DP2010-19

The Intranational Business C ycle
in Japan
Michael ARTIS
Toshihiro OKUBO
June 2, 2010

The Intranational Business Cycle in Japanby
Michael Artis, Swansea University, and CEPR*
andToshihiro Okubo, Kobe University**
December 2009
Abstract: This paper studies the intranational business cycle – that is the set of
regional (prefectural) business cycles – in Japan. One reason for choosing to examine
the Japanese case is that long time series of relatively detailed data are available. A
Hodrick-Prescott filter is applied to identify cycles in annual data from 1955 to 1995
and bilateral cross-correlations of prefectural GDPs are calculated for all pairs ofprefectures, in our results we find fairly high cross-correlations. The paper then turns toan econometric explanation of the cross-correlation coefficients in the augmented
gravity model framework. Two prefectures with similar GDPs and a shorter distance
between them lead to business cycle synchronization whilst those with larger regional
gaps in factor endowments (capital, labor and human capital) result in more
idiosyncratic business cycle.
JEL Classification : E32, F41, R11
Keywords: Intranational business cycle, Hodrick-Prescott filter, Optimal Currency
Area, Gravity Model, Heckscher-Ohlin theorem.
* Welsh Assembly Government Visiting Research Professor, Department of Economics, Universityof Swansea, Singleton Park, SA2 8PP, Swansea, UK
E-mail:Michael.Artis@manchester.ac.uk**Associate Professor, Kobe University, Research Institute for Economics and Business
Administration (RIEB), Rokkoudai-cho 2-1, Nada-ku, Kobe, 657-8501, Japan. E-mail:okubo@rieb.kobe-u.ac.jp
Acknowledgments: The work underlying this paper was largely carried out when the authors wereat the University of Manchester. The authors are grateful to Tommaso Proietti, Len Gill, George
Chouliarakis, Pierre M. Picard, Denise Osborn and participants at seminars in Kobe andManchester Universities for their advice along the way. They are also grateful to Kyoji Fukao andHyeog Ug Kwon for providing data sets and Robert O’Neill for excellent proof reading.

1. Introduction
The intranational business cycle is the set of business cycles that characterize theregions of a country. Although less commonly studied, analysis of intranational businesscycles offer a useful benchmark for comparison with results obtained from international
business cycle analysis.1 As an example, issues of adjustment and of consumption-risk
sharing, and more generally many of the predictions of real business cycle (RBC) theory,
which have been investigated at the international level, can also be analyzed at the
intranational level, often with different results. Such differences provide a challenge forthose attempting to formulate an explanation.
Intranational cycles have been studied in the past in connection with propositions in the
optimal currency area (OCA) literature, particularly with respect to risk-sharing
mechanisms (see Wincoop, 1995; Iwamoto and Wincoop, 2000 for leading examples).
However the OCA perspective with risk-sharing mechanisms is not the only one thatshould be important in studies of the intranational business cycle. Indeed, as shown
below, many of the variables that allegedly determine the degree of international businesscycle convergence, can be seen to have little effect in the intranational context.
We have chosen to investigate the intranational business cycle in Japan, this allows
us to make use of a relatively lengthy and comprehensive time series of regional accounts
and factor endowment records which exists for Japan’s 47 prefectures.2 It is useful tonote that Japan is completely divided into 47 prefectures, which are roughly equivalent to
NUTS 2 regions.
This paper studies intranational business cycles by using Japanese prefecture data
sets. We aim to investigate how highly correlated intranational business cycles are, which
regional factors affect intranational business cycle synchronization, and then to discuss
how such factors compare with those considered in the international business cycle
literature. The regional context of the intranational cycle draws attention to the need totake up some of the themes and insights contained in traditional (Heckscher-Ohlin) andnew trade (the gravity model) theory.

1

Of the handful of previous studies of the intranational business cycle the better-known are those by
Wynne and Koo (2000), Hess and Shin (1997 and 2001) Del Negro (2001) and HM Treasury (2003). A
more recent example is Partridge and Rickman (2005).
2
See Table A for 47 Japanese prefectures list for details.

