GCOE Discussion Paper Series
Global COE Program

Human Behavior and Socioeconomic Dynamics
Discussion Paper No.42

The Economic Costs of Court Decisions Concerning
Dismissals in Japan: Identification by Judge Transfers

Hiroko Okudaira

April 2009GCOE Secretariat
Graduate School of Economics

OSAKA UNIVERSITY

1-7 Machikaneyama, Toyonaka, Osaka, 560-0043, Japan

The Economic Costs of Court Decisions Concerning Dismissals in Japan:
Identification by Judge Transfers ※Hiroko Okudaira ‡This version: January 28, 2009

Abstract
The goal of this paper is to detect the degree to which court decisions control the stringency of
employment protection and to investigate how such judicial discretion affects labor market
performance. However, identification difficulty arises because court decisions are volatile against
economic and social conditions. This paper overcomes the endogeneity problem by exploiting thetriennial judge transfer system in Japan, or the exogenous allocation of judges to prefectures. A key
finding is that the prefecture employment rate is reduced by approximately 1.4% if a prefecture
receives more pro-worker judgments than pro-employer ones in a given year.

Journal of Economic Literature Classification Numbers: J65, K31, K41
Keywords: Employment Protection, Wrongful-Discharge Law, Weak Instrumental Variables※
I am grateful to Fumio Ohtake, Yukiko Abe, David Autor, Steven Davis, Kyota Eguchi, Charles Y. Horioka,
Takenori Inoki, Ryo Kambayashi, Daiji Kawaguchi, Jeong-Yoo Kim, Miki Kohara, Ayako Kondo, Jan Magnus,
Masao Ogaki, Shinichi Sakata, Masaru Sasaki and anonymous referees as well as the seminar participants at theJapan Institute of Labor and Policy Training (JILPT), Research Institute for Economy, Trade and Industry (RIETI),
National Graduate Research Institute for Policy Studies (GRIPS), Tokyo University and Osaka University for theirhelpful comments. All remaining errors are my own. This paper was presented at the 2008 Far Eastern and South
Asian Meeting of Econometric Society, the 63rd European Meeting of the Econometric Society, and the 20th Annual
EALE Conference. Author acknowledges a research grant from the Japan Society for the Promotion of Science for
Young Scientists.
‡ Graduate School of Economics, Osaka University (okudaira@iser.osaka-u.ac.jp).

1

Introduction

The empirical effects of employment protection have been widely studied; however, only a fractionof these studies focused on the economic cost of discretion exercised by courts. It has been one oflongstanding issues in labor economics to construct an index that precisely represents the de facto
stringency of employment protection. Nonetheless, a majority of the studies have left the degree of
judicial reviews intact and mainly quantified the amount of severance pay or length of advancenotice (Lazear 1990, Heckman and Pages 2000, 2004). 2 The exceptions to this are a series of studies
regarding the “employment at will” doctrine in the U.S. Autor et al. (2006) found that state
employment rate is reduced by approximately 1 % if the state court adopts the implied exceptions to
the at-will doctrine. The idea of their analysis rests on the fact that the timing of the adoptions differs
across states, thereby creating state-level variation in the dominance of case law. However, evenafter a case law becomes prevalent, it is open to a wide range of interpretations and a large amountof discretion is left to courts or judges. The stringency of employment protection may differ fromcourt to court or judge to judge. Then, the questions that arise are: does a specific court or individual
judge have persistent influence over the restrictions on dismissals? If so, in what manner do theeconomic agents react to the restrictions imposed by the court or judge?
These questions are of great significance particularly in countries with a high degree of
judicial activism. They are also important if a country has one national court system but no state
court system. Unlike the U.S. state court system, for example, it is usually difficult to find regionalvariations in regulation change in countries with no explicit legal boundaries across regions. Fromthe perspective of program evaluation, it would be ideal if researchers of such countries could also
exploit variations that arise from reasons other than the program change de jure by region.

2

Botero et al. (2004) revealed that countries with common law origin tend to embrace significantly more stringent
labor regulation than those with civil law origin. The difference arises because “common and civil law traditions
utilize different strategies for dealing with market failure: the former relying on contract and private litigation, thelatter on direct supervision of markets by the government” (Botero et al. 2004, 1340).

1

An example of lower courts in the Tokyo and Osaka Prefecture in Japan provides an insightful
and intriguing case study. Table 1 presents the ratios of worker victory for dismissal-related
litigations at the High and District Courts in Tokyo and Osaka Prefecture. The numbers in brackets
indicate the total number of litigations. The figures are obtained by dividing the number of worker
victories by the total number of litigations related to “adjustment dismissal” (Seiri Kaiko) for eachfive-year period. 3 The table indicates a remarkable difference between Tokyo and Osaka; inparticular, after the late 1970s, Tokyo Prefecture has experienced a much lower worker victory ratio,
while courts in Osaka tended to adjudge employers of abusiveness in adjustment dismissals.
Moreover, the third row presents the overall victory ratio for all 47 prefectures, from which it is evenmore evident that the two prefectures represent extreme cases.
In Japan, there is only one national court system. Precedents made at lower courts areapplicable to all lower courts but do not necessarily bind their future decisions. There exists no legal
boundary between prefectures. Although it has been not tested empirically, some legal scholars pointout that Japan has historically permitted a relatively large amount of discretion in judicial reviews in
dismissal-related litigations (e.g. Foote 2006). Since it is mandatory for plaintiffs (i.e., workers) tobring their case to a lower court in the region where defendants (i.e., firms) reside, one possible
explanation for this observation is that firms receive different enforcement levels of employment
protection across prefectures. In other words, firms in Osaka Prefecture may have been faced withmore stringent restrictions on dismissals or high firing costs than those in Tokyo Prefecture.
In previous studies, the empirical effect of employment protection has been suggestive but
tentative. Addison and Teixeira (2001) broadly surveyed cross-country studies and concluded thatstrict employment protection has a negative, if not significant, impact on employment rate. Heckman
and Pages-Serra (2000) used a cross-country dataset for OECD and Caribbean countries and3“Adjustment dismissal” is a dismissal that is undertaken as a means of employment adjustment under businessnecessity, independent of the misconduct of workers.

2

concluded that high severance payments moderately decrease total employment. However, in theirrevised work they indicated the fragility of cross-country studies in expanding samples and changing
the definition of an employment protection indicator, and emphasized the importance of awithin-country study (Heckman and Pages 2004). In this respect, a majority of the recent studies on
employment protection have focused on within-country variations in terms of restrictions. Besley
and Burgess (2004) found that the pro-worker amendment of the Indian Industrial Relations Act
reduces state output per capita and hinders welfare by increasing the urban poverty. Similarly, Autor,
Donohue and Schwab (2006) examined the effect of the “implied-contract” exception to theemployment-at-will doctrine on employment-to-population ratio and indicated that employment rate
falls significantly if state courts accept the exception.
In line with previous studies, the worker victory ratio presented in Table 1 is negatively
correlated with local labor market performance. Figure 1 compares the employment-to-population
ratio for Tokyo and Osaka Prefecture. Until the early 1970s, Osaka had only slightly higher
employment-to-population ratio than Tokyo. However, in 2000, employment-to-population ratio
became much higher in Tokyo as compared with that in Osaka. When combined with Table 1, Figure
1 indicates a persistent negative correlation between worker victory and employment-to-population
ratios. In Tokyo, courts are likely to decide in favor of employers while employment rate is high; onthe other hand, in Osaka courts are likely to decide in favor of workers while their employment rate
is low. The example of the two prefectures is at least seemingly consistent with findings in a
majority of previous works.
However, the correlation does not necessarily guarantee that any causal relationship is at work.
There are identification difficulties for detecting the economic costs of court decisions concerning
dismissals on labor market performance. A primary concern is the endogeneity problem. Case law isunder continuous revision, and judgments are volatile against changes in social norms or economic3

conditions. Ichino et al. (2003) analyzed a detailed dataset pertaining to an Italian bank and indicatedthat judges have expressed unbalanced leniency toward workers in judgments when the local labormarket is in a depression. While a court may exogenously establish new decision standards that
affect the amount of expected firing costs to firms, judge bias, such as this, or replacement of social
norms also have a reverse effect on court decisions. The bilateral causality between court decisions
and local economy makes it difficult to identify the causal effects of employment protection imposedby courts.
This paper attempts to identify the causal effects of adjustment dismissal-related court
decisions on local labor market performance by exploiting the exogenous allocation of judges toprefectures. In particular, I exploit triennial judge transfers in the Japanese judicial career system. To
do so, I first created a judgment indicator by coding adjustment dismissal precedents into pro-worker
or pro-employer judgments. Then, by using this variable, I examined how the direction of judgment
affects the labor market performance in a prefecture-level panel model. Finally, I estimatedjudge-specific effects from dismissal litigation records, and instrumented them to the direction of
judgment in the original prefecture-level panel model in order to avoid the endogeneity bias.
A main finding in this paper is that the employment rate is reduced significantly—byapproximately 1.5%—if a prefecture has a greater number of “pro-worker” judgments in a certain
year as compared with those that are “pro-employer.” This result is robust to several specification
checks, such as controlling for observable prefecture characteristics and alternative methods ofcoding judgments, as well as Instrumental Variable (IV) estimation. Additional analysis reveals thatthe incumbent workers also bear some costs by accepting the decrease in real wages, and that firms
substitute full-time workers with part-time ones who are less productive in terms of specific capitaland less expensive to fire.
The unique contribution of this paper is to identify the impact of judgments on the labor4market through the exogenous assignment of judges to prefectures, by exploiting the periodical
judge-transfer system in Japan. This paper also differs from a series of works that have investigatedthe consequence of American at-will exceptions, by focusing on the economic consequence of court
or judge discretion per se rather than the established case law. Lastly, this work provides the firstevidence to indicate the impacts of employment protection on the Japanese labor market. 42Theoretical Background

Table 1 and Figure 1 indicate a negative correlation between worker victory ratio and employment
rate in Tokyo and Osaka. Although economic theory has predicted the ambiguous effects of
employment protection on the level of employment, the correlation appears to be in line with someof the previous empirical works, which suggests that strict employment protection hinders labormarket performance. On the other hand, it is not necessarily clear whether the correlation observedin section 1 actually represents the causal effect of court discretion on labor market outcome. The
negative correlation may be reversely explained by local labor market performance or may be
spurious due to omitted variables. This section summarizes economic theories in order to relate
employment protection and labor market outcome, and introduces the possibilities of estimation bias
incurred by reverse causation and omitted variables.
Economic theories have provided unambiguous conclusions that higher firing cost reduces
employment flows. Hopenhayn and Rogerson (1993) presented a general equilibrium model andsuggested that increased firing tax increases both current and future labor cost and makes labor
reallocations more rigid (see also Bentolila and Bertola 1990). In addition, Kugler and Saint-Paul(2004) emphasized that adverse selection of workers by firms makes the unemployment
4
Ohtake and Fujikawa (2002) and Ohtake (2004) first organized the statistical information regarding the doctrineof adjustment dismissal that has been established by courts. Kawaguchi (2005) and JILPT (2006, 2007) followed;
however, neither analyzed its relation to the labor market. Okudaira (2008, in Japanese), the previous version of thispaper, presented the preliminary evidence on the impact of judgments regarding adjustment dismissals; however, she
did not present results from the IV estimations, which is of primary interest in the current paper.

