OSIPP Discussion Paper: DP-2023-E-001

Job Value: New Measure of Career Success Potential from a Job*
July 7, 2023**
Kenta Kojima†
Associate Professor, Faculty of Economics, Kansai University

Katsuya Takii‡
Professor, Osaka School of International Public Policy (OSIPP)【Keywords】 Internal labor market; Career paths; Promotion; Fast track; Job
Assignment【JEL Classification】 J45, M12, M51【Abstract】 This paper develops a new method for assigning a value to each jobthat evaluates the likelihood and speed of promotion from that job to top executive
and applies it to personnel data for Japanese bureaucrats. We find that outwardly
similar jobs within the same hierarchical rank involve very different opportunities
for promotion to top executive. We also reveal frequent real demotions and the early
selection of elite bureaucrats unable to be detected through use of hierarchical rank.
These findings suggest that assignment to a specific job can be a credible signal for
promotion to top management.
* The previous version of this paper was distributed as ``Successful and Dead-end Jobs ina Bureaucracy: Evidence from Japan.'' We are thankful to Kong-Pin Chen, Hirokuni Iijima,
Hiroaki Ino, Junichiro Ishida, Hideshi Itoh, Takao Kato, Fu-Chuan Lai, Hodaka Morita,
Fumitoshi Moriya, Toru Nakazato, Fumio Ohtake, Hideo Owan, and Shinpei Sano, alongwith seminar participants at Doshisha University, Hitotsubashi University, Hokkaido
University, Kyoto University, Osaka University, University of Tsukuba, Waseda University,
and National Central University for their helpful comments and suggestions. We also
greatly appreciate the assistance of Takashi Mihara and unnamed public officials forproviding us with useful reference materials and valuable information on Japaneseinstitutions. We gratefully acknowledge the financial assistance of the Japan Society for the
Promotion of Science through Grants-in-Aid for Scientific Research Nos. 21243024,
25245040, and 18H03636.
**We have corrected a typographical error in the numbers in the Discussion Paper uploaded onJune 19 and re-uploaded it.
† E-mail: k-kojima@kansai-u.ac.jp‡ E-mail: takii@osipp.osaka-u.ac.jp

1

Introduction

Internal labor markets have been widely investigated since Doeringer and Piore (1971), andtheir characteristics shown to be quite different from those of external labor markets. The
main reason is that in internal labor markets, employees within an organization are effectively
shielded from competition in a series of spot markets, as found in external labor markets.
Instead, employees face career paths in the organization that are stable over time. Because
it is likely that these career paths are designed to play several functions, including skill
accumulation, as a screening device and as an incentive mechanism, the accumulated evidence
on internal labor markets attracts theorists by providing several empirical regularities thatmust be explained by economic theory (e.g., Gibbons and Waldman (1999a) (1999b)).
One obstacle to a solid quantitative examination of career paths is that most importantjob characteristics are qualitative along several dimensions, including function, location, andbusiness unit. Furthermore, there are uncodified characteristics of jobs, such as skill requirements. Although the existing literature uses either an organization chart or transition
matrices between major job titles to identify the level in a hierarchical organization (e.g.,
Baker, Gibbs, and Holmstrom (1994), Lin (2005)), significant heterogeneity in job characteristics is likely to remain at the same hierarchical level. Such heterogeneity could be criticallyimportant in the analysis of promotions.
This would be particularly true for the analysis of promotions in the public sector. As
argued by Tirole (1994), because government pursues multiple goals, many of which are challenging to measure, it is difficult to provide high powered incentives through a formal salary.
Therefore, career concerns could be one of the most important motivations for hard work.
Alternatively, because the number of highly ranked positions in a hierarchical organization
is limited, it is more likely that the public sector strategically employs more lateral transfers
at the same hierarchical level to motivate workers than does the private sector.
This paper develops a new method to assign each job a value that can evaluate boththe likelihood and speed of promotion for that job to top executive and applies it to the2personnel data of an elite group of white-collar government officials working in the Japanese
Ministry of Land, Infrastructure, Transport and Tourism (MLIT). While our method could
apply to the career paths of any hierarchical organization, the personnel data available forJapanese government officials is ideal for demonstrating its power. This is because while thepromotion system of public officials in Japan appears to follow a rigid rule based solely on
the length of tenure, several case studies have revealed that public officials are also subject
to secret long-term competition for executive positions in ministries (e.g., Kishi (2015)).
We argue that our method can reveal this hidden feature of career paths for Japanese elite
officials, which cannot be uncovered by either an official organization chart or the standard
method developed by Baker, Gibbs, and Holmstrom (1994).
As in the seminal work by Baker, Gibbs, and Holmstrom (1994), we employ the transition
across jobs as the basic information used to evaluate each job. However, unlike Baker, Gibbs,
and Holmstrom (1994), which uses information on moves between major job titles to definelevels, where the major titles are selected from their data set, we estimate the transition
probabilities between all the individual jobs available in our data set. We can differentiatethese not only by job title, but also by functional department and business unit. Using
these transition probabilities, we define the value of each job using the discounted expected
probability of becoming a top executive. As a result, this evaluates not only the likelihoodof being promoted to a top executive position, but also the speed of promotion. Using thejob value, we can then extract the heterogeneity of individual jobs within the same formal
rank typically omitted in previous studies.
We find that there are significant differences in job values within the same hierarchicalrank. We also show that within the same high hierarchical rank, there are both successful
jobs, whose discounted expected probabilities of being promoted to top executive are nearlyone, and so-called dead-end jobs, whose discounted expected probabilities are nearly zero.
This suggests that the heterogeneity of jobs within the same hierarchical rank is important
for the analysis of promotions in the Japanese public sector. We also find frequent real3

demotions (or job relegations) in the sense of downgrading to a job with a lower job value.
However, this is not in the sense of downgrading to a lower hierarchical rank. This suggeststhat officials are likely to face intense but hidden competition for top executive positions
through job assignment.
We can also detect the presence of implicit fast tracks for elite officials, a matter undetected by hierarchical rank. Our data show that most officials are promoted to department
heads, a designated position, and that it is rare to observe more than two hierarchical rankdifferences for elite officials of the same tenure until they reach 30 years of tenure. Because
the majority of officials retire by 35 years of tenure, they are seemingly treated equally formost of their careers.1 However, we also find that the variation in job values within the sametenure gradually rises as the tenure of officials increases, even during the early stages of theircareers. This suggests that the public sector may select officials during a much earlier stageof their careers than we would usually consider.
To confirm the presence of secret fast tracks, we also conduct regression analysis. While
we cannot find any indication of fast tracks using hierarchical rank, we obtain robust evidence
that the job value of the first job assigned upon promotion to a lower hierarchical rank issignificantly positively associated with promotion in a later period. This confirms previous
hypotheses that the public sector selects its employees by assigning specific jobs to promising
candidates for top management early in their careers.
Finally, we also obtain robust evidence that the value of the first job assigned upon
promotion to a lower hierarchical rank is significantly negatively associated with exit inlater years. As discussed later, many officials retire as soon as they are assigned to beinga head of department because the Japanese bureaucratic system is designed to increase theoutside options of officials when they are promoted to a designated position. This negative
association suggests that an official assigned to a job with a low job value could be a signal tothem of having little promise for future promotion to top executive, and hence they choose
1

Several Japanese labor economists (e.g., Koike (2002)) point out the late selection as a common featureof internal labor markets in Japanese firms.

