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

Secondly Revised October 2023
Revised June 2023
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 Zvedelikova1
October 31, 2023

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 applicants
and the characteristics of firms that set them. Although Japanese law prohibits age discrimination in
employment, several exemptions, such as hiring young workers without prior work experience on
regular contracts, are allowed. Firms can set an age limit, require job-related experience, or searchbroadly; 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, 26% set experience requirements, and nearly
all contained some form of implied age preference. Consistent with theoretical predictions, the results
show that firms with higher capital, those with fewer employees, older firms and those located inurban centers tended to set requirements on applicants. Further, domestic firms, firms with fewer
employees, in urban centers and firms using probation periods for new hires were more likely to set
age limits. Moreover, firms setting either requirement did not seem to be sensitive to local labormarket conditions. Firms searching broadly responded to population age-related increased wage
expectations while reducing labor costs by increasing the number of working hours covered by abaseline wage.
Keywords: Mid-career recruiting; Age discrimination; Job ads; JapanJEL Classification: J42, J63, J71

1Institute of Social and Economic Research, Osaka University, 6-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan.
https://orcid.org/0000-0002-3085-7502. Email: zvedelikova@iser.osaka-u.ac.jp

1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, explicit
age limits for applicants are not uncommon. In many countries, restricting applicant eligibility basedon 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
individuals 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 legally considereddiscriminatory under the relevant legislation. This legislation allows firms to set age limits on
applicants but prevents them from simultaneously requiring previous job experience. Firms thus face
the option of setting either age limits or experience requirements to search narrowly, or setting neither
to search broadly. Exploiting this regulatory exemption allows direct analysis of what type of firms
set age limits in recruiting without the need to prove the presence of discriminatory practices firstusing the typical methods of experiments or firm surveys. As setting age limits on applicants is legal
in Japan under the circumstances examined in this paper and as the word discrimination generally has
a negative connotation, the term age targeting is used instead.
The aim of this paper is to describe the firm characteristics associated with age targeting utilizing an
original sample of online job ads for sales jobs. To do so, this paper proposes and tests a theoreticalframework of employee search, hypothesizing that firms able to conduct more costly search selectsearch strategy restricting applicant eligibility. The sample was collected by the author from Doda, an
online job ad site aimed at mid-career jobseekers, over an 18-month period from July 2018 to
December 2019, ending prior to the outbreak of the COVID-19 pandemic. The sales occupation
allows for a seamless application of a young-age-related regulatory exemption. 23.6% of the 2,683
unique ads collected included age limits. However, age limits are not the only way to convey a
preference regarding applicants’ ages; up to 96% of the ads included other age-related content. The
empirical results of this study are generally consistent with the theoretical predictions.
For the duration of the data collection period, the regional prefectural vacancy ratio was above 1,
indicating an excess of vacancies over jobseekers. Voluntarily limiting the pool of potential applicantsin a tight labor market may appear counterintuitive on the firm side and may also have serious social
consequences. Job mobility in Japan remains low. 2 Job advertisements are a crucial source of
information for workers considering a job change and for individuals considering entering the labormarket. Age restrictions in job advertisements, particularly easily accessible online job ads hosted onheavily 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 iceage,” had limited access to stable jobs and related stable income and social benefits, hindering theirability to start families and plan for retirement. These workers are now middle aged, and despite thehigher number of vacancies than jobseekers, they may no longer be eligible. Individuals who dropped
out of the labor force during their productive years, for example, to raise children or become
caregivers, might also find it difficult to return to their 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. Kitao and Mikoshiba (2020) proposedthat higher labor participation of women and the elderly, especially in regular, higher-paying jobs,
could help mitigate the negative impact of demographic changes on the Japanese macroeconomy andfiscal stability while transitionally lowering equilibrium wages.
2

Regardless of contract type, the job change rate in Japan in 2018 was 10% with significant differences basedon age and gender. Although 17.5% of males under the age of 19 changed jobs, this rate dropped to 7.7% formales aged 35–39 and 4.7% for those aged 45–49. For females, the job change rates were higher, driven up by ahigher ratio of part-timers who changed jobs more frequently, peaking in the 25–29 age group at 18.3% anddecreasing to 13.1% and 11.3% in the 35–39 and 45–49 age groups, respectively (Ministry of Health, Labourand Welfare of Japan (MHLW), 2019d).

2

This study makes several contributions. The theoretical framework proposes three mechanisms ofnarrow employee search: search costs related, youth favoritism related, and wage related. The
empirical results confirm that, in general, firms able to choose more costly narrow search and thosehighly valuing young age tended to set requirements on applicants. Firms with higher capital, older
firms and firms hiring in urban areas tended to search narrowly, setting either experience requirements
or age limits on applicants. Next, domestic firms, smaller firms, those hiring in urban areas and those
implementing a probation period were more likely to set age limits. The use of other age-related
content in job ads was variable, as it was used both alongside and in place of age limits. Further,analyzing the sensitivity of firms’ wage setting behavior to the local labor market conditions based on
the selected search strategy, only firms not setting either requirement partially responded to labormarket changes. Although ample research on age limits in recruiting exists in Japan, to the best of theauthor’s knowledge, this study is the first to use the rich information found in online job ads todirectly analyze the characteristics of firms in Japan that both set explicit age limits on applicants anduse age-related job ad content in mid-career recruiting.

2. Related literature
The economic literature on discrimination 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 are uncommon in developed countries. Empiricalstudies must thus rely on indirect methods. These typically include 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 stated preference for specific types of workers allows for skipping thisstep and focusing directly on determinants of firm behavior. This paper belongs to this stream ofliterature.
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 thepassage of anti-age discrimination legislation. Kuhn and Shen (2013) studied firms’ preferences forgender, age, height, and beauty of applicants using job ads from a Chinese online job ad site in acountry where employment discrimination is mostly unregulated. In a subsequent study Helleseter etal. (2020) expanded their dataset to include data from Mexico. Burn et al. (2022) 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, placing high value on worker’s young age, lifetime employment for regular
employees, and the corresponding low job mobility are some of the defining features of the Japanese
postwar labor market. This system is dependent on firms’ ability to hire young workers, particularly
fresh graduates. Ono (2010) demonstrated that lifetime employment was a persistent practice,particularly among highly educated male regular employees in large corporations and the public
sector. On the other hand, Hamaaki et al. (2012) argued that lifetime employment and seniority-based
wage practices were eroding, as evidenced by the flattening wage curve for older workers and related
lower retention rates of university-educated younger workers.
Preferential hiring of young workers as a long-standing practice in Japan has been extensively
researched, with most studies depending on firm surveys. Firm surveys provide information on thefirm’s internal operations; however, they tend to suffer from low response rates and response bias,
particularly regarding sensitive topics. Okunishi (2008) discovered that younger firms and those that
did not practice on-the-job training hired more mid-career workers, whereas larger firms expanded
mid-career recruitment in the three years preceding the survey. Similarly, Ōta and Yasuda (2010)concluded that firms 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) using survey data found that3

firms with more employees and firms with higher quit rates tended to search for more workers. They
further found differences in age limits in recruiting between industries and occupations.
Several studies examined age targeting in Japanese job ads. Kitaura (2003) found that in September
2001, before the implementation of anti-age discrimination legislation, only 1.6% of job ads
registered with the public employment office excluded age restrictions on applicants. This number
increased to 12.7% in October 2002, after a weak version of the legislation took effect. Tokunaga(2008) examined job ads from the 1985, 1995, and 2005 issues of a women-oriented job ad magazine
and reported that around 97% of ads contained age limits at all three time points, with the maximum
eligible age increasing over time. This current study adds to their findings by investigating theprevalence and determinants of age limits in job ads after the implementation of the current, strictest
anti-age discrimination legislation yet. To the best of the author’s knowledge, this paper is the first touse online job ads to investigate the characteristics of firms practicing age targeting in mid-career
recruiting in Japan.

3. Institutional setting
Discrimination in employment is governed by the Employment Measures Act (EMA), originallyimplemented 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 ofage. The 2007 EMA amendment changed this duty to endeavor into an obligation with six exemptions.
Every sampled job ad stipulating age limit cited the following exemption:“Recruit only new graduates who are youth, or below certain ages, in order to give themthe opportunity to develop and improve their occupational abilities over a long period ofservice.” (Sakuraba, 2009)This exemption protects the traditional recruiting process focused on recent graduates. Firms may set
upper age limits when hiring youth on indefinite contracts; however, they cannot simultaneously
require any work-related experience, licenses, or qualifications obtained only during employment,
thereby effectively treating applicants as fresh graduates. Firms seeking to hire workers on a regular,
non-fixed-term, contracts have three options: set an 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, in some cases up to 45 (MHLW, 2016,2019a). Furthermore, firms 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.
The current remaining exemptions allow age targeting in cases of hiring for artistic reasons, for jobs
with statutory age limits, to comply with mandatory retirement age, when a given age category isunderrepresented in the firm, and hiring seniors or other individuals whose employment is encouraged
by government policies.

