership type, publicly listed companies do not differ from privatecompanies in terms of likelihood and willingness to target both age and experience. Whilesubsidiaries of publicly traded companies do not differ from private companies in terms of overallnarrow search and direct age targeting at all thresholds, they are 7% more likely to target experienceand tend to use fewer indirect age targeting practices than private companies with the significancelevel of this effect standing at 1%. Because one application of indirect age practices is to signal
young-age preference when age limits cannot be set due to the inclusion of experience
requirements, one possible explanation for this result is that subsidiaries of publicly traded
companies are more welcoming to outside experience regardless of applicant age.
Examining the impact of ownership origin, domestic Japanese companies are 11.4% less likely totarget experience than foreign-owned companies, and 13.5% and 9.6% more likely to target age
overall and ages 40 and younger, respectively. This trend may indicate a company culture that relies
on internal training or seniority systems and places a premium on youth. However, these results are
significant only at 10% and 5% thresholds, and no statistically significant difference is found fortargeting ages 35 and younger, implying identical preference for very young workers. Furthermore,
foreign-owned companies tend to use more indirect age targeting methods, which is consistent withpractices used in an environment where age limits are illegal, as examined in Burn et al. (2021).
Following that, companies reporting their location in Tokyo or Osaka have, on a 1% level ofsignificance, a 16.5% higher likelihood of targeting age overall, a 16.4% higher likelihood of targetingthe 40 and younger age category, and an 11.7% higher likelihood of targeting the 35 and younger
age groups than companies reporting their location in other locations, but they do not differ in termsof experience targeting. Companies in these urban centers have access to a larger pool of applicants,
making targeted searches with uncertain realization of perceived productivity bonuses lessexpensive. Companies in urban areas also tend to use more indirect age targeting practices, possibly
taking advantage of the large jobseeker pool to reach age groups that are more precisely defined
than age limits allow.
Finally, companies that use a probation period, which is interpreted as an indicator of intensive
training practices, are on a 5% level of significance more likely to target age at all thresholds, withthe effect sizes ranging from 6.0% to 7.0%, and to employ indirect age targeting to a greater extent.
The use of a probation period is, albeit marginally, negatively associated with experience-targeted
search, which supports the hypothesis linking training costs and young-age appreciation. Okunishi(2008) and Ota and Yasuda (2010) support this interpretation by linking more developed in-company
training practices to a preference for young workers.
15

NARROW SEARCH IDENTIFICATION – OLS ESTIMATES
AGE LIMIT

EXPERIENCE

(1)

(2)

(3)

(4)

(5)

(6)

Capital (log)0.000784(0.00945)

0.00723(0.00925)

0.0101(0.00928)

0.0249***(0.00907)

0.0142*(0.00840)

0.0160*(0.00867)

Employees (log)0.0175(0.0122)0.00652(0.0120)-0.0268**(0.0120)-0.0358***(0.0101)-0.0215**(0.00919)-0.00881(0.0101)

Foundation year

0.000250(0.000785)-0.000195(0.000709)-0.000693(0.000695)-0.00238***(0.000689)-0.00132**(0.000670)-0.000965(0.000664)

Listed company-0.0838(0.0681)-0.0655(0.0587)-0.0858(0.0525)

0.0564(0.0516)0.0428(0.0459)

0.0653(0.0452)

Subsidiary of listed company-0.0265(0.0487)-0.0103(0.0483)-0.00436(0.0429)0.114***(0.0437)

0.0767**(0.0351)

0.0717**(0.0356)

Japanese company

0.177***(0.0565)

0.111*(0.0571)0.135**(0.0564)-0.226***(0.0689)-0.0964(0.0653)-0.114*(0.0653)

Tokyo, Osaka

0.128***(0.0294)0.148***(0.0310)0.165***(0.0306)

0.0401(0.0244)-0.00526(0.0227)-0.00154(0.0224)

Probation period

0.0512*(0.0296)

0.0582**(0.0286)

0.0696**(0.0272)-0.0255(0.0266)-0.0354(0.0222)-0.0424*(0.0218)











2,679
0.148

2,679
0.188

2,679
0.240

2,679
0.250

Job
Search

𝑁
𝑅

2,679
0.108

2,679
0.135

Notes: Coefficient estimates from linear probability model. Dependent variable is indicator of presence of targeting. Month and industry dummies included
in all models. Inclusion of variables controling for type of advertised job (Job) and search strategy (Search) indicated. Job controls are Starting salary,
Inclusion of overtime in salary, Education, B-to-B, B-to-C, 9AM shift, Flextime, 2 days week off, Transfer possibility. Search controls are Ads per company,
Number of duplicates, Posting period, Number of locations, Number of locations squared, Hiring 5+ workers. Robust standard errors in parentheses
clustered at company level. Significance levels: *** p<0.010, ** p<0.05, * p<0.10.