4

to leave the organization when they reach the designated position.
To explain these new findings, we follow Prendergast (1992, 1993) and develop a simple
model. Prendergast (1992, 1993) shows that promotion can be designed to encourage firmspecific human capital when the firm cannot write a wage contract that depends on neither
human capital nor performance. We modify the assumptions in Prendergast (1992) to meetthe institutional features of Japanese government employment and include job values in themodel. We show that our refined model can account for four new features: (1) there aredead-end and successful jobs of the same hierarchical rank, (2) while demotions in the senseof downgrading to a lower hierarchical rank are rare, there are frequent real demotions inthe sense of downgrading to a job with a lower job value, (3) while there is no indication of
fast tracks using hierarchical rank, we find government assigns jobs with high job values topromising candidates for top management toward the middle of their careers, and (4) many
officials assigned to jobs with low job values toward the middle of their careers leave as soon
as they are assigned to a designated position.
A key idea is that assignment to specific jobs that have high job values can be a credible
signal for promotion to top management. When organization-specific human capital is important for top managers, it is cost effective that only candidates for top manager invest in
human capital. To encourage these candidates to invest in human capital, the government
has an incentive to reveal their evaluation to officials. We show that assigning candidate executives to specific jobs can send a reliable signal for promotion to top management positions,even if there are no formal differences between these and other jobs.
There have already been several attempts to analyze personnel data and provide thestylized features of the internal labor markets in firms (e.g., Rosenbaum (1984), Baker,
Gibbs, and Holmstrom (1994), Treble, Gameren, Bridges, and Barmby (2001), Lin (2005),
Kauhanen and Napari (2012), and Gittings (2012)) and within the public sector ( e.g., Inoki(1995, 2002), Inatsugu (1996), and Gibbs (2001)). Most recently, several studies have begun
to pay attention to movement within hierarchical rank (Koike (2002), Dohmen, Kriechel, and5

Pfann (2004), Ariga (2006), Clemens (2012), Frederiksen and Kato (2017), Belzil, Bognanno,
and Poinas (2018), Jin and Waldman (2020) and Takii, Sasaki, and Wan (2020)).2 However,no studies have attempted to measure the heterogeneity of jobs in the same hierarchy, withthe exception of Clemens (2012).3
Clemens (2012) distinguishes between jobs of the same hierarchical rank using the probability of direct promotion from that job to the next hierarchical rank. In contrast, our job
value distinguishes between jobs using the likelihood and speed of promotion to top management. Thus, the job value assigns a numerical value that is comparable, not only within but
also across hierarchical ranks in terms of promotion to the top, allowing for a more comprehensive examination of the information concerning the prospects for promotion to the top
that accompanies job assignments. Owing to this, our analysis shows that the differencesin the job value of assigned positions increase with tenure or length of employment. It also
shows that not only does job value at lower hierarchical ranks increase the probability ofpromotion to the next hierarchical rank, but also that it increases the likelihood of promotion
to a higher hierarchical rank and decreases future exit. These facts help us to understand thehidden mechanisms underpinning the career paths of the Japanese bureaucracy that seem
to work in countries outside Japan.
Indeed, the idea that assignment to a particular job within the same hierarchical rank can
be a reliable signal of promotion to top management is novel and may be applicable to otherorganizations. The role of job assignment as a signal has been noted in several papers (e.g.,
Waldman (1984)). In particular, Prendergast (1992) points out that wages and promotions
can be used as signaling devices to motivate workers when it is difficult to write performancebased contracts. However, many organizations, such as those in the public sector, have little
2

A number of other studies also provide theories on the role of job rotation (e.g., Cosgel and Miceli (1999),
Ickes, and Samuelson (1987), Ortega (2001), Eguchi (2004, 2005), and Eriksson and Ortega (2006)).
3
Other studies using personnel data in Japanese firms or public sector organizations consider the heterogeneity of the jobs observed. Like our approach, these point out that the differentials in job assignment
between promising and other employees can be observed before a difference in the speed of promotion arises((Matsushige (2000), Umezaki (1999), Uehara (2007) and Ichise (2013)). However, because they do notinclude systematic measurement to quantify the prospects for promotion to top executive for each job, they
can only identify jobs with a distinguishing trait through casual observation.

6

discretion over the payment of wages and only a limited number of highly ranked positions
to offer. In this case, it is practically impossible to use wages and promotions as signaling
devices. By using the measure of job value, this paper shows theoretically and empiricallythat even in a constrained economic environment like the public sector, organizations canstrategically assign jobs at the same hierarchical rank and inform workers in advance of theirpromotion prospects.
The remainder of the paper is organized as follows. The next section explains the institutional background of the personnel system for Japanese public officials and the third
section presents our data set. The fourth section provides evidence using hierarchical rank
and the fifth section investigates career paths using a job value. The sixth section conducts
regression analyses using hierarchical ranks and job values and shows that we can detect afast track only when we use a job value. The seventh section shows that similar regression
analyses also detect that exit is negatively associated with the job values of jobs to which
officials are assigned in early career. The eighth section constructs a model to explain newfacts obtained by the job values. The final section concludes.

2

Institutional Background

There are two main groups of white-collar government officials in Japan: an elite group
destined to be future senior civil servants of ministries and a non-elite group that only has
a restricted possibility of promotion. Traditionally, the Japanese government sets different
ports of entry depending on the group (Inatsugu (1996)). The elite group of Japanesepublic officials enters the ministries by overcoming two hurdles. First, they must pass anemployment examination carried out by the National Personnel Authority that places aheavy weight on academic skills. Second, the promising candidates passing the entrance
examination will be selected through multiple rounds of interviews by the ministry they wish
to join. The personnel department in each ministry has authority to decide employment using

7

the candidate list of successful examinees in the employment examination. Finally, about athird of candidates are screened for the elite group of officials. The MLIT recruits around
20 white-collar elite officials every year.
These candidates typically enter the ministries with no full-time job experience immediately after graduating from university. Given the nature of the employment examination,
the officials of the elite group are also relatively homogeneous in terms of academic skill.
Graduates of the University of Tokyo have long occupied a prestigious position among theelite group of officials in the Japanese central government as well as in the judiciary, as
pointed out by Kawaguchi and Ma (2008).
The officials of the elite group are subject to competition for executive positions in theministry. Similar to most Japanese private large firms, the personnel department in eachministry controls the job transfers of all officials employed by that ministry until their retirement. Japanese cabinet ordinance prescribes the standard job titles and their official
level, with the hierarchy in the internal organization of a ministry consisting of the eight
official ranks listed in Table 6 in Appendix A. Because of competition, none or at most one
candidate within an entry cohort can become the Vice-Minister of any given ministry. The
ordinance also provides a formal statement on the required abilities and skills for each official
rank, although it would be difficult to grasp precisely the required skills from the statement.
The salary structure is prescribed by other laws and rules so that the range of wages has asolid link with the official job levels.
The competition for executive positions is not visible, but there is anecdotal evidence
supporting its existence. Based on an institutional analysis of Japanese public administration, Omori (2006) argues that there is a secret order of jobs within the same job title.
For instance, it is said that managers of personnel, budget and account, and general affairs
divisions in the Minister’s Secretariat at a hierarchical rank of five in Table 6 have better
prospects of being promoted to the top executive than other division managers. Kishi (2015)
also points out the role of the heterogeneity of jobs in terms of future career prospects, which8is based on many interviews with active and retired officials, primarily from the Ministry of
Finance. These face three gateways to the top executive in their careers: mid-30s, mid-40s,
and mid-50s. At each gateway, successful jobs are assigned to the most promising officials.
How well qualified an official is for a top executive position then depends on their reputation
with both the insiders and outsiders of the ministry. Our analysis provides a method thatquantifies the importance of this existing anecdotal evidence.
The salary schedule applied to officials with titles of the same official level of section
manager or lower consists of two parts: “kyu” depending on the official job level, and “gou”
depending mainly on tenure within an official job level. Hence, we can predict most of thesalary of officials if we know their official rank and tenure.
There is one notable feature of the salary schedule of Japanese public officials. Thisis that officials with titles of the same level of department head or higher, called “officialsof designated service (shitei-shoku),” receive discontinuously higher salaries as a reward fortheir promotion from the level of section manager because the salary schedule applied tothem has changed according to the remuneration act.4 Of course, their salary grows as they
go further up the job ladder. The higher salaries for designated officials also result in muchhigher retirement allowances, which ultimately depend on the salary paid for the last job of
their career.
In addition, officials who have retired as designated officials have further advantages.
First, they tend to be assigned to high socioeconomic positions such as executive officers
in public or private firms and receive greater remuneration following retirement. Second,retirement from the ministry at a higher executive position leads to higher status. This
practice, called amakudari in Japanese, provides for deferred compensation to public officials(Inoki (1995)).
4
The Annual Report on Salaries of National Public Officials 2011 published by the National Personnel
Authority states that university-graduate officials at section manager level to whom the highest salary grade
is applied receive an average of 559,810 yen per month, whereas the lowest monthly salary for designatedofficials is 724,000 yen.