4. Data
The author gathered the data for this study over 18 months, from July 2018 to December 2019, fromthe Japanese online job ad site Doda (https://doda.jp/), a website aimed at mid-career jobseekers. As
the data was not supplied by Doda or any other entity, it is limited to the firm’s job posting behavior
in the form of the job ad, which is identical to what a Doda user sees. Importantly, no data regarding
the applicants or any internal firm information beyond that included in the ad is observed. In casebasic firm information (capital, ownership structure) was missing from the ad, it was obtained fromthe firm website or other sources.
In total, 3,573 ads from 1,342 firms were collected; however, this sample contains reposts. Firms
posting multiple ads often use a mix of search strategies for an otherwise identical ad. An ad isconsidered a repost if it matches a previously posted ad by the same firm in all its job characteristics
4and search strategy attributes. Jobseekers are assumed to recognize a repost and always reject it if theterms are undesirable but are assumed to consider an ad that meets their criteria regardless of their
possibly having rejected another ad by the same firm. This study thus adopts an ad-based approachinstead of a firm-based approach, excluding duplicates of the previously posted ads while keeping
multiple ads from firms not considered reposts. After excluding reposts the sample consists of 2,683
unique ads by 1,342 firms, with 790 firms posting a single unique ad. The 10 most active firms posted
483 ads in total, 216 of which were identified as unique. Appendix A details the sample collection
process and discusses the representatives of the sample.

Variable
Narrow search

Mean
48.6%

Std. Dev.
0.500

Min
0

Max
1

Experience requirement

25.0%

0.433

0

1

Age limit

23.6%

0.425

0

1

Age index 1

3.13

1.510

0

6

Age index 2

2.37

1.322

0

5

Capital (yen)1.35E+10

1.49E+11

100,000

5.40E+12

Employees

3,297

20,957.62

307,275

Foundation year

1992

21.77

1880

2019

Listed firm

14.1%

0.348

0

1

Subsidiary of listed firm

19.2%

0.394

0

1

Japanese firm

94.7%

0.223

0

1

Tokyo, Osaka location

69.4%

0.461

0

1

Probation period

65.9%

0.474

0

1

Wage ratio
Wage ratio without
overtime
Vacancy ratio

1.55

0.299

0.62

3.77

1.41

0.301

0.62

3.77

1.57

0.126

1.24

2.21

Population age

44.97

0.810

40.54

51.01

Notes: N = 2,683 for all variables.

Table 1: Descriptive statistics
Table 1 presents the descriptive statistics. Regarding employee search strategy, firms search broadly,
not setting requirements on applicants, or narrowly. Narrow search is defined as a firm setting either
an experience requirement or an age limit citing EMA’s young age-related exemption. Firms adopted
narrow search strategy in 48.6% of ads, with 25.0% requiring prior work experience and 23.6%
setting an age limit.
Besides an explicit age limit, firms can express their age preferences by other means throughout thead. Examples of age-related ad content include firms stating that the job is suitable for fresh graduates,
referencing the young age of current employees, showing model wage growth based on (young) age
as opposed to tenure, or using photographs of distinctly young people only for visuals. Altogether, six
patterns of age-related content were identified within the highly standardized ad structure, with five inwriting and one visual. The absence or presence of each pattern was coded as a dummy variable.
These dummy variables were then summarized into two variables representing the extent to whichfirms include age-related content in their job ads: 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-related content. The average
number of such content types used was 3.1 for Age index 1 and 2.4 for Age index 2. A detailed
discussion on both age limits and age-related ad content is provided in Appendix B.

5

Next, the average registered capital of a firm posting an ad was 13.5 billion yen, with a median of
63.0 million yen.3 With a median of 215 employees, the average number of employees was 3,297. The
average firm was founded in 1992, whereas the median year of founding was 1999. Larger, older
firms tended to post a variety of ads, pushing the sample means higher. Furthermore, publicly tradedfirms posted 14.1% of the ads, and subsidiaries of one posted 19.2% of the ads. Domestic Japanesefirms posted 94.7% of the ads and 69.4% of ads were advertising jobs in Tokyo or Osaka. A probation
period was required in 66.0% of the ads.
All job ads collected specified a monthly wage offer upon completion of the probation period. In
48.3% of ads this wage included overtime pay, with the number of overtime hours covered ranging
from 10 to 80.4 To describe the wage offer, two variables were created: hourly wage ratio and hourly
wage ratio without overtime. Hourly wage rate respectively not reflecting and reflecting the included
overtime hours was weighted by the minimum wage of the prefecture the job was located in, toaccount for regional differences. The average hourly wage ratio was 1.55 and 1.41 for hourly wage
ratio without overtime, with the minimum and maximum values for both variables standing at 0.62
and 3.77, respectively. In some cases, the convoluted system of incentive pay pushed the hourly baserate below the legal minimum hourly wage.
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, in the prefecture of job’s location. The vacancy
ratio ranged from 1.24 to 2.21, indicating more jobs than jobseekers were available for all months andlocations. Next, population age represents the average population age in the city the job is in, as
determined by the 2015 census.

5. Theoretical framework
In this non-sequential search model, a firm with an opening chooses the type of candidate to invite
based on perceived productivity and corresponding search costs. Firms can search broadly, with norequirements, or narrowly, with age limits or experience requirements imposed on applicants.
Although the choice between age and experience is binary, the underlying preferences are notmutually exclusive.
This framework assumes that firms operate in a monopsonic local labor market. Burdett andMortensen (1998) proposed that firms’ wage-setting power stems from the existence of search
frictions, such as the costs to jobseekers associated with a job change. In a monopolistic setting,worker discrimination can be consistent with profit maximization as firms can set wages lower thanworkers’ marginal product.
5.1. Jobseekers
On the supply side, each jobseeker 𝑖 is assumed to possess several characteristics:
1)
2)
3)
4)
5)

productivity 𝑥𝑖 defined as 𝑥𝑖 = 𝑥̄ + 𝜀𝑖 , where 𝑥̄ is the average productivity for theadvertised job after undergoing relevant training and 𝜀𝑖 is 𝑖’s deviation from 𝑥̄
reservation wage 𝑊𝑖
experience 𝑒𝑖 relevant to the advertised job
age 𝑦𝑖location 𝑙𝑖 the jobseeker is willing to work in.

3

Corresponding to 121.9 million USD for the mean and 569,000 USD for the median as of July 1 st, 2018, thebeginning of the sample collection period.
4
Unpaid overtime and the inclusion of overtime pay in baseline wage are a standard practice in Japan. Kuroda
and Yamamoto (2016) reported that in a survey of regular employees working in firms with more than 100
employees in Japan, half of their overtime hours were unpaid.

6

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 𝑃𝑖 of a jobseeker 𝑖 applying for an opening 𝑣 with an offered wage 𝑊𝑣 , productivity
requirement 𝑥̄ 𝑣 , experience requirement 𝑒𝑣 and age limit 𝑦𝑣 by a firm located in location 𝑘 is
𝑃𝑖 = (1 − 𝐹𝑖 (𝑥̄ 𝑣 ))𝐺𝑖 (𝑊𝑣 )(1 − 𝐻𝑖 (𝑒𝑣 ))𝐽𝑖 (𝑦𝑣 ).
The number of applicants 𝐴 for opening 𝑣 is then𝐴 = 𝑃𝐿𝑘
where 𝐿𝑘 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 firm and theopening, the number of arrivals is modified to the following:𝐴 = 𝜇𝑃𝐿𝑘 .
Firms searching broadly see a higher number of suitable applicants arriving
𝐴𝐵 = 𝜇𝑃𝐵 𝐿𝑘 , 𝑃𝐵 = (1 − 𝐹(𝑥̄ ))𝐺(𝑊)than firms searching narrowly do, setting age limits or experience requirements with thecorresponding number of applicants 𝐴𝑌 and 𝐴𝐸
𝐴𝑌 = 𝜇𝑃𝑌 𝐿𝑘 , 𝑃𝑌 = (1 − 𝐹(𝑥̄ ))𝐺(𝑊)𝐽(𝑦)𝐴𝐸 = 𝜇𝑃𝐸 𝐿𝑘 , 𝑃𝐸 = (1 − 𝐹(𝑥̄ ))𝐺(𝑊)(1 − 𝐻(𝑒)).
Because there is always at least one opening in location 𝑘, higher local vacancy ratio leads to a lower
number of jobseekers arriving:lim

𝑣𝑎𝑐𝑎𝑛𝑐𝑦 𝑟𝑎𝑡𝑖𝑜𝑘 →∞𝐴 (𝐿𝑘 ) = 0.

Additionally, the existence of retirement age guarantees that the higher the average population age ina location 𝑘, the fewer jobseekers are available even in the absence of age limits.
lim

𝑎𝑣𝑒𝑟𝑎𝑔𝑒 𝑝𝑜𝑝𝑢𝑙𝑎𝑡𝑖𝑜𝑛 𝑎𝑔𝑒𝑘 →∞𝐴 (𝑦) = 0

5.2. Firms
The number of job separations firm faces, 𝑠(𝑛), is assumed to be a non-decreasing function of thenumber of employees 𝑛:𝑠 ′ (𝑛) ≥ 0.
For a firm to maintain its size, each quit is turned into an opening, the number of openings is thus anon-decreasing function of the number of employees. This setting is consistent with Kambayashi andŌta (2009), who found that firms with more regular employees and firms with higher quit rates post
more mid-career job ads in Japan.
Further, the total search cost ∑ 𝑆𝐶 firm 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.

