DISCUSSION PAPER SERIES
September
September 20122012

No.2012
No.201212-06

A fivefive-day work week system and laborproductivity/efficiency
Atsuyuki FUKAURA

Very Preliminary

A five-day work week system and labor productivity/efficiency

Atsuyuki FUKAURA

Faculty of Economics
Nagasaki University

afukaura@nagasaki-u.ac.jp

Abstract: The purpose of this paper is to clarify whether there is a relationship between
the labor productivity and the spread of five-day work week system in Japan by
applying Data Envelope Analysis (DEA). Thus this paper gives another research field
where we can use DEA to analyze the policy evaluation of local authorities. Our main
findings are twofold: the five day system is more popular in the region where theeconomic activity is highly active and in such area the efficiency measured by DEA isalso high, which means there is a negative correlation between the labor productivity
and the number of annual working days.

JEL classification: J31, J81, J82, R10
Keywords: Data Envelope Analysis, five-day work week system, labor productivity,
regional gap

1

A five-day work week system and labor productivity/efficiency
Atsuyuki Fukaura

1. Introduction
In 2012, Municipal Office of Nagasaki published the report that discussed about
the work-life balance in Nagasaki area. It emphasized that shortening of working hours
is necessary to improve the workers’ welfare in Nagasaki area. According to the data
released from Ministry of Internal Affairs and Communications quoted in that report,
annual working hours of Nagasaki are about 1,400 hours, the longest across all
prefectures in Japan.
There is the huge difference of approximately 200 hours between Nagasaki andNara, where the working hours are shortest in Japan, and this is equivalent to 25 days(eight hours / day) a year, or one month in the labor days of Nagasaki. The report did notgive the reasons in detail, but it implicitly suggested that the spread of five-day working
week system is very behind in Nagasaki. In addition, in many prefectures in themetropolis area, a wage level is generally higher than Nagasaki, although working
hours are relatively shorter (Fukaura (2012)). If so, workers in Nagasaki are to beinferior in quality as work force, in other words, their performance and productivity islow. However, judging from the general understanding that there are many cutting-edge
companies and workers having an advanced technique mainly on manufacturingindustry in Nagasaki, such reasoning seems to be strange. At all events, we can imagine
that there are some kind of relation between labor productivity and the length ofworking hours. Furthermore, having taking into account the a wage in the metropolis
and degree of the spread of five-day working week, we may assume the positive
correlation between labor productivity and a five-day working week.
In this paper, we are going to confirm whether the significant relations exist
between degree of the spread of five-day working week and the labor productivity. The
paper is organized as follows. At first we state the current situation of a five-day
working week system and confirm the more a five-day working week system spreads themore active the regional economy activity is. In other words, we try to know if the small
working days are the necessary condition of the active economy in a region. Next, withthat in mind, in order to have an index for labor efficiency, we conduct Data Envelope
Analysis (DEA). Finally we examine the relation between the index of the labor
efficiency and a five-day working week system.
2. A five-day work week system
A five-day work week system is one of the employment customs with five days on
duty and 2 days off a week, normally on Saturday and Sunday. When this system was
first introduced by Ford Corporation in the 1920s, by limiting working hours per week
up to 40 hours, nevertheless 48 hours a week were common at that time. Originally it
was intended to reduce production scale, with maintaining the employment under the
global recession in those days. However, it became clear it improved the laborproductivity because of the self-culture of the worker who took advantage of a holiday
and security of the rest time. In addition, as the negotiation power of the labor union
improved, the five days system spread more and more, which also contribute to improve2

the working conditions.
Introduction to Japan advanced in 1980s, when Japanese economy turned from ahigh growth era into the steady growth period, and toward the start of 21 century,
workers, including a public servant or a public school teacher, who works under afive-day working week, grew very rapidly.
However, while the big companies having a lot of management skill could
introduce the system easily, there were a lot of medium and small business that areforced to introduce it by a need for putting together for the shift of the big companies, orby a need for the compliance with laws and ordinances observance (because the legal
working hours in the Labor Standards Law are 40 hours, then all forms have to set 5
days week if we assume labor for eight hours for five days on weekdays). In these cases,
so-called "unpaid overtime" was caused in many occasions.
Generally speaking, there are some merits the five-day work week system has.
Primarily and most importantly, an effect to the productivity is expected, by the restdays devoted to recover the work force. For example, (1) the morale improvement ofworkers, (2) improvement of efficiency of personnel management policy, (3) improving
the fixation rate of workers, are expected. Second, there is an indication that suggests
the improvement of the working situation/environment and this attracts the talented
workers more easily. If this is the case, the labor productivity should rise as workers
under five-day work week system are increased. On the contrary, one of the reasons of
the less productivity could include a delay of the introduction of a five-day working weeksystem.
Appended Table1 shows the ratio of worker working under five-day working week
which is estimated from various sources about the working days for all prefectures but
Okinawa. Here we define workers under complete five-day working week system as
those whose annual working days are less than 250 days (19 a month day on average);
therefore, workers who take 3 off-days in every two weeks are counted as workers morethan 250 days. And, we calculated the difference between the ratio of workers with
250-300 days and those of less than 250days, and, for the analytical reason, andstandardized and prioritized them.
The reason we set a cut-off line at 250days is we can observe a remarkable feature
at that point. Figure 1 and Figure 2 below show high rank 10 areas (with astandardization index less than -1.2) and the low rank 11 areas (a standardization index
greater than 1). Because each area’s values are resembles closely, it is hard to tellindividually, but we can say the distribution of workers less than 200 days does notchange among all areas. Similarly, the ratios over 300 days are approximately equal.
Major difference occurs at the point of 200 days and 300 days. The shape of line isnearly flat in Figure 1, but in Figure 2 a clear peak exists at 250days. In addition, theprefectures shown in Figure 1 are areas where local economies are relatively active, on
the other hand, the prefectures in Figure 2 are suffering a depressed regional economy.
The degree of the regional economic activity should be understand multifactorial, butthese two figures are enough to cause doubt that long working hours may not necessary
bring about positive outcomes to the region. Kochi and Nagasaki are examples.