2Related Literature and Our Paper
A key factor for business cycle synchronization is trade, in particular, interregional
trade is much more frequent than international trade as shown in McCallum (1995) by thegravity model.3 The gravity equation explains active intra-industry trade between two
regions with similar economies (GDPs) and geographical proximity (distance), which istheoretically supported by the New Trade Theory (Helpman and Krugman, 1985).4 On
the other hand, inter-industry trade is still dominant in the current world (Brulhart, 2009).
The other type of trade explained by traditional trade theory, specifically the HeckscherOhlin theorem, is inter-industry trade in which capital (labor) abundant regions export
capital (labor) intensive goods. Differently endowed economies engage inter-industry
trade through specialization in sectors in which they have abundant factor endowments.5
Trade is crucially linked with international business cycle synchronization, which is
our main interest.6 This line of thought was effectively initiated by Frankel and Rose(1998), who showed that the traditional criteria for an optimum currency area might, in
effect, be endogenous. The traditional criteria could be read as trading off the benefits ofadditional trade against the cost of relinquishing monetary sovereignty and the associatedbenefits of stabilization policy. If it could be shown that the additional trade itself led togreater business cycle synchronization then the costs of relinquishing monetary
sovereignty would be reduced. A set of subsequent studies investigates the generalizationand sophistication of the initial relationship uncovered by Frankel and Rose (1998)(Frankel and Rose, 2001, Rose, 2000 and de Haan, et al. 2008), arguably the mostcomplete exercise in this vein was the paper by Gruben et al. (2002). A number of issues
were raised in the course of this work, first and foremost being how the trade-GDP
relationship should be rationalized. As Gruben et al. (2002) discuss, the cycle can beviewed as a response to a shock, the prevalence of intra-industry trade between countries
3

McCallum (1995) found that Canadian inter-provincial trade is 22 times as large as US-Canada trade,referred to as the border effect. See Okubo (2004) for the Japanese interregional trade case. Bergstrand(1985) explained the gravity model in the framework of new trade theory.
4
Hummels and Levinsohn (1995) found empirical evidence of active intra-industry trade amongdeveloped countries and suggested the importance of distance between trade partners. Helpman (1987)
empirically showed that developed countries with similar GDPs tend to have a high proportion of intraindustry trade.
5
Evenett and Keller (2002) empirically tested the relationship between intra-industry trade and interindustry trade in terms of factor endowment differences.
6

Traditional OCA theory, as identified with Mundell (1961), points towards a trade-off between trade andintegration benefits against loss of monetary sovereignty, the latter being assumed to imply a loss ofregional stabilization policy benefits. A high correlation between regions’ business cycles resolves thetrade-off because the common monetary policy of a currency union then appears appropriate for all theregions.

3

demonstrates their common exposure to shocks (of technological or of “taste” origin) andhence the likelihood of a synchronization in cyclical experience.7 On the other hand interindustry trade suggests a degree of specialization likely to result in a high frequency ofidiosyncratic shocks, ultimately reflected in low business cycle cross-correlations. The
study of the international business cycle has also led to the reflection that differences in
the propagation mechanism, including differences in policy response and even linguistic
and genetic differences (e.g. Spolaore and Wacziarg, 2006), are liable to produce
different business cycles. It is clear that many of these elements have no relevance in thesetting of an intranational cycle, where institutions and markets important to the
propagation mechanism are “national” in character and scope. This seems especially truein the case of Japan which is an ethnically homogeneous and highly centralized nation
exemplified by the fact that institutions, product/factor markets, financial and taxation
systems are fairly uniform across regions. At the same time, for the prefecture system weconsider here prefectural trade data do not exist, although there exists regional(aggregated at a level higher than prefectural) trade data available, as discussed below.
Nevertheless, as will become clear below, the basic idea of choosing explanatory
variables that might reasonably proxy a common, or idiosyncratic, vulnerability to shocks,
which should then predispose regions towards high or low cross correlations respectively,
are ones with potential relevance for the problem in hand. The notion that any heavy
flow of trade is likely to imply a common fate in the face of external shocks suggests thatany variables that might proxy trade, as for example those suggested by the gravity model
will prove useful explanatory information.
An advantage of using the data employed in this paper is that we are able to study
the long-run regional data with many kinds of variables, as our data spans over 40 years,
from 1955 to 1995 in 47 prefectures. The reason for excluding the data from 1995
onwards from our sample is that the impact of foreign direct investment (FDI) andforeign outsourcing to Asia were negligible before the mid 1990s. From the mid-1990s
onwards the Asian economy affected the Japanese national economy and thus regional
business cycle might be synchronized with those of other Asian countries rather thanother Japanese regions. Thus the current decade is required to draw attention on Japaneseregions as well as foreign countries. However most manufacturing firms and plants
located inside Japan before the mid-1990s operated without substantial Asian FDI,
7