9

3

Data

The data set used in this paper is the personnel records for an elite group of white-collar
officials working in MLIT. This ministry was established in 2001 through the merger of four
government offices: the Ministry of Construction (MOC), the Ministry of Transport (MOT),
the National Land Agency, and the Hokkaido Development Agency.
We mainly construct the data set using 13 editions of the Kokudo Kotsu Sho Meikan(Directory of MLIT officials) from 2003 to 2015 published by Jihyo Sha. Each edition
includes all middle managers (Kacho class) and higher-ranked officials in the internal bureausof both the ministry and the extra-ministerial agencies as of October 31 each year.5 Eachobservation contains an official’s name, date of birth, education, job title, the departments
and sections of the current job, the year and month of taking up the current job, and theofficial’s past job history.
It is a common personnel practice in the Japanese bureaucracy to exchange officials
between the central government and other organizations. Those transferred to externalorganizations usually return to the central government after a few years. The directories also
include all the top management and the head of department in some external organizations
such as local branch offices, research institutes, and educational facilities established inaffiliation with the ministry. However, data are not available for retired officials and thosetransferred to the other external organizations, including other ministries, embassies, local
governments, and public or private firms.
To fill part of these missing data, we also use iJAMP as supplemental material, which
is an internet service on public administrative information provided by Jiji Press. Thisdata source enables us to obtain information on careers for almost all middle- and high-rankmanagers in the internal bureaus of most Japanese ministries.
5

Specifically, the extra-ministerial agencies are four organizations attached to the MLIT. These are theJapan Coast Guard, the Japan Meteorological Agency, the Japan Tourism Agency, and the Japan Transport
Safety Board, along with two agencies formerly attached to the Prime Minister’s Office, being the National
Land Agency and the Hokkaido Development Agency.

10

Finally, we include the omitted jobs for some officials by referring to the files of personnel
changes for all officials in MLIT since April 1, 2004, which are publicly available on the MLIT
website. These files also allow us to specify the retirement date for retired officials, whereas
the other two aforementioned data sources do not identify whether officials not included in
the data sources have retired or were transferred to an external organization.
Through the data enrichment process mentioned above, we construct half-yearly panel
data of complete job histories of MLIT officials. Once an official is assigned to a middleor high-rank management position in an internal bureau or an affiliate of MLIT between
2003 and 2015, we thereafter have a complete job history. Hence, we can investigate thecareer paths for middle- or high-rank managers without too much concern about any sample
selection bias. In total, there are 659 officials, of whom 284 entered MOC and 375 entered
MOT during the period 1969–2000.6
To obtain information on early careers before managers enter a middle management
position, however, we need to rely on their recall of events. This presents two potential
problems. First, some managers do not report all of their early jobs. Hence, the job history
records of low-rank positions for some of these managers are not perfect. When we encounter
data that do not exhibit any job changes over the long term, we can only assume that thesemanagers have remained in the same job. Second, if some officials retire from the ministry
before becoming middle managers, then they are also omitted from our data set.
To maximize the amount of information we can extract from the data and to minimize
any potential sample selection bias, we utilize two different samples from our data set. First,
to obtain an overall picture of career paths that includes the early career, we combine ourcomplete panel data with the early career recall data and construct data profiles for theentire personnel history following entry. Hereafter, we refer to this as the sample of job
histories with recall data. To minimize any potential sample selection biases, we sometimes
6

See Appendix B for the representativeness of our data set. The gender of the officials in our data set is
unknown. Of course, it is possible to presume the gender using their names, but this cannot be done withany degree of certainty. However, as far as we can tell, it is safe to say that there are almost no women.

11utilize a subsample of these data. We report which (sub)sample is used in each figure ortable. Second, when we pay greater attention to the career paths of middle- or high-rankmanagement positions, we use the panel data of officials between 2003 and 2015 because we
can observe their full job histories. Hereafter, we refer to this as the sample of complete job
histories for middle- or higher-ranked managers.

4

Hierarchical Rank

We can potentially identify the rank of each job using the name of the title of each official. As
noted in Section 2, the standard names of job titles and their official ranks are prescribed by
Japanese cabinet ordinance, which also provides a formal statement on the required abilities
and skills for each official rank. The salary structure has a solid link with official job ranks.
It is almost certain that the official ranks of job titles play an important role in characterizing
the hierarchy of bureaucracy.
One of the difficulties in identifying the ranks of all jobs is that our data set contains
many job titles other than the prescribed standard titles; therefore, it is not clear what theirofficial ranks are. To assign a rank to all jobs, we apply the method developed in Baker,
Gibbs, and Holmstrom (1994) to the sample of job histories with recall data. We refer tothe assigned ranks as hierarchical ranks.
To start, we construct a transition matrix to represent the moves between jobs with theselected major titles in the internal bureaus of the MLIT and classify each one into one ofseven hierarchical ranks (Ranks 2–8). We additionally assign Rank 1 to the first job of each
official by taking advantage of the fact that there is a single port of entry for new graduates
into the ministry. Next, we assign appropriate hierarchical ranks to the jobs with titles otherthan the major ones based on the transition patterns between titles. Because it is common
to exchange officials between internal bureaus and other organizations, we require additional
procedures not prescribed by Baker, Gibbs, and Holmstrom (1994). We provide more details

12Table 1: Transition matrix for hierarchical ranksThis table provides the transition matrix for the hierarchical ranks using the complete job
histories for middle- or higher-ranked managers. A hyphen denotes a zero-transition probability. Moves within a box are stays, moves to the right promotions, and moves to the left
demotions.

on this procedure in Appendix C. We manage to classify all job titles in our data set into
eight hierarchical ranks, which turn out to be consistent with the official ranks prescribedby the ordinance as shown in Table 6.

4.1

Promotion and Demotion

Table 1 provides the transition matrix between the hierarchical ranks using the sample ofcomplete job histories for middle- or high-rank managers.7 In this table, moves to the rightof a boxed cell are promotions, and moves to the left are demotions. As shown, promotionsof more than one rank and demotions are quite rare, with the probability of moving one rank
below being quite low, and the probability of moving more than two ranks above also being
low. These observations are consistent with the stylized facts presented in the literature,including Baker, Gibbs, and Holmstrom (1994).

7

We report the same transition matrix using the sample of job histories with recall data in Appendix D.

13

14

This table details the percentage of officials at each hierarchical rank for given years of tenure using the sample of job histories
with recall data for those entering the ministry between 1974 and 1980. The sample accounts for 87.2 percent of all officials
from the same cohort and all have more than 35 years of tenure.

Table 2: Distribution of hierarchical ranks by tenureTable 2 details how many officials belong in each hierarchical rank at any given yearsof tenure. To examine the selection process from entry for 35 years, we use the sample ofjob histories with recall data for those entering the ministry between 1974 and 1980. Notethat these account for 87.2 percent of all officials in the same cohort and that all officials inthis sample have more than 35 years of tenure. Hence, this sample minimizes any possible
sample selection bias in early careers.
As shown, holding tenure constant, there is no significant difference in the speed ofpromotion until officials spend about 30 years of their careers in the ministry. Even if we canobserve some differences in the speed of promotion, they seem not to last long. For example,
most of the surviving officials are in Rank 3 at 12 years of tenure. Some differentials seemto appear at 15 years of tenure, but disappear at 18 years of tenure.

4.2

Retirement

The different patterns of selection procedure gradually appear after 30 years of tenure. For
example, 70 percent of the surviving officials are in Rank 6 at 31 years of tenure. The various
ranks of officials are evident at 33 years of tenure and this difference does not contract
thereafter. This suggests that the selection procedure for the Japanese bureaucracy is quite
slow. Several Japanese labor economists, such as Koike (2002), have pointed out that thelate selection is a prominent feature of the internal labor market in Japanese firms. Our
result provides similar evidence for the Japanese public sector.8
We can finalize the competition for promotion when an official retires. Many officials
seem to retire as soon as the selection appears to occur. We now more closely look at theretirement of officials. Table 3 presents the timing of retirement for 255 officials retiring overthe period 2004–2015. Panel (A) in this table shows the number of officials who have retired
at each hierarchical rank of their last jobs by tenure at retirement. As shown, most officials
8

Ichise (2013) shows that there is “quite late selection” in the National Police Agency, another Japanese
government organization. In other words, the difference in the speed of promotion does not last until theunpromising officials retire.