3

Figure １: distribution of the working days(high ranked prefecture)
40
35
30
25
20
15
10
5
0

（％）

Under 50
days

50-99
days

100-149 150-199 200-249 250-299
More
days
days
days
days than 300
days

Kanagawa

Mie

Shiga

Saitama

Aichi

Nagano

Gunma

Chiba

Shizuoka

IbarakiFigure 2: distribution of the working days (low ranked prefecture)
（％）

45
40
35
30
25
20
15
10
5
0
Under 50
days

50-99
days

100-149 150-199 200-249 250-299
More
days
days
days
days than 300
days

Akita

Hokkaido

Ishikawa

Kochi

NagasakiNiigata

Fukui

Saga

Aomori

Kagoshima

MiyazakiThis fact induces us to assume, in the high ranked prefectures, labor input ismaterialized effectively and this enables the firms to accept a five-day working weeksystem easily. As the results, it can be expected that the performance of labor input isinversely correlated with the numbers of working days, in other words, positively
correlated with the degree of the introduction of a five-day working week system.
Under this hypothesis, we follow the next two steps in order to verify the relation
between the labor efficiency and a five-day working week system. We will (1) derive theproper variables which reflect labor-input and its performance, and (2) apply DEA for4those variables in order to measure the labor efficiency of each prefecture.
3. Variables
In DEA, we define the performance of the decision making units (DMU) just on a
production-frontier as effectiveness = 1 and evaluate relative efficiency of other DMU bymeasuring how much they locate away from a frontier. In the case of single input, only
one DMU exists on a production-frontier, but several DMUs may be located on the frontier
when multiple-inputs are considered. In other words, in the case of a single input, the efficiency isdefined quantitatively (size of the production for the input), but in the two input DEA, it can take
into account the qualitative aspect, that is, the combination of inputs1.
We chose the input and output variables as follows. At first, we applied the cluster
analysis for the various kinds of variables and removed unsuitable variables foranalysis sequentially. Needless to say, we removed the variables that depended linearlywith each other (for example, per capita income and total income of the prefecture).
Such procedures yielded three groups of the variables: (1) "wage & salary" "amount ofdeposits&savings" (demand deposit +time deposit) ,(2) "commuting time” "overtime
working hours",(3) "vocational training costs" "prescribed working hours” .
Furthermore, the second and the third groups can be integrated. Finally, variables wereclassified roughly in "wage & salary" "amount of deposits&savings" as the first group,and "commuting time” "overtime working hours" "vocational training costs" "prescribedworking hours” as the second group.
Because the latter four variables express the hours devoted to work and the social
investment for work force improvement, we do these with the input variable in DEA. On
the other hand, output variables are "wage & salary" "amount of deposits&savings”.
They reflect respectively the flow and stock factor of the distributed value to labor
input2.
4. Results of DEA
4-1 D-efficiency
Appended Table 2 summarized the results. D-efficiency values are shown in adescending order and surplus, weight and reference DMU are also displayed. For
reference, the five-day working week introduction index is also added3。9 prefectures arelocated on the production-frontier (D-efficiency=1). Three major urban areas, Tokyo,
1

Because DEA does not need any specific assumptions about the form of the production function it iseffective when it is hard to assume the production function including the specific section in the firm
analysis. But the assumption on the scale effect is needed. On the other hand, it is said that theprobabilistic frontier analyses including the estimate of the parameter of the production function aresuitable in the effectiveness evaluation for the whole industry. We performed the parametric analysis
with the assumption of decreasing return.
2 The source of data is as follows.
“wage & salary”: Ministry of Health, Labor and Welfare wage structure basics statistics survey).
“Deposits&savings”: Bank of Japan "finance economic statistics monthly report."“commuting time”, “prescribed working hour”, “overtime working hours”: Ministry of Internal Affairs
and Communications "basic survey of social living practices."“Vocational training costs”: Ministry of Internal Affairs and Communications "local finance statistical

yearbook."