See Kenen (2000), Firdmuc (2002), Imbs (2004), Caldéron et al. (2007), Inklaar et al. (2008) for morediscussion.

4

foreign outsourcing and hollowing-out. For this reason our sample period beginningseveral decades before the mid-1990s, seems appropriate for discussing regional businesscycle synchronization in Japan.
Our main findings are that 1) Japanese prefectures have fairly high positive
business cycle correlations over several decades, however the imbalance of economic
growth across regions and factor movements in earlier years exacted a toll in reducing thesynchronicity of the regional cycles during such periods. The high cross-correlations
reflect the homogeneity of Japanese society in aspects such as legal, political andeconomic institutions, culture, and language and support an optimal currency areaargument. 2) Augmented gravity model variables have a considerable explanatory powerin the cross-correlations. Higher GDPs and shorter distance between prefectures increase
are shown to increase correlations. 3) The variables based on the Heckscher-Ohlin
theorem also have explanatory power. Larger capital-labor ratio gaps and larger humancapital gaps reduce business cycle synchronization driven by active inter-industry trade insectoral specialization across regions. In particular, recent decades (1980s-1990s) see
increased explanatory power in factor endowment differences.
This paper is organized into 7 sections, in the next section we discuss ways toidentify the cycle and provide some comparisons with other intranational cycles, we thenshow that the cohesion of the Japanese intranational cycle in comparison with othercountries. Subsequent sections study how cross-correlations can be explained by regional
factors (sections 3, 4 and 5). We also consider that another purpose of an intranational
cycle investigation such as this is to measure and identify the extent and nature of theconsumption risk-sharing mechanisms that exist in section 6. Finally, section 7 sets out
some conclusions.

2. Identifying the Business Cycle
Traditional business cycle analysis recognizes two types of cycle. There is the“classical” cycle, which can be recognized from the fact that it involves an absolutedecline in economic activity from the peak and an absolute rise in activity from thetrough. The NBER for the US and the CEPR for the Euro Area provide chronologies ofsuch cycles. Clearly such cycles do not exist in growth economies and they are relatively
rare for European economies and Japan. The second type of cycle is a deviation or
growth (occasionally growth rate) cycle where the underlying idea is that the business5

