Transcript Productivity and Growth of Japanese Prefectures

Productivity and Growth of
Japanese Prefectures
Prepared for the 3rd World KLEMS Conference, Tokyo,
May 19-20, 2014.
Joji Tokui (Shinshu University and RIETI)
Kyoji Fukao (Hitotsubashi University and RIETI)
Tsutomu Miyagawa (Gakushuin University and RIETI)
Kazuyasu Kawasaki (Toyo University)
Tatsuji Makino (Hitotsubashi University)
This presentation is based on our two papers.
Joji Tokui, Tatsuji Makino, Kyoji Fukao, Tsutomu Miyagawa, Nobuyuki
Arai, Sonoe Arai, Tomohiko Inui, Kazuyasu Kawasaki, Naomi Kodama
and Naohiro Noguchi (2013), “Compilation of the Regional-Level Japan
Industrial Productivity Database (R-JIP) and Analysis of Productivity
Differences across Prefectures,” The Economic Review, Vol. 64 No. 3,
pp.218-239 (in Japanese).
Kazuyasu Kawasaki, Tsutomu Miyagawa and Joji Tokui (2014),
“Reallocation of Production Factors in the Regional Economies in Japan:
Towards an Application to the Great East-Japan Earthquake.”
Contents
1. Construction of Regional-Level Japan Industrial
Productivity (R-JIP) Database
2. The change in prefectural productivity differences
and its causes (1970-2008)
3. Factor reallocation and its efficiency among
prefectures and industries
１．Construction of Regional-Level
Japan Industrial Productivity (R-JIP)
Database
Main Features of R-JIP Database
• 47 prefectures in Japan
• 23 industries (13 manufacturing + 10 nonmanufacturing)
• 1970-2008 (annual data)
• Value added, capital input, labor input
• Input data are constructed taking quality into account.
(1) time-series quality change for both capital and
labor
(2) cross-sectional quality difference for labor
5
Relationship between R-JIP and JIP
• The control totals of regional-level value added, capital, and labor are
2011 JIP data.
• The value added deflator for each industry calculated from the 2011
JIP data is used.
• The investment deflator and capital depreciation rate for each
industry calculated from the 2011 JIP data is used.
• The capital cost and capital quality for each industry calculated from
the 2011 JIP data are used.
• In contrast, we calculate regional-specific working hours, labor costs,
and labor quality for each industry.
6
The R-JIP Database is
available on RIETI’s
website (in Japanese
only at the moment)
http://www.rieti.go.jp/jp
/database/RJIP2012/index.html
7
Construction of relative regional labor quality
data
• Each prefecture’s relative labor quality is estimated taking its
employment structure into account.
• The number of employees cross-classified by prefecture, industry, sex,
age, and educational background is from the Population Census (1970,
1980, 1990, 2000, 2010).
• The data for 2008 are estimated through linear interpolation between
2000 data and 2010 data.
• The construction of the prefecture-level labor quality index is based
on the cross-sectional index number approach of Caves, Christensen,
and Diewert (1982).
8
Tokyo
Kanagawa
Osaka
Hyogo
Kyoto
Hiroshima
Fukuoka
Aichi
Yamaguchi
Saitama
Shizuoka
Chiba
Wakayama
Okayama
Toyama
Kagawa
Nara
Nagasaki
Nagano
Mie
Ehime
Gumma
Hokkaido
Ishikawa
Fukui
Tochigi
Yamanashi
Gifu
Miyagi
Shiga
Oita
Tottori
Ibaraki
Tokushima
Saga
Niigata
Fukushima
Yamagata
Kumamoto
Shimane
Kochi
Miyazaki
Akita
Iwate
Kagoshima
Aomori
Okinawa
The difference in labor quality across prefectures in 1970
(Tokyo=1)
1.000
0.950
0.900
0.850
0.800
0.750
0.700
0.650
0.600
9
Tokyo
Kanagawa
Aichi
Hiroshima
Osaka
Nara
Hyogo
Kyoto
Shiga
Toyama
Shizuoka
Yamanashi
Mie
Yamaguchi
Kagawa
Saitama
Okayama
Fukuoka
Gumma
Ishikawa
Tokushima
Tochigi
Fukui
Chiba
Ehime
Ibaraki
Gifu
Nagano
Miyagi
Oita
Tottori
Wakayama
Shimane
Fukushima
Saga
Kumamoto
Yamagata
Niigata
Nagasaki
Kochi
Hokkaido
Akita
Iwate
Miyazaki
Kagoshima
Okinawa
Aomori
The difference in labor quality across prefectures in 2008
(Tokyo=1)
1.000
0.950
0.900
0.850
0.800
0.750
0.700
0.650
0.600
10
• Differences in regional labor quality have shrunk in the 40 years since
1970.
• But they still remain. Labor quality in the prefecture with the highest
level is 1.3 times that of that with the lowest level.
2. The change in prefectural
productivity differences and its causes
(1970-2008)
• Some people are commuting across prefectural borders. In that case,
the prefecture where they inhabit and where they work are different.
• Since in our database value added data are compiled in the prefecture
where production is taken place and labor input data are compiled in
the prefecture where they work, we focus on labor productivity
instead of the per capita income of each prefecture.
Decomposition of factors underlying regional
differences in labor productivity
We decompose prefectural labor productivity into three factors:
prefectural TFP differences, the capital-labor ratio, and labor quality.