5

-to-employment flow even more rigid when firing cost is higher. In other words, firms are more
likely to hire from a pool of job-seekers who are already employed since they are less likely to be“lemons.” A high firing cost exacerbates the option value of hiring from the employed (Kugler andSaint-Paul 2004). 5
On the other hand, it is inconclusive whether or not a higher firing cost reduces the level of
employment. In his simple model of labor contract, Lazear (1990) indicated that increased severance
pay is not completely undone unless there is an efficient labor contract. The employment
consequence of increased severance pay is theoretically ambiguous, depending on the elasticity oflabor supply or the ability of workers to accept lower wages (Lazear 1990). Similarly, Bentolila andBertola (1990) argued that the impact of firing cost on firing and hiring are rather asymmetric due todiscounting and voluntary quits of workers, and it is not necessarily clear if an increase in firing cost
reduces the level of employment. Thus, the theoretical impact of employment protection depends
entirely on the parameter values in the labor market, and empirical studies play a role in determining
the actual impact of increased firing costs on the level of employment.
However, contrary to theoretical speculations, the local labor market can also reversely
influence judgments regarding adjustment dismissals. As Ichino et al. (2003) argued, judges may bebiased against the unfavorable condition of the labor market. In the case of Italian litigations, judges
are subjectively compassionate with workers and adjudicate in favor of them when the local
unemployment rate is high (Ichino et al. 2003). Conversely, judges may also adjudicate in favor offirms during recessions. Undoubtedly, the four prerequisites may be more easily satisfied as the
economic conditions worsen. The first prerequisite of the doctrine stated in section 2.1 is a decision
standard that involves the business conditions of firms. Therefore, local labor market performance
may partially explain the variations in Court Decision, although court or judge discretions may also
5
Recently, researchers are increasingly studying the impact of EPL on productivity, which is partially explainedby the rigid labor reallocation effects. See Autor et al. (2007), Petrin and Sivadasan (2006), and OECD (2007b).

6

exogenously provide additional interpretations of the doctrine at the same time.
Unique to the analysis of court discretions, it is also important to consider the litigation
selection mechanism because not all disputes are brought into trials and the characteristics of thelitigation depend on the underlying dispute characteristics in the region. For example, suppose that
employers in Osaka are so irrational and ruthless that they rush to fire their hard-working employees
without any valid reasons, making an excuse that it was part of their restructuring plan. Further
assume that the employers in Osaka also lack in their ability to accurately predict the probability of
their winning when the case reaches trial. Then, it is not a surprise even if courts in Osaka convict
more employers for their abusiveness in adjustment dismissals than courts in Tokyo. Meanwhile,
such irrationality of management may also have repealed business opportunities in Osaka, thereby
dampening employment. If this is the case, the negative correlation observed in Table 1 and Figure1
is merely spurious.
More formally, Priest and Klein (1984) analyzed the incentive mechanism operating between
parties, and clarified that the plaintiff victory ratio incorporates the characteristics of disputes, suchas costs of litigations and settlements, as well as the relative stakes of both parties. According to theirselection hypothesis, the plaintiff victory ratio in litigations does not necessarily reflect theunderlying distribution of plaintiff success for an entire sample of disputes, and tends to be 50%
regardless of the original distribution of success. In other words, a rational litigation process only
selects those disputes to go into trial whose expected ratio of winning is almost fifty-fifty, ceterisparibus. Deviation from 50% occurs if, for example, the stakes of parties are asymmetric as in casesof malpractices, injuries, and product liability. Figures 1 and 2 indicate a large regional variation injudgments, and this may partially support the possibility that correlation is generated by the omitted
variables specific to prefectures, such as irrationality of agents or asymmetry of stakes.

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3

Institutional Background3.1

Employment Protection in Japan

Japan has a unique legal system. Its Labor Law is originally based on German laws but heavily
follows precedents, which portrays the American influence of common law on account of its shortoccupation after the Second World War. The introduction of American law allowed judges a more
liberal and teleological interpretation of the existing civil law system after the War (Araki 2002).
Similarly, the power of judicial reviews is a by-product of the 1947 Constitution, which organized
the present trial system, although “judge-made law constitutes only a fraction of the entire picture ofmaking and administrating public policies in Japan” (Itoh 1991, 195). An example of the exceptional“judge-made law” is the one for employment protection, the four prerequisites in the Doctrine of
Abusive Adjustment Dismissals (hereafter, referred to as the doctrine of four prerequisites). Unlike
European countries, there has been no written statute that demands just cause to dismiss workers. 6
Instead, Japanese courts have established strict prerequisites for regulating abusive adjustment
dismissals. 7
An adjustment dismissal (Seiri Kaiko) is a dismissal that results from business necessities,
independent from the behavior of the worker. The doctrine of four prerequisites in adjustment
dismissals, although not stipulated in a written statute, has de facto required a firm to satisfy thefollowing four prerequisites in order to curtail their employees on account of economic necessity: 81) There must be a need to reduce the number of employees
6

It must be noted that the Japanese government codified the Doctrine of Abusive Dismissal in the Labor Standards
Law in 2003 (Labor Standards Law, Art. 18, Par. 2), although the provision only generalized the existing case law anddoes NOT stipulate the four prerequisites. Also see footnote 6.
7
The doctrine of four prerequisites for adjustment dismissal has been derived from the Doctrine of Abusive
Dismissal, which was originally a “modification” by courts to the Civil Code provision, which states that “if theemployment is not for a definite period, either party may make a request to terminate the contract at any time…”(Civil Code, Art. 627, Par. 1). In essence, judges de facto altered this written statute by the Doctrine in the face of aserious economic downturn immediately after the Second World War, when the cost of being fired was exceptionallyhigh for workers. See Sugeno (2002, 473–493).
8
Prominent examples include Hamada v. Ohmura-Nogami, 242 Rodo Hanrei 14 (Nagasaki D. Ct. Dec. 24, 1975)
and Shimazaki v. Toyo Oxygen, 30 Rodo Minshu 1002 (Tokyo High Ct. Oct. 29, 1979)8

2) Resorting to adjustment dismissals be necessary for attaining personnel reduction3) The selection of the person or persons to be dismissed be appropriate
4) The procedures be appropriate

These four prerequisites have strictly limited the ability of firms to adjust the number of
employees and flexibly achieve their optimal production level. Ohtake (2004) studied all publishedadjustment dismissal litigation records and statistically revealed that Japanese courts have rigorously
required defendant firms to experience a reduction in sales in the previous fiscal term, in order forfirms to satisfy the first prerequisite. As a result, firms must hoard unproductive labor until they meetthe standards required by courts. The second prerequisite is satisfied only if firms have made theirbest efforts to avoid adjustment dismissals—before dismissing workers; for example, they mustsuspend hiring mid-career and new graduates, reallocate workers within a company, farm out
workers to related companies, and solicit early retirement (Ohtake 2004, Sugeno 2002). By soliciting
early retirement, firms also have to undertake the risk of forgoing productive labor. Ohtake (2004)
found, in his probit model of worker victory, that it was around the mid-1970s—in the midst of theoil crisis—when the four prerequisites had arisen. Similarly, Kawaguchi (2005) pointed out that atleast until the 1990s courts had literally required firms to satisfy all four prerequisites in order tolegitimately dismiss workers. Thus, the doctrine of four requirements has de facto imposed stringent
employment protection or high firing costs on firms in Japan for decades.
In relation to other countries, Japan is regarded as a country with relatively stringent
employment protection. OECD (1999) created an index to represent the rigidity of the Employment
Protection Legislature (EPL) and placed Japan in the 7th rank of most strict EPL from among 27
countries. 9 OECD (2007) also denoted that employment protection is particularly restrictive for

9

Many other studies also constructed the EPL index. However, some only quantified the length of advance noticeor severance pay (Lazear 1990, Heckman and Pages 2000, 2004) and ignored the restriction imposed by courts, suchas the doctrine of four prerequisites. Thus, these indexes fail to capture the actual stringency of EPL in the Japaneselabor market.