7

Broad search is associated with search cost 𝑆𝐶(𝐴𝐵 ), narrow search results in search cost 𝑆𝐶(𝐴𝑌 ) for
age limits or 𝑆𝐶(𝐴𝐸 ) for experience requirements. Search costs are decreasing in the number ofapplicants 𝐴, 𝑆𝐶′(𝐴) < 0, reflecting the additional costs of narrow search, such as the costs needed toreach jobseekers with specific characteristics 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, alarger jobseeker population, and a higher attractivity coefficient, and increase with tighter productivity
or experience requirements. Costs incurred during the subsequent stages of the hiring process, such as
interviews, are not considered.
For a single opening, firm chooses the type of applicant to invite based on maximizing the following
profit function 𝜋:𝜋 = 𝑚𝑎𝑥{𝐸[𝑅(𝑈𝑖 ; 𝐷 𝑗 )] − 𝐷 𝑗 (𝑊 𝑗 + 𝑆𝐶(𝐴𝑗 ) + 𝑇𝐶𝑗 )}𝑤ℎ𝑒𝑟𝑒 𝑈𝑖 = 𝑥𝑖 + 𝐷 𝑗 𝑥 𝑗 , 𝑗 ∈ (𝐵, 𝑌, 𝐸), 𝑥 𝐵 = 0
𝑠. 𝑡. 𝐴𝑗 ≥ 1
𝑆𝐶(𝐴𝑗 ) ≤ 𝑡𝑐
where 𝑅(𝑈𝑖 ) is a revenue function, 𝑅′(𝑈𝑖 ) > 0 and 𝑅 ′′ (𝑈𝑖 ) < 0. The variable of choice is 𝐷 𝑗 , 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 narrow search imposing age limits and 𝐸 for narrow search requiringexperience. The term 𝑥 𝑗 is a firm's perceived bonus in productivity associated with a given type ofworker. A firm’s perceived young-age productivity bonus 𝑥 𝑌 can stem both from taste-based or
statistical discrimination, 𝑥 𝐸 is firm’s perceived productivity bonus from relevant experience and𝑥 𝐵 = 0. 𝑇𝐶𝑗 is the cost of training allocated to a worker hired through the respective type of search,where 𝑇𝐶 𝑌 > 𝑇𝐶 𝐵 > 𝑇𝐶 𝐸 . 𝑊𝑗 is the wage associated with the productivity level 𝑥̄ and 𝑊 𝑌 < 𝑊 𝐵 <𝑊𝐸.
A firm 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.
𝑞
𝑗

𝑗

𝑗

𝑗

𝛱 = 𝑚𝑎𝑥 ∑{𝐸[𝑅𝑣 (𝑈𝑣𝑖 ; 𝐷𝑣 )] − 𝐷𝑣 (𝑊𝑣 + 𝑆𝐶𝑣 (𝐴𝑣𝑗 ) + 𝑇𝐶𝑣 )}𝑣=1
𝑞𝑠. 𝑡. ∑ 𝐴𝑣𝑗 ≥ 𝑠(𝑛)𝑣=1𝑞

∑ 𝑆𝐶𝑣 (𝐴𝑣𝑗 ) ≤ 𝑡𝑐
𝑣=1

The dataset compiled for this study allows for a direct observation of the chosen search strategy 𝐷 𝑗 .
Based on this model, three hypotheses are proposed:
Hypothesis 1: Firms for which narrow search is comparatively more costly tend to search narrowly
less.
This includes firms that can allocate fewer resources to search activities, firms experiencing
more quits, or firms struggling to attract applicants, such as those in locations with fewer
jobseekers or firms with low attractivity coefficient.
Hypothesis 2: Firms highly valuing young age tend to set age limits more.
These are firms with a high perceived young age productivity bonus 𝑥 𝑌 . Firms with highly
developed on-the-job training programs or those with an established rigid inner structure8

might place more value on young age. Firms investing heavily in training workers, whichmight be observed as utilization of a probation period, might want to gain the benefits over along period of employment. The impact of unobserved firm inner structure or culture mightmanifest as a difference between younger and older firms or between domestic Japanese andforeign-owned firms.
Hypothesis 3: Firms able to offer lower wages without discouraging potential applicants tend to
search narrowly.
A firm able to set wage offer 𝑊𝑗 lower than a corresponding competitive wage without theloss of arriving jobseekers can use this difference to cover additional costs stemming fromnarrow search.
Besides search type, firms reveal their preference for young age by including age-related content intheir ads. In ads that set experience requirements but are unable to set age limits simultaneously, suchcontent may be used as a substitute for age limits. In ads with an age limit, age-related content may be
used to communicate specific age preference more precisely.
6. Narrow search and firm characteristics
6.1 Narrow search and firm characteristics – empirical strategy
This section examines the association between firm characteristics and a narrow employee searchstrategy. Search strategy dummy variable for job ad j at firm i is regressed on firm characteristics anda set of controls using the following linear probability model:
𝑆𝑒𝑎𝑟𝑐ℎ 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦𝑖𝑗 = 𝛼0 + 𝜶𝟏 ∗ 𝑭𝒊𝒓𝒎 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒊 + 𝛼2 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑗 + 𝜀𝑖𝑗(1)
Search strategy takes three forms based on the employed search type: Age limit for narrow search
imposing age limits, Experience requirement for narrow search imposing experience requirements,
and Narrow search indicating the use of either one. The coefficient of interest is 𝛼1 , a vector ofparameter estimates of variables describing firm characteristics. Controls is a vector of control
variables always including local labor market variables, posting month and industry dummy variables,
and a combination of variables describing job characteristics, or search intensity. Term 𝜀𝑖𝑗 is the error
term. Standard errors are for all models clustered at the firm level.
Further, to examine the extent to which firms reveal their preference for young age throughout the job
ad using age-related content, categorical variables Age index 1 and Age index 2 for job ad j of firm iare similarly regressed on firm characteristics and a set of controls in a linear regression model:
𝐴𝑔𝑒 𝑖𝑛𝑑𝑒𝑥𝑖𝑗 = 𝛽0 + 𝜷𝟏 ∗ 𝑭𝒊𝒓𝒎 𝒄𝒉𝒂𝒓𝒂𝒄𝒕𝒆𝒓𝒊𝒔𝒕𝒊𝒄𝒔𝒊 + 𝛽2 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑗 + 𝛽3 ∗ 𝑆𝑒𝑎𝑟𝑐ℎ 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦𝑖𝑗
+ 𝜀𝑖𝑗
(2)
including Search strategy in the form of narrow search type dummy variables as additional controls.
Age index 1 summarizes indicators for the presence of five text-based and one visual-based agerelated ad content types, thus ranging from 0 to 6. Age index 2 excludes visual age-related content
dummy to take values from 0 to 5. Standard errors are analogously clustered at the firm level.
6.2 Narrow search and firm characteristics – results and discussion
Firms may select specific search strategies flexibly, based on the vacancy to fill or on the searchintensity required to reach a suitable candidate. Table 2 displays the results of the search strategy
model from Equation 1 with a variable set of controls and an outcome variable Age limit in columns(1)-(3) and Experience requirement in columns (4)-(6). These are the two mutually exclusive search
9strategy types directly observed in the ads; it is, therefore, intuitive to use them to examine how
controlling for the job characteristics and search intensity changes the impact of the firm
characteristics of interest.
Columns (1) and (4) show the estimation results including only controls common to all specifications.
Without controlling for job characteristics and search intensity, results for Age limit in column (1)show the only highly statistically significant characteristics to be the firm origin and job location.
Japanese firms and firms hiring for jobs located in Tokyo and Osaka were more likely to set age limits
than their respective counterparts, consistent with hypotheses 1 and 2. Turning to column (4), firms
requiring experience were of the type assumed to be able to select more costly search strategies(hypothesis 1): wealthier firms, firms with fewer quits, and older firms, which may have higher
recognition value than young firms. Furthermore, firms assumed to have a more flexible internal
structure (foreign-owned firms and subsidiaries of publicly traded firms) were also significantly more
likely to require experience. However, firms may be inclined to restrict eligibility only for specifictypes of sales jobs; the addition of job-related controls in columns (2)-(3) and (5)-(6) filters this effectout, weakening the significance of the effect of firm characteristics for both types of narrow search.
Columns (3) and (6) show results with the addition of controls separating out the effect of searchintensity. Column (3) results show that the firm characteristics associated with age limits survive theinclusion of search-related control variables, generally gaining significance. On the other hand,controlling for search intensity for experience requirements in column (6) causes little change. While
the significance of the coefficient estimates varies with the controls, their direction and effect sizes
generally remain stable. The specification used in columns (3) and (6), including the complete set ofcontrol variables, thus revealing underlying preferences, is selected for the remainder of this section.
Table 3 displays the results for the adopted specification for all outcome variables from Equations 1
and 2, including the results from Table 2 columns (3) and (6). This allows for a comparison of theeffects of the firm characteristics on different search strategy types as well as age-related ad content
use. Column (1) shows the results of an overall narrow search, with columns (2) and (3) respectively
displaying the results for searches requiring experience and restricting age. Columns (4) and (5) thenshow the relationship between the firm characteristics and the revealed preference for young age inthe form of age-related ad content at the top of the selected search type.
The results in Table 3 generally align with the proposed hypotheses. Firms with higher capital arehighly significantly more likely to choose an overall narrow search. However, the effect on
experience requirements and age limits is only marginal or non-significant. This result suggests that
wealthier firms undergo a more costly search while not necessarily preferring young age orexperience specifically. An increase in capitalization is also associated with the decreased use of agerelated content in columns (4) and (5), confirming that these firms do not show a preference for youngage.
Next, 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 firms do not differ from smaller firms in terms of requiring experience or
showing a preference for young age through ad content but are less likely to choose an overall narrowsearch strategy as well as set age limits. A 1% increase in the number of employees results in a 2.5%
lower likelihood of setting an age limit. In contrast to young age, experience is assumed to bring arealized productivity bonus, possibly offsetting higher search costs.
Further, younger firms also behave in accordance with hypothesis 1. They are less likely to select anoverall narrow search than older firms but do not differ in terms of experience requirements and theuse of age limits. However, younger firms are highly significantly more likely to include age-related
content in their ads for either definition of age index in columns (4) and (5). They display a strong
preference for young age without acting on it, possibly due to low attractivity caused by low namerecognition or uncertainty about the future, leading to higher search costs.
Analyzing the role of firm ownership type, publicly listed firms, and their subsidiaries do notsignificantly differ from private firms in terms of the likelihood of employing an overall narrow10