15

Table 3: Exit behavior of officials(A) The timing of retirement(B) Retirement at Rank 6

Panel (A) details the number of officials that have retired at each hierarchical rank of theirlast job by tenure at retirement. Panel (B) shows the timing of retirement only for thosethat have retired at Rank 6 in relation to tenure at promotion to the rank. Both tables use
the sample of complete job histories for middle- or higher-ranked managers.

16

have retired at Rank 6 or higher before the mandatory retirement age.
In actuality, there is an institutional arrangement in the Japanese bureaucracy designed
to motivate officials to retire following promotion to Rank 6. As noted in Section 2, officialsof designated service belonging to Ranks 6–8 receive discontinuously higher salaries as areward for their promotion from Rank 5 according to the remuneration act, which result inmuch higher retirement allowances. Besides, officials who have retired at Rank 6 or higher
tend to be assigned to higher amakudari positions as executive officers in public or private
firms and receive greater remuneration following retirement. Thus, being a designated official
is economically important in that it has a considerable influence on lifetime wages. Under
this system, officials are likely to have a great incentive to remain in service until promotion
to Rank 6.
Interestingly, most officials retire as soon as they are promoted to Rank 6. Panel (B) in
Table 3 details the years of tenure at retirement only for the 131 officials who have retired at
Rank 6 in relation to tenure at their promotion to that rank. We find that 26.7 percent ofthese retired one year after their promotion to Rank 6 and that 71.7 percent retired withinfour years after their promotion. This exit behavior suggests that they are making the best
possible use of amakudari because the outside options of officials significantly improve when
they are promoted to Rank 6. We can also conjecture that because officials who do notretire tend to be promoted to Rank 7, they are already aware of their opportunities of being
promoted to Rank 7 when promoted to Rank 6.

5

Job Value

The promotion and retirement pattern observed in the previous section suggests that all
officials are equally treated during most of their careers before they become officials of designated service (Rank 6). Once promoted to Rank 6, most officials soon retire. For their part,the remaining officials are promoted to top executives. Therefore, officials seem to recognize

17their own likelihood of being promoted to top executive before being promoted to Rank 6. A
relevant question is then when and how they learn about their future promotion likelihood.
We would like to argue that the career path itself reveals how they are evaluated by theirsupervisors.
For this purpose, we develop a method to value each individual job by not only theprospects but also the speed of promotion to top executive. If an official is promoted to top
executive with a high probability shortly after being assigned to a certain job, we assign the
job a high value. In our method, we utilize richer information on jobs than in the method of
identifying the hierarchical ranks employed in the previous section because individual jobs
are distinguished by organizations and functional departments as well as job titles.

5.1

Definition

More specifically, let Vj∗ ≡ (1 − δ) Vj be the value of job j, where Vj satisfies

Vj = wj + δnXP (k|j)Vk , for all j,

(1)

k=1

and wj is the instantaneous value of job j; δ is a discount factor; n is the number of available
jobs including retirement; and P (k|j) is the probability that an official currently belonging
to job j would be transferred to job k in the next period, which can be calculated from thehalf-yearly panel data from personnel records.9 Using the matrix notation such as












 V1 
 w1 
 P (1|1) · · · P (n|1) 
.
 . 
 .
.. 
...
 . 
 .
.
V=
. 
 . ,w =  . ,P =  .
,
 
 


Vn
wn
P (1|n) · · · P (n|n)
9

If there exists a job s first appearing in the last period of the data set, it is impossible to compute thevalues of the job s and the previous jobs that are transferred to job s because there is no information on job
transitions to other jobs from job s. We assume that P (s|s) = 1 and P (k|s) = 0 for any k ̸= s for such job
s. In this way, the value of job s is set to be 0.

18

(1) can be expressed as
V = w + δPV.
Hence, we can compute the job value byV∗ = (1 − δ) (I − δP)−1 w,(2)where V∗ = (1 − δ) V and I is an identity matrix.
We assume that wj = 1 if job j indicates a top management position or retirement after
top executive, and wj = 0 otherwise. We set the probability when retiring as P (r|r) = 1 and
P (k|r) = 0 for any k ̸= r, where r denotes retirement. Under these assumptions, Vj∗ = 1
when j indicates either a top executive or retirement after top executive, and Vj∗ = 0 when
j denotes retirement after jobs other than top executive. Hence, the value of a job is close
to one when the official in the job has a high probability of shortly being promoted to top
management, while the value is close to zero either when the job has a high probability ofretirement without being promoted to top management or when it is expected to take along time to become a top executive from this job. Thus, our measure provides a simple
prediction of career success, which reflects both the likelihood and speed of promotion fromeach job to top executive.
To estimate the job value, we must specify which jobs are the top management positions.
We identify these based on the formal rules and directives by the National Personnel Authority about salary grades for officials of designated service.10 Top executives are defined
as jobs in the internal bureaus of ministries accompanied by Grade 5 salary or more.11 The
set of top executives defined in this way is a subset of jobs at hierarchical ranks 7 and 8,
10

The rule of the National Personnel Authority No. 9–42 provides an ambiguous expression of the links
between individual jobs and salary grades for designated officials. The particular rules are prescribed by the
directives of the National Personnel Authority, which are not publicly available. However, specific links wererevealed by the formal answers submitted to an ordinary session of the Diet (the Japanese parliament) toquestions from a member of the House of Councilors in May 2011.
11
There are eight salary grades for officials of designated service according to rule No. 9–42 by the National
Personnel Authority.

19

87 percent of which are top management positions as defined here. We assume that once
an official reaches a top management position, they remain assigned to a top management
position. Because we are interested in promotion to the top management position, once anofficial is promoted to the top executive, we omit that data thereafter.

5.2

Successful and Dead-end Jobs

Assuming that δ = 0.97, which is frequently used by many macroeconomists as being mostappropriate, we compute the job values for all individual jobs. The distribution of thejob values using the sample of complete job histories for middle- or high-rank managers ispresented in Figure 1.12 The box plot shows that while jobs at a high hierarchical rank
have on average high job values, there are still large variations in job values within the samehierarchical rank. Within the jobs at Rank 5 or 6, in particular, there are both successful
jobs, whose job values are nearly one, and dead-end jobs, whose job values are nearly zero.
When an official is assigned to a successful job, that official would be in a position near the
top executive.
The lower images in Figure 1 show that the variations in job values gradually increase
with the hierarchical rank, which may suggest that officials are gradually screened within theorganization. Within Rank 4, the share of the jobs whose job values are higher than 0.15 isvery low, but a small number of jobs whose job values are higher than 0.3 are observed within
Rank 5. The jobs at Rank 6 can be divided into three groups: a very few successful jobs,
some middle-valued jobs, and many dead-end jobs. This confirms our previous conjecturethat assigned jobs make many officials realize their likelihood of being promoted to top
executive when promoted to Rank 6.
To investigate how the heterogeneity of job values develops through officials’ tenure, wematch the calculated job values to individual panel data and plot the relationship between
the value of the job each official belongs to and the tenure of the officials. Figure 2 depicts
12

We report the same box plot using the sample of job histories with recall data in Appendix D.

20

Figure 1: Distribution of job values within the same hierarchical rank

The box plot and histograms detail the distribution of job values calculated using the sample
of complete job histories for middle- or higher-ranked managers by hierarchical rank.

21

Figure 2: Distribution of job values by tenure

This figure details the distribution of job values. Darker colored points identify that more
officials belong to that job value. The job values use the sample of job histories with recall
data for those entering the ministry between 1974 and 1980. The sample accounts for 87.2
percent of all officials from the same cohort and all have more than 35 years of tenure.

the result using the sample of job histories with recall data for those entering the ministry
between 1974 and 1980. A darker point in the figure indicates that more officials belong tothat job value. As expected, this shows that the variance of job values gradually increases as
tenure becomes longer. The increase in the variance appears even before 20 years of tenure,but certainly accelerates after 20 years. Hence, it is likely that the competition for promotion
becomes more intense after 20 years of tenure. The majority of officials seem to be screenedout before 20–25 years of tenure. Finally, after 36 years of tenure, there is a clear separation
between officials in jobs that almost guarantee their being promoted to top executive andofficials in the jobs that have no chance of being promoted to the top executive.