9regular workers in Japan, proposing reforms in the EPL. One reason of this proposal was theuncertainty that court-mandated law delivers. It is possible that the uncertainty in the interpretationof courts of the four prerequisites discourages firms to hire new regular workers (OECD 2007a).
Indeed, the four prerequisites have been under continuous revision, and the wording of the doctrineis open to a wide range of interpretations. While it blurs the de facto stringency of employment
protection and may have discouraged new hiring, it also implies that a large discretion is left tocourts or judges in interpreting the doctrine of four prerequisites.
As a matter of fact, it is well-known among legal scholars and professions that the Tokyo
District Court has attempted, particularly after the 1990s, to relax the prerequisites and to allow
firms to exercise adjustment dismissal with more ease (Tsuchida 2002, Mori 2001). With regard to
the second prerequisite mentioned above, for example, the “scope” of labor contract is one of thequestions at issue. The Tokyo District Court has tended to define the relatively narrow scope of thelabor contract by requiring firms to reallocate workers only within an affiliated company and not
across all related companies (Saitoh v. Chase Manhattan Bank, 609 Rodo Hanrei 63, Tokyo D. Ct.
Feb. 27, 1992). Another issue is the legitimacy of hiring new workers immediately before or after the
adjustment dismissal. The case of Uenishi v. Meiji Shoin (779 Rodo Hanrei 27, Tokyo D. Ct. Jan. 12,
2000) recognized the legitimacy in the second prerequisite; however, despite this, firms hired new
employees around the time of the adjustment dismissals. This was considered to be a radical
judgment for relaxing employment protection (Ukai 2001). These examples indicate the possibility
that the enforcement of employment protection differs across regions, and that it was much less strictin Tokyo due in part to judge discretion. Figure 1 supports these observations of legal scholars.
This paper takes advantage of regional variations in the standards of interpreting adjustment
dismissal by courts in order to investigate the empirical impact of employment protection in Japan.
In particular, I applied a method similar to the one in Burgess and Besley (2004) and generate a10

variable that represents the direction of judgments accumulated in each prefecture from a precedent
dataset of adjustment dismissal. Then, I use this variable to estimate its impact on labor market
outcome in prefecture-level panel data. In the next section, I explain a variable construction
procedure and examine the regional patterns in judgments regarding adjustment dismissals.
Prior to constructing the variable, it is useful to provide a brief summary of the Japanesejudicial system. First, all litigations are bench trials in Japan and they have no juries.10 Judges decidequestions of fact in addition to questions of law (Ramseyer and Rasmusen 2003). Second, Japan
employs a three-instance trial system, and parties usually have three opportunities to contest in
courts—the District Court at each prefecture, High Court at each regional block, and Supreme Court
as a final stage (see Appendix for more details). Finally, Japan is a country with a low frequency of
litigations. The number of newly filed labor-related cases in 1998 was only approximately 3000
cases in Japan, while it was almost 600,000 cases in Germany (Araki 2002). However, this fact does
not devaluate the significance or impact of litigations in establishing the case-law. To be certain, theaccumulation of precedents has often led to the enactment of case law by the central government(see footnote 5).

3.2 Exogenous Judge Transfers
In order to identify the causal impact of employment protection exercised by courts, it is crucial tofind the random allocation of stringency in employment protection for each prefecture, independent
from labor market conditions. This paper utilizes the variations that result from the periodicaltransfer system of Japanese judges. About every three to five years, the Supreme Court Secretariat,
which manages judicial human resources, transfers judges from prefecture to prefecture and fromcourt to court. 11 Judges usually do not stay in the same prefecture or court for too long, especially
1011

The jury system will be introduced in 2009 for serious criminal cases.
An exception is judges of the Supreme Court who are appointed by the Cabinet and removed by voters at the11when they are young. 12 It is important to note that judges cannot practically reject their postings,
although they are nominally entitled to do so (Ramseyer and Rosenbluth 1993). 13 Thus, judges mustobey any appointments offered to them by the Secretariat.
However, the appointments may not necessarily be random. First of all, the Japanese judicialtransfer system has an innate incentive scheme for judges to be promoted at an earlier stage ortransferred to “prestigious” courts, such as those in metropolitan areas. Examples of fast track arepositions at courts in Tokyo or administrative positions at the Secretariat, while examples ofunfavorable appointments are positions at branch offices or rural courts (Nishikawa 2005).
Ramseyer and Rasmusen (2003) examined the detailed career records of judges and revealed thatjudges are promoted to Sokatsu, an administrative position, significantly earlier if they were assigned
their first positions at the Tokyo District Court, but significantly later if they were assigned their first
positions at branch offices. Ramseyer and Rasmusen (2003) also presented some evidence to showthat the Secretariat retaliated by assigning judges a position at a branch office of courts in rural areas
if judges adjudicate for the illegitimacy of the Constitute or express any anti-government opinions.
This works as an incentive for judges to not take such actions and so that the Secretariat transfers
them to the “better” courts or metropolitan areas where local labor market performance may or may
not be relatively fine. What is worse, since the Secretariat takes judges’ location preferences into
account to a certain but unknown degree, it is possible that ideology of individual judges inadjustment dismissal litigations selects them into a region whose political background matches withtime of Lower House election (although they never have been removed from position in this manner).
12 In order to grasp the idea, let us take a standard example of Judge X, who appears in our sample of adjustment
dismissal precedents several times; Judge X passed the national bar exams in 1987 and after a two-year mandatory
training period at the Legal Research and Training Institute, began his career at Tokyo (No.13) District Court in 1990;
in the third year of his career, he was transferred to Kagoshima (No.46) District Court, where he spent three years
until he was assigned an out-of-court position at the Takamatsu (No.37) branch office of the Legal Affairs Bureau;
thereafter, he returned to the Tokyo (No.13) District Court in 1998; however, he was again transferred to Saga
(No.41) District Court in 2001 (Nihon Minshu 2004).
13“Nominally, a judge can refuse any posting he dislikes. In fact, he refuses at his peril. By 1969, Judge Shigeharu
Hasegawa had worked in Hiroshima for seventeen years. The Secretariat then assigned him to an out-of-town
position, formally a promotion. Hasegawa, however, had a sick wife and did not want it. He declined the promotion,
and when the time came for his next ten-year appointment he found himself out of work” (Ramseyer and Rosenbluth
1997, 156).

12

their own taste. 14 For instance, pro-worker judge may prefer to be transferred to the region where
unions exercise relatively large political power.
In order to confirm that judge assignments are exogenous to labor market performances or to
the regional factors that incur selection of litigations, I test whether a current judgment determinesthe ‘quality’ of a next destination for each judge. More specifically, I take rank differences inoutcome measure between judge’s current prefecture (court) and the next prefecture (court), andregress them on judge’s decisions regarding dismissal litigations at a current prefecture. I pick up
judges those who dealt with adjustment dismissal cases at least once in our dataset which will be
introduced in section four. Then, for each judge, I truck the first transfers before which judges gave
any decisions in regard with dismissals. The transfer data used in this analysis consists of 382
judge-transfers identified by Zen Saibankan Keireki Soran (the ZSKS hereafter) between 1951 and2003. 15
Table 3 reports the average pro-employer judgments among judges by rank-difference
categories. Column (3) and (4) presents the results for the employment-to-population ratio, a mainindicator which will be analyzed in this paper. In all columns, there is no consistent pattern in rankdifferences both in labor market outcomes and regional dispute factors. I also show regression
coefficients and standard errors of a simple regression of rank difference on judgment variable at thesecond bottom of the tables, however, no judgment variables are significant. Thus, the Supreme
Court Secretariat does not ‘retaliates’ judges by assigning them to poor-performing prefectures justbecause they adjudicated in favor of workers or employers. Judges are transferred at least in a way
14
Judge transfers usually involve three steps. First, the Secretariat asks judges to fill in their preferences in ‘Judge
Card No.2’ Sheet (Saibankan Daini Kaado). This sheet includes information about family members, such as
occupation and health condition etc, as well as how many and what kind of litigations they had dealt with previously.
Then in the second step, the Secretariat asks top managers in each court about their evaluations about each judge. In
the final step, the Secretariat assigns judges to the next courts by taking all these information into account, although ascholar points out that the decision process is pretty much a ‘Black Box’ (Nishikawa 2005).
15
ZSKS records detailed information about when and where judges are transferred. ZSKS describes all positions
held by every judge who were educated after World War Two. Some transfers are excluded from the analysis in casejudges retire or turned to be attorneys. Ramseyer and Rasmusen (2003) use the same dataset to test the effects ofjudge ideology on their promotion.

13

that is exogenous to labor market performance or regional dispute factors.

4

Variable Construction: Direction of Judgments

Precedent dataset used in this paper has been taken from one of the most prevalent reports, “Judicial
Information System (JIS)” provided by Dai Ichi Hoki Co. 16 JIS encompasses every released
precedent in approximately ninety published periodical reports since the Meiji era and comprises
over 300 thousand cases from all courts. Ohtake (2004) organized a dataset of wrongful-adjustment
dismissal precedents from JIS in order to analyze the development of the doctrine of four
prerequisites. His dataset was obtained by typing “adjustment dismissal (Seiri Kaiko)” in the JIS key
word search for identifying reports where plaintiff worker(s) claims the defendant firm for its
wrongfulness in adjustment dismissals. 17 The sample period ranges from January 1950 to December
2001, which is a total of 260 adjustment dismissal cases. I use this same group of precedents toconstruct a variable to represent the enforcement level of employment protection by courts. 18
In estimating the economic consequence of court decisions, it is important to correctly define
the information set that constrains the behavior of economic agents (i.e., firms and workers). A key
to this definition is the perception of court decisions by agents. When firms maximize their profit,
they estimate the expected costs of firing using only the available information. Similarly, workers
decide to invest in firm-specific or general human capital in accordance with the expected
probability to be fired (Wasmer 2006), which is in part constructed through their beliefs against
employment protection imposed by the judiciary. Both firms and workers are affected by new
information through newspapers or published precedent reports that are easily accessible to the16Japan has another popular database, LEX DB, that is often used in legal professions, although the JIS and LEX
DB are mostly mutually inclusive because of their compatible data sharing system.
17
The dataset excludes any precedents that did not involve dismissals but were included in the results of the key
word search. Certain legal scholars indicate that it is difficult to precisely distinguish “adjustment dismissal” cases
from other types of dismissal litigations, where, for example, workers were fired due to their incompetence (Ogawa et
al. 2007). This paper includes those cases, and thus, broadly defines “adjustment dismissal.”
18
Fumio Ohtake (Osaka University) kindly provided me with his organized dataset.