search strategy or setting age limits. Yet, subsidiaries of publicly traded firms are more likely torequire experience and tend to use less age-related ad content than private firms. One possible reason
behind age-related content use is signaling young-age preference when age limits cannot be set due tothe inclusion of experience requirements. This result thus suggests that subsidiaries of publicly tradedfirms are more welcoming to outside experience regardless of the applicant’s age.
Examining the ownership origin, domestic Japanese firms do not differ from foreign-owned firms in
the likelihood of selecting a narrow search strategy overall but differ in the types of narrow searches
employed. They are marginally less likely to require experience while simultaneously more likely to
use age limits. This result may indicate a firm culture that places a premium on youth, in accordancewith hypothesis 2. On the other hand, foreign-owned firms tend to place more age-related content intheir ads. The difference in statistical significance between Age index 1 in column (4) and Age index
2 in column (5) suggests that this trend is driven by conveying youth preference using visuals, which
is a more subtle approach than referencing young age in writing. This result is consistent withmethods used in an environment where age limits are illegal, as examined by Burn et al. (2022).
Next, consistently with hypothesis 1, firms filling a vacancy in Tokyo or Osaka have a 13.6% higher
likelihood of employing a narrow search than in other locations, driven entirely by age limits use.
Firms in these urban centers have access to a larger pool of applicants, making this search type withthe uncertain realization of perceived productivity bonus less costly. Firms in urban areas also utilize
age-related ad content to a higher degree, possibly taking advantage of the large jobseeker pool toreach age groups more precisely defined than age limits allow.
Finally, firms that use a probation period interpreted as an indicator of intensive training practices are
7.2% more likely to set age limits while also placing more age-related content in their ads.
Additionally, the use of a probation period is, albeit marginally, negatively associated with experience
requirements, which further supports hypothesis 2, associating training costs with youth favoritism.
Okunishi (2008) and Ōta and Yasuda (2010) support this interpretation by linking more developed infirm training practices to a preference for young workers. Examining the difference between theimpact on Age index 1 and 2, including visual-based age-related ad content in column (4) leads to alower statistical significance of age index. This result suggests that, in addition to age limits, thesefirms predominantly relay their youth preference straightforwardly in writing.
To check the robustness of these results, three approaches are adopted. First, the possibility oferroneous inference stemming from multiple hypothesis testing is addressed in two ways. Appendix C
Tables C9 and C10 report familywise error rate (FWER) adjusted p-values using the Holm-Bonferroni
method for Tables 2 and 3, respectively. Appendix C Tables C11 and C12 then present the results
using Equation 1 for Age limits and Experience requirements, including each firm characteristics
variable separately without additional controls and with the job and search-related controls. The
results generally align with the above discussion. Second, while this section examined the use of age
limits at any threshold, Appendix C Table C13 shows the results for a stricter definition, setting theeligibility cut-off age to 40 and 35 years. The results for these alternative definitions are consistent
with the main ones. Finally, the employed models were for all outcomes additionally estimated using
probit (Equation 1) and ordered logit (Equation 2), producing generally qualitatively identical resultswith higher statistical power (results not reported for brevity). These additional analyses show theresults to be robust.
In conclusion, firms employed a narrow search strategy in 48.6% of the ads, roughly evenly split
between requiring experience and setting age limits. The determinants of overall narrow search andspecific types of narrow search generally agree with the hypotheses derived from the presented
theoretical framework: firms that can afford more costly narrow search are more likely to searchnarrowly, and firms assumed to derive productivity bonus from young age to use age limits. However,as this study does not have information about the inner workings of the observed firms and is limitedby the data source, more detailed research into the determinants of preference for a young age isneeded.

11

NARROW SEARCH TYPESAGE LIMIT

EXPERIENCE REQUIREMENT

(1)

(2)

(3)

(4)

(5)

(6)

Capital (log)0.00124(0.00938)

0.00741(0.00922)

0.00998(0.00924)

0.0241***(0.00907)

0.0138*(0.00837)

0.0157*(0.00865)

Employees (log)0.0181(0.0122)0.00723(0.0119)-0.0252**(0.0119)-0.0360***(0.0101)-0.0217**(0.00917)-0.00889(0.0100)

Foundation year

0.000308(0.000776)-0.000129(0.000704)-0.000632(0.000692)-0.00246***(0.000686)-0.00134**(0.000671)-0.000976(0.000666)

Listed firm

-0.0744(0.0676)-0.0589(0.0584)-0.0815(0.0521)0.0549(0.0518)

0.0422(0.0454)0.0654(0.0452)

Subsidiary of listed firm

-0.0211(0.0481)-0.00647(0.0475)-0.00196(0.0423)

0.113***(0.0437)

0.0756**(0.0350)

0.0706**(0.0355)

Japanese firm

0.179***(0.0578)

0.114*(0.0583)

0.137**(0.0574)-0.222***(0.0693)-0.0958(0.0658)-0.113*(0.0656)

Tokyo, Osaka location

0.114***(0.0330)

0.133***(0.0350)0.151***(0.0333)

0.0239(0.0250)-0.0194(0.0227)-0.0150(0.0230)

Probation period

0.0539*(0.0297)0.0600**(0.0287)

0.0718***(0.0273)-0.0234(0.0266)-0.0339(0.0220)-0.0415*(0.0215)

✓

✓
✓

✓

✓
✓

2,683
0.151

2,683
0.189

2,683
0.242

2,683
0.253

Job
Search
𝑁
𝑅2

2,683
0.111

2,683
0.138

Notes: Coefficient estimates from linear probability model. Dependent variable is an indicator of narrow search type use. Labor market controls, and Monthand industry dummies included in all models. Inclusion of variables controlling for type of advertised job (Job) and search intensity (Search) indicated. Job
controls are Starting wage, Inclusion of overtime in wage, Education, B-to-B, B-to-C, 9AM shift, Flextime, Days off schedule, Transfer possibility. Searchcontrols are Ads per firm, Number of reposts, Posting period, Number of locations, Number of locations squared, Hiring 5+ workers. Robust standard errors
in parentheses clustered at the firm level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

Table 2: Narrow search types

12

SEARCH STRATEGYNARROW
SEARCHEXPERIENCEREQUIREMENT

AGE
LIMIT

AGE INDEX
1

AGE INDEX
2

(1)

(2)

(3)

(4)

(5)

Capital (log)0.0257***(0.00840)

0.0157*(0.00865)

0.00998(0.00924)-0.0526*(0.0309)-0.0507*(0.0285)

Employees (log)-0.0341***(0.0127)-0.00889(0.0100)-0.0252**(0.0119)0.0701*(0.0396)0.0476(0.0364)

Foundation year

-0.00161**(0.000743)-0.000976(0.000666)-0.000632(0.000692)

0.0110***(0.00240)0.00961***(0.00218)

Listed firm

-0.0161(0.0524)0.0654(0.0452)-0.0815(0.0521)0.141(0.178)0.109(0.168)

Subsidiary of listed firm

0.0686(0.0432)

0.0706**(0.0355)-0.00196(0.0423)-0.622***(0.131)-0.547***(0.121)

Japanese firm

0.0238(0.0707)-0.113*(0.0656)

0.137**(0.0574)-0.363**(0.172)-0.249(0.154)

Tokyo, Osaka location

0.136***(0.0354)-0.0150(0.0230)

0.151***(0.0333)

0.253**(0.111)0.212**(0.0948)

Probation period

0.0303(0.0284)-0.0415*(0.0215)

0.0718***(0.0273)0.228**(0.0894)

0.208***(0.0801)

𝑁
𝑅2

2,683
0.201

2,683
0.253

2,683
0.189

2,683
0.245

2,683
0.229

Notes: Coefficient estimates from linear probability model in columns (1) – (3), linear regression model for columns (4) – (5). Dependent variables incolumns (1) – (3) are dummy variables, dependent variable in column (4) ranges from 0 – 6, in column (5) from 0 – 5. All models control for labor market
characteristics, month, industry, job characteristics and search intensity. Robust standard errors in parentheses are clustered at the firm level. Significancelevels: *** p<0.01, ** p<0.05, * p<0.1.

