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Productivity and Growth of Japanese Prefectures Prepared for the 3 rd 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) - PowerPoint PPT Presentation
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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.”
Contents1. Construction of Regional-Level Japan Industrial
Productivity (R-JIP) Database2. 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 non-
manufacturing)• 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)
7
http://www.rieti.go.jp/jp/database/R-JIP2012/
index.html
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
The difference in labor quality across prefectures in 1970 (Tokyo=1)
0.600
0.650
0.700
0.750
0.800
0.850
0.900
0.950
1.000
Toky
oKa
naga
wa
Osa
kaHy
ogo
Kyot
oHi
rosh
ima
Fuku
oka
Aich
iYa
mag
uchi
Saita
ma
Shizu
oka
Chib
aW
akay
ama
Oka
yam
aTo
yam
aKa
gaw
aN
ara
Nag
asak
iN
agan
oM
ieEh
ime
Gum
ma
Hokk
aido
Ishi
kaw
aFu
kui
Toch
igi
Yam
anas
hiGi
fuM
iyag
iSh
iga
Oita
Tott
ori
Ibar
aki
Toku
shim
aSa
gaN
iigat
aFu
kush
ima
Yam
agat
aKu
mam
oto
Shim
ane
Koch
iM
iyaz
aki
Akita
Iwat
eKa
gosh
ima
Aom
ori
Oki
naw
a
9
The difference in labor quality across prefectures in 2008 (Tokyo=1)
0.600
0.650
0.700
0.750
0.800
0.850
0.900
0.950
1.000
Toky
oKa
naga
wa
Aich
iHi
rosh
ima
Osa
kaN
ara
Hyog
oKy
oto
Shig
aTo
yam
aSh
izuok
aYa
man
ashi
Mie
Yam
aguc
hiKa
gaw
aSa
itam
aO
kaya
ma
Fuku
oka
Gum
ma
Ishi
kaw
aTo
kush
ima
Toch
igi
Fuku
iCh
iba
Ehim
eIb
arak
iGi
fuN
agan
oM
iyag
iO
itaTo
ttor
iW
akay
ama
Shim
ane
Fuku
shim
aSa
gaKu
mam
oto
Yam
agat
aN
iigat
aN
agas
aki
Koch
iHo
kkai
doAk
itaIw
ate
Miy
azak
iKa
gosh
ima
Oki
naw
aAo
mor
i
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.
We decompose prefectural labor productivity into three factors: prefectural TFP differences, the capital-labor ratio, and labor quality.
Decomposition of factors underlying regional differences in labor productivity
14
L
i
LirL
iLir
i
Vi
Vir
i
ir
i
irKi
Kir
i
Vi
Vir
iir
Vi
Vir
i i
irVi
Vir
r
Q
QSSSS
HH
ZZ
SSSS
RTFPSS
HH
SSVV
log21
21
log- log21
21
21
log21log
23
1
23
1
23
1
23
1
: Labor Productivity
: TFP Difference
: Capital-Labor Ratio
: Labor Quality
Decomposition of differences in regional labor productivity in 1970 (in logarithm)
15
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
Kana
gaw
aTo
kyo
Osa
kaM
ieCh
iba
Shig
aYa
mag
uchi
Hyog
oW
akay
ama
Nar
aAi
chi
Oka
yam
aSh
izuok
aHi
rosh
ima
Kyot
oTo
chig
iTo
yam
aSa
itam
aIb
arak
iGi
fuIs
hika
wa
Ehim
eFu
kuok
aGu
mm
aO
itaKa
gaw
aN
agan
oAk
itaHo
kkai
doN
iigat
aTo
kush
ima
Miy
agi
Fuku
iSa
gaFu
kush
ima
Tott
ori
Iwat
eAo
mor
iYa
mag
ata
Yam
anas
hiM
iyaz
aki
Koch
iKu
mam
oto
Nag
asak
iKa
gosh
ima
Shim
ane
Oki
naw
a
TFP Difference
Capital-Labor Ratio
Labor Quality
Labor Productivity
Decomposition of differences in regional labor productivity in 2008 (in logarithm)
16
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
Toky
oO
saka
Chib
aAi
chi
Oita Mie
Kyot
oKa
naga
wa
Wak
ayam
aSh
iga
Shizu
oka
Hiro
shim
aYa
mag
uchi
Hyog
oIb
arak
iTo
chig
iFu
kuok
aTo
yam
aHo
kkai
doN
agan
oO
kaya
ma
Gifu
Fuku
shim
aSa
itam
aN
ara
Toku
shim
aKa
gosh
ima
Ishi
kaw
aAk
itaGu
mm
aKa
gaw
aFu
kui
Saga
Niig
ata
Yam
anas
hiM
iyag
iAo
mor
iIw
ate
Miy
azak
iYa
mag
ata
Ehim
eSh
iman
eTo
ttor
iKu
mam
oto
Koch
iO
kina
wa
Nag
asak
i
TFP Difference
Capital-Labor Ratio
Labor Quality
Labor Productivity
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:
i
irirr zlz