14

public.
JIS is an ideal data source in this respect. JIS does not contain all the litigations that have been
filed in courts; however, it includes every released report of litigations. The Supreme Court
Secretariat, the administrative office of courts, does not release all precedents. It is said that the
Secretariat tends to allow “époque-making” or “rare” cases to be published in precedent reports. 19 In
fact, JIS is often used by legal scholars and contains all the influential precedents that have in factestablished the doctrine of four prerequisites. Since workers and firms as well as their attorneys
observe released precedents but never unreleased ones, JIS must accurately capture, to a certain
extent, the information set of agents that constrains their behavior.
The variable construction method used in this paper is very similar to the one proposed by
Besley and Burgess (2004), where they quantified the amendments to the industrial relations law by
Indian states. I code each of the 260 cases either pro-worker or pro-employer in order to indicate thedirection of judgment in adjustment dismissal litigations. In identifying whether a judgments is
pro-worker or pro-employer, I employ a simple rule: Each court decision was regarded as
pro-worker if a defendant firm was charged of illegitimate or abusive dismissal, and pro-employer ifplaintiff workers lost the case. Some samples were considered neutral if the defendant firm wins thecase but a plaintiff worker also obtains some compensation. 20 For the purpose of quantitative
analysis, I code each pro-worker judgment as one, each pro-employer judgment as minus one, andeach neutral judgment as zero. Then, I allocate them to each prefecture if it is held at the District
Courts, to all prefectures under the jurisdiction if it is held at the High Courts, and to every
prefecture if it is held at the Supreme Court. A zero is assigned if a prefecture observes no judgments
in adjustment dismissals in a given year. In years with multiple judgments, I sum up to obtain the
total values of precedents and transform them into an indicator of the general direction of change—a19

Comments from a seminar participant (judge) at the Legal Research and Training Institute (November 9, 2006,
Tokyo).
20
Such cases comprise 13.8 % of the sample.

15

plus one is assigned if the total value is positive, and a minus one is assigned if the total value isnegative. This creates a prefecture-level panel data of indicator of direction of judgment from 1950
to 2001.
As an example of this procedure, it is instructive to calculate a judgment indicator for Tokyo
Prefecture in 1979. Tokyo Prefecture witnessed three major adjustment dismissal-related litigations
at Tokyo District and High Court in 1979. One was filed in the High Court (Nakamoto v. Nissan
Motor Co., 717 Jurist 138, Tokyo H. Ct. Mar.12, 1979), where a defendant was sentenced for
abusing the right of dismissal. This judgment is ascribed a code of plus one. The other two cases
were declared as legitimate (Abe v. British Airways, 332 Rodo Hanrei 28, Tokyo D. Ct. Nov.29,
1979; Shimazaki v. Toyo Oxygen, 30 Minshu1002, Tokyo D. Ct. Oct. 29, 1979), and are ascribed acode of minus two. These makes up a total of minus one. Therefore, Tokyo Prefecture in 1979 iscoded as minus one.
As a final step, I accumulate these judgment indicators over time in each prefecture beginningfrom 1950. There are three obvious reasons why the accumulation commences in 1950. First, theAllied High Command introduced a new set of labor regulations between 1945 and 1947,
immediately after the Second World War. 21 It brought into effect the three labor laws common to all
prefectures, which has been the benchmark for industrial relations after the Second World War. 22
Upon these laws, courts established the Doctrine of Abusive Dismissal, and later the four
requirements in adjustment dismissals. The second reason is the fact that severe recession around the1950s caused a number of collective labor disputes. It has been stated that these disputes generated astrong demand for employment protection and accelerated the establishment of the Doctrine of
Abusive Dismissal (Chuma 1998). 23 Lastly and most obviously, JIS has very few adjustment

21

Trade Union Law in 1945, Labor Relations Adjustment Law in 1946, and Labor Standards Law in 1947.
See Sugeno (2002) for thorough discussions on the historical development of Japanese labor law.
23
The early stage disputes include firms that are nowadays well-known international corporations: Hitachi Ltd.
Dispute in 1950, Nissan Motor Co. and Toyota Motor Co. Dispute in 1953 (Chuma 1998).
22

16

dismissal cases filed prior to 1950. Since all prefectures have the same starting point, theaccumulated judgment indicators represent the overall direction of judgments made in the past. I
define this accumulated variable as “Court Decision”; a positive value of this variable implies thatcourts have been likely to sentence for the illegitimacy of adjustment dismissal or make pro-worker
decisions in the past.
Figure 2 presents a graph of accumulated direction of judgments (Court Decision) for eachprefecture. There are clear regional variations. Consistent with the observation in Table 1, Tokyo(No.13) exhibits an extreme pro-employer Court Decision, whereas Osaka (No.27) indicates a strong
pro-worker trend. Note that prefectures in the same High Court jurisdictions have a similar pattern,which is partially as a result of adding High Court judgments to all prefectures in the same
jurisdiction. In essence, prefectures under the jurisdiction of Tokyo H.C. [c] and Fukuoka H.C. [h]accumulated pro-employer judgments, while those under Osaka H.C. [e] and Hiroshima H.C. [f]accumulated pro-worker judgments. In addition to regional patterns, Court Decision has somecommon trends. For example, the divergences from zero are concentrated in the late 1970s in a
majority of the prefectures. The 1970s witnessed a serious nationwide recession caused by the oilcrisis, and firms were inevitably forced to reduce the number of their employees. As a result, thenumber of litigations related to adjustment dismissal increased dramatically, which accumulated
precedents, thereby establishing the doctrine of four prerequisites (Ohtake 2004). Another common
trend is an overall pro-employer movement in the late 1980s and early 1990s. This may be due to thefact that courts commonly accepted the lenient view toward the second prerequisite around thetime. 24
While the variable construction procedure in this paper is rather similar to the one in Besley
and Burgess (2004), this paper has an implicit but important modification. In the Indian case of24

Firms used to be required to “go inevitably bankrupt” unless they curtailed their employees. However, courts
relaxed their views, and now it is sufficient to dismiss workers if firms are in a “high degree of business difficulties”(Sugeno 2002, Osaka Gyomeikan ,685 Rodo Hanrei 49, Osaka D. Ct., Oct. 20, 1995).

17

Besley and Burgess (2004), they accumulated state-specific amendments to the Indian Industrial
Relations Act, a written law. The amendment persists within the state forever unless it is abolished.
On the other hand, in the case of the doctrine of four prerequisites in Japan, where they employ one
national court system but no prefecture (i.e., state) court system, the revision of the doctrine isapplicable to all prefectures; however, at the same time, it does not necessarily bind future judgments,
even within the same prefecture. Only Supreme Court decisions bind lower court judgments. Thus,
Court Decision does not represent the accumulated revisions of a law persistent within a prefecture.
Instead, Court Decision represents the accumulated information of agents regarding judicial
environments, including judicial decision standards or judge discretion exercised thus far within aprefecture. This idea largely relies on the fact that a prefecture of the first trial is exogenously given
to both parties. When firms are considering the dismissal of workers due to their business necessity
and uncertain regarding whether or not their adjustment dismissal satisfies the four prerequisites,
rational firms calculate the likelihood of winning the case when it goes to a trial, taking into
consideration historical judgment records of courts in their prefectures.
There are some evidences that support this idea. Foote (2006) suggested that the revision of
the Doctrine of Abusive Dismissals is one of a few examples where Japanese courts have been
involved in policy-making. He provided the example of Tokyo District Court in the 1950s, where
judges at Division 10 of Tokyo District Court had a strong influence on the preliminary debate of theDoctrine by declaring their opinions in published works. As already mentioned, the Tokyo District
Court is famous for leading decisions in the 1990s that relaxed the four prerequisites. Judge
convocation (Saiban-kan Kaido) is another fact that supports that courts have generated judicial
climate by regions. Judge convocation is held by the Supreme Court Secretariat, occasionally within
the High Court jurisdiction area, in order to provide judges with an opportunity to exchange theiropinions and, more importantly, consolidate their views (Nishikawa 2005). Thus, there are some18

reasonable grounds to believe that the economic agent perceives that judicial climate or court
discretions differ significantly from region to region.
However, the drawback of this idea is that information regarding which and how many
precedents the economic agent preserves in the information set is not available. Firms may notnecessarily recognize all the past precedents or past judicial climate. This implies that firms andworkers may exclude the old past precedents from their information set with the passage of time. Fora moment, let us assume that firms and workers never forget past information and let all past
precedents remain in their information set. I return to this issue in Section 5 and propose some newalternative variables to relax this assumption.

5

Identification Strategies and Data

5.1

Baseline Model

The baseline model of this paper begins with an ordinary least squares (OLS) estimation with somecovariates to adjust for observable differences in prefecture characteristics. Let E denote theemployment to population ratio and CD denote pro-worker Court Decision, as defined in section
2.2; let subscript p refer to a prefecture and t refer to a year. An OLS regression model is presented inequation (1):Ept = α + β1CDpt-1 + Xptβ2 + δt + εpt1 . (1)One-year lagged pro-worker Court Decision, CDpt-1 , is used assuming that it takes one year forfirms and their attorneys to recognize new precedents and incorporate the expected cost of firing into
their maximization behavior. Prefecture characteristics (X) are controlled in order to account for the
possible correlation between Court Decision and local public policies and demographics. Year19

dummies (δt ) are also included in order to capture unobserved year effects, including technologicalprogress and enactments or amendments of laws common to all prefectures (e.g., major amendmentof the Labor Standards Law in 1987). 25
In order to measure employment (E), I draw on the Basic Survey on Wage Structure (WageCensus) annual files for the years 1985 to 2000 and calculate employment-to-population ratio byprefecture-year group. 26 The Wage Census stands out from other surveys in that it draws a large
sample of establishments and their regular workers (over 70 thousand establishments and 1.5 million
workers in 2001), and comprises details regarding employment and wage information of workers bya variety of categories—for example, prefecture, industry, sex, and age. In section 6, I make use ofthis advantage of Wage Census to present results for employment and hourly wages for ten
subgroups distinguished by sex and industry. Panels A and B of Table 2 provide summary statistics
for the main variables used in this paper. Appendix Table 1 shows more details about data sourcesand variable constructions. Okuaira (2008), previous version of this paper, discusses the economic
background to include control variables, X in equation (1).