Table 3: Search strategy

13

7. Narrow search and wage offer
Hypothesis 3 from section 5 proposes that firms able to suppress wages choose more costly narrowsearches. This section tests this hypothesis by analyzing the relationship between search type andwage setting behavior based on local labor market conditions. A firm offering a competitive wage
should theoretically increase this offer in the face of a tightening labor market. Using US online job
ads, Azar et al. (2020) examined the association between labor market concentration and wage offers
and observed lower wage offers in markets with fewer firms. Assessing the relationship between local
labor market conditions and the wage offer may thus provide some evidence as to whether firms
utilizing different search strategies respond differently to labor market conditions. This analysis uses
both the sample of unique ads and the original dataset containing reposts to capitalize on the dynamic
nature of the labor market.
The aim of this analysis is to examine the current sample for the presence of indicators consistent withhypothesis 3; it does not intend to detect or measure market power. Additionally, this analysis
assumes low job mobility, which is consistent with the observed mobility in the Japanese labor market,
assuming that mid-career jobseekers generally search for jobs in the city of their residence or in cities
with comparable average population age. Because of these considerations, this analysis is limited andshould be interpreted accordingly.
7.1 Narrow search and wage offer – empirical strategy
To examine the association between the wage offer and the local labor market conditions, wage offer
variables Hourly wage ratio and Hourly wage ratio without overtime of job ad j at firm i are regressedby a linear regression model on variables describing the local labor market and a set of controls forfull and search type subsamples using the following model:
𝐿𝑜𝑔(𝑊𝑎𝑔𝑒 𝑜𝑓𝑓𝑒𝑟𝑖𝑗 )= 𝛼 + 𝜷 ∗ 𝒍𝒐𝒈(𝑽𝒂𝒄𝒂𝒏𝒄𝒚 𝒓𝒂𝒕𝒊𝒐𝒊𝒋 ) + 𝜸 ∗ 𝑷𝒐𝒑𝒖𝒍𝒂𝒕𝒊𝒐𝒏 𝒂𝒈𝒆𝒊𝒋 + 𝛿 ∗ 𝐶𝑜𝑛𝑡𝑟𝑜𝑙𝑠𝑖𝑗+ 𝜇 ∗ 𝑆𝑒𝑎𝑟𝑐ℎ 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦𝑖𝑗 + 𝜀𝑖𝑗(3)Hourly wage ratio and Hourly wage ratio without overtime depict the wage offer as an hourly wage
weighted by the local minimum wage, respectively, ignoring and accounting for the number ofovertime hours included in the wage offer. Vacancy ratio is the number of vacancies to the number ofjobseekers in the prefecture of job’s location 1 month before the posting month. Population age
represents the average population age in the city the job is in. Controls is a vector of control variables
including month and industry dummy variables and a combination of variables describing firm, job
characteristics and search intensity. Search strategy is a vector of dummy variables indicating
experience requirements and age limits use. Standard errors are clustered at the firm level for the
sample of unique ads and two-way clustered at the firm level and a job ad level for the sample of allads.
7.2 Narrow search and wage offer – results and discussion
For all ads, the local labor market is characterized by an excess of vacancies over jobseekers, with anaverage of 1.57 openings per jobseeker. A higher vacancy ratio indicates a tighter labor market. The
expected sign on the vacancy ratio estimates in Equation 3 for both outcomes is thus positive. A
negative or statistically insignificant result would imply that firms, on average, do not respond tolabor market tightness. Similarly, the expected impact of average population age on both outcomes iseither null or positive. Firms in locations with older workers may reflect the average level ofproductivity of potential applicants in the wage offer, as productivity generally increases withexperience and, correspondingly, age.
Table 4 shows the estimation results using Equation 3 with an expanding set of controls. Panels A andB present the results for the sample of unique ads, reflecting the circumstances at the time of theinitial ad posting. Panels C and D contain identical analysis for the sample containing reposts,
14

leveraging the dynamics of the labor market. The mechanism behind not reposting an ad is unclear
from the available data; a firm 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 unique ad. While the sample used for analysis in panels C-D moreaccurately showcases a casual jobseeker's experience on Doda, it likely includes multitudes ofcomparatively unpopular ads or ads that have not been updated to match jobseekers’ demands. Panels
A-B and C-D thus analyze different firm behavior with panels C-D expected to show a weaker link
between wage offer and labor market conditions. 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.
While the direction of the effect of the vacancy ratio on both wage offer measures is as expected forall models and samples, neither estimate reaches statistical significance. This result implies that, on
average, firms do not respond to the tightness of the local labor market by increasing wage offer.
However, examining the time of the initial posting in panels A and B, a higher population average age
is associated with a higher wage offer, especially when job characteristics and search intensity arecontrolled for. One year increase in average population age resulted in a 1.30% increase in the Hourly
wage ratio in column (4) and a 1.36% increase in Hourly wage ratio without overtime in column (8),estimated with less precision. As expected, this trend no longer holds for the sample containing
reposts in panels C and D once overtime hours are considered. While the sample firms were notsensitive to local labor market tightness, which is more transient than the population age profile, they
appear to consider the likely heightened wage expectations of prospective applicants, with theinclusion of overtime pay weakening this association. Furthermore, the inclusion of a specific numberof overtime hours weakening or effectively erasing this relationship suggests that the use of overtime
hours in wage offer is strategic.

15

WAGE SETTING BEHAVIORPANEL A: OVERTIME NOT CONSIDERED
UNIQUE ADS

Vacancy ratio (log)
Population age

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

0.0984(0.0740)0.0125*(0.00653)

0.0920(0.0774)
0.00968(0.00654)

0.0905(0.0727)
0.0127**(0.00605)

0.0808(0.0683)
0.0130**(0.00579)

0.0723(0.0705)
0.0124(0.00792)

0.0742(0.0754)0.00944(0.00782)

0.0829(0.0727)
0.0125*(0.00741)

0.0797(0.0743)0.0136*(0.00706)

✓

✓
✓

✓
✓
✓

✓

✓
✓

✓
✓
✓

2,683
0.099

2,683
0.177

2,683
0.190

2,683
0.095

2,683
0.168

2,683
0.184

Firm
Job
Search

𝑁
𝑅2

2,683
0.042

PANEL C: OVERTIME NOT CONSIDERED
ALL ADS

Vacancy ratio (log)
Population age

2,683
0.060

PANEL D: CLEARED OF OVERTIME HOURS
ALL ADS

(9)

(10)

(11)

(12)

(13)

(14)

(15)

(16)

0.124
(0.0896)0.0143**(0.00727)

0.123(0.102)0.00938(0.00698)

0.116(0.0853)
0.0132**(0.00644)

0.113(0.0870)0.0133**(0.00636)

0.0576(0.0772)
0.0107(0.00880)

0.0673(0.0909)
0.00621(0.00843)

0.0769(0.0853)0.0101(0.00791)

0.0780(0.0850)
0.0113(0.00754)

✓

✓
✓

✓
✓
✓

✓

✓
✓

✓
✓
✓

3,573
0.117

3,573
0.196

3,573
0.217

3,573
0.110

3,573
0.177

3,573
0.196

Firm
Job
Search𝑁
𝑅2

PANEL B: CLEARED OF OVERTIME HOURSUNIQUE ADS

3,573
0.058

3,573
0.077

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 job location 1 month before ad posting. Population age isaverage population age in city of job location. Posting month and industry dummy variables included in all models. Inclusion of variables controlling forfirm characteristics (Firm), type of job advertised (Job) and search intensity (Search) indicated. Robust standard errors in parentheses are clustered at thefirm level for panels A and B and two-way clustered at the firm and ad level for panels C and D. Significance levels: *** p<0.010, ** p<0.05, * p<0.10.

Table 4: Wage setting behavior

16

To further test these findings, Table 5 shows the results of the specification from Table 4 including
the full set of controls (columns (4), (8), (12), (16)) for search type-based subsamples. The chosenmodel accounts for the main factors influencing search type selection, revealing baseline firm
behavior. In accordance with Table 4, the results presented in panels A-B and C-D use the sample ofunique ads and the sample containing reposts, respectively. Columns (1), (5), (9), (13) show theresults for a subsample of ads containing age limits, columns (2), (6), (10), (14) for a subsample ofads with experience requirements, columns (3), (7), (11), (15) for these two subsamples combined fora narrow search subsample, and finally columns (4), (8), (12), (16) for a subsample of ads with neither
requirement.
The results for subsamples of ads containing narrow search or its types (columns (1)-(3), (5)-(7), (9)(11), and (13)-(15)) do not confirm any association between either measure of wage offer and local
labor market conditions. The relationship between the wage offer and the average population age inTable 4 thus appears driven by ads with a broad search strategy. This result lends support to the
hypothesis that firms able to suppress wages choose a more costly narrow search regardless of its type.
Furthermore, the trend of firms nominally raising wage offers while compensating for higher laborcosts by increasing working hours is more pronounced in the broad search subsamples compared tothe original full samples. For the broad search subsample of unique ads, a one-year increase in the
average population age leads to a 1.97% increase in the Hourly wage ratio, and a 1.90% increase in
the Hourly wage ratio without overtime, with the significance decreasing from a 1% to a 5% level.
While firms searching broadly appear to raise wages in the face of age-related higher wage
expectations, they seem to include overtime pay in baseline wage to suppress the hourly rate.
The collective evidence presented in this section aligns with hypothesis 3. Indirectly analyzing thelink between wage-setting behavior and search strategies, firms selecting an overall narrow searchstrategy or either of its types do not seem to respond to labor market tightness by increasing wages.
Likewise, firms searching broadly do not react to labor market tightness but seem to adjust their wage
offers to jobseekers’ average wage expectations based on the population age while strategically using
overtime hours to push the per-hour rate downward. These results are consistent with the hypothesis
proposing that firms able to keep wages low adopt more costly narrow search strategies. However, theanalysis in this section is limited, and more direct methods are needed to ascertain how firms adjust
wage offers in response to changing labor market conditions.