Next, the national average of the capital-labor ratio in industry i, denoted by z_
i, and the national average of
the labor input share in that industry, denoted by l_
i, are obtained by taking the simple average across all prefectures:
r
iri zz471
、 r
iri ll471
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 z
_
i using the national average
labor input share in each industry l_
i , as weights:
i
ii zlz
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=l_
I and zir=z_
i:
iiiir
iiir
iiiir
iiiiri
iiir
iii
iirir
lzzzllzzll
lzzzllzlzl
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):
i
iiiri
iiiri
iii
irir lzzzzllzlzl
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 TFP
Share effect Within effect Share effect Within effect Within effect
Agriculture, forestry, and fisheries -0.18 6.60 30.30 26.72 4.33
Mining -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
Processed metals -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
Electricity, gas and water utilities 1.01 5.00 -2.19 -4.05 2.39
Wholesale and retail trade -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
Nonmanufacturing excl. primary industry subtotal 2.54 61.42 -92.57 52.42 16.76
Total 11.77 88.23 -21.76 121.76 100.00
Result of decomposition by industries (2008)(3) 2008
Capital-labor ratio Labor quality TFP
Share effect Within effect Share effect Within effect Within effect
Agriculture, forestry, and fisheries -30.47 13.10 7.07 4.92 -7.18
Mining -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
Chemicals 11.85 6.32 5.28 1.93 1.25
Petroleum and coal products 5.67 2.99 0.78 0.20 13.43
Ceramics, stone and clay -0.01 1.29 0.15 1.33 2.59
Basic metals 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
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
Service activities (government) -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
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 non-government 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.
the prefecture’s growth of labor productivity=capital deepening (within effect) + capital deepening (share effect) +capital reallocation effect + labor reallocation effect +TFP (within)
ri rr r Kri ri r ri
i r
ri r ri r ri rr Kri ri Lri ri
i ir r r
Yri rii
k kG y s G k G Lk
R R y y k ks G k s G LR y k
s G TFP
In 1980s capital reallocation effect was negative almost every prefectures in Japan.
Shiga
Tochigi
Tokyo
Shizuoka
Yamanashi
Fukui
Mie
Saitama
Ibaraki
Toyama
Kagoshima
Aichi
Niigata
Miyagi
GunmaNara
Nagano
Fukushim
a
Nagasaki
YamaguchiKyo
to GifuTottori
Miyazaki
Hyogo
Chiba
Kanagawa
Ishika
wa
Yamagata
Okaya
ma
Kumamoto
Hirosh
ima
Saga
Shimane
Akita
Iwate
Kochi
Tokushim
a
AomoriOsa
kaEhim
e
Okinawa
Oita
Hokkaido
Kagawa
Fukuoka
Wakaya
ma
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
5.00
6.00
7.00
Effect of Factor Reallocation on the Prefectural Labor Productivity (1980-1990)
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.
Yamanashi
Akita
Saga
KagoshimaTottori
MieIbaraki
NaganoOsa
ka
Tokush
ima
Gifu
Fukush
ima
Kyoto
Yamagata
Shizuoka
Hyogo
Tokyo Fukui
Shimane
Saitama
ShigaAich
i
Fukuoka
Niigata
Okinawa
Iwate
Miyazaki
Oita
Aomori
Tochigi
Kagawa
Kumamoto
Toyama
Chiba
Nagasaki
Wakaya
maNara
Hirosh
ima
Hokkaido
Yamaguchi
Okaya
ma
Ishika
waMiya
gi
Gunma
KanagawaKoch
i
Ehime
-3.00
-2.00
-1.00
0.00
1.00
2.00
3.00
4.00
Effect of Factor Reallocation on Prefectural Labor Productivity (2000-2008)
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.