Because DMU’S economic activities is very large, so here we applied the Decreasing Return to Scale
Model.
3

5

Osaka, and Nagoya are included here, and this would be a straightforward result
because output is measured by the nominal value distributed to workers.
In addition, we can classify these prefectures in two groups about weight. The firstincludes Kanagawa, Tokyo, Aichi, Osaka, Nara, and Gifu, whose weights of the “wage &salary” are large, that is; the output in the sense of “flow” is significant. These areas are
the advanced areas in applying 5-days work week system and strongly affected by thethree major economic zone mentioned above. On the other hand, the second group is thegroup which the contribution of the “stock” is large (Mie, Nara, Tottori, Kagawa).
Among this group, a five-day work week system is not so common, except Mie. It is
worth to note that Tottori, Kagawa are peculiar.
On the result that Kagawa, Tottori are efficient, we have strong sense ofincongruity when we consider the economic condition/environment of these two
prefectures. It is thought that in Kagawa the amount of deposits&savings acted to raise
an efficiency (Kagawa is the top prefecture as to the deposit&savings) and Overtime
working hours does the same in Tottori where it is shortest. The effect of these variables
appeared in excessively in DEA and strongly influenced the result4。
D-efficiency expresses how much the potential productivity of the input is realized,
and we can know the cause of low efficiency by examining surplus. If surplus is big, thenthe production must be small, compare to the input level (or input is excess). For
example, in Chiba, if all inputs are multiplied by 0.994 uniformly and decrease
Overtime working hours by 2.53 hours, prescribed working hours by 22.9 hours andcommuting time by 0.65 hours, then its D-efficiency rises to 1. Here we can say
D-efficiency of the local prefectures is generally low, and the lowness of each Kyushu
prefectures is outstanding (the 24th place of Fukuoka is the best in Kyusyu area, andthree last are all Kyushu prefectures). Tohoku tends to be approximately similar.
4-2 Input and output
Next, we examine the character of input and output variables.
(1) Prescribed working hours
Prescribed working hours tend to be spent excessively in all prefectures, but
especially in the high-ranked prefectures, it is longer than the low-ranked ones. In otherwords, we can maintain a present efficiency, with decreasing the working hours, that is,prescribed working hours works on the reduction of the efficiency. If the working hours
are the disutility for workers and we assume the decreasing return of scale, this is anatural result.
However, weight of prescribed working hours is very high in Kanagawa, Aichi, andMie. This means that prescribed working hours can give an effect to improve efficiency.
This contradicts the above implication. It is unknown why it improves the efficiency in
Kanagawa etc. One possible explanation is here; if the firms carry out the well-designed
personnel management and the process control, the working hours may help improvethe efficiency, even if they are long. Thus, an active introduction of such management
know-how is helpful in the low ranked prefectures.
4

Weight is calculated as the optimal weight for each variable. The bigger weight is the higher thevariable is evaluated for giving the output. It is not necessary for DEA to put a specific assumption
about a form of production function, and it also has the advantage that analysis can be done with a few
samples, but, on the other hand, there is the difficult point that a result depends on an abnormal value.
Kagawa and Tottori may correspond here.

6(2) Vocational training costs
The weight of vocational training costs is high in the metropolis prefectures withhigh efficiency, so it implies the employment policy works effectively in these
prefectures. Generally, they are the high-density area and the job opportunity isabundant. Therefore, it can be expected that the “Hello Work Office” (job placement
service office) functions well, that, in other words, economy of scale acts in the job
placement or the vocational training.
There are four prefectures of Nagano, Hokkaido, Ehime, Kyoto that a relatively
big surplus occurs as to vocational training costs (therefore, its weight is 0), and thissuggests there is a room to reduce vocational training costs of these areas.
(3) Commuting time
The surplus of commuting time is observed in Chiba, Saitama, Shiga, and Tochigi.
These prefectures are included in the commutable area to Tokyo, Osaka, and it very
often needs over two hours to commute. Intuitively, the long commuting time isconsidered to exhaust the labor power and brings the less efficiency. However, the laborof these prefectures is very efficient in itself. That means a long commuting time andhigh efficiency are positively correlated with each other, and this contradicts ourintuition.
We can explain this quiz as follows; in one aspect the long commuting time
discourages workers, but we have to focus on the other side of commuting time. Actually,
it is often reported that the self-study opportunity before arriving the office tends tobecome popular recently, for example, attending the career-up seminar, the crossindustrial association and so on. This reminds us the side-effects of commuting time for
improving the labor efficiency. We cannot deny the possibility that such an effect occurs.
On the other hand, in the low ranked prefectures, contributions of commuting
time is large (i.e., its weight is large). By examining the original data, it can be easilyfound that commuting time of the low-ranked prefecture is short without exception. So
we can imagine, because workers do not get tired in commuting, they can concentrate
into their jobs, which buoys up the efficiency. This may be the very naturalinterpretation. However, commuting time is the only factor to contribute in Ehime,
Yamagata, Yamanashi, Iwate, Fukushima, Aomori, Saga and Nagasaki (weight= 0).
After all, advantage of the short commuting time is cancelled by other factors whichjeopardies the efficiency, so it cannot help improve the total efficiency.
(4) Overtime working hours
A clear relation between Overtime working hours and the labor efficiency cannot
be derived. But in many cases, in the area where weight is positive, it is the maximum
weight among all inputs (e.g., Tokushima, Toyama). Because weight expresses a
response to the efficiency-improvement when an input is decreased by one unit,
decreasing overtime working hour will be the most effective way for efficiency
improvement in Tokushima, Toyama.
All three inputs (prescribed working hour, commuting time, overtime working
hours) are necessary hours for workers, but the derived results above show that lengthof these hours is not simply related to the efficiency. The question is how the firmmanager can utilize prescribed working hour and commuting time, in other words, thepersonnel management and process control should be focused if we try to utilize theprescribed hours effectively, and on the same time, the manager needs to encourage
7