17

WAGE SETTING BEHAVIOR AND SEARCH STRATEGYPANEL A: OVERTIME NOT CONSIDERED
UNIQUE ADS(1)Experience
requirement(2)

Narrow
search(3)

Broadsearch(4)

Vacancy ratio (log)0.204(0.168)-0.0501(0.131)0.0366(0.105)Population age

0.0219(0.0139)0.000888(0.0122)

𝑁
𝑅2

634
0.380

670
0.256

Subsample

Age limit(5)

Experience
requirement(6)

Narrow
search(7)

Broadsearch(8)0.117(0.0712)0.129(0.213)

0.0984(0.146)0.114(0.125)0.0880(0.0779)

0.00552(0.00926)

0.0197***(0.00695)

0.0167(0.0138)

0.00527(0.0148)0.00782(0.0101)

0.0190**(0.00849)

1,304
0.291

1,379
0.174

634
0.363

670
0.262

1,304
0.299

1,379
0.151

PANEL C: OVERTIME NOT CONSIDERED
ALL ADS(9)

Experience
requirement(10)

Narrow
search(11)

Broadsearch(12)

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)

𝑁
𝑅2

869
0.414

795
0.271

1,664
0.308

Subsample

Vacancy ratio (log)

Age limit

PANEL B: CLEARED OF OVERTIME HOURSUNIQUE ADS
Age limit

PANEL D: CLEARED OF OVERTIME HOURS
ALL ADS(13)

Experience
requirement(14)

Narrow
search(15)

Broadsearch(16)0.132*(0.0764)0.0187**(0.00743)

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

0.162(0.123)0.00853(0.0101)

0.0452(0.0872)
0.0144(0.00947)

1,909
0.233

869
0.457

795
0.285

1,664
0.347

1,909
0.162

Age limit

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 of Hourly
wage ratio without overtime. Vacancy ratio (log) is log of vacancy ratio in prefecture of job location 1 month before ad posting. Population age is average
population age in city of job location. Month, industry, firm, job and search intensity control variables included. Robust standard errors in parentheses are
clustered at the firm level for panels A and B and two-way clustered at the firm and ad level for panels C and D. Significance levels: *** p<0.010, ** p<0.05, *p<0.10.

Table 5: Wage setting behavior and search strategy

18

8. Conclusion
The labor market in rapidly aging Japan is generally characterized by low job mobility and apersistent excess of vacancies over jobseekers. However, under certain circumstances, firms in Japan
can legally set explicit age limits on applicants. One permitted exemption is hiring young workers on
regular contracts without requiring previous job-related experience, a statute protecting the traditional
practice of preferentially hiring fresh graduates. Firms, therefore, face a choice between setting age
limits or requiring experience, thus searching narrowly, or neither, searching broadly. Online job adsites are an easily accessible source of information for people contemplating a 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.
While Japan grapples with a declining workforce and mounting pressure on the social security system,
many individuals willing to enter regular work contracts may have aged out of job opportunities. Thisis particularly problematic for young people contemplating starting families and, likewise, for middleaged individuals who temporarily left the labor force to raise children or care for aging relatives. With
many productive years ahead, age limits may hinder them from utilizing their human capital andrealizing their earnings potential. Underemployed workers simultaneously contribute less to the social
system during their productive years, and they are more likely to find themselves in unstable financial
situations both during employment and after retirement. This places additional and continuous strain
on the social security system. Similarly, individuals contemplating a job change or relocation may feeltrapped in jobs or locations they no longer prefer due to the barriers to job mobility imposed by age
limits. The opportunities for extended employment of the elderly are also socially desirable and could
help alleviate pressure on the labor market and the pensions system.
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 firm characteristics related to the use of age limits and other job ad content showing age
preference on applicants. Out of the 2,683 unique job ads from 1,342 firms, 23.6% included explicit
age limits, with the most frequent eligibility cut-off age at 35 years. Additionally, up to 96% of ads
included content revealing a preference for a young age other than age limit.
This paper proposes a theoretical framework of a firm’s employee search and tests it against the data.
Three mechanisms behind narrow search are proposed: search costs, youth favoritism, and wage
related. Empirically testing the model's implications, this paper observes that, generally, firms that canafford more costly search strategies tend to search narrowly. Firms with fewer employees, firms
hiring in urban centers, and firms instituting a probation period for new hires were more likely to set
age limits than their counterparts. However, the use of other age-related ad content did not show aclear trend, implying that the inclusion of young-age references in the body of a job ad is situational.
Further, indirectly analyzing the link between wage setting behavior and employee search strategy,
consistent with theoretical predictions, firms conducting a narrow search do not seem to respond tochanging labor market conditions.
In 2019, the Japanese government announced a plan to promote mid-career hiring, increase householdincome, and secure employment opportunities until age 70 (Cabinet Office of Japan, 2019a). The
practice of large firms traditionally once a year hiring fresh graduates was also called out, highlighting
the need for more flexible hiring practices. As a part of a policy to increase household income, thegovernment 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 (Cabinet
Office of Japan, 2019b). As a result, the “employment ice age” generation was added to the EMA
exemption permitting age restrictions on applicants whose employment is encouraged by government
policies. This change allowed firms to set age limits on this age group specifically. Because of thetiming of the legislative change, no conclusions can be drawn about this exemption application in thecurrent study. However, no job ad in the sample cited the broad employment-encouragement
exemption as a basis for age limits.

19

Understanding which firms utilize the legislative exemptions on age discrimination is crucial forpolicymakers. This knowledge can be used to promote employment opportunities at all ages,
incentivize firms to adopt more inclusive hiring practices, or verify whether the exemptions allowing
age limits are applied in accordance with their original purpose. Using a previous amendment to the
EMA, Sasaki and Yasui (2014) investigated the impact of stricter anti-age discrimination rules
adopted in 2007. They concluded that the legislative tightening resulted in increased employment of
the elderly in both part-time and full-time jobs without displacing younger workers. While theJapanese government appears aware of the difficult situation many mid-career workers face due tolimited and age-restricted employment opportunities, perhaps reducing rather than increasing
exemptions from anti-age discrimination legislation should be the way forward.
While this study, to the best of the author’s knowledge, provides the first evidence on the link
between a narrow search strategy, particularly age limits, in Japanese online job ads and firm
characteristics, it is limited by its data source. Since this paper analyzed only a specific section of thelabor market, which is not representative of the overall one, it cannot offer a discussion of broader
trends. More research is needed to generalize the findings to other occupations and recruiting channels
on the demand side, as well as to examine the supply side, including jobseekers’ abilities, needs, andlimitations related to age limits.
However, an aging population and the associated social and fiscal challenges are not unique to Japan.
While this study provides valuable insights into a specific segment of the Japanese labor market
within the context of Japanese legislation, its broader implications may prove useful in other countries.
Understanding the mechanisms behind firms’ tendencies to impose age restrictions on applicants may
promote the efficient allocation of limited resources to investigate discriminatory practices, especiallyin settings that explicitly prohibit age discrimination in employment. Comparative research is anotheravenue for future studies that can contribute to the promotion of inclusive hiring practices on a global
scale.

Declarations of interest: none.
Funding: This work was supported by KAKENHI grant number 23K18803 and 20H05631 from theJapan Society for the Promotion of Science.

Acknowledgment: I want to express my sincere gratitude to Hideo Akabayashi and Soichi Ota for
their patient guidance and support. I also want to thank the participants of various seminars andconferences I had the opportunity to present this research at for their valuable comments.

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22Appendix A: Sample collection and representativenessThe sample was collected by the author from July 2018 to December 2019 from the Japanese midcareer online job ad site Doda. A firm with an opening to be filled with a mid-career worker is facedwith a choice of a recruiting channel, such as the public employment office, referrals, or online job adsites. Although the selection likely depends on the job itself, the jobseekers the firm wants to reach,and the corresponding costs, the popularity of online job ad sites in Japan is growing, evidenced by anincreasing number of registered Doda users. Doda, founded in 2007, claimed to have approximately
4,100,000 registered users as of March 31st, 2018, with this number growing to 5,450,000 by March
31st, 2020. (Persol, 2018, 2020). 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 than highly specialized positions. Since
2010, Internet usage and overall digitalization of all aspects of society further increased and thenumber 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 firm looking to fill a sales, not a highly specialized, position became morewidespread. However, as this study only examines the online job ad site recruiting channel, itsconclusions can only be viewed in the appropriate context.
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 specificsubset of jobseekers based on age, occupation, or industry. Second, Doda claimed to host the most job
ads among popular Japanese online job ad sites at the start of the sample collection period. Finally, thestructure 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 firms draft the ads, thereby
resulting in highly standardized language and ad structure while ensuring the ad complies withapplicable legal requirements. Except for job ads for entities without capital, such as religious
institutions or municipalities announcing job fairs, all new complete ads, ads with both firm press
page and job ad page, for full-time positions uploaded to the sales (eigyō, 営業) category werecollected.
Doda allows businesses using its services to select from various ad formats. Ads with a dedicated firm
PR page and a dedicated job-posting page, as collected for this study, appear preferentially in both theoverall list of ads regardless of posting date and the criteria-based search using filters. Doda
categorizes job postings into 15 categories based on occupation. This study chose the sales category
because sales jobs can generally be transitioned into without specific education, licenses, or otherqualifications, 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, contract
employee, and other. Approximately 95% of the ads posted in the sales category during the sample
collection period advertised regular contract jobs. If the firm’s characteristics were not described in
the ad, the information was obtained elsewhere, such as on the firm’s website or in job ads on otherjob ad sites. In all cases, the ad included information about the industry wherein the firm operates.
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. All job ads collected contained a monthly wage offer upon completion of the probation
period, with 48.2% of ads including overtime pay in the offer. When wage offer was presented as arange, the lower bound was used, and for ads advertising vacancies in multiple locations, the wage
offer corresponding to the reported firm location was recorded. Table A1 shows the average monthly
wage offer broken down by age, experience, and educational requirements. Panel A describes the23

wage offer in its current form, and Panel B reports it cleared of overtime using the following
formula5:
𝑊𝑎𝑔𝑒 𝑜𝑓𝑓𝑒𝑟 𝑤𝑖𝑡ℎ𝑜𝑢𝑡 𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 = 𝑤𝑎𝑔𝑒 𝑜𝑓𝑓𝑒𝑟 ∗165.
165 + 𝑜𝑣𝑒𝑟𝑡𝑖𝑚𝑒 ℎ𝑜𝑢𝑟𝑠