cycle is identified as a cycle relative to a trend. It is the concept of the deviation cyclethat we utilize in our investigation, consequently we need to use a filter to measure thetrend so that the cycle, measured as deviations from the trend, can be identified. In ourcase, where the original data are annual, there is a reasonable presumption that highfrequency noise, such as seasonal effects, is already filtered out. On this basis we use a
Hodrick-Prescott (HP) filter with a “lambda” value (dampening factor) set at 6.25,
following the suggestion of Ravn and Uhlig (2002), this corresponds to a maximum
periodicity of the cycle of 10 years just as the popular “lambda” value of 1600 does fordata observed at a quarterly frequency.8 The filter is applied to the log of the GDP series
for each prefecture and for Japan as a whole.9 Figure 1 shows the national cycle
identified in this way, alongside it the cycles for Tokyo, Osaka (the second largest city)and for Aichi (the capital of which is Nagoya, the third largest city in Japan). Perhaps not
surprisingly the cycle for Tokyo closely follows that for Japan, Tokyo itself accounts for
15 to 20 per cent of Japanese GDP and the wider Tokyo Area for 30 per cent over recent
decades.10 It is clear from Figure 1 that Osaka and Aichi (Nagoya) follow the national
cycle less closely, with more deviations evident.11
Our preferred tool of analysis from here on is the bilateral cross-correlation between
the cyclical deviates for any (and all) pairs of prefectures. When econometric explanation
is attempted we use Fisher’s z-transformation of this cross-correlation of HP-filtered
GDPs to remove the potential of a limited dependent variable problem.12 Bilateral crosscorrelation tools can be used to compare the Japanese intranational cycle with that for the
US (US gross state product (GSP) data being used) and with that for a synthetic EuroArea (the data are just the data on the national business cycles for the countries thateventually formed the EuroArea-12,. prior to the entry of Slovenia into the Euro Area)13.
US intranational data have been used before, as providing a presumptive benchmark for a
8

There remains a degree of controversy about the procedure, as exemplified most recently in the paper
by Meyers and Winker (2005), following earlier papers by Harvey and Jaeger (1993), Burnside (1998)
and Canova (1998) among others. However, an effective counter criticism can be found in Kaiser andMaravall (2001, 2002).
9
We test whether detrended GDP variables are stationary by using unit root tests. As a result of ADF
tests, all 47 series of the variable are stationary. The report of the results is omitted to save the space.
10
The Tokyo Area is defined in our paper as Tokyo, plus its adjacent prefectures of Kanagawa,
Saitama and Chiba. In terms of population size Tokyo accounts for less than 10 per cent in recent
decades, but the Tokyo Area has 30 per cent.
11
Generally more localized regional business cycles might be expected to be more volatile than theaggregate national cycle as more localization implies more specialization.
12
See the section “definitions” in the Data Appendix for Fisher’s z-transformation.
13
The Euro-12 countries are composed of Austria, Belgium, Finland, France, Germany, Greece,
Ireland, Italy, Luxembourg, the Netherlands, Portugal and Spain. See the Data Appendix for data
source.

6

currency area to reach (see Hess and Shin, 1997; Wynne and Koo, 2000; HM Treasury2003), whilst the countries forming the Euro Area have indeed formed a new currency
union. The top and middle panels of Figure 2 show the distribution of bilateral crosscorrelations of the cyclical deviates for the 50 States over the periods 1990-1997 and1997-2005 whilst the bottom panel of Figure 2 does the same for the EuroArea-12
countries over the period 1975-1995. Turning to GDP correlation across Japanese
prefectures, five panels of Figure 3 provide the same information for Japan over 4
separate sub-periods (1955-1964, 1965-1974, 1975-1984, and 1985-1995) and over theentire period. It is clear that the Japanese distribution changed shape considerably overthe period, reflecting what we know to be some turbulent periods of structural change.
The more recent distributions suggests a greater degree of cohesion, exemplified by
fewer or no negative values and a clustering around high positive values, than can befound in the earlier periods or for the other countries.
Moran’s I statistic, which tests for spatial autocorrelation (Moran, 1948, 1950),
indicates an absence of this phenomenon throughout the sample period, values of these
indices are provided in an Appendix.14 As shown in Figure A a value of almost zero for
Moran’s I (approximately 0 to -0.1) implies that GDP fluctuations are spatially random
and thus have no positive or negative spatial correlations with neighboring prefectures.
The absence of spatial autocorrelation tells us that we need not use spatial econometrics
concepts to explain cross-correlations.15

3. Explaining the Cross-correlations
In this section we study the pattern of cross-correlations that we identified in theregional business cycle data in Section 2. In so doing we note that there are some otherimportant considerations to be taken account of here. First, the incidence of structuralchange across Japanese regions suggests that it would be unreasonable to treat the period