1
  S
2


1
 S  S
2
V
 H ir 
 Vr  23 1 V
log    S ir  S i log
 : Labor Productivity
 V  i 1 2
 Hi 
23
V
1 V
: TFP Difference
  S ir  S i RTFPir
i 1 2

  Z ir 
 H ir  : Capital-Labor Ratio
 S log  - log

 H i 
i 1
  Zi 
23
 QL 
V 1
L
1 V
L
  S ir  S i
S ir  S i log irL  : Labor Quality
Q 
2
i 1 2
 i 
23

V
ir
V
i

K
ir
K
i

14
Kanagawa
Tokyo
Osaka
Mie
Chiba
Shiga
Yamaguchi
Hyogo
Wakayama
Nara
Aichi
Okayama
Shizuoka
Hiroshima
Kyoto
Tochigi
Toyama
Saitama
Ibaraki
Gifu
Ishikawa
Ehime
Fukuoka
Gumma
Oita
Kagawa
Nagano
Akita
Hokkaido
Niigata
Tokushima
Miyagi
Fukui
Saga
Fukushima
Tottori
Iwate
Aomori
Yamagata
Yamanashi
Miyazaki
Kochi
Kumamoto
Nagasaki
Kagoshima
Shimane
Okinawa
Decomposition of differences in regional labor
productivity in 1970 (in logarithm)
0.5
0.4
0.3
0.2
0.1
TFP Difference
Capital-Labor Ratio
Labor Quality
0.0
Labor Productivity
-0.1
-0.2
-0.3
15
Tokyo
Osaka
Chiba
Aichi
Oita
Mie
Kyoto
Kanagawa
Wakayama
Shiga
Shizuoka
Hiroshima
Yamaguchi
Hyogo
Ibaraki
Tochigi
Fukuoka
Toyama
Hokkaido
Nagano
Okayama
Gifu
Fukushima
Saitama
Nara
Tokushima
Kagoshima
Ishikawa
Akita
Gumma
Kagawa
Fukui
Saga
Niigata
Yamanashi
Miyagi
Aomori
Iwate
Miyazaki
Yamagata
Ehime
Shimane
Tottori
Kumamoto
Kochi
Okinawa
Nagasaki
Decomposition of differences in regional labor
productivity in 2008 (in logarithm)
0.5
0.4
0.3
0.2
0.1
TFP Difference
Capital-Labor Ratio
Labor Quality
0.0
Labor Productivity
-0.1
-0.2
-0.3
16
Results:
• Differences in prefectural TFP, capital-labor ratios, and
labor quality all contribute to the differences in
regional labor productivity.
• The most important reason for the decline in regional
labor productivity differences in the past 40 years is
the narrowing of differences in the capital-labor ratio
across prefectures.
• In contrast, substantial differences in prefectural TFP
levels remain and are now the main cause for
differences in labor productivity across prefectures.
17
Which industries contribute to the decline in regional
labor productivity differences in the past 40 years?
To do this analysis, first we use following decomposition of each prefecture’s relative factor intensity
into share effect and within effect.
The prefecture-level capital-labor ratio (i.e., for all industries together) in prefecture, zr , can be represented
as the weighted average of the capital-labor ratio in each industry zir, where the weights are given by
industries’ labor input share lir measured in terms of man-hours:
zr 
l
ir
z ir
i
_
Next, the national average of the capital-labor ratio in industry i, denoted by zi, and the national average of
_
the labor input share in that industry, denoted by li, are obtained by taking the simple average across all
prefectures:
zi 
1
 zir
47 r
、 li

