Hi-Stat

Discussion Paper Series

No.28

Labor Input Data of the JIP Database

Yoko Takahashi and Tatsuji Makino
April 2004

Hitotsubashi University Research Unit
for Statistical Analysis in Social Sciences
A 21st-Century COE Program

Institute of Economic Research
Hitotsubashi University
Kunitachi, Tokyo, 186-8603 Japan
http://hi-stat.ier.hit-u.ac.jp/

Labor Input Data of the JIP Database

International Workshop on the Industry Productivity Database
February 27- March 1, 2004
Solaria Nishitetsu Hotel, Fukuoka, Japan

Yoko Takahashi* and Tatsuji Makino***

Doctoral student, Gakushuin University
Doctoral student, Chuo University**

The quality of labor in Japan has changed considerably over the past thirty years. Contributing
factors are the rapid aging of the population, the rising educational level of the workforce, greater
diversity in employment patterns, and changes in the industrial structure. So when the worker’svarious attributes (labor qualities) are assumed to be homogeneous, it is impossible to measure thereal labor input. As a result, the estimation result of TFP will also be unreliable. It is thus necessary
to find an appropriate way of accurately measuring the quality of labor.
The labor input data of the JIP database aims to estimate labor input adjusted for differences in thequality of labor. In order to use JIP labor data together with other JIP data such as Input-Output
Tables and capital stock, we have to rearrange and make the labor data into the same industry
classification and the same period as JIP database. Then, using the rearranged labor data, we cancreate a Divisia index for labor input adjusted for labor quality, and also we can estimate TFP much
more correctly.
Taking over from Kawai (2003) 1, we extended the data and tried to improve the estimation method.
In this paper, we briefly outline the characteristics of the labor input data and discuss the estimation
method employed by Kawai (2003). Moreover, we explain how the data and the estimation method
could be improved.

1. Outline of the labor input data of the JIP database
The JIP labor input data contain the following series: the number of workers, working hours, andhourly wages for every year from 1970 to 2000. For each of these series, a breakdown by industry,
employment status, sex, education, and age is available.
Table1 Characteristics of the JIP labor data(×31)(×84)

Employment status(×2)

Sex(×2)

Education(×4)

Age(×15)1970 - 2000
JIP industry classification
Employee + Executive
Self-employed + Family worker
MaleFemale
Elementary school
Senior high school
Junior college or technical college
College or university, graduate school
15 - 19 years old
20 - 24 years old…

PeriodIndustry classification

80 - 84 years old
85 years old and over

It is the one of maximum contributions that Kawai (2003) created the JIP Labor data in the detailed
classification of service industries (38 sectors).

1

This paper especially section 1-4 bases on “Keizaibunseki” (2003, ESRI, No.170, Section 2). Please show that if
you want to know much detail.

2. Data source and estimation method
The sources for the labor input data are as follows. Data on the number of workers is taken from thePopulation Census which is published every five years. This data is used as the benchmark and data
for the intervening years are filled by linear interpolation between the benchmark, and by RAS
method. Since there is no detailed classification data like Census of Manufactures, Kawai (2003)
used Population Census data in Control Total.
The working hours and hourly wages by worker category were made from Monthly Labor Survey
and Basic Survey on Wage Structure. Kawai (2003) mainly used Monthly Labor Survey, but thatsurvey does not contain the detailed category data. So he filled the blank by the data from BasicSurvey on Wage Structure.
The figures on the compensation of employees estimated from JIP labor data differ from the figuresin the JIP input-output table. Then Kawai (2003) is trying to make the data consistent by calculating
the rate of deviation between two values, and multiplying working hours by the square root of that,and multiplying hourly wages by the square root.

3. Estimation of quality adjusted labor input
Quality adjusted labor input can be derived as follows. First, it is assumed that the labor input partin the production function is (weakly) separable. If the labor input function is set as

L = L( MH 1 , MH 2 , L , MH n ) ( MH i : man hours ofworker i), the labor input index adjusted forlabor quality (Divisia quantity index) will be defined as:
•(1)

L=n

∑

∂ ln Li =1 ∂ ln MH

Ld ln MH idti=n

∑
i =1

•

νiMH iν i = wi MH in∑ w MH (Share of wage bill)iii =1MH i

The difference between the labor input index and the growth rate of man hours in this formula is thelabor quality index. i.e. it is defined as:
(2)

••

•

QLMH

Q=L−MH=n

∑
i =1

•

ν i dindi = MH i

∑ MH (Share of man hours)
ii =1

It becomes possible to estimate the labor input index and the labor quality index, using this formulaand the JIP labor data on the number of workers, working hours and hourly wages.