14

Another statistic for spatial autocorrelation is Geary’s C statistic. The result is consistent with that ofMoran’s I and indicates no spatial autocorrelations. To save the space, the result is not reported.
15
In Moran’s I statistic if we observe a value of +1 (-1), then the same (different) characteristic
regions are perfectly clustered (dispersed). A value of zero indicates a random spatial pattern (i.e. there
is no spatial autocorrelation). If we observe spatial autocorrelations through Moran’s I statistic, then we
have two possibilities and distinguish between them by using spatial econometric techniques. The firstis spatially correlated error terms, in which the solution comes from the inclusion of the spatial lag ofthe dependent variable. The other is residual or nuisance spatial autocorrelation, which may arise fromthe omission of relevant variables or from measurement errors (See Anselin, 1988 for more details).

7

as homogeneous.16 We therefore divide the sample into four sub-periods of ten years eachand average the variables over these decades. Secondly, we use GMM, nominating as
instruments one period lagged values of our independent variables with some
identification tests.17 Thirdly, the left hand side dependent variable, the set of bilateralcross-correlation coefficients between prefectures i and j at time t, ijt , is potentially alimited dependent variable as the values are bounded between -1 and +1; to overcome thepotential bias involved in not recognizing this we apply Fisher’s “z” transformation to the1  1  ijt . In this paper the transformed cross-correlation ofdata, defined as  ijt  log2  1  ijt HP filtered real GDPs,  ijt , is regressed on the independent variables.18 Following theargument above we consider as independent variables the following; product of twoprefecture real GDPs, the GDP per capita gap between two prefectures, the capital-laborratio gap, infrastructure per-capita gap, human capital gap, area, geographical distance, adummy for adjacency, manufacturing ratio, and manufacturing specialization index.19
One period lagged values of these are used as instruments in GMM estimation.
As discussed in Frankel and Rose (1997) and Gruben et al.(2002), the expected
signs of real GDP and distance are positive and negative respectively, because the gravity
equation indicates that two economies with higher GDPs and smaller intermittent
distances leads to synchronization of business cycles through active intra-industry trade.
The variables of capital-labor ratio gap and human capital gap are expected to be negativesign. The Heckscher-Ohlin type of inter-industry trade increases idiosyncrasies inbusiness cycles. As a proxy for the capital-labor ratio difference, the gravity equation
sometimes uses the GDP per capita gap (Hummels and Levinsohn, 1995). Thus, the sign16

The reason for taking 4 sub-samples is that almost every ten years from 1955 Japan experienced
critical changes. For instance, the high-speed transport system and highway networks were firstdeveloped in the mid 1960s, and then a rapid economic growth period prevailed until the middle of the
1970s. Manufacturing sectors shifted to machinery after the oil crisis after the middle 1970s; then there
was the Plaza Accord of 1985, which appreciated the Japanese yen, promoting Japanese FDI andinternational trade. The highway networks and transport system were spread all over Japan and werecompleted in the middle of the 1980s. Along with globalization this powerfully affected firm location
within Japan. See Fujita and Tabuchi (1996) for regional and sectoral structural changes in the post-war
Japan.
17
General considerations suggest that there will be a substantial amount of endogeneity in the data,
which requires the use of an appropriate estimation technique. Thus we use the 2-step GMM with
underidentification test using the Anderson Cannon correlation LM statistic, the weak identification
test of Cragg and Donald and the overidentification test using Sargan Statistic.
18
Even if we use nominal GDP, all main results as shown below can keep the same.
19
These are all more or less self-explanatory but a detailed definition of each appears in the Data
Appendix.