22

While Figure 2 provides important information on how job variability changes as tenure
increases, caution should be exercised in its interpretation. The values of early jobs must be
low and have small variations, as by definition they are heavily influenced by the discountfactor δ. To remove this, we construct a measure of relative job value by dividing an official’s
job value at some year of tenure by the mean of the job value over officials with the samelength of tenure.
Table 4 details the distribution of relative job value by tenure using the sample of job
histories with recall data for those entering the service of the ministry between 1974 and1980. In contrast to the distribution of the hierarchical ranks by tenure in Table 2, we canobserve a gradual increase in the variation of relative job values. The proportion of relativevalues between 0.75 and 1.25 is down to 80 percent up until 16 years of tenure, and further
declines to 70 percent at 20 years of tenure, 50 percent at 26 years of tenure and 17 percent at
30 years of tenure. While it is true that the variation in relative job values rapidly increases
after 20 years of tenure, the increase up until 20 years of tenure cannot be ignored. This
leads us to surmise that the ministry might also differentiate among officials, even early intheir careers. While we previously found that the ministry seems not to formally differentiate
between officials until 30 years of tenure, the results in this table suggest the possibility ofthe tacit selection of promising officials through job assignment.
We also examine the persistence of the values of jobs in which officials belong. For thispurpose, we construct a transition matrix among quintiles of job values calculated using thesample of complete job histories for middle- or high-rank managers. Table 5 provides theresult. As shown, most of the bottom quintile remain in the bottom quintile and most of
the top quintile either remain in the top quintile or are promoted to the top executive in
the next 6 months. This confirms a strong persistency for the job values in which officials
belong for low- and high-value jobs. That is, there are sets of almost always dead-end jobs
and almost always successful jobs in the Japanese bureaucracy.

23

24

This table details the percentage of employees belonging to each range of relative job values for the given years of tenure. The
relative job value is constructed by dividing an official’s job value for some year of tenure by the mean of the job value overofficials with the same length of tenure. The values are calculated using the sample of job histories with recall data for thoseentering the ministry between 1974 and 1980. The sample accounts for 87.2 percent of all officials from the same cohort and all
have more than 35 years of tenure.

Table 4: Distribution of relative job values by tenureTable 5: Transition matrix between job values

This table details the transition matrix among quintiles of job values using the sample ofcomplete job histories for middle- or higher-ranked managers. A hyphen denotes a zerotransition probability.

This table also demonstrates that we observe frequent large promotions and real demotions for the set of jobs with job values between 0.2 and 0.8. This means that the government
aggressively selects officials from those in jobs with values between 0.2 and 0.8. In particular,
frequent real demotions may attract special attention. Those assigned to jobs with values
between 0.6 and 0.8 can expect promotion with a probability of about 18 percent in the next
6 months. However, they can also be demoted to low-value jobs with a probability of about
10 percent in the next 6 months. It is thus likely that officials in jobs with values between
0.2 and 0.8 face intense competition.

5.3

Measurement Errors in Job Values

The number of individual jobs, which are characterized by the organization, department,function and business unit, and job title to which they belong, is quite large. Therefore, thenumber of observations available for estimating an individual’s probability of changing jobs
may be reduced, and the job values calculated using the job transition probabilities could
contain measurement errors because of the small sample size.
To assess the significance of measurement error, we calculate job values using the sample
drawn by bootstrap sampling. We first resample the officials with replacement, and then25

replicate their job histories with recall data for the frequencies for which they are selected.
Using the replicated data, we calculate the job values of all individual jobs experienced by
the selected officials. We conduct 100 bootstrap replications.13
Panel (A) of Figure 3 depicts the distribution of the coefficient of variation of each of thejob values calculated using bootstrap sampling for each hierarchical rank. The results showthat the coefficient of variation for most job values is less than 0.5. This means that thestandard deviations of most job values are less than half of the means of the job values, which
we consider quite small. This figure also shows that the coefficients of variation gradually
increase with hierarchical rank, suggesting that the lower the hierarchical rank, the morereliable the job value. This also suggests that although job values in lower hierarchical ranks
is likely to be cumulatively influenced by measurement error in the transition matrices,because the discount factor has larger impacts on the construction of job values in the lower
rank, reliability is greater.
As Figure 1 illustrates that the heterogeneity of job values is apparent in the hierarchicalranks of 4 to 6, we check how much measurement error influences the heterogeneity in theseranks. We use the sample of jobs with hierarchical ranks 4–6 and divide the mean bootstrap
value of the job values into categories in 0.2 increments. Panel (B) of Figure 3 plots the
distribution of the coefficients of variation of the job value for each category.
As illustrated, the scatter is very small for jobs with a mean value of 0.8 or more. Thissuggests that it is almost certain that successful jobs are present. It is less obvious thatdead-end jobs exist because the coefficients of variation for jobs of less than 0.2 are muchlarger. Hence, we estimated 95 percent confidence intervals for the mean job value for jobs
with mean job values less than 0.2. The results show that the upper bound of the confidence
interval is less than 0.215 for 99 percent of these jobs. This suggests that while there is some
impact of measurement error, it is difficult to reject the existence of dead-end jobs.
In sum, while there are some job values that we may need to concern ourselves with,13

We confirm that the bootstrap does not cause any systematic bias in job values. See Appendix E for
details.

26

Figure 3: Coefficients of variation for job values using bootstrap sampling(A) By hierarchical rank(B) By mean value

This figure plots the distribution of the coefficients of variation for each job value calculatedusing bootstrap sampling for each hierarchical rank in Panel (A) and for each 0.2 incrementof mean value of jobs with hierarchical ranks 4–6 in Panel (B).

27

about the possible influence of measurement error, most job values are reliable. We conduct
the regression analysis including a caution regarding a potential measurement problem inthe following section.

6

Existence of Fast Tracks

In Section 4, we revealed that the speed of promotion to a higher hierarchical rank is roughly
the same for all officials. However, we also obtained suggestive evidence that the government
may select promising officials much earlier during their careers. The following more closely
investigates the presence of fast tracks by conducting regression analysis.
For this purpose, we estimate the probability of promotion to a higher rank using thefollowing probit model:

Yit∗ = β0 + β1s Vi (s) + β2 T Pit + β3 Dit + β4 (T Pit × Dit ) + βx Xit + βt + εit , 1 if Y ∗ > ā,
it
Yit =

 0 if Yit∗ ≤ ā,(3)

where Y ∗ is a latent variable; V (s) is the job value of the job to which officials are assigned
when first promoted to hierarchical rank s ∈ {3, 4, 5}; T P is tenure in the previous hierarchical rank; D is a dummy variable indicating that an official is assigned to the current
hierarchical rank by demotion from the previous rank; Xit is a vector of other control variables; βt is a dummy for period t; and ε is a normally distributed error term.

Yit is an

outcome variable that takes a value of one if an official i in period t is promoted to a higher
hierarchical rank in the next period t + 1 or zero otherwise. The parameter ā is the thresholdfor the outcome. As control variables Xit , we use tenure in the current hierarchical rank,
total tenure in the ministry, the squared terms of these two tenure variables, and dummy
variables indicating the current hierarchical rank, education, and the ministry an official
joined.
28

The education variable is a dummy variable that takes a value of one if an official graduated in law at the University of Tokyo and zero otherwise. As noted in Section 2, public
officials graduating in law at the University of Tokyo have long occupied a prestigious position among the elite group of officials in the Japanese central government. About 80 percentof the officials in our data set graduated from the University of Tokyo, 80 percent of whom
are law graduates.
We are mainly interested in the value of the coefficient β1s , which provides informationon the degree of association between the job value of the job to which officials are assigned
when first promoted to hierarchical rank s and the future probability of promotion to ahigher hierarchical rank. The definition of early selection, which we consider an indicator ofa fast track, does not require causality. Therefore, if the β1s estimate is positive, it can beinterpreted as a sign of fast tracks without concern about causal interpretation.
For comparison, we are also interested in the estimate of the coefficient β2 , which is theassociation between the duration in the previous hierarchical rank and the future probabilityof promotion to a higher hierarchical rank. This parameter corresponds to the definition of
fast tracks by Baker, Gibbs, and Holmstrom (1994) and Ariga et al. (1999). If the use ofhierarchical rank detects fast tracks, then the estimate of β2 should be negative. We showthat while the use of hierarchical rank cannot detect fast tracks, that of job value can.
For this estimation, we utilize the sample of complete job histories for middle- or highrank managers between 2003 and 2015. Like the calculation of job values, once an official
is promoted to top executive, we omit any following data because no more variation in thehierarchical rank can be observed.
As discussed in Section 5.3, the job values could contain measurement error due to small
sample size. Given that the regression analysis specifies V (s) as an explanatory variable, we
may need to take this into account. Because we are not interested in causal interpretation,
bias due to measurement error is not a main concern (if anything, the true association would
be stronger than the association without measurement error; hence, our estimates would