workers to do a self-culture activity during commuting time.
However, such a relation was not seen in Overtime working hours. This makes us
conclude a holiday attendance, for example, does not enhance workers’ ability or labor
will5。
(5) Output
As for three prefectures of Fukui, Shimane, Kochi, DEA results tell us
wage&salary is low. For example in Kochi, the total wage bill can be increased toapproximately 28 billion yen if inputs are used efficiently. However, in almost all
prefectures, no surplus of wage&salary is detected. Therefore, to improve the labor
efficiency, it should be done by the adjustment of the input, rather than the outputs.
As mentioned before, the stock side can be expressed by the deposits&savings andthe flow side by the wage&salary, respectively. When we compare the both weights, they
are Kanagawa, Aichi, Tokyo, Osaka, Gifu, Chiba and Saitama that weight of the wage &salary exceeds that of deposits&savings. This is the very natural result because thehigh wage level was evaluated substantially to improve D-efficiency6.
4-3 Referenced prefectures
The Appended Table also shows the list of prefectures which refer the prefectureswith D-efficiency=1. Kagawa is the most referred prefecture and Aichi is the next. But
we have to remember Kagawa (and Tottori) strongly received huge influence of the data
itself, then it is safe to say Aichi is the typical prefecture, in the sense that it is themodel prefecture of the labor efficient area7。
When we remember there are many prefectures with surplus of vocational
training costs, it follows that we should strengthen the employment policy more for
Aichi. This is because the labor market is very tight in Aichi where manufacturingindustry is in boom, the more intensive employment policy is needed to help the firms
who demand the work force immediately. On the other hand, the employment policy
helps the job applicants, too. From the view point of the job applicants, we are to devotea policy effort to the area where the labor resources have the less competitiveness. But
such an implication, i.e., upbringing of long-term labor resources, does not come out of
DEA directly because DEA is the method assuming the present labor market.
It is Hyogo, Tochigi, Ibaraki, Kyoto that refers Tokyo, and this is the clear evidence
indicating their working condition is the metropolis type. Situation is similar about
Osaka.
Kanagawa and Mie have some striking features and contrastive to Aichi. Both are
top-ranked efficient prefectures but only one prefecture (Fukui) refers Kanagawa and
Mie is referred by only two (Shiga, Ibaraki). That means that these are in a peculiar
position among 46 prefectures. Or, graphically speaking, Kanagawa and Mie locate on
5

Of course Overtime working hours are paid more than prescribed hours financially. However, theincrease of overtime hours may reflect the lack of the management ability of the firm. This may
discourage workers in spite of higher financial compensation.
6 All input data used in this DEA is about the men, but output data came from the local statistics
which includes the female. Therefore, a notice must be paid to remember the contribution of the inputs
may be overestimated. The reason we basically exclude the female labor from the analysis is because
we did not take into account the difference of working types (e.g., part-time, or full-time). This is
deeply related the female labor condition.
7 Traditionally Kagawa has the strong ties with the Osaka economic zone, and, the economic/social
unification with Honshu has been advancing by the Honshu-Shikoku Bridge. These factors nay
contribute to the results, so the detailed examination must be done.

8the production–frontier, but in the place very near of the axis of the input variables. It is
not obvious from where this uniqueness comes. But, in both prefectures, because
weights of prescribed working hours and vocational training costs balance relatively
well, it may be able to say that the firm’s effort to improve the labor-efficiency, including
personnel management and the process control, and the employment policy aresynchronizing relatively well.
5. Labor-efficiency and five-day working week system
Finally, we examine the relation between D-efficiency and degree of the spread of
five-day working week system. If labor input is used more efficiently, it is thought that
we can shorten the working days with keeping the output the same level. In order toverify this, a regression analysis was carried out, where the dependent variable is afive-day working week index (shown in Append Table 1) and the independent isD-efficiency.
The result was given in Table 1. If Nagasaki’s D-efficiency is improved andbecomes 1, a five-day working week index is decreased by 1.55(from 1.13 to -0.43). Thisis almost the same level of Miyagi. Workers who work more than 250 days are 47.46% in
Miyagi and 53% in Nagasaki. Then, in Nagasaki, approximately 22,000 workers canenjoy a five-day working week newly (the male’s working population is approximately
410,0008).
Table 1: regression analysis(a dependent variable: Five-day working week index)
PartialStandard
variables
regression
F-value t- valueerror
coefficient
D-efficiency-3.3068
0.915413.0484 -3.6123
Constant

2.75430.757413.22423.6365p-value
0.00080.0007(R2= 0.228)

The results of the regression to per capita income are given in Table 2. According
to the table, if D-efficiency is improved by 0.5, per capita prefecture income will risearound 700,000 yen. This corresponds to the gap between 2,940,000 yen, the average
per capita income of the prefectures with D-efficiency=1 and 2,150,000 yen of Nagasaki
with D-efficiency = 0.52.

variables
D-efficiency
Constant

Table 2: regression analysis(a dependent variable: per capita income)PartialStandard
regression
F-valuet- value p-valueerror
coefficient
1397.7896
318.3704
19.27614.39050.00011532.2477
263.4143
33.8360
5.81690.0000(R2= 0.340)

By integrating Table1, Table2 and DEA results, at the risk of oversimplification, the8Of course, this does not mean Miyagi is a model prefecture of Nagasaki, but just a numericalexample.