8

of GDP per capita should be also negative. The variable recording the infrastructure (percapita) gap is expected to be negative or positive, depending on policy targets andoutcomes, possibly because the practice of successive Japanese governments has been to
reward lagging areas with substantial public sector infrastructure investment (per capita),
so that the variable acts as a “branding” or for other reasons. Finally, the variables
relating to the manufacturing ratio and manufacturing specialization index reflect how
manufacturing is crucial in the economy and thus are expected to be positive particularly
in early years as Japan experienced a drastic growth in the 1960s and 70s, during which
manufacturing played a crucial role in the business cycle. When two prefectures have alarge proportion of manufacturing, business cycle is more synchronized.
We now investigate whether these assumptions are borne out in an empiricalinvestigation. Given the commonly held association between trade and business cycle
synchronization we commence with a model which is based on the gravity model of trade.
Here we can consider GDP and distance as variables, as in the simple model,
supplemented by a dummy for a shared border and area. Starting with the classicalgravity model, the first set of results (see the first panel of Table 1) yield the expected
positive sign on GDP products and the expected negative sign on the distance parameter.
This indicates that larger intra-industry trade across prefectures leads to common
vulnerability to shocks, thus resulting in business cycle synchronization. Interestingly, themagnitude of the distance parameter declines over time before becoming insignificant.
This indicates that decreased transport costs and weaker geographical aspects haveaffected business cycle synchronization through time.
The second set of results, shown in the second panel of Table 1, include results
regarding an additional variable, namely the GDP per capita gap, which is the absolutedifference in GDP per capita between prefectures i and j. This variable is used as a proxyfor factor endowment difference and the wealth gap. As expected, the parameter on thisvariable is significantly negative in all periods while the GDP and distance parameters
remain positive and negative respectively. The results indicate that regional difference infactor endowments should be further investigated.
To investigate the impact of factor endowment differences in accordance with the
Heckscher-Ohlin theorem, the “gravity model” variables are supplemented by factor
endowment variables, i.e. capital-labor ratio gap, human capital gap and infrastructure
per capita gap. As expected, in the results reported in the third panel of Table 1 thecapital-labor ratio gap between two prefectures has a significantly negative relationship9with GDP cross-correlations in the latter two periods and the human capital gap issignificantly negative in only the last period. In later periods endowment gaps havestronger explanatory power. Both terms represent a Heckscher-Ohlin type of interindustry trade, which leads to idiosyncratic vulnerability to shocks and results in low
cross-correlations. Hence inter-industry trade, as well as intra-industry trade, plays asignificant role in explaining business cycle correlations in recent periods. On the other
hand the infrastructure per capita gap parameter is significantly negative in the first
period, albeit the late two periods cannot see. Prefectures with similar levels of
infrastructure are more likely to link with each other, however prefectures with highlevels of per-capita infrastructure are less likely to link with those endowed with a low
level of infrastructure.
In the fourth and fifth panels of Table 1 the explanatory variables are further
augmented by variables relating to the role of manufacturing in the prefecture. The
prefectures percentage of national manufacturing, CL, has a significantly positive sign,
regardless its insignificance in the last period. Prefectures with a high percentage ofmanufacturing are likely to be more correlated in GDP. The coefficient on the
manufacturing specialization index, CV, changes sign over the periods. Similar to CL,
CV is significantly positive before becoming insignificantly negative in the last period.
This implies that manufacturing substantially contributed to business cycle correlation in
the 1960s to the 1970s, whereas other sectors such as service and non-manufacturing
played a role in leading business cycle correlation after the mid 80s, consistent with theexperience of Japanese manufacturing growth. Finally, we investigate model
specifications related to instruments. All panels of Table 2 report the values of anunderidentification test (Anderson Cannon correlation LM statistic), Cragg Donald’s
weak identification and the Sargan Statistic for overidentification test below theestimation results. Based on the results of these tests all of our model specifications arereasonable.
To summarize, the arguments of a simple gravity-style trade model, as represented
here GDP and simple distance, and the Heckscher-Ohlin model, as represented by thecapital-labor ratio gap and the human capital gap, are the most reliable sources ofbusiness cycle differences across the prefectures.