5.2

Estimation of Judge Effects

The original idea of IV analysis in this paper is adapted from Kling (2006), where he identified theeffects of incarceration length on the future labor market outcome by using the random assignmentof judges to cases as instrumental variables. I apply his idea to the analysis of the employment
protections imposed by courts. In particular, I compare groups of otherwise similar prefectures
which experience pro-worker or pro-employer judgments because they were randomly assigned tojudges who displayed different levels of leniency in adjudicating the abusiveness of adjustment
25
Robust standard errors are clustered by prefectures in order to permit an arbitrary serial correlation within theprefecture (Bertrand, Duflo, Mullainathan 2004).
26
The Wage Census is a survey conducted each year by the Ministry of Health, Labor and Welfare, and theirsample encompasses private establishments with over five regular employees and public establishments with over ten
regular employees.

20dismissals. This method first estimates judge-specific effects, or the decision standard of judges, in a
linear probability model of employer victory. Let Z denote judge indicators, and let the subscript irefer to a dismissal case. Let Prob (Employer Victory) take the value of one if the employer
successfully avoids the accusation of illegitimate adjustment dismissal:

Prob(Employer Victoryi ) = Ziπ + Qiθ + L2iβ4 + νi. (3)In order to confirm that the allocation of judges is actually randomly determined by the Secretariat, I
include a set of interaction terms of five-year period dummies and High Court jurisdiction dummies
2(Q). Equation (3) also controls for some dismissal characteristics (L ): a set of dummies for types of
dismissals and levels of courts (i.e., District and High Courts) and an indicator for collegiate court.
In estimating judge effects, it is best to have all the information of adjustment dismissal
litigations that the judges are involved in. Unfortunately, the Supreme Court Secretariat does notrelease all litigation records, and unpublished records are not easily accessible to the public. The JIS
used in this paper includes every released precedent, and the estimation of equation (3) only with the
JIS sample may bias the estimates, depending on the manner in which the discretion of judges differs
between released and unreleased litigations. What is worse, I do not have enough samples ofadjustment dismissal cases; the sample size of adjustment dismissal cases (e.g., 260 cases) is too
small relative to the sample size of judges for efficiently estimating judge effects.
In order to overcome the difficulty of sample size, I first augment the dataset with the othertypes of dismissal litigations whose issues are also in its abusiveness. Instead of typing “adjustment
dismissal (Seiri Kaiko)” in the JIS search system as done before, this time I typed “dismissal(Kaiko)” to obtain all types of dismissal-related litigations from 1949 to 2004: punitive dismissal,
union-shop dismissal, and dismissals due to worker incompetence, in addition to adjustment

21

dismissal. 27 Then, the sample is limited to a case where the plaintiff is a worker(s) and defendant a
firm.
In matching the estimated judge effects, Z i πˆ m , with the prefecture-level panel model inequation (1), I follow several steps so as to avoid the endogeneity problem latent between judge’s
current decision and local labor market:(a) pool litigation records of all dismissal-related litigations.
(b) assign judge dummies to each litigation by assuming that an individual judge independently
gives decisions. This means that one case with a panel of three judges is expanded to threedifferent cases, and each judge is coded as one in one of the three cases in a judge dummy.
(c) further expand the dataset to account only for cases at the previous courts after which theestimated judge effects will be instrumented with CD. That is, estimate equation (3) only withlitigation records in previous courts, and then instrument it with the decisions at a current court.
This method allows us to exclude the endogenous concerns latent between current decision andthe economic condition of current prefecture (court). Expansion of dataset is necessary to avoid
double counts of an individual judge in dummies: the litigation records at all previous courts are
expanded depending on how many times each judge is transferred to the another prefecture in thepooled litigation record constructed in step (a).
(d) Pool all the expanded records and run a regression of equation (3) to obtain the estimates ofjudge dummies.
(e) Among the estimated judge-effects, take up those which are only relevant in creating Court
Decisions.
(f) Match judge effects with prefecture-level panel model in equation (1). In case that a matrix ofjudge-effects is not full-column rank in equation (1), take averages to replace those who are in
mulitcolinearity.
(g) instrument the estimated judge-effects with pro-worker Court Decision CD in equation (1).

These procedures leave twenty-five judges in total who are instrumented to Court Decision inequation (1).
Recent econometric literature emphasizes the importance of checking the weakness of27
The sample period is extended from 1950–2001 to 1949–2004, assuming that a judge effect is time-invariant,and that both past and future litigations provide information regarding judge discretion.

22

instruments for detecting finite-sample bias (Bound, Jaeger and Baker 1994, Stock, Wright and Yogo
2002, Murray 2006, to mention a few), although the observations discussed above indicate thatjudges do matter. It is also known that finite-sample bias in 2SLS becomes even more deteriorated
when the number of instruments is large, relative to the number of observations (Hahn and Hausman
2002a, 2002b). As this may be the case in this study (36 judge effects relative to 752 observations), I
will also present results from the alternative estimation method as well as conventional statistics for
detecting weak instruments (Stock and Yogo 2004).

6

Estimation Results

6.1 Main Results
The first column of Table 4 examines a simple regression of employment-to-population ration Court
Decision. Pro-worker Court Decision is negatively associated with employment, but at a modest
significance level (p-value = 0.221). This rough result indicates that at least some statistical
relationship exists between the two variables; however, any possibilities of spurious or reverse
causation cannot be excluded at this point.
In order to control for observable prefecture characteristics (X) in equation (1), I add log
prefecture population, log public investment per capita, female ratio, population ratio of age 15-24,
and two indicators for political attributes of governors: one for governors who execute leftist policy,
and the other for those who have held any positions at the Ministry of Internal Affairs andCommunications (Soumu Sho, hereafter referred to as MIAC) at some point in his or her career.
Columns (2) to (4) indicate a significant effect of pro-worker Court Decision—the prefecture
employment-to-population ratio decreases by 1.4 log points if a prefecture has more pro-worker
judgments (i.e., adjustment dismissal is illegitimate) than pro-employer judgments (i.e., adjustment
dismissal is legitimate) in a certain year. This is a comparable effect found in Autor et al.

23(2006)—the adoption of the implied-contract exception to the “at-will” doctrine reduces the state
employment-to-population ratio by 0.8 to 1.7 log points. 28 Column (5) checks the sensitivity of theresult by excluding outliers from the sample. As is evident from Figure 2, Tokyo and Osaka
prefecture display extreme trends in Court Decision. This is due to the fact that the litigations are
related to the two prefectures where they have a large number of company headquarters,
encompassing almost half of the total precedents in the sample (48%). In column (5), the estimate of
Court Decision is negative and significant. To summarize, I found negative and moderately
significant impact of pro-worker court decisions on prefecture employment rate, after controllingobservable and unobservable prefecture characteristics. For the remainder of the analysis, I present
the estimates with the same group of controls as those in column (4).
Panel A of Table 5 presents the results for IV estimations. For comparison, in column 1, thebaseline model of OLS regression is replicated from column (4) of Table 4. Each cell represents foran estimates from separate regressions. Column 2 of Panel A presents the estimates from the 2SLSmodel with judge effects instrumented with Court Decision. The 2SLS result provides rather similar
results to those obtained in OLS estimation. According to column 2, the employment rate is reduced
by approximately 1.6 log points if a prefecture observes more illegitimate adjustment dismissal cases
than legitimate one. The point estimates are over two standard errors from zero.
At the bottom of Table 5, the first stage statistics are presented in order to diagnose therelevance of judge effects on pro-worker Court Decision shocks. Testing the overall significance ofjudge effects yields an F-statistics of 10.94. According to the critical values presented in Stock and
Yogo (2004), this suggests that the bias of 2SLS may be 10 to 20% that of OLS. Thus, the estimateswith three other alternative methods are also presented in order to mitigate the finite sample bias

28

I test the overall significance of prefecture effects in column (3) and find that these effects are jointly different
from zero at the less than 1 % significance level. Moreover, the Hausman specification test significantly rejects thenull that prefecture effects are orthogonal to the other regressors (p-value is very close to 0.000). Thus, prefecture
effects are included in all the following estimations.

24

problem—Limited Information Maximum Likelihood (LIML), Fuller’s (1977) modification of
LIML (Fuller) 29 , and Jackknife IV estimator proposed by Angrist et al. (1999) (JIVE). 30 The firsttwo, as well as 2SLS, are k-class estimators. LIML is proved to possess better finite sample
properties than 2SLS, while LIML and 2SLS share the same asymptotic distribution. On the other
hand, LIML is also known to have no moments, and Fuller’s (1977) LIML provides the modification.
JIVE is also estimated for a robustness check, although recent Monte Carlo simulations support theuse of LIML or Fuller’s modification of LIML rather than JIVE in terms of its bias and inference(Davidson and MacKinnon 2006, Murray 2006).
Columns 3 to 5 in Panel A provide the estimation results. LIML and Fuller’s modification of
LIML do not change the point estimates for Court Decision, nor do they reduce the statisticalsignificance for the two estimates. Similarly, JIVE in column 5, although statistically insignificant,shares the same signs for the point estimates. Even after dealing with possible finite sample bias
deteriorated by weak instruments, the results are rather similar to the ones obtained in OLS or 2SLS.
In fact, partial R-squared here equals 0.363, and judges effects appear to be relevant rather than weak
instruments in the first stage regression. This supports observations in section 3.2 that judges domatter in trials in Japan.

6.2

How Do Workers Share the Costs?