Table 5: Narrow search – identification

16

NARROW SEARCH – OLS ESTIMATES
AGE LIMIT +EXPERIENCE

EXPERIENCE

AGE LIMITUNDER 40UNDER 35

AGE INDEX 1

AGE INDEX 2

(1)

(2)

(3)

(4)

(5)

(6)

(7)

Capital (log)0.0262***(0.00843)

0.0160*(0.00867)

0.0101(0.00928)

0.00237(0.0119)-0.00113(0.00803)-0.0498(0.0308)-0.0485*(0.0284)

Employees (log)-0.0356***(0.0127)-0.00881(0.0101)-0.0268**(0.0120)-0.0346***(0.0120)-0.0321***(0.0104)

0.0690*(0.0396)0.0472(0.0364)

Foundation year

-0.00166**(0.000737)-0.000965(0.000664)-0.000693(0.000695)-0.00169**(0.000838)-0.000763(0.000629)

0.0113***(0.00239)

0.00991***(0.00217)

Listed company-0.0205(0.0536)

0.0653(0.0452)-0.0858(0.0525)-0.0966(0.0601)

0.0150(0.0525)0.145(0.178)0.116(0.167)

Subsidiary of listed company

0.0673(0.0446)

0.0717**(0.0356)-0.00436(0.0429)-0.0572(0.0386)

0.00436(0.0370)-0.611***(0.131)-0.536***(0.121)

Japanese company

0.0212(0.0710)-0.114*(0.0653)0.135**(0.0564)0.0957*(0.0511)0.0624(0.0469)-0.363**(0.168)-0.250*(0.150)Tokyo, Osaka location

0.163***(0.0339)-0.00154(0.0224)

0.165***(0.0306)

0.164***(0.0283)

0.117***(0.0259)0.275***(0.101)0.230***(0.0880)

Probation period

0.0272(0.0287)-0.0424*(0.0218)

0.0696**(0.0272)

0.0600**(0.0277)

0.0615**(0.0253)

0.223**(0.0884)

0.203**(0.0790)

𝑁
𝑅

2,679
0.197

2,679
0.250

2,679
0.188

2,679
0.140

2,679
0.116

2,679
0.244

2,679
0.228

Notes: Coefficient estimates from linear probability model in colums (1)–(5), linear regression model for colums (6)–(7) . Dependent variables in columns (1)–(5) are dummy variables,
dependent variable in columns (6) ranges from 0–6, in column (7) from 0–5. All models control for month, industry, job and search strategy characteristics. Robust standard errors inparentheses are clustered at company level. Significance levels: *** p<0.010, ** p<0.05, * p<0.10.

Table 6: Narrow search17In conclusion, the determinants of the likelihood of overall narrow search and specific types oftargeted search generally agree with the hypotheses derived from the presented theoretical model:
companies that can afford more expensive narrow search are more likely to search narrowly. Other
factors, such as location, company ownership type, or the use of a probation period, influence thetype of search chosen, implying that in-company structure and company culture play an importantrole. However, as the sample used in this study excludes information about the inner workings of theobserved companies and is limited by the data source, more detailed research into the determinantsof preference for young age is needed.

7.2. Company market power and narrow search
For all ads in both the restricted and full samples, the local labor market is characterized by anexcess of vacancies over jobseekers, with on average 1.57 openings per jobseeker. A higher vacancy
ratio indicates a tighter labor market, which should be positively correlated with wage offer in acompetitive environment. As a result, the expected signs on the vacancy ratio estimates in equation(3) for both outcome variables are positive. A negative or statistically insignificant result would imply
that the company has market power. Similarly, the expected impact of average population age on
both outcomes is either null or positive. Companies in locations with older workers may reflect the
average level of productivity of potential applicants in the salary offer, as productivity generally
increases with experience and correspondingly age.
However, this analysis does not intend to measure the degree of market power, instead it aims todetect its indicators in the sample used in this study with all its limitations. The local labor market isdefined spatially based on the company reported location and related data availability; thus, thevariables describing the local labor market may not accurately reflect the conditions that companies
face. This setting also assumes low job mobility, which is consistent with the observed mobility in theJapanese labor market, assuming that mid-career jobseekers generally search for jobs in the city of
their residence or in cities with comparable population age. Because of these considerations, thisanalysis of labor market power is limited and should be interpreted accordingly.
Table 7 shows the estimation results using equation (3) with an expanding set of controls. Panels A
and B present the results for the restricted sample, reflecting the circumstances at the time of theinitial ad posting. Panels C and D contain identical analysis for the full sample, including ad reposts,
taking advantage of the changing nature of the labor market during the sampling period. The
mechanism behind not reposting an ad is unclear from the available data; a company may stop
posting an ad once the advertised position is filled, when they deem the cost of advertising to be too
high, or when they update an ad that is not attracting desirable applicants, resulting in a new type ofad. While the sample used for analysis in panels C-D more accurately showcases a casual jobseeker's
experience on Doda, it likely includes multitudes of comparatively unpopular ads or ads that havenot been updated to match jobseekers’ demands. Panels A-B and C-D thus analyze different
company behavior with panels C-D expected to show a weaker link between salary offer and labormarket conditions, understood as a stronger evidence of market power. Further, panels A and C
show results for the variable Hourly wage ratio, not considering overtime hours in the hourly wage
calculation, and panels B and D display the results for Hourly wage ratio without overtime, clearing
the hourly wage of included overtime hours. However, the realized overtime hours might eventually
be lower than the number included in the salary offer, pushing the hourly wage closer to the figures
used in panels A and C.

