On Regional Business Cycles in Japan:
An Analysis by Prefectural Composite Indexes
Kazumi Asako a, Takashi Onodera b*, and Atsuko Ueda caIER, Hitotsubashi Universityb

Japan Center for Economic Researchc

Faculty of Political Science & Economics, Waseda University----------------------------------------------------------------------------------------------------------------------------Objective of the paper- To characterize regional-specific business cycles compared to the national business cycle.
- To investigate influential factors to affect regional business cycles in the case of Japan.
----------------------------------------------------------------------------------------------------------------------------Analysis method- Constructing monthly Composite Index (CI) for 47 prefectures over the period of 1985-2010, fromfour economic indicators. (Normalized to be 100 in 2005 for all prefectures and nationwide.)- Characterizing disparity of regional business cycles from national business cycle, by matching eachprefectural CI sequence to the nationwide CI sequence. (Analysis-A)- Investigating factors that exert influences on regional economies to explain deviation of prefecturalCIs from the national CI. (Analysis-B)----------------------------------------------------------------------------------------------------------------------------Distribution of disparity measured by the standard deviation of [PCI(t)-JCI(t)] (N=47)
PCI(t): Prefectural CI
JCI(t): Nationwide CI
Range: [1.9, 12.7]
Average: 5.2
Median of 4.8
Indicated prefectures arethose with large disparity
from the nationwide CI.

Figure 1. Geographicaldistribution of regional
disparityAnalysis – A: Matching analysis
A-1. Matching method
Matching CI sequences of each prefecture: PCI(t) and Japan: JCI(t) . N=301 (1985-2010).
(1) Adjusting leads and lags in the timing of business cycle, by Dynamic Programming Matching(DPM).
(2) Linear transformation to adjust:- CI levels and magnitude of variations- Time trend- Structural change between 1985-2001 and 2002-2010 (long expansion phase in 2002-2008)
ε(t)
s.t.

CCI(t)
CCI(t)

βββ ·PCI(t) β ·t
β ·PCI(t) β ·t(t)(t)for 1985-2001
for 2002-2010

where a sequence of CCI(t), {CCI(1), CCI(2), …}, represents a rearranged national CI of JCI(t) after
adjusting leads and lags of business cycle by DPM.
A-2. Result of matching(1) Performance of introducing DPM and structural change
Table 1. Minimum and average R2 for 47 prefectures
Without structural change
Minimum R2
Average R2
Without DPM
0.330
0.784
With DPM
0.524
0.902

With structural change
Minimum R2
Average R2
0.706
0.885
0.827
0.961

Improved R2 : Average: +0.076～0.118 (by DPM), +0.059～0.101 (by structural change)
Minimum: + 0.121～0.194 (by DPM), +0.303～0.476 (by structural change)(2) Time trend
Table 2. Summary of time trend estimate
Average

MedianMinimum

Maximum

up to Dec 2001

0.000

0.012

-0.160Jan 2002 and after

0.050

0.037

-0.046

No. of prefecture
Negative

Positive

0.080

19280.194

938* Positive (Negative): Prefectural CI declines (rises) against national CI.
Before the structural change (-1990s): 19 prefectures show better performance than an average.
After the structural change (2000s):
Only 9 prefectures show better performance than an average.

Figure 3. R2 for matching with DPM and structural change- R2 with both DPM
and the structuralchange applied- 14 prefectures: below
the average of 0.96
From the lowest:
1. Okinawa (0.827)
2. Iwate (0.840)
3. Shimane (0.865)
4. Akita (0.868)
5. Kochi (0.888)
6. Tottori (0.911)
6. Niigata (0.911)
8. Ehime (0.912)

Figure 5. Time Trend before and after Structural Change
Only 9 prefectures
show better
performance thannational average after
the structural change:Nagoya are:Gifu, Aichi, Mie
Wakayama
Tokushima
Kyushu area:
Fukuoka, Oita,Miyazaki,
Kagoshima

Analysis – B: Factors to influence deviation of regional economies
B-1. Analysis Method
Panel data analysis (fixed-effects model, dynamic panel model with the Arellano-Bond estimators)
i for 47 prefectures, t for fiscal years of 1990-2008
Pub(i,t)Loan(i,t)Export(i,t)+ ,,or,,Pub(i,t)
Loan(i,t)Export(i,t)+ ,where Pub(i,t): Ratio of public investment amount to gross prefecture product
Loan(i,t): Growth rate of outstanding lending of financial institutions
Export(i,t): Export demand to the machinery industry
Figure 6. Transition of three factors at the national level
Lending, export(%) 40Public investment (%)
10830