9following interpretation seems a fair generalization as a background of the introductionof a five-day working week system. If the firm use prescribed working hours efficiently
and workers can utilize their commuting time as a self-culture opportunity, labor input
yields output more effectively. This is reflected by wages primarily, and followed by thegrowth of the deposits&savings. These finally improve the prefecture income per capita.
On the other hand, shortening of the working days lead to improvement of the working
condition, and enhance an effect of the employment policy. This kind of good circulation,
income and working environment, is an important key factor to boost up the whole
regional economy.
Micro-economics teaches us a labor supply curve bends backwardly, as wages rise,through an income effect. If we can understand the spread of five-day working weeksystem as a result of an income effect, then it is explicable theoretically that
introduction of a five-day working week advances as per capita income and wages rise.
From the view point of the firm manager or human resource director it is criticallyimportant to understand how they connect working hours and commuting time to thelabor efficiency effectively. In this sense, it is natural to say the grass-root-likemanagement technique such as TQC and TQM are still important and effective, and“morning activities” (asa-katsu in Japanese new word, which means the self–culture
activities before the duty hours), which has been getting to popular recently, is more
likely to be a trigger for shortening of working hours. In this sense, an improvement of
the motivation of worker oneself is another key factor. Conversely, in prefectures of
Tohoku and Kyushu, because the personnel management and the self-culture activity
are not so refined, a vicious circle that disturbs the effective use of working hours occurs,that is, a poor personal management and self-culture activity lead the long working
hours followed by the low productivity.
Of course, this may the exaggerated picture. However, it is necessary for Tohoku,
Kyushu prefectures with having low D-efficiency to promote the precise time
management, but not to utilize Commuting time of workers because it is originallyshort. Rather, the firm managers should try to refine their time management method,
and raise workers’ concentration during a prescribed working hour.
Finally, for reference, we show Table 3 showing the results of regression of theminimum wage of 2011 to D-efficiency. We know from the table the minimum wage ishigh in the area where D-efficiency is high, and it will rise approximately 19 yen if
D-efficiency is improved by 0.1. As the facts, the difference of the minimum wage
between Nagasaki and Miyagi is 29 yen and the difference of D-efficiency is about 0.1.
However, the most impressive implication from Table 3 is the high correlation
between the minimum wage and D-efficiency. The minimum wage is determined byconsidering many social factors as well as the economic ones, but the essential
determinant must be a labor productivity of the area concerned9.
Recently, Japanese government set a policy objective to make a minimum wage
800yen nation-widely in next few years. Improvement of the effectiveness necessary toachieve this goal can be calculated as shown in Appended Table 3, by using a result ofTable 3. At a glance, it is absolutely impossible to realize 800yen, as far as economic
reality is concerned.

9

According to Minimum Wage Law, the minimum wage must be determined in consideration of cost ofliving of the worker and ability to pay of the employer, and the consistency with other policy measures
for the public assistance, in order to maintain the minimum standards of wholesome and cultured
living. It is not allowed to decide minimum wage simply only by labor productivity. However, it will beclear that a minimum wage in defiance of labor productivity cannot be sustainable.

10Table 3 regression analysis(a dependent variable: Amount of 2011 minimum wage)
PartialStandard
variables
regression
F-valuet- value p-valueerror
coefficient
190.6981
39.4764
23.3355
4.83070.0000D-efficiency
536.7003
32.6621270.0068
16.4310.0000Constant(R2= 0.413)
6. Conclusion
D-efficiency is one possible index to measure the efficiency. However, considering
the result of 0.529, D-efficiency of Nagasaki, it can be seen that the regional economy of
Kyushu is facing the severe situation, as well as Tohoku area.
The following difficulties are left in this paper. At first, there is no easy way tomeasure the working condition quantitatively or numerically. The cultural and social
factors like "a working style" must have an important influence on the use of the local
labor resources, or commuting time. It is not easy to take such elements into theanalysis directly, and what we should work on is to interpret the statistical results fromthe wide social point of view. Such an explanation was partial in this paper, and thismay cause the biased results, in Kagawa and Tottori for example.
Second concern is about the input and output variables. If this is an analysis of thebusiness organization’s activity, then such variables are relatively easily determined.
Because we had no clear assumption about the DMU as mentioned earlier, we wereforced to rely on the cluster analysis to find the possible variables, and this means thetheoretical base of our variables was not so sound.
Furthermore, the physical productivity of the labor was not reflected. For example,
it will be natural to suppose that the amount of factory shipment reflects the efficiencyof labor input.
When some problems are left about the analysis technique, the overestimatedresults are provided or otherwise the underestimated. If our results are underestimated,
it is expected that there would be a larger difference between areas. On the contrary, thedifference is smaller if a result is emphasized excessively.
A problem may be settled by using more general DEA, but probably a more
important point is to consider whether DEA is appropriate method as the analyticaltechnique to meet an object of this paper. The prefectures we assumed as DMU here are
the abstract units and do not mean the concrete decision making institutes such as alocal authority or the central government office and so on. In this sense our prefectures
are the virtual organizations. If we focus on the effectiveness of an employment policy, we
should have referred the concrete decision making institutes such as the local laborbureau. In such a case, the meaning of the analysis could be clearer.
However, even if so, the labor productivity difference of Nagasaki and Tokyo is nota small so as to disappear in the short term. In this sense, this paper roughly supports
the tough situation that a low-ranked prefectures face.