18

MARKET POWER ANALYSIS – OLS ESTIMATES
OFFERED HOURLY WAGE TO MINIMUM WAGE RATIO (log)
PANEL A: OVERTIME NOT CONSIDERED
UNIQUE ADS

Vacancy ratio (log)
Population age

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.100
(0.0737)
0.0125*(0.00655)

0.0937(0.0773)
0.00968(0.00653)

0.0917(0.0724)0.0126**(0.00604)

0.0813(0.0680)
0.0129**(0.00578)

0.0732(0.0707)0.0121(0.00793)

0.0752(0.0754)0.00921(0.00780)

0.0835(0.0726)
0.0124*(0.00736)

0.0800(0.0740)0.0135*(0.00701)



















2,679
0.098

2,679
0.176

2,679
0.189

2,679
0.094

2,679
0.168

2,679
0.183

Company
Job
Search𝑁
𝑅

Vacancy ratio (log)
Population age

2,679
0.042

2,679
0.059

PANEL C: OVERTIME NOT CONSIDERED

PANEL D: CLEARED OF OVERTIME HOURS

ALL ADSALL ADS

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

0.125
(0.0896)0.0143**(0.00727)

0.123(0.102)0.00939(0.00698)

0.116(0.0853)
0.0132**(0.00644)

0.114(0.0870)0.0133**(0.00637)

0.0601(0.0770)0.0108(0.00882)

0.0701(0.0905)
0.00641(0.00845)

0.0790(0.0850)
0.0102(0.00793)

0.0803(0.0846)
0.0115(0.00757)



















3,557
0.117

3,557
0.196

3,557
0.217

3,557
0.111

3,557
0.177

3,557
0.197

Company
Job
Search𝑁
𝑅

PANEL B: CLEARED OF OVERTIME HOURSUNIQUE ADS

3,557
0.059

3,557
0.078

Notes: Coefficient estimates from linear regression model. Dependent variable in panels A and C is log of Hourly wage ratio, in panels B and D log ofHourly wage ratio without overtime. Vacancy ratio (log) is log of vacancy ratio in prefecture of reported company location month before ad posting.
Population age is average population age in city of reported company location. Month and industry dummies included in all models. Inclusion ofvariables controling for company characteristics (Company), type of job advertised (Job) and search strategy (Search) indicated. Robust standard errors
in parentheses are clustered at company level for panels A and B and two-way clustered at company and ad level for panels C and D. Significance levels:*** p<0.010, ** p<0.05, * p<0.10.

Table 7: Market power analysis

19

MARKET POWER ANALYSIS BY SEARCH TYPE – OLS ESTIMATES
OFFERED HOURLY WAGE TO MINIMUM WAGE RATIO (log)
PANEL A: OVERTIME HOURS NOT INCLUDEDUNIQUE ADS(2)Narrow
search(3)

Broadsearch(4)

0.215(0.168)-0.0517(0.129)0.0423(0.106)

Population age

0.0229(0.0140)0.000122(0.0123)

𝑁
𝑅

634
0.378

670
0.262

Subsample

Age limit

Experience(1)

Vacancy ratio (log)

PANEL B: OVERTIME HOURS INCLUDEDUNIQUE ADS(6)Narrow
search(7)

Broadsearch(8)0.132(0.214)0.0905(0.142)0.119(0.124)0.0872(0.0782)0.0194***(0.00697)

0.0177(0.0139)0.00482(0.0149)

0.00822(0.0101)

0.0185**(0.00851)

1,375
0.170

634
0.361

670
0.264

1,304
0.299

1,375
0.149

Age limit

Experience

(5)

0.116
(0.0719)0.00595(0.00928)
1,304
0.292

PANEL C: OVERTIME HOURS NOT INCLUDEDALL ADSPANEL D: OVERTIME HOURS INCLUDEDALL ADSAge limit

Experience

Narrow
search

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

Population age

0.231(0.155)0.0216(0.0143)-0.0119(0.131)-0.00250(0.0124)