This section tests two hypotheses in order to reveal how and by whom the economic cost of
pro-worker Court Decision are borne. The first hypothesis refers to a trade-off between wages andemployment. The point was first clarified in Lazear (1990), where he introduced the Coasean typecontract that completely nullifies an increase in severance pay or firing cost. In essence, if workers
and firms agree to workers paying a bond that is exactly equal to the mandated severance pay, no
29

Fuller’s parameter is set to 2, although the result is robust to parameter values.
JIVE excludes the ith observations for Z’X, where the first stage fitted value for the ith observation is obtainedby Zπ(i) = (Z’Z)-1Z’X. Namely, I estimated the JIVE 2 estimator proposed by Angrist et al. (1999).
30

25

employment consequence must follow (Summers 1989, Lazear 1990). Thus, if efficient Coasean
contract were to not hold at all, pro-worker Court Decision would result only in employment
reduction and there would be no reduction in wages.
The second hypothesis refers to the substitution effect between full-time and part-time workers.
The doctrine of four requirements includes only those who are regularly employed. Firms have an
incentive to evade the increased firing costs by employing part-time workers who are not covered by
the doctrine and thus are much less expensive to fire. Autor (2003) constructed a model to suggestthat the increase in firing cost induces firms to outsource the occupations where specific humancapital investment is relatively unimportant. Thus, if courts impose high firing costs, then part-time
to full-time employment rates are expected to increase, particularly in occupations that require
general rather than specific human capital investment.
Table 6 tests the first hypothesis by examining the effect of pro-worker Court Decision onhourly wages (Panel B) as well as employment (Panel A) for ten subgroups distinguished by sex andindustry. Similar to the baseline specification, both employment and hourly wage correspond forfull-time employees (Ippan Rodosha), and part-time workers are not included. The same
specifications are used as those in the Fuller’s modification of the LIML model in column 4 of Table
5, but with a replaced dependent variable. 31
Two important findings arise from the estimation results. The first finding is that the
pro-worker Court Decision has a relatively large negative impact on the male employment rate in the
Manufacturing (column 2) and Finance & Insurance sectors (column 5), while no significant effects
are observed on male or female employment rates in other sectors. In the Manufacturing sector, themale employment rate is reduced by approximately 2 log points if a prefecture experiences more
pro-worker judgments than pro-employer ones. The second finding refers to the hourly wage in
31
The employment rate in this table is defined by the number of employees in the industry divided by prefecture
population. Hourly wage is measured in terms of the prices in the year 2000 (see Appendix Table 1).

26panel B. Court Decision significantly reduces hourly wages in all sectors, for both male and female
workers. The only exception is the insignificant effect on female wage rate in the Finance &Insurance sector (column 10), but it has negative signs. It must be noted that in the Manufacturingsector, male employment is relatively less undone by the decrease in wage (panels A and B ofcolumn 2), as compared with the other sectors. The estimates for hourly wages contrast with thosefound in Autor et al. (2006), in which no significant effect of the implied contract adoptions on log
hourly wage was obtained. Coasean contract partially negates an increase in firing costs imposed by
pro-worker judgment on adjustment dismissal in Japan, whereas it does not do so in the U.S. case of
exception to the employment-at-will doctrine. While potential male workers who are unemployed
and searching for a job are seriously damaged by pro-worker judgments, particularly in the
Manufacturing and Finance & Insurance sectors, the incumbent workers also accept certain costs
imposed by pro-worker judgments through a reduction in their real wages. 32
Table 7 similarly tests the second hypothesis—whether pro-worker Court Decision substitutes
full-time workers with part-time workers. The dependent variable is defined by 100 times logarithmof part-time female employment divided by full-time male and female employment. Until very
recently, Wage Census tabulated female part-time employment only, partially because a majority ofthe part-time positions used to be occupied by female workers. In order to check the degree ofsubstitution between female part-time and all full-time workers, the part-time female employment
was divided by female and male full-time employment rather than female full-time employment
only.
Substitution to part-time workers occurs in a rather different manner across industries. The
bottom line of Table 8 indicates that pro-worker Court Decision has replaced full-time workers with
32
There are two possible explanations for why workers in the Manufacturing and Finance & Insurance sectors aremost severely affected by Court Decision. First, incumbent workers in these sectors have relatively strong vested
interests as compared with those in other sectors. Second, firms, particularly in the Manufacturing sector, can be morecautious with regard to compliance in dismissing workers. Almost half of the cases in the JIS sample (129 out of 260)
involve the Manufacturing sector.

27

part-time ones for all industries overall, although at the moderate significance level. This is in
keeping with the prediction of Autor (2003). However, the remainder of the table indicates that thistotal effect masks a large discrepancy among industries. The second row indicates that pro-worker
Court Decision increases the part-to-full-time-ratio by almost 7 log points in service sectors. On thecontrary, pro-worker Court Decision does not substitute full-time with part-time workers but reduces
part-time employment in the Manufacturing sector. There are two possible interpretations for thisreduction. First, female part-time workers may be a complement, rather than a substitute, to full-timeworkers in the Manufacturing sector; second, the incumbent full-time workers in the Manufacturingsector are strong insiders so that firms find it too costly to replace them with part-time workers andinstead reduce the number of part-time female workers. In fact, unions have been usually formed
only with full-time workers, and part-time workers did not belong to any unions, until recently the“Regional General Unions” (Godo Roso) has begun to include “those who are hard to organize, suchas…part-time workers” (Sugeno 2002, 499).
In summary, the economic costs of pro-worker Court Decision are borne in variant
ways—employment opportunities in the Manufacturing and Finance & Insurance sectors are reduced
significantly. The incumbent workers also bear some cost in the form of a reduction in real hourly
wage, while unstable part-time jobs have increased in the overall industry. Thus, everyone appears to
end up bearing some of the costs incurred by firing restrictions exercised by courts, although theincumbent full-time workers, particularly in the Manufacturing sector, have some ability to impute
some of their burden to outsiders.

7

Conclusion

The purpose of this paper was to provide an empirical assessment of relationship between labormarket outcomes and employment protection enforced by court discretion. I exploited regional

28

variations in judgments and generate a variable, Court Decision, in order to represent theenforcement level of case law, or the doctrine of four prerequisites in adjustment dismissals in Japan.
Table 1 and Figure 2 present a sharp regional difference in the direction of judgments concerning
adjustment dismissals. Particularly, in the Tokyo Prefecture, courts tended to sentence in favor of
employers while in Osaka Prefecture, they are likely to adjudicate in favor of workers.
However, the difficulty in exploiting the direction of judgments lies in the identification of thecausality, since court decisions are easily affected by economic conditions and social norms. In order
to circumvent the problem, I exploited the exogenous allocation of judges to prefectures by taking
advantage of the periodical judge-transfer system in Japan. Specifically, I estimated judge-specificeffects in OLS model of worker victory in all dismissal-related precedent records, and instrumented
the relevant judge effects to the original prefecture-level panel model.
The results are in keeping with those obtained in previous works. Pro-worker Court Decisionor strict employment protection significantly reduces the employment-to-population ratio byapproximately 1.4 log points, after controlling for observable and unobservable prefecture
characteristics. They are also robust to instrumental variable estimations. This paper also indicatedthat the economic costs of pro-worker judgments are ubiquitous. Stringent employment protection
not only deprives employment opportunities to potential workers but also requires the incumbent
workers to accept a decline in their hourly wages. This finding contrasts with the one in Autor et al.
(2006), where they found that the efficient Coasean contract did not hold in the U.S. labor market,
and thus only employment was affected by the exception to the at-will doctrine. Moreover, firms
replace full-time with female part-time workers when courts are likely to adjudicate in favor ofworkers. This replacement effect is largest in service sectors where specific capital is less required.
On the other hand, in the Manufacturing sector, the incumbent workers or “insiders” have a stronger
bargaining power and owe relatively lower costs by transferring some of their burden to “outsiders”

29(Lindbeck and Snower 1986).
It is important to emphasize that the results presented in this paper must be handled withcaution. First, this paper provides no evidence of the benefit of employment protection imposed bycourts. Several previous studies insist that the employment protection induces investment in specifichuman capital (Autor 2003, Wasmer 2006). Further research is necessary in order to find the net
impact of court discretions concerning dismissals in Japan. Second, this study focused on the
direction of judgments only and did not test for the effect of uncertainty brought by courts.
Uncertainty in firing costs may increase the option value to hoard workers (OECD 2007a), but thiseffect is ignored.
Finally and most importantly, a question that may have arisen in the minds of readers is why
the worker victory ratio persistently differs from Osaka to Tokyo, despite the fact that judges are
exogenously transferred to prefectures. There are two possible explanations for this. A first
explanation refers to multiple equilibria that arise from reverse causation. If judges are sympathetic
toward workers and their decisions are biased by bad local labor market conditions, and if
pro-worker judgments also increase firing costs and reversely hinder the labor market outcome, twoequilibria may arise: 1) high employment rate and pro-employer judgments and 2) low employment
rate and pro-worker judgments (Ichino et al. 2003). It is also possible that certain exogenous but
unknown mechanism of judge transfers explains the divergence in Tokyo and Osaka. It is informallybelieved that the Japanese judicial ladder has two top levels, one in Tokyo and the other in Osaka.
The two cliques may have tugged each other by proposing opposing interpretations of existingcase-law, although this issue will remain within the compass of imagination until further research is
conducted.

30Appendix 1: The Japanese Court Structure
Japan maintains a three-instance trial system. In a typical case of adjustment dismissal litigation, thelegal procedure involves three possible court levels: District, High, and Supreme Courts. Appendix
Figure 1 describes the Japanese court structure. In the first stage of a trial, workers file their cases atthe District Court in a prefecture where a defendant firm is located. Every prefecture has one District
Court and a few of its branches. The only exception is Hokkaido Prefecture (northern island), where
they have four District Courts. This adds up to a total of fifty District Courts. With the first court
judgment, either workers or a firm may appeal to the second court level, the High Court. The High
Court comprises four to eleven District Courts located in the vicinity. Their jurisdiction area
corresponds to the geographical region block, which is often referred to in practical use. For example,
Osaka High Court includes six prefectures in Kansai or the Western area—Shiga (No.25), Kyoto(No.26), Osaka (No.27), Hyogo (No.28), Nara (No.29), and Wakayama (No.30) Prefecture. Finally,
either of the two parties may appeal to the third court level, the Supreme Court, which is located inTokyo.