4. Sensitivity Analysis
10One of the key factors for intranational business cycle synchronization is interregional
trade. Unlike in the international business cycle literature there does not exist data
recording trade between the 47 prefectures thus we cannot directly measure the impact oftrade, and so use the Gravity type estimations in accordance with New Trade Theory.
Japan, however, does have regional trade data sets in the Inter-regional input-output (IO)
Table assembled by the METI (Ministry of Economy and International Trade of Japan).
Although the data are published every five years (data are not reported per annum) andare not at prefecture level, but for 8 regions, it is worthwhile to test at the regional level.
Roughly, 8 Japanese regions almost correspond to a NUTS 1 region.20
Using this data, we estimate the impact of trade on intranational business cycle, however,since we aggregate the 47 prefectures to 8 regions, we are forced to abandon someindependent variables such as factor endowment gaps and the manufacturing
specialization index, because each region includes big cities, rural areas. Similarly
regions may be a combination of manufacturing (capital abundant), service districts(human capital abundant) and agricultural (labor/land abundant) areas. Regions are mucheconomically homogenous than prefectures. As a result of less sectoral specialization at
the regional level, gaps of factor endowment ratio and the infrastructure per capita
substantially decline between the regions.21 Thus this section adopts the original gravity
model and considers whether GDP and distance can be good proxies for trade. Here,trade is measured by the bilateral trade in GDP of two regions, i.e. Trade Tradeij
GDPi * GDPj,

where Tradeij denotes bilateral trade flows between region i and j. An alternative measureis trade intensity, given as TradeIntensity Tradeij * Total Trade
Tradei * Trade j, where Total Tradedenotes all interregional trade flows and Tradei is all bilateral trade in region i. In thisregional analysis, as the number of observations is smaller than in the prefectural analysis,
we use instrumental variable (IV) panel regressions. As in the prefectural regressions
instrumental variables are the independent variables lagged one period. Table 2 reports
the results for these regressions, the first column reports the result of gravity estimation,
which is consistent with the prefectural level estimations. The second and third columns
20

See Table A for the classification of 8 regions.
We tried to test some regional estimation using capital-labor ratio and human capital gap, but results
are insignificant in all cases.

21

11

report the results for trade variables instead of GDP and distance. The interregional trademeasures are significantly positive, thus we conclude that larger interregional tradepromotes interregional business synchronization, consistent with the findings of
McKinnon (1963), and the gravity model variables are a good proxy for trade variables.
Our regional level results support our prefectural results based on an augmented gravity
model.

5. Discussion
Our results exhibit good fits to augmented gravity equations, higher GDPs andsmaller geographical distance increase the correlation between prefectures. The relevanceof the gravity equation in explaining business cycle convergence was initially highlighted
in the empirical OCA literature as it was shown that active (intra-industry) trade between
countries (Frankel and Rose, 1997; 1998) and a high openness of trade (McKinnon, 1963)
synchronize business cycles. Our results show that these hypotheses from international
business cycle studies are applicable to intranational business cycles. They confirm, to an
extent, that the set of Japanese prefectures constitutes an optimal currency area
We have also provided evidence that differing factor endowments reduce businesscycle synchronization. In particular the recent years there is a significant impact ofdiffering factor endowment gaps on synchronization. The 80s and early 90s saw thefragmentation of production process across Japanese regions and increased intermediate
input trade and parts and component trade, in which capital (labor or human) abundantregion specializes in capital (labor or human capital) intensive production process in themechanism of the Heckscher-Ohlin theorem. This might substantially amplify the impactof factor endowment gaps on business cycle correlations in the recent years.
We also investigated the role of the infrastructure per capita gap, the coefficients on
the variable are significantly negative in the 1960s and 70s, i.e. there is a negative impactof a public investment gap on business cycle synchronization. That is, two prefectureswith higher infrastructure per capita in rural areas have a higher correlation, whilst these
prefectures have a lower correlation with prefectures with lower infrastructure. In Japan
public capital / infrastructure per capita is higher in rural areas and lower in cities in orderto reduce the core-periphery wealth gap. In the 1970s Japanese governments invested inpublic capital through fiscal policy in rural areas. This implies that the development of

12

industrial infrastructure, including highways, road networks, ports and airports, in rural
areas does not greatly contribute to business cycle synchronization between core andperiphery areas. Instead the investment fortifies connections with other rural areas andboosts the correlation with them. This suggests a paradox, that more public capital
investment in rural areas by the central government reduces, or does not increase,business cycle synchronization. If we supplant our findings on this point to a European
setting, it could be inferred that EU Structural Funds, in particular public investments inpoor peripheral regions, are not appropriate, and might actually be harmful, in the senseof reducing business cycle synchronization, which is harmful to an optimal common
currency area.