0.0663(0.101)0.00441(0.0100)

0.134*(0.0764)0.0186**(0.00744)

0.162(0.195)0.0177(0.0127)0.158(0.151)0.00281(0.0145)

0.162(0.123)0.00853(0.0101)

0.0512(0.0862)0.0142(0.00953)

𝑁
𝑅

869
0.414

795
0.271

1,664
0.308

1,893
0.234

869
0.457

795
0.285

1,664
0.347

1,893
0.162

Subsample

Vacancy ratio (log)BroadsearchAge limit

Experience

Narrow
searchBroadsearchNotes: Coefficient estimates from linear regression model. Dependent variable in panels A and C is log of Hourly wage ratio, in panels B and D log ofHourly wage ratio without overtime. Vacancy ratio (log) is log of vacancy ratio in prefecture of reported company location month before ad posting.
Population age is average population age in city of reported company location. Month and industry dummies included in all models. Inclusion ofvariables controling for company characteristics (Company), type of job advertised (Job) and search strategy (Search) indicated. Robust standard errors inparentheses are clustered at company level for panels A and B and two-way clustered at company and ad level for panels C and D. Significance levels:*** p<0.010, ** p<0.05, * p<0.10.

Table 8: Market power analysis by search type20

The effect of the vacancy ratio on both measures of salary offer is not statistically significant for allmodels and samples, implying that companies do not respond to the tightness of the local labormarket with wage competition. However, examining the time of the initial posting in panels A and B,
higher population age is associated with a higher salary offer, especially when job characteristics andsearch strategies are controlled for. For models in columns (3)–(4) and (7)–(8), clearing the salaryoffer of overtime hours weakens the significance of the effect from 5% to 10%. One year increase inaverage population age resulted in 1.29% increase in Hourly wage ratio in column (4) and 1.35%
increase in Hourly wage ratio without overtime in column (8). However, this trend no longer holds
for the entire sample in panels C and D, where the association between population age and salaryoffer disappears once overtime hours are considered. While the sample companies were notsensitive to local labor market tightness, which is more transient than population age profile,
indicating the presence of market power on their side, they appear to consider the likely heightenedsalary expectations of prospective applicants, with the inclusion of overtime pay in baseline salary
weakening the association. Furthermore, the inclusion of a specific number of overtime hours that
results in a weakening or effectively erasing of the relationship suggests a strategic use of overtime
hours in salary offer.
To put these claims to the test, Table 8 shows the results of the model used in Table 7 columns (4),(8), (12), and (16) for targeting type-based subsamples. The chosen model accounts for the main
factors influencing search type selection, as described by the theoretical model revealing baselinecompany behavior. In accordance with Table 7, the analyses presented in panels A-B and C-D use theredacted and full samples, respectively. Columns (1), (5), (9), (13) show the results for a sample ofads with age limits, columns (2), (6), (10), (14) for a sample of ads with experience requirements,
columns (3), (7), (11), (15) for a sample of ads searching narrowly, and finally columns (4), (8), (12),(16) for a sample of ads with neither requirement.
Columns (1)-(3), (5)-(7), (9)-(11), and (13)-(15) do not confirm any association between either
measure of salary offer and local labor market conditions for all targeted subsamples. The
relationship between salary offer and average population age in Table 7 appears driven by nontargeted ads, supporting the hypothesis that market power allows companies to choose a narrowsearch strategy regardless of targeting type. Furthermore, the trend of companies nominally raisingsalary offers in response to a tightening local labor market while compensating for higher labor costs
by increasing working hours is more pronounced in the non-targeted subsample compared to theoriginal samples, and even more so in the non-targeted subsample including reposts, as expected.
For the sample of unique non-targeted ads, one year increase in average population age leads to
1.94% increase in Hourly wage ratio, with this effect being highly significant, and 1.85% in Hourly
wage ratio without overtime, with the significance standing at 5%. While companies not targeting
their ads appear to engage in wage competition, they seem to use overtime hours covered bybaseline salary to suppress the hourly rate growth.
The collective evidence presented in this section points to the presence of monopsony in theJapanese labor market. Further, indirectly analyzing the link between wage setting behavior andtargeted search practices, companies directly targeting their searches do not seem to engage inlabor market tightness induced wage competition. Likewise, companies searching broadly do notrespond to labor market tightness but seem to adjust their salary offers to jobseekers’ average salary
expectations based on population age, while strategically using overtime hours to push the realsalary offer downward. These results are consistent with the proposed hypothesis stating thatmarket power enables companies to undergo more costly targeted search. However, the analysis in
this section relies on indirect methods only, is restricted to the sample used and is not designed toprovide definitive evidence of monopsony or its degree in the Japanese labor market.