1
 lir
47 r
_
Further, the capital-labor ratio for Japan as a whole across all industries, denoted by z, is obtained as the
_
weighted average of the national average capital-labor ratio in each industry zi using the national average
_
labor input share in each industry li , as weights:
z 
l
i
i
zi
The difference between the capital-labor ratio for each prefecture as a whole and the capital-labor ratio for
Japan as a whole can then be decomposed as shown below by regarding the product lirzi as a non-linear
_
_
function of lir and zir and linearly approximating in the neighborhood of lir=lI and zir=zi:
l
ir
i

  l

z


z ir   l i z i   l ir  l i z i   z ir  z i l i
i
i
ir
 li
i
i





 z   l ir  l i z   z ir  z i l i
i
i
i
Given that the second term on the right-hand side equals zero, we obtain the following relationship (where
we use the fact that the sum total of the labor input shares in each prefecture has to be equal to 1):
l
i
ir





zir   l i z i   lir  l i z i  z   zir  z i l i
i
i
i
where the first term on the right-hand side represents the contribution of the fact that a
prefecture has, e.g., above-average labor input shares in industries with a capital-labor
ratio that is above the national average (share effect), while the second term represents
the contribution of differences between the capital-labor ratios of the industries in a
particular prefecture and the national average capital-labor ratios for those industries
(within effect).
Next, we define each industry’s contribution based on the covariance
between factor intensity and labor productivity in the prefecture as follows.
Contribution of the share effect for industry i.
Contribution of the within effect for industry i.
For capital labor ratio and labor quality we can decompose between
share effect and within effect. For TFP we can calculate only within effect.
Result of decomposition by industries (1970)
(1) 1970
Capital-labor ratio
Labor quality
Share effect
Within effect
TFP
Share effect
Within effect
Within effect
Agriculture, forestry, and fisheries
-0.18
6.60
30.30
26.72
Mining
4.33
-0.71
-0.09
-10.22
3.46
2.30
Food and beverages
0.14
3.04
-0.35
4.53
12.91
Textile mill products
-1.37
1.87
-1.37
7.22
8.07
Pulp and paper
0.30
-1.27
0.57
1.35
1.25
Chemicals
5.48
2.77
6.81
2.00
13.43
Petroleum and coal products
4.28
0.15
1.07
0.14
9.28
Ceramics, stone and clay
0.18
0.96
0.77
2.04
4.32
Basic metals
6.05
3.92
14.86
1.91
-0.00
-0.85
1.09
3.90
1.73
3.74
General machinery
0.67
1.59
9.65
2.07
7.60
Electrical machinery
-1.22
1.07
1.04
5.12
6.36
Transport equipment
-1.11
1.26
8.55
1.50
5.81
Precision instruments
-0.30
0.23
0.22
0.57
0.29
Other manufacturing
-2.13
3.61
5.01
8.99
3.55
Construction
-0.50
1.91
4.01
13.48
8.81
1.01
5.00
-2.19
-4.05
2.39
-1.01
3.25
-2.93
23.23
19.86
Finance and insurance
0.23
2.31
1.08
-4.37
0.80
Real estate
2.73
1.61
2.71
-1.84
-5.73
Transport and communications
2.29
33.69
-4.70
-0.65
-10.08
Service activities (private, not for profit)
-0.31
9.94
-16.62
17.25
3.38
Service activities (government)
-1.89
3.70
-73.92
9.37
-2.69
Manufacturing subtotal
10.12
20.30
50.72
39.16
76.61
2.54
61.42
-92.57
52.42
16.76
11.77
88.23
-21.76
121.76
100.00
Processed metals
Electricity, gas and water utilities
Wholesale and retail trade
Nonmanufacturing excl. primary industry subtotal
Total
Result of decomposition by industries (2008)
(3) 2008
Capital-labor ratio
Labor quality
Share effect
Agriculture, forestry, and fisheries
Within effect
TFP
Share effect
Within effect
Within effect
-30.47
13.10
7.07
4.92
-7.18
-1.05
1.37
-0.27
0.73
-0.07
Food and beverages
2.95
5.30
-0.19
5.09
7.01
Textile mill products
0.39
3.35
0.13
2.07
0.00
Pulp and paper
0.28
-2.62
0.22
0.87
0.57
11.85
6.32
5.28
1.93
1.25
5.67
2.99
0.78
0.20
13.43
-0.01
1.29
0.15
1.33
2.59
6.19
7.13
3.89
2.47
1.81
Processed metals
-3.82
0.62
1.67
2.05
0.97
General machinery
-1.93
3.72
6.06
5.31
3.77
Electrical machinery
-2.26
-10.52
-1.02
10.90
-0.95
Transport equipment
-1.09
5.52
6.64
4.69
6.84
Precision instruments
-0.00
0.45
0.03
0.96
-0.30
Other manufacturing
-4.00
7.42
3.75
6.55
1.95
Mining
Chemicals
Petroleum and coal products
Ceramics, stone and clay
Basic metals
Construction
9.28
1.10
-5.43
7.10
11.72
Electricity, gas and water utilities
-8.78
24.96
-3.24
-1.42
-2.57
Wholesale and retail trade
-1.69
8.43
0.77
13.63
25.27
Finance and insurance
-1.71
1.07
0.96
0.91
8.12
Real estate
54.77
-15.92
3.39
-1.81
-0.64
Transport and communications
11.82
21.76
4.72
2.97
0.87
Service activities (private, not for profit)
-5.72
-2.31
-5.23
36.71
25.09
-13.27
-11.96
-62.59
24.28
0.44
Manufacturing subtotal
14.23
30.99
27.40
44.43
38.95
Nonmanufacturing excl. primary industry subtotal
44.70
27.13
-66.65
82.37
68.29
Total
27.41
72.59
-32.45
132.45
100.00
Service activities (government)
Summary of the industrial decomposition result
• Main causes of the remaining differences of prefectural labor productivity
occurred in non-manufacturing sector.
• Notable development from 1970 to 2008 are:
(1)For Capital labor ratio, the share effect of non-manufacturing increased
greatly over time. Particularly, real estate, and transport and
communications. These industries concentrated in high labor productivity
prefectures.
(2)For labor quality, the within effect of non-manufacturing increased greatly
over time. Particularly, wholesales and retail trade and non-government
services. In these industries labor quality is high in high labor productivity
prefectures.
(3)For TFP, the within effect of non-manufacturing increased greatly over
time. Particularly, construction, wholesales and retail trade and nongovernment services.
3. Factor reallocation and its
efficiency among prefectures and
industries
Calculation formula for factor reallocation effect
• Our calculation is based on the Sonobe and Otsuka (2001)’s formula, which
decompose the prefecture’s growth of labor productivity into four parts.
 k  kr 
G  yr    r  sKri G  kri    r  ri
 G  Lri 
kr
i