620

410

200-101985

1990
Public investment rate

1995

2000
Growth of lending

2005

2010

Machinery export

Figure 7. Prefectures with high public investment ratio
Average public investment
ratio during the sample period
Range [3.0%, 14.9%]
Average: 8.2%
From the highest:
1. Shimane (14.9%)
2. Kochi (13.1%)
3. Akita (12.6%)
4. Okinawa (11.8%)
5. Kagoshima (11.8%)
6. Hokkaido (11.3%)
7. Iwate (10.8%)
8. Tottori (10.8%)“: large CI disparity*: low R2 of matching**: bothPublic investment rate:- Reduced by half from 8% in the early 1990s to 4% in the late 2000s.
- Large disparity among prefectures from 3% in Tokyo to 15% in Shimane.
Growth of money lending- Declined over the period of 1990s, negative in the late 1990s and in the early2000s.
- Recovered in the long expansion phase in 2002-8.
Export demand to the machinery industry- Approximately 25% in the early 1990s, and increased to nearly 40% in the late 2000s.
- Production rate of the machinery industry distributes from 0.1% to 20% (Shiga & Aichi).
B-2. Estimation result over the sample period
Table 4: Estimation Result of Panel Data Analysis: 1990-2008
PCI(t)-JCI(t)PCI(t)-CCI(t) (after timing adjustment)
Fixed-effects
Dynamic panel
Fixed-effects
Dynamic panel
Lag of dependent
0.872 (0.024) ***0.883 (0.019) ***Public investment ratio
1.584 (0.095) ***0.345 (0.079) ***1.427 (0.084) ***0.411 (0.055) ***Growth rate of loans
0.051 (0.016) ***0.175 (0.034) ***0.074 (0.014) ***0.121 (0.024) ***Machinery export
0.779 (0.297) ***0.827 (0.248) ***0.766 (0.265) ***0.967 (0.171) ***Constant-11.724 (1.412) *** -5.008 (1.132) *** -10.671 (1.258) *** -6.052 (0.780) ***R-squared
0.327
0.343
Notes: Sample includes 47 prefectures by fiscal year of 1990-2008. Standard errors are in parentheses, and ***, **, and* indicate 1%, 5%, and 10% significant levels respectively.
Dependent variable- All of public investment ratio, growth rate of lending, and machinery export contribute to push up
prefectural CI.
Table 5: Estimation Result of Panel Data Analysis: 1990-2001, and 2001-2008
Dependent: PCI(t)-CCI(t) (afer timing-adjustment)
Fixed-effects
Fixed-effects
Dynamic panel
Dynamic panel
1990-2001
2002-2008
1990-2001
2002-2008
YearLag of dependent
0.832 (0.046) ***0.584 (0.028) ***Public investment ratio
0.310 (0.122) **1.118 (0.119) ***0.218 (0.100) **0.546 (0.086) ***Growth rate of loans
0.031 (0.011) ***0.016 (0.055)
0.205 (0.036) *** -0.041 (0.035)
Machinery export
0.198 (0.479)
1.047 (0.272) ***1.525 (0.465) ***1.151 (0.172) ***Constant
1.554 (1.785)-10.016 (1.370) *** -5.379 (1.571) *** -7.371 (0.941) ***R-squared
0.022
0.250
Notes: Sample includes 47 prefectures by fiscal year. Standard errors are in parentheses, and ***, **, and * indicate 1%,5%, and 10% significant levels respectively.

- Public investment: positive & significant, more influential in the 2000s than in the 1990s.
- Growth of lending: Positive & significant only in the 1990s.
- Machinery export: Positive & significant only in the 2000s.

Figure 9: R2 of fixed-effects model with structural change by prefecture

Note: Prefectureswith large disparity inFigure 1 are indicated.
Those in brackets areexplained less bypanel data analysis.
Summary
1. Matching analysis- Performance of the matching analysis looks fairly well: R2 > 0.82, average R2 = 0.96
- Deviations of regional business cycle are well explained by leads and lags of the timing, time trend,and the structural change.
- Only 9 prefectures show better performance in time trend than national average in the 2000s
2. Influential factors- Public investment rate, growth rates of money lending, and export demand contribute to regionaleconomies.
- Across the structural change, public investment becomes more influential, and export demand
replaces to money conditions of influential.
3. “Deviated” prefectures
Explained well by both Analysis-A & B: Nagano, Yamanashi and Nara
Explained well by Analysis-A, but not B: Wakayama and Miyazaki
Explained well by Analysis-B, but not A: Tottori and Shimane
Not explained well:
Akita and Okinawa; Iwate, Niigata, and Ehime