21

8. Conclusion
The labor market in rapidly aging Japan is generally characterized by low job mobility and apersistent excess of vacancies over jobseekers. However, companies in Japan can legally set explicit
age limits on applicants under certain circumstances. One permitted exemption is the hiring ofyoung workers on regular contracts without requiring previous job-related experience, a statuteprotecting the traditional practice of preferentially hiring fresh graduates. Recruiting companies thus
face a choice between setting age or experience requirements or neither. Using an original sample
of job ads for regular contract sales jobs collected from July 2018 to December 2019 from a large
Japanese online job ad site targeting mid-career jobseekers, this study analyzes the company
characteristics related to the use of age limits and other practices showing age preference on
applicants. Out of the total 3,557 job ads, containing 2,679 unique ads from 1,341 companies, 24%
included explicit age limits, with the most frequent eligibility cut-off age standing at 35 years of age.
Additionally, up to 96% of ads included other content revealing preference for young age, dependingon the definition. Online job ad sites are an easily accessible source of information for people
contemplating job change or entering the workforce. This tool designed to promote job mobility
might thus come to discourage jobseekers if they find themselves excluded from visible job offers.
By proposing a theoretical framework of targeted employee search, three possible mechanisms
behind targeted search are introduced: search costs related, appreciation for young age related, andlabor market monopsony related. Empirically testing the model's implications, this paper observes
that in general, companies that can afford more expensive search strategies tend to search narrowly,
setting either age or experience requirements on applicants. Companies with fewer employees,
domestic companies, companies located in urban centers and companies that use a probation
period for new hires were more likely to set age limits than their counterparts. The use of indirect
age targeting did not show a clear trend, implying that the inclusion of young-age references in the
body of a job ad is situational. Further, indirectly analyzing the role of labor market power, the link
between wage setting behavior and local labor market conditions is examined, concluding that,consistent with theoretical predictions, only the sample of non-targeted ads responded to the stateof labor supply, albeit in a limited way. Companies that do not target their ads were found torespond to the average age of the population in the city where they are located, possibly reflecting
higher wage expectations. However, including overtime pay in the baseline salary weakened thisassociation, implying that businesses use overtime hours to manipulate labor costs. Furthermore,regardless of the sample or subsample examined or the wage offer definition used, offered wage did
not appear to be related to labor market tightness as represented by the local vacancy ratio,
indicating that companies do not engage in labor market tightness induced wage competition.
In 2019, the Japanese government announced a plan to promote mid-career hiring, increasehousehold income, and secure employment opportunities until the age of 70 (Cabinet Office ofJapan, 2019a). The practice of large companies traditionally once a year hiring fresh graduates was
also called out, highlighting the need for more flexible hiring practices. The government
implemented a three-year program to support the employment of the now middle-aged“employment ice age” generation, who struggled to find secure jobs as fresh graduates, as part of its
policy to increase household income (Cabinet Office of Japan, 2019b). The program primarily
targeted jobseekers through counseling, training programs, and support from local government
offices, while also organizing seminars for small and medium-sized businesses to introduce thebenefits of a diverse workforce. As a result, the “employment ice age” generation was added to the
EMA exemption permitting the use of age restrictions on applicants whose employment isencouraged by government policies, allowing businesses to specifically target this age group.
Because of the timing of the legislative change, no conclusions can be drawn about this specificexemption application in this study; however, no job ad in the sample cited the broad employmentencouragement exemption as a basis for age limits. Using a previous amendment to the EMA, Sasaki
and Yasui (2014) investigated the impact of stricter anti-age discrimination rules adopted in 200722

and concluded that the legislative tightening resulted in increased employment of the elderly in bothpart-time and full-time jobs without displacing younger workers. While the Japanese government
appears aware of the difficult situation many mid-career workers face due to limited and agerestricted employment opportunities, perhaps fewer rather than more exemptions from anti-age
discrimination legislation should be the way forward. Particularly if, as the study’s findings suggest,
the presence of market power emboldens companies to limit eligible jobseekers based on age,making crafting specific company-targeted policies difficult.
While this study, to the best of the author’s knowledge, provides the first evidence on the link
between age targeting in Japanese online job ads and company characteristics and company market
power, the results are limited by the data source which is not representative of the overall labormarket. As this paper analyzed only a specific section of the job market, it cannot provide a
discussion of larger trends. More research is needed to generalize the findings to other job types andrecruiting channels on the demand side and examine the supply side: jobseekers’ abilities, needs,
and limitations related to age targeting and company market power.