 R  Rr 
 yri  yr
kri  kr 
 r  sKri  ri
G
k

s


  ri   Lri 
 G  Lri 
Rr
yr
kr
i
i




  sYri G TFPri 
i
the prefecture’s growth of labor productivity
=capital deepening (within effect) + capital deepening (share effect)
+capital reallocation effect + labor reallocation effect
+TFP (within)
In 1980s capital reallocation effect was negative almost every prefectures in Japan.
Effect of Factor Reallocation on the Prefectural Labor Productivity (1980-1990)
6.00
5.00
4.00
3.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
Capital Deepening: Within (%)
Capital Deepening: Share (%)
Capital Reallocation (%)
Labor Reallocation (%)
TFP (%)
In 2000s capital reallocation effect was positive in relatively high labor productivity growth
prefectures.
Effect of Factor Reallocation on Prefectural Labor Productivity (2000-2008)
4.00
3.00
2.00
1.00
0.00
-1.00
-2.00
-3.00
Capital Deepening: Within (%)
Capital Deepening: Share (%)
Capital Reallocation (%)
Labor Reallocation (%)
TFP (%)
Summary of the factor reallocation effect
• Labor reallocation effect was positive almost every prefectures in
Japan from 1980s through 2000s.
• But, in 1980s capital reallocation effect was negative almost every
prefectures in Japan.
• In 2000s capital reallocation effect turned to be positive in relatively
high labor productivity growth prefectures.
• But, in relatively low productivity growth prefectures capital
reallocation effect still remained negative in 2000s.
Thank you.