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36

Table 1. Worker Victory Ratio for Adjustment Dismissal Litigationsyear

1950-2000 1950-60 1961-70 1971-80 1981-90 1991-2000

Tokyo High and District Court

0.30

0.20

0.00

0.38

0.10

0.40

Osaka High and District Court

0.80

0.50

0.50

0.86

0.88

0.82

All High and District Courts

0.52

0.28

0.39

0.58

0.52

0.61

Source: Judicial Information System (Hanrei Taikei).
Note: Worker victory ratio is calculated by dividing the number of worker victories by the number of total
litigations. The sample is limited to the litigations that are involving adjustment dismissals. The original dataset
has been taken from Ohtake (2004), as discussed in section 2. The third row shows the victory ratio for every
high and district court of all 47 prefectures, including Tokyo and Osaka Prefecture. The number of total cases are
given in parentheses.

37

74
73
72
71
70
69
68
67
66
65
64
2000

1995

1990

1985

1980

1975

1970

1965

Tokyo Prefecture
Osaka Prefecture

Figure 1. The Employment to Population Ratio (%): Tokyo vs. Osaka
Source: National Census.

38

5
-5
5
0
-5
5
-5
5

28 Hyogo [e]

0

0

-5

35 Yamaguchi [f]

0
-5

0

0

5
0

-5

-5

0
-5

0
-5

0
-5

0
-5

0
-5

1950 1960 1970 1980 1990 2000

1950 1960 1970 1980 1990 2000

1950 1960 1970 1980 1990 2000

1950 1960 1970 1980 1990 2000

1950 1960 1970 1980 1990 2000

0

5

47 Okinawa [h]

-5

0

5

46 Kagoshima [h]

-5

0

5

45 Miyazaki [h]

-5

0

5

44 Oita [h]

-5

-5

0

5

43 Kumamoto [h]42 Nagasaki [h]541 Saga [h]40 Fukuoka [h]55

39 Kochi [g]

-5

-5

0

0
-5

0

38 Ehime [g]537 Kagawa [g]5536 Tokushima [g]

-5

0
-5

-5

0

5

5

34 Hiroshima [f]
5

-5

-20

0

5

20

27 Osaka* [e]33 Okayama [f]5

21 Gifu [d]

0

5
-5

0

5
-5

0

5
0
-5
5
0
-5

0

5
20
-20

0

5
0
-5

-5

0

5

-5

0

5
-5

0

5
-5

0

5
0
5
0
5
0
5
0

14 Kanagawa [c]20 Nagano [c]26 Kyoto [e]32 Shimane [f]7 Fukushima [b]13 Tokyo* [c]19 Yamanashi [c]25 Shiga [e]31 Tottori [f]530 W akayama [e]5529 Nara [e]6 Yamagata [b]12 Chiba [c]18 Fukui [d]24 Mie [d]

-5

0

5

23 Aichi [d]

-5

-5

0

5

22 Shizuoka [c]5 Akita [b]11 Saitama [c]17 Ishikawa [d]

-5

0

5

16 Toyama [d]

-5

-5

0

5

15 Niigata [c]4 Miyagi [b]10 Gunma [c]

-5

0

5

9 Tochigi [c]

-5

-5

0

5

8 Ibaraki [c]3 Iwate [b]

-5

0

5

2 Aomori [b]

-5

-5

0

5

1 Hokkaido [a]1950 1960 1970 1980 1990 2000

1950 1960 1970 1980 1990 2000

Figure 2. Direction of Accumulated Judgments (Court Decision) by Prefecture
Note. Tokyo (No.13) and Osaka (No.27) occupies 48% of the total cases, and different vertical scales are used for the two prefectures. [ ] denotes High Court
jurisdiction; [a] under Hokkaido (Sapporo) H.C., [b] under Miyagi (Sendai) H.C., [c] under Tokyo H.C., [d] under Aichi (Nagoya) H.C., [e] under Osaka H.C., [f]under Hiroshima H.C., [g] under Kagawa (Takamatsu) H.C., and [h] under Fukuoka H.C.

39

Table 2. Summary Statistics
A. Dependent Variable
100 × log (Employment/Population)

SamplePeriod1985-2000

No. ofObservations
752

Standard
Deviation
22.129

Min.

Mean

Max.

-223.429-139.010-70.361

B. Regressors
Court Decision
Court Decision before sample period
Governor from MIAC
Leftist governor
Log prefecture population
Log public investment, 2000 price
Unionization index
Ratio of settlements mediated by courts
Average number of plaintiffs per case1985-2000
1950-1984
1985-2000
1985-2000
1985-2000
1985-2000
1987-2000
1987-2000
1987-2000

752
1576
752
752
752
752
652
652
652

3.235
1.521
0.422
0.258
0.720
0.302
0.032
0.265
13.823-17-11
0
013.326
11.7350.095
01-0.648-0.1970.231
0.072
14.493
12.595
0.170
0.429
2.281

11
6
1
1
16.30613.4130.278
1351

C. Variable Specification Checks
Ratio of worker victory in the last 3 years
Ratio of worker victory in the last 5 years
Ratio of worker victory in the last 10 years
Ratio of worker victory for all cases since 1950

1985-2000
1985-2000
1985-2000
1985-2000

752
752
752
752

35.52135.01731.94815.78600
025.00037.52833.39441.32152.086

100
100
100
100

40

Table 3 Exogeneity of Judge Transfers
Employer Victory Rate by Rank-differnce, Averages among Judges
Population(1)
Victory rate for firms− ( NextRankt − CurrentRank t ) within a current court-30 or less
-20 to -11-10 to -1
0 to 910 to 19
20 to 29
30 or more
Total
Regression coefficient
Sample transfer period
Number of total transfers(2)
Judgment right before atransfer (1= firm victory)

0.43
0.46
0.43
0.47
0.40
0.50
0.31
0.44
-2.234(1.915)

0.42
0.52
0.44
0.48
0.38
0.48
0.31
0.44
-2.054(1.683)
1951-2003
382

Employment-to-Population Ratio(3)(4)
Victory rate for firms
Judgment right before a
within a current court
transfer (1= firm victory)

0.47
0.52
0.36
0.44
0.57
0.50
0.37
0.46
-2.874(4.016)

0.41
0.58
0.40
0.45
0.61
0.50
0.36
0.46
-1.656(3.534)
1985-2000
160

Note: Rank difference is a difference in prfecture ranks between a judge's current court and his or her next destination. All variables are ranked in descendingorders. Regression coefficent stands for the estimates from the regression of the rank difference on the cumulative win rate or judgment right before a transfer.
Robust standard errors are given in parentheses.

41

Table 3 continued.
Unemployment Rate
(5)
(6)
Victory rate for firms
Judgment right before a− ( NextRankt − CurrentRank t ) within a current court
transfer (1= firm victory)-30 or less
-20 to -11-10 to -1
0 to 910 to 19
20 to 29
30 or more
Total
Regression coefficient
Sample transfer period
Number of total transfers

0.38
0.50
0.41
0.43
0.41
0.47
0.38
0.43
-1.181(1.782)

0.42
0.40
0.43
0.44
0.39
0.51
0.33
0.43
-0.555(1.528)
1965-2003
352

Public Investment per capita(7)(8)
Victory rate for firms
Judgment right before a
within a current court
transfer (1= firm victory)

0.44
0.45
0.22
0.46
0.48
0.45
0.43
0.43
1.556
(2.423)

0.46
0.44
0.23
0.46
0.44
0.50
0.43
0.43
1.366
(2.068)
1965-2000
328

Note: Rank difference is a difference in prfecture ranks between a judge's current court and his or her next destination. All variables are ranked in descendingorders. Regression coefficent stands for the estimates from the regression of the rank difference on the cumulative win rate or judgment right before a transfer.
Robust standard errors are given in parentheses. Unemployment rate is linearly interpolated since observations are only avaialble for every five-year period.

42

Table 3 continued.
Unionization Index(9)(10)
Victory rate for firms
Judgment right before a− ( NextRankt − CurrentRank t ) within a current court
transfer (1= firm victory)-30 or less
-20 to -11-10 to -1
0 to 910 to 19
20 to 29
30 or more
Total
Regression coefficient
Sample transfer period
Number of total transfers

0.46
0.52
0.41
0.46
0.38
0.50
0.32
0.43
-3.538(2.180)

0.46
0.56
0.36
0.45
0.40
0.52
0.32
0.43
-2.471(1.914)
1965-2002
347

Ratio of Settlements Mediated by Courts
(11)
(12)
Victory rate for firms
Judgment right before a
within a current court
transfer (1= firm victory)

0.67
0.62
0.43
0.39
0.49
0.47
0.40
0.45
-1.591(2.297)

0.67
0.67
0.45
0.40
0.51
0.37
0.40
0.45
-2.772(1.956)
1987-2002
180

Note: Rank difference is a difference in prfecture ranks between a judge's current court and his or her next destination. All variables are ranked in descendingorders. Regression coefficent stands for the estimates from the regression of the rank difference on the cumulative win rate or judgment right before a transfer.
Robust standard errors are given in parentheses. P-values for significance test of regression coefficients are; p-value = 0.105 in column (1); p-value = 0.197 in

43

Table 4. The Impact of Pro-Worker Court Decision on Employment(1)

Court Decision [t-1]

100 × ln (Employment/Population)
(2)
(3)
(4)

-1.561(1.257)-2.331(0.396)-1.703(0.539)0.461(1.652)-1.865(1.645)-91.357(30.968)
3.774(3.046)-1.437(0.498)0.038(1.870)-1.736(0.268)-93.714(35.233)
2.353(2.994)0.742(1.150)97.314(59.064)-1.464(0.761)0.721(1.738)-2.887(1.359)-100.338(36.714)0.406(3.004)0.886(1.143)35.155(61.326)

No
No
0.051
752

Yes
No
0.351
752

Yes
Yes
0.432
752

Yes
Yes
0.440
752

Yes
Yes
0.453
720

Leftist governor
Governor from MIAC
Log prefecture population
Log public investment
Female population/ total population
Population of age 15-24/ total population

Year effectPrefecture effect
Adjusted R-sq.
No. of observations(5)
No Tokyoand Osaka

Note: Standard errors, adjusted for clustering at the prefecture level, are given in parentheses. The data are for the fourty-seven
prefectures for the period 1985-2000.