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25Appendix 1: Summary statistics
ALL JOB ADS
Std.
Dev.
Min

Max

N

UNIQUE JOB ADS
Std.
Mean
Dev.
Min

1

1,341

2679

693.789385.0971

1341

37.266

1

173

2679

4.983

6.489

1

34

2.771

5.564

1

42

2679

0

0

0

0

3557

1.812

0.892

0.250

5

2679

2.017

0.890

0.250

5

Hiring 5+ workers

3557

0.497

0.500

0

1

2679

0.502

0.500

0

1

Multiplelocations

3557

0.701

0.458

0

1

2679

0.686

0.464

0

1

Number oflocations (50 cap)

3557

17.322

20.667

1

50

2679

15.06319.4371

50

Age limit

3557

0.244

0.430

0

1

2679

0.237

0.425

0

1

Under 40

3557

0.216

0.411

0

1

2679

0.209

0.407

0

1

Under 35

3557

0.163

0.369

0

1

2679

0.157

0.364

0

1

Experience

3557

0.224

0.417

0

1

2679

0.250

0.433

0

1

Age index 1

3557

3.068

1.592

0

6

2679

3.132

1.510

0

6

Age index 2

3557

2.331

1.378

0

5

2679

2.368

1.322

0

5

Age in text

3557

0.563

0.496

0

1

2679

0.583

0.493

0

1

Recent graduate

3557

0.455

0.498

0

1

2679

0.478

0.500

0

1

Age in salary

3557

0.588

0.492

0

1

2679

0.589

0.492

0

1

In-company age

3557

0.316

0.465

0

1

2679

0.314

0.464

0

1

PR page - text

3557

0.432

0.495

0

1

2679

0.430

0.495

0

1

PR page - photo

3557

0.737

0.440

0

1

2679

0.764

0.425

0

1

Capital stock3557

1.45E+10

1.43E+11

1000005.40E+122679

1.35E+10

1.49E+11

1000005.40E+12

Employees

3557

3725.1220665.98

23072752679

3301.759

20968.41

2307275

Foundation year3557

1991.591

21.437

1880

2019

2679

1992.244

21.766

1880

2019

Listed company

3557

0.184

0.387

0

1

2679

0.141

0.348

0

1

Subsidiary oflisted company

3557

0.181

0.385

0

1

2679

0.192

0.394

0

1

Domestic

3557

0.942

0.235

0

1

2679

0.947

0.223

0

1

Tokyo, Osaka

3557

0.697

0.460

0

1

2679

0.694

0.461

0

1

Probation period

3557

0.643

0.479

0

1

2679

0.660

0.474

0

1

Starting salary

3557

241820.045554.26

1000006000002679

243621.5

47048.52

100000600000Overtime insalary

3557

0.478

0.500

0

1

2679

0.483

0.500

0

1

Overtime hours

3557

18.64821.749

0

80

2679

18.46921.437080

Wage ratio

3557

1.541

0.291

0.62

3.77

2679

1.552

0.299

1

4

Wage ratio
without overtime

3557

1.398

0.291

0.62

3.77

2679

1.409

0.301

1

4

Variable
SearchN

Mean

Company

3557

715.123385.354

Job ads percompany

3557

16.712Ad duplicates

3557

Posting period(month)Max

TargetingCompany

Job

26

Education
None

3557

0.679

0.467

0

1

2679

0.688

0.463

0

1

High school

3557

0.200

0.400

0

1

2679

0.192

0.394

0

1

Vocational, 2year college

3557

0.019

0.137

0

1

2679

0.023

0.149

0

1

University

3557

0.101

0.302

0

1

2679

0.097

0.296

0

1

B-to-B

3557

0.442

0.497

0

1

2679

0.455

0.498

0

1

B-to-C

3557

0.294

0.456

0

1

2679

0.308

0.462

0

1

9 AM shift

3557

0.375

0.484

0

1

2679

0.353

0.478

0

1

Flextime

3557

0.051

0.219

0

1

2679

0.057

0.232

0

1

2 days week off

3557

0.480

0.500

0

1

2679

0.493

0.500

0

1

No transfer
Local labormarketVacancy ratio

3557

0.608

0.488

0

1

2679

0.606

0.489

0

1

3557

1.571

0.127

1.240

2.210

2679

1.567

0.126

1.240

2.210

Population age

3557

44.9560.787

40.54351.0072679

44.9650.810

40.54351.007

Table A1: Summary statistics

27

Appendix 2: Indirect age targeting
Indirect age targeting refers to the practice of indicating a preference for young workers in a job ad
in ways other than explicitly setting an age limit. Within the highly standardized job ad structure, six
patterns were identified, five written, and one visual.
The first type of indirect age targeting is the inclusion of the words “recent graduate” (dai ni
shinsotsu, 第二新卒) in the body of the ad, in expressions such as the position being suitable forrecent graduates, a large number of recent graduates joining the company, or that recent graduates
are encouraged to apply. In 47.8% of the restricted sample, the term “recent graduate” was used.
The second type of indirect age targeting is a reference to a specific age group in the ad's text. The
typical language referencing age was pointing out the young age of employees, using phrases suchas young employees playing an active role in the company or that all employees being approximately
the same young age creates a comfortable work environment. Figure A2 depicts the distribution of
the referenced average age (age if a specific age is mentioned, average age for age range, none if
only the words “young” and similar were used), revealing that it is concentrated in the late 20s andearly 30s. According to the actual use of age limits, where the maximum cut-off age was set at 49,
ads containing references to people in their 50s and up were not considered targeted. This type ofindirect age targeting was present in 58.3% of ads. The lowest average age mentioned in targeted
ads was 22, the average was 29.8, and the maximum was 48.