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文誌 58(1), pp.54-61
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谷口守，秋永淳一郎，阿部宏史,2000,地方中心都市の環境評価構造とその都市間比較及びDEA分析への展開，土木計画学研究・論文集，No.17，pp.423-430．
丸山昭治､2002、郵便事業における生産性と効率性の計測 －国際比較の観点からー、郵政研究所月報､pp.14-30.
深浦厚之､2012、非正規労働の社会的機能と地域経済格差 －東京都と長崎県の比較―
長崎大学ディスカッションペーパー No.2012-04

12Appended Table 1
Working

~

～

～

～

～

～
300
299

days
(per month)

50
(4)

99

149

(6)

(10)

199
(15)

249

（23）

(19) *(**)－(*)

Weighted(**)－

average

(*)

~
(25)

Standardized
index

**
Kanagawa

2.72

3.64

7.4

8.59

36.8

31.95

8.89

226

-4.85

-2.2631

Mie

2.3

3.18

5.94

8.79

36.55

33.73

9.5

230

-2.82

-1.8038

Shiga

2.64

3.47

6.52

8.23

35.65

33.85

9.64

229

-1.8

-1.5730

Saitama

2.39

3.27

6.51

9.4

35.68

34.2

8.55

228

-1.48

-1.5006

Aichi

2.25

3.03

6.03

8.56

36.01

35.12

9

231

-0.89

-1.3671

Nagano

2.25

3

5.87

9.04

35.34

34.55

9.94

231

-0.79

-1.3445

Gunma

2.07

3

5.83

8.85

35.61

34.99

9.65

231

-0.62

-1.3061

Chiba

2.33

3.36

6.98

9.75

34.48

34.19

8.91

228

-0.29

-1.2314

Shizuoka

1.81

2.94

5.85

8.04

36.23

36.45

8.69

232

0.22

-1.1160

Ibaraki

2.48

3.14

6.31

9.16

34.41

35.24

9.25

230

0.83

-0.9780

Tokyo

2.76

3.63

6.73

8.67

33.45

34.41

10.34

229

0.96

-0.9486

Hyogo

2.77

3.31

6.52

9.14

34.06

35.03

9.18

228

0.97

-0.9463

Tochigi

2.3

3.08

5.67

8.7

34.85

36.05

9.34

231

1.2

-0.8943

Yamanashi

2.48

3.52

6.25

8.91

32.69

34.57

11.57

231

1.88

-0.7404

Miyagi

2.28

2.77

5.62

8.25

33.62

36.98

10.48

233

3.36

-0.4056

Osaka

2.68

3.2

6.35

8.94

32.25

35.71

10.89

231

3.46

-0.3830

Kyoto

3.08

3.97

6.91

9.07

30.44

35.1

11.43

228

4.66

-0.1115

Nara

2.88

3.44

6.5

8.9

31.18

36.01

11.09

230

4.83

-0.0730

Yamaguchi

2.38

3.87

6.1

8.64

31.84

36.81

10.36

231

4.97

-0.0413

Shimane

2.99

3.52

5.99

7.71

32.36

37.53

9.9

231

5.17

0.0039Hiroshima

2.73

3.32

6.27

8.15

31.85

37.58

10.1

231

5.73

0.1306

Gifu

2.24

3.03

6.06

8.6

31.78

37.86

10.43

233

6.08

0.2098

Toyama

2.25

3.16

4.97

7.28

32.98

39.41

9.94

235

6.43

0.2890

Oita

2.53

3.09

5.85

8.47

30.18

37.1

12.77

234

6.92

0.3999Wakayama

2.11

3.03

5.9

8.45

30.74

37.81

11.94

234

7.07

0.4338

Fukuoka

2.28

3.16

6.18

8.44

30.9

38.03

11.02

233

7.13

0.4474

Fukushima

2.06

3.01

5.41

7.89

31.4

38.87

11.37

235

7.47

0.5243

Kumamoto

2.38

2.95

5.22

8.32

29.44

37.58

14.11

236

8.14

0.6759Okayama

2.68

3.05

5.6

7.95

31.21

39.4

10.11

233

8.19

0.6872

Yamagata

2.07

2.88

5.1

7.35

31.46

39.73

11.41

237

8.27

0.7053

Tottori

2.3

3.31

5.51

6.89

31.24

39.6

11.16

235

8.36

0.7257Tokushima

2.76

3.31

5.69

8.17

29.08

37.7

13.3

234

8.62

0.7845

Iwate

2.52

3.6

5.61

8.21

30.15

39.13

10.77

233

8.98

0.8659

Ehime

2.15

3.08

5.55

7.96

29.91

39.07

12.29

236

9.16

0.9066IKagawa

2.27

2.65

4.84

7.72

31.08

40.46

10.98

237

9.38

0.9564

Akita

2.6

3.36

5.43

9.19

29.67

39.4

10.35

233

9.73

1.0356

Hokkaido

2.01

2.95

5.71

8.07

29.92

39.72

11.63

236

9.8

1.0514

Ishikawa

2.34

2.99

5.28

7.53

30.65

40.5

10.7

236

9.85

1.0628

Kochi

2.59

3.2

5.39

8.25

27.91

38.05

14.61

236

10.14

1.1284

Nagasaki

2.26

2.72

4.77

8.38

28.87

39.05

13.95

238

10.18

1.1374

Niigata

2.41

3.19

5.85

7.55

31.31

41.54

8.14

233

10.23

1.1487

Fukui

2.23

3.39

5.12

7.4

30.44

40.78

10.63

236

10.34

1.1736

Saga

2.04

2.72

5.23

8.66

28.88

39.55

12.93

237

10.67

1.2483

Aomori

2.04

2.82

4.8

8.43

29.56

40.45

11.89

237

10.89

1.2981

Kagoshima

2.5

3.35

5.34

9.06

27.39

38.54

13.82

235

11.15

1.3569Miyazaki

2.08

2.62

5.54

7.87

28.03

39.73

14.14

238

11.7

1.4813

All

2.48

3.29

6.22

8.71

32.97

35.95

10.38

231

2.98

0

Note 1: Data from the Employment Status Survey(2007)
Note 2: an average of the working days in parenthesis
Note 3: The ratio(%) of the worker who work in each annual working days
Note 4: "(**)-(*)" is a difference of "... 299 days" and "~249 day"Appended Table 2（resultｓ of DEA）
slacksprefecture

Index

Defficiencyof(**)－(*)