44

Table 5. Dealing with the Endogeneity Concern
(1)

(2)

(3)

(4)

OLS

2SLS

LIML

Fuller(5)
Angrist et al.
unbiased JIVE2

A. 100 × ln (Employment/Population)
Court Decision [t-1]standard error
robust standard error-1.437(0.302)(0.498)-1.626(0.515)-1.679(0.535)-1.676(0.539)-5.926(4.338)

B. 100 × ln (Job Creation Rate)
Court Decision [t-1]standard error
robust standard error-5.068(1.635)(1.815)-5.038(2.790)-5.033(2.783)-5.034(2.775)-2.773(1.325)

C. 100 × ln (Job Destruction Rate)
Court Decision [t-1]standard error
robust standard error
First-stage F-statistics for H0:
judge effects = 0
First-stage Partial R-squared
No. of excluded Instruments
No. of observations-4.066(1.814)(1.469)-4.784(2.507)-4.866(2.478)-4.859(2.470)-3.094(1.218)

-

13.79

13.79

13.79

9.33

752

0.344
25
752

0.344
25
752

0.344
25
752

25
752

Note: Standard errors are given in parentheses in columns (2) to (5). Standard error, adjusted for clustering at the prefecturelevel, is given in parentheses in column (1). The data are for the fourty-seven prefectures for the period 1985-2000. All
estimation controls for prefecture dummies, year dummies, log population, log public investment, indicator variable for leftist
governor, indicator variable for governor from MIAC, female ratio, and population ratio for age 15-24. Column (1) in panel A
replicates the estimates from column (4) of Table 4. Column (4) sets the fuller parameter = 2.

45

Table 6. The Impact of Pro-Worker Court Decision on Employment and Hourly Wage by Gender and Industry
(1)

(2)

Males
(3)

Construction Manufacturing Services(4)
Wholesaleand Retail(5)
Finance andInsurance

(6)

(7)

Construction Manufacturing

Females(8)Services(9)(10)
Wholesale and Finance andInsurance
Retail Trade

A. 100 × ln (Employment/Population)
Court Decision [t-1-0.016(1.479)-2.230(0.731)0.014(1.091)-2.542(1.522)-2.022(1.181)0.617(2.173)-1.803(0.924)-0.067(0.939)0.641(1.725)0.529(1.543)

Year effectPrefecture effectNo. of observations

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

B. 100 × ln (Hourly Wages)
Court Decision [t-1-0.721(0.383)-0.369(0.264)-0.595(0.375)-1.295(0.459)-0.875(0.419)-0.274(0.630)-0.397(0.269)-1.275(0.350)-1.394(0.363)-0.535(0.621)

Year effectPrefecture effectNo. of observations

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Yes
Yes
752

Note: All models are estimated by Fuller's LIML. Fuller's parameter is set equal to 2. Standard errors are given in parentheses. The data are for the fourty-seven prefectures for the period 19852000. All estimation controls for prefecture dummies, year dummies, log population, log public investment, indicator variable for leftist governor, indicator variable for governor from MIAC,
female ratio, and population ratio for age 15-24. "Wholesale and Retail Trade" sector includes drinking and eating places.

46

Table 7. The Impact of Pro-Worker Court Decision
on Female Part-time Employment
100 × ln (Part-time Female Employment /Full-time Total
Employment)
(1)
(2)
(3)
(4)
OLS

2SLS

LIML

FullerManufacturing-3.009(2.121)-1.612(2.274)-1.546(2.201)-1.559(2.195)Services

2.933(1.442)5.631(2.560)5.923(2.527)5.896(2.519)Wholesale andRetail Trade-0.921(1.317)2.109(2.520)2.291(2.450)2.262(2.442)All industry-0.515(1.337)1.740(1.517)1.856(1.471)

1.835(1.466)

Note: Standard errors are given in parentheses. The data are for the fourty-seven
prefectures for the period 1985-2000. All estimation controlls for prefecture dummies,
year dummies, log population, log public investment, indicator variable for leftist
governor, indicator variable for governor from MIAC, female ratio, and population
ratio for age 15-24.. The number of observations is 752 in all estimations.

47

Appendix Table 1. Data Source and Variable Construction
Variable

SourceConstruction

Log (employment/population)

Wage Census, Population Estimatesobtained by dividing number of full-time employees (ordinary
employees, Ippan Rodosha ) by population.

Log (hourly wage, 2000 price)

Wage Census, Consumer Price Index obtained by dividing annual wage by number of hours worked
annually. Annual wage includes: annual contractual cash
earning, bonus and term-end allowance. Hours worked includes:
actual number of overtime worked and actual number ofscheduled hours worked. Note that it covers only full-time
employees (Ippan Rodosha ).

Log (part-time female employment /full- Wage Census
time total employment)

obtained by dividing the number of part-time female employees
by full-time male and female employees.

Court Decision

Judicial Information System, (Hanrei positive if pro-worker judgments have been accumulated;
Taikei, by Dai Ichi Hoki )negative if pro-employer judgments have been accumulated.
The original dataset is provided by Ohtake (2004), whichcontains 260 adjsutment dismissal litigation records from 1950
to 2001. See section 2.2 for a construction of the variable.

Leftist governor

The Biographic Dictionary ofequals one if leftist governor, zero if not. Govrenors are
Politicians, (Seijika Jinmei Jiten, by classified as leftiest if there is any description of "hard-left" in
Nichigai Associate )the Biographic Dictionary of Politicians , or he or she is amember of left-wing parties and also has experienced apresident of local labor committees. They include: Toshihumi
Tanaka (1.Hokkaido), Jiro Iwagami (8.Ibaraki), Yawara Hata
(11.Saitama), Ryokichi Minobe (13.Tokyo), Kazuji Nagasu(14.Kanagawa), Torazo Ninagawa (26.Kyoto), Ryoichi Kuroda(27.Osaka), Yukiharu Miki and Shiro Nagano (33.Okayama),
Goro Abe (36.Tokushima), Tadao Maekawa (37.Kagawa), Taichi
Uzaki and Hachiji Okuda (40.Fukuoka), Kaoru Kinoshita(44.Oita), Choubyou Yara, Kohichi Taira, Jyunji Nishime,
Masahide Ohta (47.Okinawa).

Governor from Ministry of Internal
Affairs and Communications (MIAC)

The Biographic Dictionary ofequals one if a governer is from MIAC, zero if not. Governors
Politicians, (Seijika-Jinmei-Jiten, by are classified as "governer from MIAC" if he or she had held any
Nichigai Associate )positions at the Ministry before he or she was elected as agovernor. There are 46 out of 216 governors from the MIAC
between 1950-2000.

Log prefecture population
Log public investment per capita, 2000
price

Population Estimates
System of Prefecture Account,
Consumer Price Index, Pupulation
Estimates
System of Prefecture Account, Basic
Survey on Labuor Unions

Unionization index

public investment is obtained from prefecture public gross fixed
capital formation in "System of Prefecture Account".
obtained by dividing number of union members by number ofworkers (Shu Gyosha ).

Ratio of settlements mediated by courts JILPT (2006), Appendix Tables

If no dismissal litigation was filed in courts, I take an average ofvalues of previous and next period when they filed litigations.
This procedure excludes five observations (1987 of Saga
Prefecture and 1987-1990 of Tottori Prefecture) since the data
begins in 1987.

Average number of plaintiffs (workers ) JILPT (2006), Appendix Tables
per casesame as above.

48

Appendix Table 2 Is A Judgement Shock Accurately Defined?
(1)

Dependent variable: 100 × log (Employment/Population)
(2)
(3)
(4)
First-stage F-statistics for
2SLS
LIML
Fuller
H0: judge effects = 0-0.151-0.145-0.0833.74(0.042)(0.041)(0.028)

Ratio of Worker Victoryin the last 3 years

OLS-0.025(0.014)in the last 5 years-0.016(0.018)-0.078(0.476)-0.832(0.472)-0.511(0.244)

1.56

in the last 10 years-0.002(0.030)-0.106(0.051)-0.434(0.145)-0.379(0.125)

2.22

for all cases since 1950

0.081
(0.085)-0.087(0.136)-1.059(0.479)-0.865(0.398)2.49

Note: Standard errors are given in paretheses for columns (2) to (4). Standard errors, adjusted for clustering at prefecture level, are given inparenthese for column (1). Fuller's parameter is set equal to 2. The data are for the fourty-seven prefectures for the period 1985-2000. All
estimation controls for prefecture dummies, year dummies, log population, log public investment, indicator variable for leftist governor,
indicator variable for governor from MIAC, female ratio, and population ratio for age 15-24. The number of observations is 752 in all
estimations.

49

Appendix Table 3 First Stage Tabulation with Conservative Instruments
Dependent Variable

First Stage
Court Decision [t-1]Second Stage
100 × ln

Court Decision [t-1]Judge1_fukuoka1 [t-1]Judge2_osaka [t-1]Judge3_osaka [t-1]Judge4_tokyo [t-1]Judge5_osaka [t-1]Constant

Year effectPrefecture effectFirst-stage F-statistics for H0:
judge effects = 0
First-stage Partial R-squared
No. of excluded Instruments
Sample period
Number of observations-2.691(0.814)-5.412(3.118)-1.496(1.587)-10.383(2.028)2.604(6.314)0.313(0.968)-0.657(0.046)
Yes
Yes

Yes
Yes

5.95
0.041
5
1985-2000
752

Note: Standard errors are given in paretheses. Fuller's parameter is set equal to 2. The data are for the
fourty-seven prefectures for the period 1985-2000.

50

Supreme Court (SC)# of SC1# of prefectures under
the jurisdictionSC

47# of judges per case

Final Appeal

5-15*High Court (HC)# of HCs8# of prefectures under
the jurisdiction

HC

HC

.

HC

.

1-11

# of judges per caseAppeal3District Court (DC)# of DCs
Hokkaido Prefecture

DC

DC

DC

DC

.

.

.

.

.

DC

4

the other 46 prefectures

1

total

50

# of judges per case1 or 3*

Appendix Figure 1. The National Court System in Japan (typical adjustment dismissal
case)
NOTE. * The number of judges per case depends on the social significance of each case.

51