Figure A2: The average age mentioned in the ad’s body
Similarly, mentioning a young age on an ad’s PR page is considered a separate, third type of indirect
age targeting. Interviews with employees, typically including their ages, were frequently featured on
the PR page. When a specific age of an employee was mentioned, the maximum age considered astargeted was set to 44 years old to provide leeway for assessing indirect age targeting in visuals usedon the PR page. Other references to youth, such as the words “young,” “new graduate,” or “recent
graduate,” are also considered examples of age targeting. On the PR page, 43.0% of ads in the
restricted sample made reference to a young age.
The inclusion of average employee age in company overview is the fourth type of indirect age
targeting. This information was included in 31.4% of the ads, and Figure A3 depicts the distributionof average in-company age in these ads. Assuming a typical retirement age of 60, only ads with aposted average employee age of less than 40 were classified as targeted. Out of all ads, 29.0% statedthat the average age of employees was under 40, which corresponds to 92.0% of ads containing thisinformation, indicating that it is used strategically to age target. In targeted ads, the minimum
average employee age was 24, the average was 31, and the maximum was 39.6.
28

Figure A3: Average in-company age
The inclusion of age in model salary is the fifth type of indirect age targeting. Job ads typicallyshowed examples of salary growth that an applicant could expect, with the majority of ads including
age in addition to the length of employment and some ads only including age. Age in model salary
was used in 58.9% of ads. The distribution of maximum age included is presented in Figure A4. Outof the ads including age in model salary, 69.5% of ads set the maximum age to 35 or less. Maximum
age higher than 45 was included in 5% of the ads, and over 50 in 0.8% of the ads. However, when thelength of employment was considered, indicating the age an applicant would need to join thecompany to earn the example salary, all ads with age in model salary were classified as targeted. Inmodel salary, the minimum, average and maximum ages were 22, 33.8, and 67, respectively.

Figure A4: Maximum age in model salary
The sixth and final type of indirect age targeting takes into account the visuals that the company
chose to use on its PR page. If only images of young people are shown in an advertisement, it isconsidered targeted. In terms of the graphics used on the PR page, the company posting an ad has
several options. Some companies choose not to include photographs of their employees and insteadhighlight their products, offices, or customers, whereas others use illustrations or photographs withno visible faces. After opening a full ad on Doda, the PR page appears first, creating a first impressionof the company. As a result, the choice of visuals is an important tool for conveying information,
including preferences regarding the age of applicants. However, all ads were evaluated by the29author only, resulting in a high degree of subjectivity. As previously stated, some companies disclose
the age of employees featured on the PR page; in this case, the maximum targeted age was set at 44.
When age was not disclosed and the pictures were not of clearly young people, information fromthe ad’s body was used as a guide. If no age-related information was available, the ad was deemed
untargeted. To ensure consistency, ads from the same company were cross-referenced. However,
the issue of subjectivity remains unresolved, therefore any analysis utilizing indirect age targeting
data employs indicators both with and without this type. Pictures of only young people were
included in 76.4% of ads.
TABLE A5: Indirect age targeting in subsamples
ALL
Recent graduate
Age in text (ad)Age in text (PR)Age in model salary
Average in-company age
Targeted PR pictures

AGE LIMIT

EXPERIENCENOREQUIREMENT(2679)

(634)

(670)

(1375)

47.8%
58.3%
43.0%
58.9%
31.4%
76.4%

52.5%
65.3%
55.2%
67.5%
35.8%
84.2%

28.5%
51.5%
34.9%
50.0%
31.5%
75.5%

55.1%
58.3%
41.3%
59.3%
29.2%
73. 2%

Table A5 shows the prevalence of each type of indirect age targeting in the sample – the total
sample, the sample of ads with age limits, the sample of ads requiring experience, and finally the
non-targeted sample. In general, ads that set age limits make the most use of indirect age targeting,possibly to target more precisely within the eligible age range.
PANEL AAge index 1
0
1
2
3
4
5
6
Total

TABLE A6: Indirect age targeting indicesALL

AGE LIMIT

EXPERIENCENOREQUIREMENT(2679)

(634)

(670)

(1375)