A. OVERTIME NOT CONSIDEREDREQUIREMENTEDUCATIONAll
No requirement
High school
Vocational, 2-year college
University

NONE(N = 1,379)
238,279
239,858
231,073
229,432
245,664

AGE LIMIT(N = 634)
233,827
241,018
215,465
221,853
231,813EXPERIENCE(N = 670)
263,923
258,314
247,936
265,613285,513

AGE LIMIT(N = 634)
209,907
213,665
195,274
214,092
227,310EXPERIENCE(N = 670)
243,081
232,663
235,565
258,429
268,792

B. CLEARED OF OVERTIMEREQUIREMENTEDUCATIONAll
No requirement
High school
Vocational, 2-year college
University

NONE(N = 1,379)
216,052
217,644
206,104
218,264
231,824

Table A1: Average monthly wage offer – sample
To assess the data’s representativeness, Table A2 shows the average monthly wage in Japan inJapanese yen by the age group and contract type in Panel A and the average first job starting monthly
wage by educational level in Panel B.

A. BY AGE GROUP AND CONTRACT TYPE
2018
Under 19
20 – 24
25 – 29
30 – 34
35 – 39
40 – 4445 – 49
50 – 54
55 – 59
60 – 64
65 – 69Over 70
Average

2019

REGULAROTHER

REGULAR

OTHER

179,200
213,200
245,700
282,400
313,300
342,100
372,800
400,000
400,200
316,700
283,300
281,000
323,900

165,500
182,100
198,200
204,900
207,700
205,600
206,100
204,300
206,200
236,500
208,200
199,500
209,400

180,200
214,600
249,500
284,800
317,100
344,400
368,900
398,600
396,300
325,100
286,500
274,700
325,400

168,000
180,800
198,900
204,700
207,600
208,200
208,100
206,600
205,500
237,900
216,500
195,800
211,200

MHLW (2019b, 2020b)

5

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

24

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)

Table A2: Average monthly wage – population
Comparing Tables A1 and A2, the offered wage 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 wage in ads with an age limit and requiring university
education is for master’s degree holders lower than the average first job wage. Age restrictions may
thus be intended to discourage primarily non-regular workers from applying. Even job advertisementswith the most stringent requirements, such as experience and a university degree, offer a lower wage
than that a worker on a regular contract in the 35–39 age group earns on average. The job offers
posted in the sales category on Doda generally seem to be aimed at younger workers, specificallythose below the age of 30 in the case of regular workers.
In terms of the data’s main limitations, because the sample was collected from a single source, forregular contract jobs in a single occupational category only, it is not representative of the Japanese
mid-career job market. In 2018, 37.8% of workers in Japan were in non-regular jobs (Statistics
Bureau of Japan, 2019). In 2016, 96.6% of firms in Japan had fewer than 50 employees (Ministry of
Economy, Trade and Industry, 2018), well below the sample median of 215. Further, Tokunaga(2008) reported that while in 2005, 96.1% of ads in a women-oriented job ad magazine contained age
limits, for public employment office the percentage of ads setting explicit age limit was 46.7% withadditional 12.1% referencing an upper limit, and these numbers decreased to 29.9% and 10.4%,
respectively, by September 2007. Although the period covered in Tokunaga (2008) ended almost 11
years before the start of the sample collection period for the current study, it shows the marked
difference between the public employment office and a targeted job ad medium. The prevalence ofage limits in ads posted on Doda is also likely much higher than in ads available through the public
employment office and by extension in the universe of job ads. Furthermore, firms that do not set age
limits in their job ads may engage in age discrimination later in the recruiting process, particularly ifthey choose to set experience requirements while preferring to hire young applicants.

25

Appendix B: Age targeting
This study examines age targeting from two perspectives: direct, in the form of explicit age limits, andimplied, in the form of age-related job ad content. First, explicit age limits citing EMA young-age
related exemption, were present in 23.6% of ads with varying eligibility cut-off ages. Examining the
maximum eligible age, 3.7% of all ads accepted only applicants aged 30 or younger, 15.7% accepted
only applicants aged 35 or younger, and 20.9% of ads set the maximum eligible age to 40 or younger.
Figure B3 depicts the maximum eligible age distribution in ads with age limits. 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 B3: Age limit by cut-off age
Compared to the previous literature, which primarily focused on the prevalence of age restrictions, notits determinants, the use of age limits decreased from close to 100% (Kitaura, 2003; Tokunaga, 2008)
to about one-fourth of ads. Although the ad source for each study differed, prohibiting directcomparison, it is an expected result following the implementation and tightening of anti-ageism
legislation. Regarding the eligibility cut-off ages, Kitaura (2003) reported that before theimplementation of 2001 EMA revision, the maximum eligible age was, on average, set to 43.9 years(September 2001) and increased to 46.3 years afterward (October 2002). Tokunaga (2008) also
reported that the eligibility cut-off age increased over time. For regular workers in 1985, 76.1% ofsurveyed ads allowed applicants 35 and younger and 93.9% 40 and younger. For 1995, these numbers
decreased to 62.7% and 85.8%, and 42.9% and 78% in 2005. The prevalence of age limits set to any
age and the cut-off ages of 40 and 35 in the 2005 Tokunaga sample compared to the current sample is
4.1, 3.7, and 2.7 times larger, respectively. The much larger decrease in the overall use of age limits
compared to the specific eligibility cut-off ages is a consequence of the 2007 EMA amendment
banning age discrimination yet allowing, among others, young-age-related exception. However, up to
96% of the ads in the current sample included other age-related content, almost identical to theprevalence of age limits for all the surveyed years in Tokunaga (2008).
Next, firms are free to indicate a preference for young workers in a job ad in other ways than
explicitly setting an age limit. Within the highly standardized job ad structure, six patterns of agerelated content were identified, five written, and one visual. The first type of age-related content is theinclusion of the term “recent graduate” (dai ni shinsotsu, 第二新卒) in the body of the ad, in
expressions such as the position being suitable for recent graduates, a large number of recent
graduates joining the firm, or that recent graduates are encouraged to apply. The term “recent graduate”
was used in 47.9% of the sample.
The second type of age-related content 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 such as
young employees playing an active role in the firm or that all employees being approximately thesame young age creates a comfortable work environment. Figure B4 depicts the distribution of the26referenced average age (age if a specific age is mentioned, average age for age range, none if only thewords “young” and similar were used), revealing that it is concentrated in the late 20s and early 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 coded as containing this type of agerelated content. This type of content was present in 58.2% of ads. The lowest average age mentioned
was 22, the average was 29.8, and the maximum was 48.

Figure B4: 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 agerelated content. 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 as
implying young-age preference was set to 44 years old to provide leeway for assessing 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-related content. On the PR page, 42.9% of ads referred
to a young age.
The inclusion of average employee age in firm overview is the fourth type of age-related ad content.
This information was included in 31.4% of the ads. Figure B5 depicts its distribution in these ads.
Assuming a typical retirement age of 60, only ads with a posted average employee age younger than
40 were classified as including age-related ad content. Out of all ads, 28.9% stated that the averageage of employees was under 40, which corresponds to 92.0% of ads containing this information,
indicating that it is used strategically to relay age preference. In these ads, the minimum average
employee age was 24, the average was 31, and the maximum was 39.6.

Figure B5: Average in-firm age
27

The inclusion of age in model wage is the fifth type of age-related content. Job ads typically showed
examples of wage growth that an applicant could expect, with most ads including age in addition to
the length of employment and some ads only including age. Age in model wage was used in 58.8% ofads. The distribution of maximum age included is presented in Figure B6. Out of the ads including
age in model wage, 69.5% of them set the maximum age to 35 or younger. A maximum age higher
than 45 was included in 5% of the ads, and over 50 in 0.8% of the ads. However, when the length of
employment was considered, indicating the age an applicant would need to join the firm to earn theexample wage, all ads with age in model wage were classified as including age-related content. Inmodel wage, the minimum, average and maximum ages were 22, 33.8, and 67, respectively.

Figure B6: Maximum age in model wage
The sixth and final type of age-related content takes into account the visuals that the firm used on its
PR page. If only images of young people are shown, it is considered age-related. In terms of thegraphics used on the PR page, the firm posting an ad has several options. Some firms choose not toinclude photographs of their employees and instead highlight their products, offices, or customers,
whereas others use illustrations or photographs with no visible faces. After opening a full ad on Doda,
the PR page appears first, creating a first impression of the firm. 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 the author only, resulting in a high degree of subjectivity. As
previously stated, some firms disclose the age of employees featured on the PR page; in this case, themaximum age indicating the presence of age-related visual content was set at 44. When age was notdisclosed and the pictures were not of clearly young individuals, information from the ad’s body wasused as a guide. If no age-related information was available, the ad was deemed as not including agerelated content. To ensure consistency, ads from the same firm were cross-referenced. However, theissue of subjectivity remains unresolved, therefore any analysis examining firms’ youth preference
utilizing age-related content employs indicators both with and without this type. Pictures of only
young workers were included in 76.4% of ads.
Table B7 shows the prevalence of each type of age-related content in the sample – the total sample,
the sample of ads with age limits, the sample of ads requiring experience, and finally the sample
containing neither requirement. In general, ads that set age limits make the most use of age-related
content, possibly to target jobseekers more precisely within the eligible age range.