Prescribed

Vocational

workingtraining

hours

cost

Commutingtime

Excess

DepositWage

working

&

&

hours

savingssalaryKanagawa

1-2.2631

0

0

0

0

0

0

Mie

1

-1.8038

0

0

0

0

0

0

Aichi

1

-1.3671

0

0

0

0

0

0

Tokyo

1

-0.9485

0

0

0

0

0

0

Osaka

1

-0.3829

0

0

0

0

0

0

Nara

1

-0.0730

0

0

0

0

0

0

Gifu

1

-0.2098

0

0

0

0

0

0

Tottori10.7256

0

0

0

0

0

0

Kagawa1

0.9564

0

0

0

0

0

0

Chiba

0.994

-1.231422.97422

00.658555

2.530304

00

Shizuoka

0.979-1.1160

17.40502

0.596316

01.775696

0

0

Fukui

0.959

1.173523.95509

0.06402

00.933971

00.02101

Tokushima

0.955

0.784400.150423

0

0

0

0

Saitama

0.944-1.5006

0.39395

00.552599

0.790293

0.867849

0IINagano0.921-1.3445

17.99719

1.008675

00.30142200Niigata

0.901

1.148721.93064

0.430165

0

0

0

0

Hokkaido

0.889

1.051441.25623

3.05774

00.507024

00Ishikawa

0.865

1.062717.122010.254748

0

0

0

0

Shiga

0.854

-1.5730000.034296

1.695182

00

Okayama

0.848

0.687110.03752

001.332303

0

0

Shimane0.828

0.003922.66179

0.47005801.133089

00.065822

Toyama0.827

0.28899.089782

0

0

0

0

0

Hyogo

0.821

-0.94630000.579107

00

Hiroshima

0.819

0.130600.512545

00.963068

0

0

Ehime

0.81

0.906629.05415

8.05348202.404048

00

Yamagata

0.809

0.705239.53737

0.69347802.543331

00

Yamanashi

0.786-0.7404

7.88032

0.339149

00.668951

00Tochigi

0.775-0.8942000.000559

0

0

0

Kochi

0.765

1.128324.29512

0.15976902.113494

00.028159

Gunma

0.764

-1.3066

3.113853

002.921303

00Ibaraki

0.764-0.97790000.559086

00

Fukuoka

0.731

0.4473

1.077987

0.627738

0

0

0

0

Kyoto

0.726

-0.111407.497135

0

0

0

0

Yamaguchi

0.72-0.0413

1.147684

0.154405

0

0

0

0

Wakayama

0.719

0.4337

0

0

0

0

0

0

Oita

0.673

0.39988.905946

0.31647

0

0

0

0

Iwate

0.672

0.865913.28332

0.619057

00.983537

00Fukushima

0.659

0.5242

12.83927

0.272323

01.38382300Miyagi

0.622-0.4055

13.35974

0.269886

0

0

0

0

Akita

0.601

1.0356

7.643447

0.127162

0

0

0

0

Kumamoto

0.6

0.675810.02360.307963

01.175888

0

0

Saga

0.592

1.2482

11.72803

000.867869

00Aomori

0.586

1.298021.23770.275197

00.446087

00

Kagoshima

0.583

1.356818.4754

0.142688

0

0

0

0

Miyazaki

0.566

1.481319.98472

0.05363401.480189

00

Nagasaki

0.529

1.13781.121641

0.209052

00.320418

00Appended Table 2(continued )weights
prefecture

Kanagawa

Prescribed

Vocational

workingtraining

hours

cost

0.33730.662689

Commuting

Excesstime

working hours
0

III

0

Deposit

Wage

&

&

savingssalary01Mie0.45260.547389

000.710092

0.289908

Aichi

0.452

0.548048

0

0

0

1

Tokyo

1

0

0

0

0

1

Osaka

0

0.659098

00.340902

0

1

Nara

0

0.515996

00.484004

0.714466

0.285534

Gifu

0

1

0

0

0.172820.82718

Tottori

0

0

0

1

1

0

Kagawa01

000.862583

0.137417

Chiba

0

1

0

0

0.053994

0.939906

Shizuoka

0

0

1

0

0.744801

0.234058

Fukui

0

0

1

0

0.959213

0

Tokushima

0.182

0

0.006825

0.811176

0.951091

0.003903

Saitama