4. Decomposition of the labor input index
In this section, we explain the transition of the quality adjusted labor input index and the result of
the factor decomposition. Figure 1 it decomposes the annual average rate of change the labor input
index of the all industries, the non-service industry, and service industry in three periods, the wholeterm, and 1970- 85, and 1986-98 into the quality of the number of laborers, working hours.
The overall number of workers increased only slightly during the observed period. However, thisoverall trend hides strongly diverging industrial trends: the number of workers fell sharply in thenon-service industries, while it grew strongly in the service industries. There was only slight increasein the total number of workers from 1986 onwards.
Working hours increased somewhat both in the non-service and the service industries and therefore
overall. However, we think there are some problems with this data on working hours, which we will
go into in section 5.
Similarly, the quality of labor is improving both in the non-service industries and the service
industries and therefore overall. However, the growth rate halved after 1985.
The labor input index increased slightly for the non-service sector until 1985, but then declined.. Inservice sector, the index grew throughout the entire period, though it halved after 1985.
Consequently, in all industries, although 2.02% of growth is shown during the whole term, thespeed of expansion became to be half since 1986 year, as compared with the steep rise until 1985.
However, the rate of increase of total man-hours is much lower than that of the labor input index,
meaning that labor input will be underestimated if man-hours are used as the relevant measure.
(Insert Figure 1)
As we mentioned first, the greatest contribution of this JIP labor data is created the data of service
industries. While the number of workers has shrunk in almost all non-service industries, it has grownin almost all service industries. The labor quality index has increased in many industries as a resultof rising educational levels and the rise in the number of skilled workers. The upward trend in labor
quality has been particularly pronounced in wholesale, retail, transportation, telecommunications, inthe service industries and chemicals, electrical machinery in the manufacturing industries.
(Insert Figures 2 and 3)

5. Some Problems
Four major problems with the JIP labor data remain, which we are planning to solve.
(1) Working hours
As a result of the revision of the Labor Standards Law in 1989 as well as the prolonged recession
over the past decade, working hours in Japan decreased sharply in the 1990s. This is shown clearly,for example, in the Monthly Labor Survey (Figure 4).

(Insert Figure 4)
On the other hand, in JIP labor data, the working hours are continuing to increase every industry
since 1970 (Figure 5). Transition of working hours is completely different from our real feelings.
Hayashi and Prescott (2002) argue that the prolonged recession in Japan can be explained by thefalling TFP growth rate. This TFP is estimated under the situation of a rapid reduction of working
hours. If TFP is estimated using the JIP labor data which working hours are increasing, TFP value
may be wrong.
(Insert Figure 5)

This problem is considered to be caused by the adjustment method of compensation of employees.
Now in order to keep consistency between the compensation of employees estimated from JIP labor
data and that in a JIP input-output table, working hours are multiplied by the square root of thedeviation rate2. If the deviation rate is continuing rising for a certain reason, working hours might
continue to increase by such adjustment method.
(2) Discontinuity
There are some discontinuities in the data. The number of workers, the man hours, and the labor
input index values for 1985 look like outliers. Then, it turns out that there was an unusual divergencein a service industry in 1985 when I compared to officially published data (Figure 6).It is necessary
to clarify this cause.

(Insert Figure 6)(3) Service industry
In the service industry, even Control Total part is estimated by linear interpolation. However, wehave worry in using the RAS method for the data which made by linear interpolation. Moreappropriate estimation will be attained by combining many kinds of surveys for service industries.
(4) Secondary job
The number of workers in the JIP labor data is estimated by using as a benchmark data from thePopulation Census which is performed every five years. However, the data on the number ofworkers in the Population Census does not include secondary jobs. So there is the possibility that
we underestimate the number of workers.