29

only be conservative). However, the reliability of our estimate is likely to be influenced by
the degree of measurement error. To deal with this problem, we also estimate the standard
deviations of the parameters using the same sample extracted by bootstrapping in Section
5.3. Following Chapter 10 in Hansen (2022), we obtain bootstrap percentile-t confidence
intervals using the distribution of the bootstrap t-ratio.
We conduct regression analyses and obtain the sample means and sample standard deviations of β1s and β2 using the same sample extracted by bootstrapping in Section 5.3. Then
we construct bootstrap percentile-t confidence intervals. Figure 4 reports the results.
Graphs (1) to (3) are for different model specifications. Graphs (1), (2), and (3) are theresults of the models that include an initial job value at Ranks 5, 4, and 3 as an explanatory
variable, respectively.
Panel (A) in Figure 4 reports the bootstrap percentile-t confidence intervals of coefficients
β1s . The diamond-shaped dot within the interval is the coefficient estimated using theoriginal sample with complete job histories for middle- or high-rank managers between 2003
and 2015. This shows that initial job values at Ranks 4 and 5 are significantly and positively
associated with the probability of promotion, while the association of the initial job value at
Rank 3 on the probability of promotion is not statistically significant.
This fast track cannot be detected by hierarchical rank. Panel (B) shows that we cannot
reject the hypothesis that the coefficients of tenure in the previous rank, β2 , is zero for all
specifications and all samples. That is, we cannot detect any indication of fast tracks fromthe use of hierarchical rank.
In sum, we find the indication of fast tracks using job values, which cannot be detectedby hierarchical rank.

30

Figure 4: The bootstrap percentile-t confidence intervals of the estimated coefficients for thepromotion probability model

The figures show the bootstrap percentile-t confidence intervals of the coefficients of key
variables for the promotion probability models. Panel (A) reports the confidence intervalsof the coefficients for initial job values of hierarchical Ranks 3–5, and Panel (B) for tenurein the previous rank.

31

7

Exit Rates

In this section, we analyze the association between job value and retirement using the same
specification as our analysis of promotion probability in the previous section. The outcome
variable Y is changed to a variable that takes a value of one if the worker retires in the next
period and zero otherwise. If the estimated coefficient β1s is negative, then assignment to a
job with a low job value when promoted to hierarchical rank s predicts the greater likelihoodof being followed by resignation. As in the previous section, we estimate the models using thesame sample extracted by bootstrapping in Section 5.3 and obtain the bootstrap percentile-t
confidence interval.
Figure 5 depicts the confidence intervals for the coefficients of the job values of jobs
when first promoted to hierarchical Ranks 3–5.14 In contrast to the analysis of promotion
probabilities, initial job values at Ranks 4 and 5 are significantly and negatively associatedwith the probability of retirement. This suggests that an official assigned to a job with alow job value could be given a signal that they show little promise for future promotion to
being a top executive, and hence, they may choose to leave the organization.

8

TheoryIn this section we modify the model in Prendergast (1992) to meet the institutional featuresof Japanese government employment and include job values. We show that our refined model
can account for four new features: (1) there are dead-end and successful jobs of the samehierarchical rank, (2) while demotions in the sense of downgrading to a lower hierarchicalrank are rare, there are frequent real demotions in the sense of downgrading to a job with alower job value, (3) while there is no indication of fast tracks using hierarchical rank, we findgovernment assigns jobs with high job values to promising candidates for top management
14

In the estimation with the bootstrap samples, the model does not converge in a very few cases. The
results are presented after excluding these cases, but this does not have a significant impact on the overall
results.

32

Figure 5: The bootstrap percentile-t confidence intervals of the estimated coefficients for theexit rate model

The figure plots the bootstrap percentile-t confidence intervals of the coefficients of initial
job values of hierarchical Ranks 3–5 for the exit rate models.

toward the middle of their careers, and (4) many officials assigned to jobs with low job values
toward the middle of their careers leave as soon as they are assigned to a designated position.

8.1

Model

We assume that the career of officials can be divided into three periods and that there isone measure of officials in each generation. For a simple analysis, we assume that discountfactor is one and agents are risk neutral. The first period is an exploration period where thegovernment randomly assigns a job to officials and explores the ability of these officials. At
the end of the first period, the government realizes the ability of the officials, but officials do
not. We consider that officials experience jobs with hierarchical ranks of 1 to 3 in the first
period. The second period is a training period in that the government strategically assigns
a job to the officials and officials strategically decide if they invest in organization-specifichuman capital. We assume that the second period occurs with hierarchical ranks of 4 and33

5. The third and final period is where some officials are assigned to top management jobs
and others decide whether they should exit. We consider the third period starts when they
become designated officials, which is more than or equal to Rank 6.
Assume that there are two types of job, difficult and easy. We consider jobs more than orequal to hierarchical Rank 7 as difficult jobs and below Rank 7 as easy jobs. Hence, all jobs
in the first and second periods are easy jobs. The quality of services for each job, y ∈ R, isdetermined by the production function, fx (a, h):y = fx (a, h) ,which depends on the type of jobs, x ∈ {D, E}, where D is a difficult job and E is an easyjob, the level of ability, a, where a is uniformly distributed in [0, 1], and the organizationspecific human capital of officials operates the job, h ∈ {0, q}, which is h = q > 0 if theofficials train and h = 0 if not.
For our simple analysis, we assume that the accumulation of organization-specific skill
improves the productivity of difficult jobs, but does not improve those of easy jobs:

fD (a, q) > fD (a, 0) ,∂fD (a, h)> 0, ∀a, h.
∂ay ∗ (a) = fE (a, 0) = fE (a, q) , ∀a,

(4)
(5)

Equations (4) and (5) imply that organization-specific human capital is useful for difficult
jobs but not for easy jobs. Equation (5) is more than necessary, but it simplifies our analysis.
We also assume that the values of the officials’ outside option varies by period:fD (a, q) ≥ R3 + c, ∀a
R3 > z (a) ≥ max {R2 − R3 , R1 − R2 } , ∀awhere z (a) ∈ {fD (a, 0) , y ∗ (a)}, Rt is the value of the officials’ outside option at period t,34and c > 0 is the cost of accumulating skills. These ranges of outside options guarantee theexistence of an equilibrium that is consistent with the evidence. Several remarks are in orderconcerning this assumption. First, the condition implies that fD (a, q) − fD (a, 0) > c for alla. This means that if the government assigns an official to a difficult job, it is efficient forthe official to invest in organization-specific skills. Second, the assumption of R3 > z (a),where z (a) ∈ {fD (a, 0) , y ∗ (a)}, indicates that from a societal perspective it is desirable forall officials that had an easy job in their third period or were given a difficult job without
organizational human capital in their third period to exit the government. As discussed in
Section 2, when officials become designated officials, their retirement allowances substantially
increase and Japanese “amakudari ” practice provides attractive exit options from Japanese
government jobs. The assumption of R3 > z (a) is designed to capture this institutional
regulation.
As in Prendergast (1992, 1993), we assume that human capital h, ability a, and output
y are not verifiable, but period and hierarchical rank (and, therefore, the type of job) areverifiable. Therefore, the wage contract of officials is a function of job type and period. Thisis consistent with the salary system of Japanese officials, which is a function of hierarchicalrank and tenure.
j
Let wt,x≥ 0 denote the wage of job type x in period t when government chooses job

assignment strategy j.

jBecause all jobs in periods 1 and 2 are easy jobs, wt,Ddoes notjexist and wt,E= wtj for all j when t = {1, 2}. Because of the assumption of R3 > z (a) ∈{fD (a, 0) , y ∗ (a)}, and fD (a, q) ≥ R3 + c, it is optimal for government to retain officialsthat invest in human capital and are assigned to a difficult job, but to encourage any otherjofficials to exit. Hence, the individual rationality constraint at period 3 should be w3,E
< R3 ,
jand w3,D≥ R3 for all j.