3.8%
11.8%
19.2%
23.2%
21.9%
14.6%
5.4%
100%

1.4%
5.2%
16.4%
23.0%
23.7%
23.0%
7.3%
100%

6.4%
16.0%
23.3%
24.9%
19.3%
7.5%
2.7%
100%

3.6%
12.9%
18.6%
22.5%
22.4%
14.3%
5.9%
100%

ALL

AGE LIMIT

EXPERIENCE

7.6%
20.4%
26.7%
23.6%
16.2%
5.5%
100%

4.3%
14.2%
24.0%
23.7%
26.5%
7.4%
100%

13.4%
26.4%
28.4%
20.9%
8.1%
2.8%
100%

PANEL BAge index 2
0
1
2
3
4
5
Total

NO
REQUIREMENT
6.3%
20.4%
27.2%
25.0%
15.3%
5.9%
100%

The various types of age targeting are added together to form a categorical variable age index, whichshows the extent to which the company indirectly targets its ads. Variable Age index 1 includes all six
types and thus ranges from 0 to 6, whereas Age index 2 excludes targeting in PR images and thus
ranges from 0 to 5. Table A6 describes Age index 1 and Age index 2. The most frequently used
number of indirect age targeting types in the restricted sample was three (23.2%) for Age index 1,30

and more ads used all 6 types (5.4%) than none (3.8%). In terms of Age index 2, the most ads (26.7%)
used two types, with 5.5% using all five and 7.6% using none. Comparing subsamples, similarly toTable A5, the least indirect age targeting was generally observed in the sample of ads requiringexperience and the most in ads containing an age limit.

31

Appendix 3

AGE LIMIT – OLS ESTIMATES
(7)

(8)

Listed company-0.00940(0.0584)-0.0483(0.0486)

Subsidiary oflisted company

0.00307(0.0451)-0.00722(0.0404)

Capital (log)
Employees (log)Foundation
year

Japanesecompany(1)(2)

0.00573(0.00715)-0.000264(0.00722)

(3)

(4)

0.0120(0.00844)-0.0128(0.00927)

(5)

(6)

-4.10e-05(0.000673)

0.000143(0.000700)

(9)

(10)

0.170***(0.0513)0.136**(0.0541)

Tokyo, Osaka
location
Probation
period(11)(12)

0.128***(0.0297)

0.152***(0.0302)

(13)

(14)

0.0394(0.0309)

0.0558*(0.0288)

Job







Search







2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
𝑁
0.079
0.151
0.086
0.153
0.077
0.151
0.078
0.152
0.084
0.155
0.094
0.174
0.079
0.155
𝑅
Notes: Coefficient estimates from linear probability model. Dependent variable is indicator of presence of age limit. Month and industry dummies included in all models. Inclusion of variables controling for typeof advertised job (Job) and search strategy (Search) indicated. Job controls are Starting salary, Inclusion of overtime in salary, Education, B-to-B, B-to-C, 9AM shift, Flextime, 2 days week off, Transfer possibility.
Search controls are Ads per company, Number of duplicates, Posting period, Number of locations, Number of locations squared, Hiring 5+ workers. Robust standard errors in parentheses clustered at company
level. Significance levels: *** p<0.010, ** p<0.05, * p<0.10.

Table A6: Age limit – extended identification

32

EXPERIENCE – OLS ESTIMATES
(7)

(8)

Listed company

0.106**(0.0504)

0.105**(0.0421)

Subsidiary oflisted company

0.143***(0.0397)0.0884***(0.0322)

Capital (log)
Employees (log)Foundation
year

Japanesecompany(1)(2)

0.0216***(0.00700)

0.0196***(0.00649)

(3)

(4)

0.0120(0.00844)

0.0181**(0.00810)

(5)

(6)

-0.00237***(0.000725)-0.00138**(0.000678)

(9)

(10)

-0.270***(0.0670)-0.125*(0.0641)

Tokyo, Osaka
location
Probation
period(11)(12)

0.0590**(0.0252)

0.0115(0.0225)

(13)

(14)

-0.0127(0.0270)-0.0342(0.0220)

Job







Search







2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
2,679
𝑁
0.098
0.241
0.086
0.236
0.096
0.237
0.102
0.241
0.101
0.236
0.087
0.233
0.084
0.234
𝑅
Notes: Coefficient estimates from linear probability model. Dependent variable is indicator of presence of experience requirements. Month and industry dummies included in all models. Inclusion of variables
controling for type of advertised job (Job) and search strategy (Search) indicated. Job controls are Starting salary, Inclusion of overtime in salary, Education, B-to-B, B-to-C, 9AM shift, Flextime, 2 days week
off, Transfer possibility. Search controls are Ads per company, Number of duplicates, Posting period, Number of locations, Number of locations squared, Hiring 5+ workers. Robust standard errors inparentheses clustered at company level. Significance levels: *** p<0.010, ** p<0.05, * p<0.10.

Table A7: Experience – extended identification

33