6. Other suggestions for improvements”
While the problems just mentioned take the highest priority, suggestions for other extensions andimprovements of the JIP labor data are as follows.
(1) Estimation of marginal labor productivity
In the estimation of the quality adjusted labor Divisia index, hourly wages are assumed to be equal
to marginal productivity.
However, aging and a seniority wage system make the advanced age workers who enjoy hourly pay
higher than actual marginal productivity of their own, and that lead to overestimating the labor input.
So we are looking for a method of estimating the marginal productivity of the workers but wagedata.
Crepon, Deniau and Sebastien (2002), Hellerstein, Neumark and Troske (1999), and Ilmakunnas
2Deviation rate is defined as follows.
Employees × (Monthly working hours × 12) × Hourly wages
Deviation rate ≡Compensation of employees in JIP input - ouput tableand Maliranta (2002) estimate the marginal productivity of workers using micro-data. By creating
panel data based on time series and industry data of JIP database, we can estimate the marginal
productivity of workers in a similar way.
(2) Occupational category
The present JIP labor data does not contain any information on workers’ occupations. Suchinformation, however, would make it possible to examine the effect of workers in particularoccupations, such as those in IT-related fields, on TFP in each particular industry.
(3) International Comparison
When we compare the same data as JIP database in a foreign country, we probably take care forwage data. As far as we know, there are few countries that collect annual wage data at theestablishment level as is the case in Japan (South Korea has this kind of wage data). Many othercountries obtain wage data from the income data of household surveys. For this reason, wage data inJapan and in other countries may not be comparable.
(4) OtherIn accordance with the revision of the whole JIP database, such as (1) the shift to 93SNA, (2)correspondence to the revised 2002 Japanese standard industrial classification, (3) using 2000
input-output table (Ministry of public management, home affairs, posts and telecommunications), it
is necessary to revise the JIP labor input data.

Figure1 Decomposition of labor input
4.0%

3.0%

ServiceIndustry Total

Non-Service
2.0%

1.0%

0.0%

-1.0%

-2.0%

Number of workers

Working hours

45. Other manufacturing

44. Precision machinery & equipment

43. Other transportation equipment
42. Ships

41. Motor vehicles

40. Other electrical machinery

39. Equipment and supplies for household use

38. Electrical machinery

37. General machinery equipment

36. Metal products

35. Non-ferrous metals
34. Other steel

33. Steel manufacturing

32. Stone, clay & glass products

31. Coal products

30. Petroleum products

29. Other chemicals

28. Chemical fiber

27. Basic chemicals

26. Rubber products

25. Leather and leather products

24. Publishing and printing

23. Pulp, paper, paper products
22. Furniture

21. Lumber and wood products

20. Apparel and accessories

19. Fabrics and other textile products
18. Spinning
17. Silk
16. Tobacco
15. Beverages
14. Other foods

13. Rice polishing, flour milling

12. Processed marine products

11. Livestock products

10. Quarry, gravel extraction, other mining

9. Crude oil, natural gas exploration
8. Metal mining

7. Coal, lignite mining

6. Marine products
5. Forestry

4. Veterinary, Farming services

3. Livestock, poultry

2. Other cultivation and seed planting

1. Rice, wheat production

Quality adjusted labor

Quality

Working hours

Number of workers

Quality adjusted labor

70-98

Quality

86-98
70-85
70-98
86-98
70-85
86-98
70-85
70-98

Figure2 Decomposition of labor input (Non-service, 1970-1998)
4.0%

2.0%

-2.0%
0.0%

-4.0%

-6.0%

-10.0%-8.0%-12.0%-14.0%

Figure3 Decomposition of labor input (Service, 1970-1998)
8.0%

6.0%

4.0%

2.0%

0.0%

-2.0%

-4.0%

Figure4 Average working hours per worker(Industry total, Annual Average, 1970=100)
100

95

90

85

80

83. Others (non-profit)
82. Medical, hygiene (non-profit)
81. Public administration

Source: Monthly Labor Survey, Ministry of Health, Labor and Welfare

80. Medical, hygiene (public)
79. Education (public)
78. Other services for individuals
77. Laundry, hair-cutting , public bath
76. Inns
75. Restaurants
74. Broadcasting
73. Entertainment
72. Other services for businesses
71. Rental of office equipment and goods
70. Advertising
69. Other public services
68. Medical, hygiene (private)
67. Research
66. Education (private, non-profit)
65. Mail
64. Telegraph, telephone
63. Other transportation, packing
62. Air transportation
61. Water transportation
60. Road transportation
59. Railway
58. Housing
57. Real estate
56. Insurance
55. Finance
54. Retail
53. Wholesale
52. Waste disposal
51. Water supply for industrial use
50. Waterworks
49. Gas, heat supply
48. Electricity
47. Civil engineering
46. Construction

Year00959085807570

Quality adjusted labor
Quality
Working hours
Number of workers

Figure5 Index of working hours (JIP Database, 1970=1.00)
1.25

1.20

Industry Total
Non-ServiceService

1.15

1.10

1.05

1.00

0.95
7075

80

85

9095

Year

Figure6 Index of labor input (Service, 1970=1.0)
2.4

2.2

2.0

Quality adjusted labor
Man hours
Number of workers

1.8

1.6

1.4

1.2

1.0

0.8
7075

80

85
Year9095