We assume that the measure of jobs in the first and second periods is 1, but the measureof difficult jobs is #D ∈ (0, 1). Hence, only some officials can be assigned to difficult(a,h)> 0 for all a and h, government has an incentive to assign officials
jobs. Because ∂fx∂a35

with a ≥ a∗ , where #D = 1 − a∗ , to difficult jobs if all of these officials have invested inorganization-specific human capital in advance. Although the government knows the abilityof officials at the beginning of the second period, the government cannot commit itself toassign able officials to difficult jobs until they invest in human capital.
We assume that in the second period, the government can choose two different job assignment strategies j ∈ {s, n} , where s is a selective job assignment strategy and n is a
nonselective job assignment strategy. The selective job assignment strategy assigns a particular job to officials with a ≥ a∗ , while the nonselective job assignment strategy randomly
assigns jobs to officials. We show that government optimally chooses the selective assignment
strategy, and the empirical predictions of the equilibrium given this strategy are consistent
with four empirical features revealed in our analysis using job values.
We first explain the equilibrium under the selective job assignment strategy. To makeclear the relationship between our findings and theoretical prediction, we assume that the
government assigns officials with ability a to jobs with job values a in the hierarchical rankof 4 and 5 in the second period. Because all jobs in the hierarchical rank of 4 and 5 are easyjobs, the quality of jobs and wage payments in the second period are identical. However,this job assignment strategy provides a credible signal about how government assesses thecompetence of its officials. Because the officials know that the government has an incentive
to assign officials with ability a ≥ a∗ , where #D = 1 − a∗ , to difficult jobs in the third period
as long as they invest in organization-specific human capital, officials assigned to jobs withjob values of a ≥ a∗ believe that they will be assigned to difficult jobs in the third period ifthey invest in human capital.
To provide these officials with an incentive to invest in human capital, the wage payment
must satisfy the following incentive compatibility constraint.

s
w3,D− R3 ≥ c

36

(6)

s
Note that when equation (6) is satisfied, w3,D
≥ R3 is always satisfied.

Because the job assignment strategy in the second period also reveals that officials assigned to jobs with job value a < a∗ will not be assigned to difficult jobs in the third period,
they do not have any incentive to invest in organization-specific human capital. Because they
expect that they will retire at the beginning of the third period, the following participation
constraint must be satisfied in the second period.

R3 + w2s ≥ R2

(7)

Note that in using the two equations (6) and (7), the participation condition of officials withability a ≥ a∗ , w3,D − c + w2s ≥ R2 is always satisfied.
In the first period, the government does not know the ability of the officials, and therefore
must randomly assign jobs to them. Because officials also do not know their ability, they
consider that they will be assigned to successful jobs with probability 1 − a∗ and assigned todead-end jobs with probability a∗ in the second period. Hence, the participation constraint
in the first period is
R1 ≤ (1 − a∗ ) V2,D + a∗ V2,E + w1s ,(8)s− c + w2s , and V2,E = R3 + w2s .
where V2,D = w3,DGovernment is assumed to solve the following cost minimization problem

Cs =

min

w3,D ,w2 ,w1s#Dw3,D+ w2s + w1s

subject to equations (6), (7), (8) and #D = 1 − a∗ , where C j are the optimal labor costs
when the government chooses job assignment strategy j ∈ {s, n}. It is easy to see that all
constraints bind, and we obtains
w1s = R1 − R2 , w2s = R2 − R3 , w3,D
= R3 + c37andC s = R1 − (1 − #D) R3 + #Dc.

(9)

The empirical predictions of this equilibrium are consistent with four features obtained byour measure of job values. The model predicts that there will be (1) dead-end and successfuljobs of the same hierarchical rank in the second period, (2) a decrease in job values from thefirst period to the second period, but no decrease in hierarchical rank,(3) no indication of afast track from the observation of hierarchical rank, but there is a fast track in that officials
assigned to a job with job value a ≥ a∗ at the beginning of the second period are guaranteed
to be assigned to difficult jobs, and (4) officials assigned to a job with job value a ≤ a∗ atthe beginning of the second period exit at the beginning of the third period.
To show that the selective job assignment strategy is optimal, we need to examine thecosts when the government chooses a nonselective job assignment strategy. In this case,equations (6), (7), (8) can be replaced by the followingnc ≤ (1 − a∗ ) w3,D
− R3



(10)

n
R2 ≤ (1 − a∗ ) w3,D
+ a∗ R3 + w2n − c(11)nR1 ≤ (1 − a∗ ) w3,D
+ a∗ R3 + w2n − c + w1n(12)Equation (10) is an incentive compatibility condition that encourages investment in humancapital. Equations (11) and (12) are participation conditions in the second and first periods,
respectively. The main difference between Equations (6), (7), and (8) and Equations (10),(11), and (12) is the differences in the expectations of officials on the timing of investingin human capital. Because officials do not know their ability, and therefore the possibilityof promotion to difficult jobs when they invest in human capital under the nonselective job
assignment strategy, all officials are encouraged to incur investment cost, c, even though thisinvestment cost might be wasted.

38

As a result, the solutions to the cost minimization problem of the government change ton= R3 +
w1n = R1 − R2 , w2n = R2 − R3 , w3,Dc.
#D

Note that the only difference between the solutions for the selective and nonselective allonscation strategies is w3,D
> w3,D. Because all officials are unsure if they will be assigned todifficult jobs, the benefits of investing in human capital are reduced by the probability of
the assignment to difficult jobs, (1 − a∗ ) in Equation (10). As a result, the government is
nobliged to increase w3,D
to encourage officials to invest in human capital. The optimal labor

cost under the nonselective strategy is

C n = R1 − (1 − #D) R3 + c.

(13)Comparing Equations (9) and (13), we obtainC n − C s = (1 − #D) c > 0.

Hence, the optimal solution must be a selective job assignment strategy.
Proposition 1 If noncontractible organization-specific human capital is important at the
top of an organization, there exists an equilibrium in which the government can place highly
capable employees in high value jobs at the beginning of the second period, and the empiricalpredictions of the equilibrium are then consistent with the four empirical features found by
our empirical study using job values.
The intuition behind the proposition is as follows. Because organization-specific humancapital is important for top but not middle managers, it is cost effective that only candidates
for top manager positions invest in human capital. To encourage only these candidates
to invest, the government has an incentive to reveal their evaluation to officials. For this39

purpose, the job assignment strategy to the same hierarchical rank can be designed to provide
a credible signal.

8.2

Discussions

The model indicates that assignment to a particular job of the same hierarchical rank can
be considered a signaling device against the possibility of promotion. However, if the government has an incentive to reveal its assessment of an official’s competence, we could realistically believe there are many other ways to send a similar signal. Why then should job
assignments be used as a signaling device?
We consider two possibilities. First, as assignment to a job is public information, thesignal provided is more transparent and reliable. If it were not public information, thegovernment might have an incentive to promise promotions to all officials. Thus, officials
may be less likely to trust private information obtained from the government. Therefore, byusing the job assignment strategy as a signaling device, officials no longer need to worry about
the possibility that the government might lie, thereby promoting investment in organizationspecific human capital.
Second, although we constructed a simple model in which job assignment has no role otherthan that of a signaling device, actual job assignments may play other important roles. For
example, if one workplace is a better place to learn organization-specific human capital thananother, then better workers will be assigned to such workplaces. Such a possibility caneasily be incorporated into the model.
Suppose that the level of organizational human capital depends on the job assignment in
the second period.
q = ϕ (a, v) ,

∂ϕ
≥ 0,
∂a∂v

where a is the staff member’s ability and v is the job value of the job assigned in the second
period. Using Becker’s (1973) theory, we show that the optimal assignment is positive40

assortative under these conditions. Without loss of generality, assume that v is also uniformly
distributed in [0, 1]. Positive assortative matching means that a = v when a ≥ a∗ . In thiscase, the selective job assignment strategy is not only cost effective, but also improves thequality of services provided by the government. Thus, the new role of job assignment is
simply an enhancement of the benefits of the selective job assignment strategy.