28

Recent graduate
Age in text (ad)Age in text (PR)Age in model wage
Average in-firm age
PR pictures

ALL

AGE
LIMIT

EXPERIENCEREQUIREMENTNOREQUIREMENT(2,683)

(634)

(670)

(1,379)

47.9%
58.2%
42.9%
58.8%
28.9%
76.4%

52.5%
65.3%
55.2%
67.5%
35.6%
84.2%

28.5%
51.5%
34.9%
50.0%
27.3%
75.5%

55.2%
58.2%
41.2%
59.1%
26.5%
73. 2%

Table B7: Age-related content in subsamples
The indicators for the presence of these age-related content types are added together to form acategorical variable age index, which shows the extent to which a firm shows youth preference in its
ads. Variable Age index 1 includes all six types and thus ranges from 0 to 6, whereas Age index 2
excludes the visual-based type and thus ranges from 0 to 5. Table B8 describes Age index 1 and Age
index 2. The most frequently used number of age-related content types was three (23.3%) for Age
index 1, and more ads used all six types (5.4%) than none (3.8%). In terms of Age index 2, the mostads (26.7%) used two types, with 5.5% using all five and 7.6% using none. Comparing subsamples,
correspondingly to Table B7, the smallest number was generally observed in the sample of ads
requiring experience and the largest in ads containing an age limit.
PANEL AAge index 1
0
1
2
3
4
5
6
Total

ALL

AGE
LIMIT

EXPERIENCEREQUIREMENTNOREQUIREMENT(2,683)

(634)

(670)

(1,379)

3.8%
11.8%
19.3%
23.3%
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%
12.8%
18.6%
22.6%
22.3%
14.2%
5.9%
100%

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

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

NO
REQUIREMENT
6.2%
20.5%
27.3%
24.9%
15.3%
5.9%
100%

PANEL BAge index 2
0
1
2
3
4
5
Total

ALL
7.6%
20.5%
26.7%
23.6%
16.1%
5.5%
100%

Table B8: Age indices29

Appendix C:
HOLM-BONFERRONI METHOD ADJUSTED P-VALUES TO TABLE 2
AGE LIMIT

Capital (log)
Employees (log)Foundation year
Listed firm
Subsidiary of listed firm
Japanese firm
Tokyo, Osaka location
Probation period

EXPERIENCE

(1)

(2)

(3)

(4)

(5)

(6)

0.897
0.755
1
1
1
0.026
0.003
0.494

1
1
1
1
0.896
0.424
0.003
0.334

0.937
0.233
0.788
0.547
0.963
0.152
0.003
0.079

0.073
0.005
0.014
0.904
0.071
0.018
0.686
0.395

0.53
0.187
0.348
0.744
0.299
0.493
0.395
0.561

0.424
0.777
0.541
0.688
0.360
0.570
0.513
0.464

Notes: Holm-Bonferroni method familywise error rate adjusted p-values of OLS estimates from Table 2: Narrow search types. Obtained using Stata
command “mhtreg”.

Table C9: FWER adjusted p-values to Table 2

30

HOLM-BONFERRONI METHOD ADJUSTED P-VALUES TO TABLE 3

Capital (log)
Employees (log)Foundation year
Listed firm
Subsidiary of listed firm
Japanese firm
Tokyo, Osaka location
Probation period

AGE LIMIT +EXPERIENCE

EXPERIENCE

AGE
LIMIT

AGE INDEX
1AGE
INDEX 2

(1)

(2)

(3)

(4)

(5)

0.037
0.082
0.202
0.783
0.481
1
0.008
0.880

0.516
0.759
0.521
0.687
0.472
0.525
0.526
0.481

0.908
0.228
0.759
0.603
0.969
0.184
0.003
0.084

0.455
0.352
0.003
0.651
0.002
0.303
0.077
0.044

0.368
0.695
0.003
0.724
0.002
0.638
0.068
0.046

Notes: Holm-Bonferroni method familywise error rate adjusted p-values of OLS estimates from Table 3: Search strategy.
Obtained using Stata command “mhtreg”.

Table C10: FWER adjusted p-values to Table 3

31

AGE LIMIT

Capital(log)
Employees(log)Foundationyear
Listed firm
Subsidiaryof listedfirmJapanesefirmTokyo,
Osaka
location(1)(2)

0.00614(0.00710)-0.000218(0.00722)

(3)

(4)

0.0127(0.00914)-0.0123(0.00927)

(5)

(6)

-4.74e-05(0.000677)

0.000159(0.000705)

(7)

(8)

-0.00791(0.0580)-0.0486(0.0487)

0.00666-0.00566(0.0451)(0.0404)

Probation
period(9)(10)

0.167***(0.0519)0.135**(0.0548)

(11)

(12)

0.127***

0.151***(0.0297)(0.0303)

(13)

(14)

0.0402(0.0310)0.0569**(0.0288)

Job
✓
✓
✓
✓
✓
✓
✓
Search
✓
✓
✓
✓
✓
✓
✓
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
𝑁
0.078
0.150
0.079
0.151
0.076
0.150
0.076
0.151
0.083
0.154
0.093
0.172
0.078
0.153
𝑅2
Notes: Coefficient estimates from linear probability model. Dependent variable is the indicator of the presence of age limit. Labor market controls, and Month and industry dummies included in all models. Inclusion of variables controlling for typeof advertised job (Job) and search intensity (Search) indicated. Robust standard errors in parentheses clustered at the firm level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

Table C11: Age limit

32

EXPERIENCE REQUIREMENT

Capital(log)
Employees(log)Foundationyear
Listed firm
Subsidiaryof listedfirmJapanesefirmTokyo,
Osaka
location(1)(2)

0.0215***(0.00698)

0.0197***(0.00645)

(3)

(4)

0.0116(0.00839)

0.0179**(0.00802)

(5)

(6)

-0.00237***(0.000721)-0.00138**(0.000675)

(7)

(8)

0.106**(0.0500)

0.106**(0.0418)

0.143***

0.0880***(0.0394)(0.0322)

Probation
period(9)(10)-0.272***(0.0670)-0.128**(0.0640)

(11)

(12)

0.0582**0.0105(0.0252)(0.0224)

(13)

(14)

-0.0110(0.0268)-0.0330(0.0219)

Job
✓
✓
✓
✓
✓
✓
✓
Search
✓
✓
✓
✓
✓
✓
✓
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
2,683
𝑁
2,683
2,683
2,683
2,683
2,683
𝑅2
0.100
0.242
0.088
0.237
0.098
0.238
0.103
0.242
0.103
0.238
0.089
0.234
0.086
0.235
Notes: Coefficient estimates from linear probability model. Dependent variable is the indicator of the presence of experience requirements. Labor market controls, and Month and industry dummies included in all models. Inclusion of variables
controlling for type of advertised job (Job) and search intensity (Search) indicated. Robust standard errors in parentheses clustered at the firm level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

Table C12: Experience requirement

33

AGE LIMIT BY ELIGIBILITY CUT-OFF AGE
40 YEARS AND YOUNGER

35 YEARS AND YOUNGER

(1)

(2)

(3)

(4)

(5)

(6)

Capital (log)-0.00423(0.0112)-0.000259(0.0123)

0.00253(0.0119)-0.00568(0.00752)-0.00295(0.00811)-0.00108(0.00801)

Employees (log)0.00564(0.0115)-0.00325(0.0115)-0.0334***(0.0120)-0.00678(0.00917)-0.0118(0.00919)-0.0308***(0.0103)

Foundation year

-0.000915(0.000813)-0.00120(0.000852)-0.00163*(0.000837)-0.000493(0.000626)-0.000452(0.000639)-0.000710(0.000627)

Listed firm

-0.0824(0.0646)-0.0785(0.0616)-0.0941(0.0598)

0.0249(0.0554)0.0213(0.0525)0.0182(0.0523)

Subsidiary of listed firm

-0.0666(0.0418)-0.0608(0.0397)-0.0547(0.0386)

0.00486(0.0392)

0.00214(0.0370)0.00630(0.0370)

Japanese firm

0.127**(0.0514)0.0772(0.0516)

0.0975*(0.0522)0.0886*(0.0476)0.0491(0.0468)

0.0640(0.0481)

Tokyo, Osaka location

0.130***(0.0273)0.145***(0.0284)

0.160***(0.0293)0.0917***(0.0234)

0.101***(0.0247)

0.107***(0.0259)

Probation period

0.0484*(0.0279)

0.0523*(0.0285)0.0621**(0.0277)

0.0589**(0.0242)0.0600**(0.0246)

0.0637**(0.0254)

✓

✓
✓

✓

✓
✓

2,683
0.111

2,683
0.141

2,683
0.098

2,683
0.116

Job
Search
𝑁
𝑅2

2,683
0.089

2,683
0.077

Notes: Coefficient estimates from linear probability model. Dependent variable is an indicator of the presence of age limits by eligibility cut-off ages. Labor
market controls, and Month and industry dummies included in all models. Inclusion of variables controlling for type of advertised job (Job) and searchintensity (Search) indicated. Robust standard errors in parentheses clustered at the firm level. Significance levels: *** p<0.01, ** p<0.05, * p<0.1.

Table C13: Age limit by eligibility cut-off age
34