0

1

0

0

0

0.944284

Nagano00100.788725

0.131948

Niigata000.384784

0.615216

0.790832

0.109755

Hokkaido

0

0

1

0

0.578434

0.310588

Ishikawa

000.339615

0.660385

0.817778

0.047358

0.96390.036105000.817866

0.036224

Okayama

00.620088

0.379912

00.494734

0.353253

Shimane

0

0

1

0

0.8280270

Toyama00.420402

0.030914

0.548684

0.64240.184944

Hyogo

0.93850.035792

0.025742

00.718193

0.102431

Hiroshima

0.884500.115451

00.757571

0.061109

Ehime

0

0

1

0

0.733693

0.075959

Yamagata

0

0

1

0

0.731693

0.077539

Yamanashi

0

0

1

0

0.734908

0.050621

Tochigi

0.32570.055077

00.619233

0.696353

0.078489

Kochi

0

0

1

0

0.764884

0

Gunma

0

0.649202

0.35079800.430817

0.332762

Ibaraki

0.95780.019495

0.022728

00.711062

0.053354

Fukuoka000.436417

0.563583

0.535795

0.195365

0.159800.009245

0.830952

0.711995

0.014342

Yamaguchi

000.369969

0.630031

0.661878

0.05815

Wakayama

0.11

0.024042

0.015719

0.850259

0.69337

0.025435

Oita

0

0

0.373788

0.626212

0.62390.048647

Iwate

0

0

1

0

0.604016

0.068345

Fukushima

0

0

1

0

0.548872

0.110559

Miyagi000.380051

0.619949

0.522529

0.099906

Akita

0

0

0.367397

0.632603

0.566598

0.034775

Shiga

Kyoto

IV

Kumamoto

0

0

1

0

0.512524

0.087216

Saga

00.558586

0.44141400.420169

0.171542

Aomori001

00.511823

0.074651

Kagoshima000.390215

0.609785

0.516733

0.065861

Miyazaki

0

0

1

0

0.509067

0.056759

Nagasaki

0

0

1

0

0.468848

0.059824

Appended Table2(continued, reference sets)
prefecture

Kanagawa

Mie

Aichi

Tokyo

Chiba
Shizuoka
Fukui
Tokushima
Saitama
NaganoNiigata
Hokkaido
Ishikawa
Shiga
Okayama
ShimaneToyama
Hyogo
Hiroshima
Ehime
Yamagata
Yamanashi
Tochigi
Kochi
Gunma
IbarakiFukuoka
Kyoto
Yamaguchi
Wakayama
Oita
Iwate
Fukushima

V

Nara

Osaka

Gifu

Tottori

KagawaMiyagi
Akita
Kumamoto
Saga
Aomori
Kagoshima
MiyazakiNagasaki
total

2

1

13

4

3

6

4

3

27

AppendenTable3 Efficiency improvement required to realize 800 yen
Minimum

Improvement

Minimum

Improvement

wage(2011)

requiredwage(2011)

required

Hokkaido

705

0.500

Mie

717

0.437

Aomori

647

0.805

Shiga

709

0.479

Iwate

645

0.816

Kyoto

751

0.258

Miyagi

675

0.658

Osaka

786

0.074

Akita

647

0.805

Hyogo

739

0.321

Yamagata

647

0.805

Nara

693

0.563

Fukushima

658

0.747

Wakayama

685

0.605

Ibaraki

692

0.568

Tottori

646

0.811

Tochigi

700

0.526

Shimane

646

0.811

Gunma

690

0.579

Okayama

685

0.605

Saitama

759

0.216

Hiroshima

710

0.474

Chiba

748

0.274

Yamaguchi

684

0.611

Tokyo

837

－0.195Tokushima

647

0.805

Kanagawa

836－0.189Kagawa667

0.700

Niigata

683

0.616

Ehime

647

0.805

Toyama

692

0.568

Kochi

645

0.816

Ishikawa

687

0.595

Fukuoka

695

0.553

Fukui

684

0.611

Saga

646

0.811

Yamanashi

690

0.579

Nagasaki

646

0.811

Nagano694

0.558

Kumamoto

647

0.805

Gifu

707

0.489

Oita

647

0.805

Shizuoka

728

0.379

Miyazaki

646

0.811

Aichi

750

0.263

Kagoshima

647

0.805

prefecture

prefectureVI