9

Conclusion

This paper develops a method to assign each job a value that evaluates the likelihood andspeed of promotion from each job to top executive and applies this method to investigate
the career paths of officials in the Japanese public sector. We find that outwardly similarjobs within the same hierarchical rank yield major differences in the probability of being
promoted to top executive. We also detect frequent real demotions and the presence of
implicit fast tracks for elite officials, matters unable to be detected through use of hierarchicalrank. We also provide a model that can account for this new evidence. Our model showsthat if noncontractible organization-specific human capital is important at the top of anorganization, assignment to a specific job of the same hierarchical rank can be a credible
signal for promotion to top management.
Because it seems a reasonable conjecture that organizations utilize movements withinhierarchical rank for several purposes, it could be possible that the great heterogeneity ofjobs within the same hierarchical rank can be found in other institutions. Because themethod provided in this paper is applicable to any organization with a clear career goal ofpromotion, it would be useful for systematically revealing this hitherto hidden feature ofcareer movement. For this reason, we trust it can help provide further important evidence
about internal labor markets.

41

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Table 6: Standard job titles and their official ranks
Hierarchical rank Standard title
8
Vice-Minister7
Director-General
6
Director
5
Section Manager
4
Office Manager
3
Assistant Section Manager
2
Chief Official
1
Official
This table summarizes the standard job titles and their official ranks in Japanese ministries
prescribed by the “Cabinet Ordinance on Standard Job Titles.”A

Standard Job Titles and Official Ranks

Table 6 lists the standard job titles and their official level prescribed by Japanese cabinet.

B

Number of Officials by Entry Year

Figure 6 illustrates the representativeness of our data set. The dark shaded bar in Figure 6
represents the number of the officials included in the data set for each entry year. The lightshaded area represents the officials we could not capture because they either retired or were
not yet promoted to middle manager. As shown, while our data set covers more than 70
percent of all officials who entered the two ministries in each entry year during the period
1974–1988, it does not for other entry years.

C

Assignment of Hierarchical Ranks

This section discusses the detailed procedures for the assignment of hierarchical ranks.
Firstly, we select the 19 major titles from the 178 different titles in the internal bureausof the MLIT and the four ministries antecedent to the MLIT.15 The major titles are the 18
15
We first focus on the job titles in the internal bureaus of MLIT. This is because there are a great variety
of external organizations in the data set and because the rank of one title in an internal bureau is likely to46

Figure 6: Number of officials by entry year

This figure shows by entry year the numbers of officials captured. The darker (lighter) shaded
areas are officials included (not included) in the data set. On the left side of the figure arethose that entered the MOC and on the right side those that entered the MOT.
Source: National Personnel Authority annual reports.

47

titles that account for at least 0.5 percent of the observations with job titles, as well as the
top title to which one of the 18 titles are moved. These major titles cover 87.4 percent of
the total observations with internal job titles.
Using the sample of job histories with recall data, we construct the transition matrix topresent moves between jobs with the major titles in the internal bureaus. Table 7 shows thematrix. Because the names of the lowest-rank titles are blank in our data set, the titles thatshould be categorized at Rank 1 do not appear in the transition matrix. The earliest jobs
are, however, moved only to Title A within the major titles, and no one enters this title fromthe other titles. Thus, it is natural to categorize Title A as Rank 2. The moves from Title
A are then to Titles B, C, and D in the internal bureaus, save transfers at the same title(A) and transfers to external organizations, so these three titles are categorized as Rank 3.
Iterating this process, the major titles can be classified into the seven ranks. And we assign
Rank 1 to the first job for each official by taking advantage of the fact that there is a single
port of entry for new graduates in the ministry.
Next, we assign appropriate hierarchical ranks to the jobs with titles other than the major
ones based on the transition patterns between titles. As discussed, it is common to exchange
officials between the central government and other organizations. Hence, we need to assign ahierarchical rank to a job in other organizations. One difficulty is that the same title appears
not only in the internal bureaus of the MLIT but also in the external organizations. As it
is likely that the same title in different external organizations has a different role, we allowthat a job title in one external organization can be at a different hierarchical rank from thesame title in another external organization. We assign a unique title if an official’s title isblank or an official is studying abroad. In the end, we have 1,236 different titles except forthe major ones to assign hierarchical ranks.

be different from that of the same title in an external organization.

48

49

This table details the transition matrix among the major titles selected from the different titles in the internal bureaus of theministry using the sample of job histories with recall data. “Other” are titles other than the major titles in the internal bureaus,
and “External” are all titles in external organizations. A hyphen denotes a zero-transition probability. Moves within a box arestays, moves to the right (except Other and External) promotions, and moves to the left demotions.

Table 7: Transition matrix between major job titles

There is a caveat. Because we have many minor titles not only in the internal bureaus but
also in an external organization, we must extrapolate the hierarchical ranks of some minor
titles using a procedure not prescribed in Baker, Gibbs, and Holmstrom (1994). Supposethat an official is appointed to a position X that has an unknown rank x. Suppose thats/he has spent t1 years to be appointed to the position since s/he had been appointed to adifferent position that has Rank n. Suppose also that s/he is appointed to a new position
that has Rank m in t2 years later. The estimated rank x of the position X is the weighted
average of the two known ranks:x=

t2 n + t1 m.
t1 + t2

This measure reflects the relative distance between the two positions with known ranks: if
t1 < t2 , then the rank of the position X should be relatively closer to Rank n. Because we
can observe several officials who were appointed to some position X, we assign the closest
integer to the average of x over all officials belonging to position X as the hierarchical rankof this position.
There are some officials belonging to position X but we are unable to calculate theirweighted average because we are unable to locate the two positions with known rank needed
for this calculation. To assign the hierarchical rank to these positions, we conduct thefollowing iteration. We first estimate the hierarchical rank of minor titles that we cancalculate using the weighted average for all officials belonging to only major titles. Because
this increases the number of positions that can be utilized to calculate the weighted averagefor some officials, we iterate this procedure until there is no job that we can assign a rank
using this procedure. In the second round, we conduct the same procedure for the minor
titles that we can calculate a weighted average for the 90 percent of officials. We repeat thisiteration by changing the criterion from 90 percent to 80, 70, and then 66 percent. This
iteration allows us to assign a rank to 86.4 percent of titles.
Even when iterating this procedure, we cannot estimate the ranks of some titles if they
move to retirement or if there is no title to which they are moved in the next period. For
50

each official we set x =rank of the job in the previous period+0.5 in the former case andx =rank of the job in the previous period in the latter case. Then we assign the closest
integer to the average of x across officials to the rest of the titles.
As a result of the process stated in this subsection, all job titles are successfully categorized into eight hierarchical ranks, which are consistent with the official ranks of standard
job titles prescribed by the cabinet ordinance. We consider that jobs at Ranks 7 and 8
in the internal bureaus of all ministries can be regarded as top executives. This is because
Director-General, Title Q in Table 7, at Rank 7 has the ultimate authority and responsibility
for each policy field under the jurisdiction of each bureau that is the primary department of
the central offices in the ministry.

D

Comparison Between Samples

In Section 4.1, we discuss the patterns of promotion and demotion based on the transition
matrix between the hierarchical ranks using the sample of complete job histories for middleor high-rank managers. In Section 5.2, we discuss the distribution and transition of job
values using the same sample. In this section, we reveal the robustness of the result evenwhen using the sample of job histories with recall data.
Table 8 details the transition matrix between the hierarchical ranks using the sample ofjob histories with recall data. Figure 7 shows the distribution of job values calculated using
the same sample. The results are almost the same at Rank 4 or higher as those using thesample of complete job histories. But we need to be careful about the values for the jobs at
Rank 3 or lower because the job history in the early career stage is partly omitted.

51

Table 8: Transition matrix between hierarchical ranksThis table details the transition matrix among the hierarchical ranks using the sample of job
histories with recall data. A hyphen denotes a zero-transition probability. Moves within a
box are stays, moves to the right promotions, and moves to the left demotions.

Figure 7: Distribution of job values within the same hierarchical rank

The box plot provides the distribution of job values calculated using the sample of job
histories with recall data by hierarchical rank.

52

Figure 8: Average job values using bootstrap sampling

For each individual job, this figure plots the job value computed using the original sample
on the horizontal axis and the average of job values computed using bootstrap sampling on
the vertical axis. The solid line is the 45-degree line.

E

Measurement Errors in Job Values

Figure 8 plots each job value computed using the original sample and the average of job
values computed using bootstrap sampling. For most jobs, each plot is located on the 45degree line, and thus confirms that bootstrapping does not cause systematic bias in the job
values.

53