6. Additional Discussion-- consumption risk-sharing
A stylized fact that comes strongly out of the data is that institutions in Japan appear
to permit a high degree of consumption risk-sharing. We took the consumption data forthe 47 prefectures in our working sample and filtered them in the same way as the GDP
data. We also calculated bilateral cross-correlations of the cyclical deviates ofconsumption for each pair of prefectures. Figure 4 plots these consumption crosscorrelations against the GDP cross correlations. RBC theory predicts that (in thepresence of complete asset markets) consumption-smoothing should result in
consumption cross-correlations which are higher than the corresponding output
correlations at business cycle frequency. Based on the graphical evidence displayed in
Figure 4 this would lead us to expect that the majority of the observations would lie
below the 45 degree line, as they do. This provides a counter-example to the well-known“consumption/output” anomaly first uncovered by Backus et al. (1993). In their study,
and subsequent work, the international evidence points to consumption correlations being
lower than output correlations. The contrary finding lends weight to the presumption thatJapanese prefectures constitute a standard for an optimal currency area, but leaves open
the question of the quantification of the channels through which this is achieved, an issue
which will be the subject of a subsequent paper, (see Iwamoto and Wincoop (2000) for
further discussion). The fact that the channels (and degree) of consumption risk-sharing
may vary across countries requires documentation and provides a natural complement to
the resolution of the puzzle that international capital mobility seems to have increased

13

drastically without affecting conventional measures of risk-sharing between countries(see Artis and Hoffmann, 2006)

7. Conclusions
In this paper we have identified the intranational business cycle in Japan using GDP
data for prefectures over the period 1955-1995. In the first section we compared it withthose for the US and the Euro Area. In the Japanese case, examined in this paper, thedegree of business cycle synchronization within the country emerges as strikingly high bycomparison with that for the US and the Euro Area.
The paper moves on to explain the patterns of business cycle synchronization
summarized in the set of bilateral cross-correlations. Our econometric explanation of thepattern of bilateral cross-correlations between the prefectures of Japan draws heavily on afeature of earlier international cross-correlation work, the idea that trade models –specifically the gravity model and the Heckscher Ohlin trade model – can help to explain
business cycle associations. We find that variables that can be associated with gravity
model explanatory variables, such as GDP and distance, and with Heckscher-Ohlin
variables, like the capital labor ratio gap and human capital gap, supplemented by
endowment variables and other variables are highly significant in explaining the bilateralbusiness cycle cross-correlation coefficients in a GMM estimation framework. This is
gratifying from several points of view: it underscores the remarkable versatility of thegravity model and allows us to integrate our knowledge of the development of theJapanese economy with modern trade theory.
A feature of working currency unions is that some mechanisms usually exist to
facilitate consumption risk-sharing, we notice that overall risk-sharing between theprefectures is a more prominent phenomenon than the widely reported consumptionoutput puzzle would lead us to expect, however its precise measurement and explanation
remain a matter for investigation in a future paper.

Data Appendix
The number of Japanese prefectures became 47 after the Okinawa prefecture wasreturned from the United States in 1972. Due to data availability problems for Okinawa

14

Prefecture before 1972 and its position as both a geographical and economic outlier ourestimation sample is restricted to the 46 mainland prefectures from 1955 to 1995. Many
prefecture data sets for factor endowment data are taken from Fukao and Yue’s “Japanese
prefecture data base”(Hitotsubashi University, Tokyo, Japan) (http://www.ier.hitu.ac.jp/~fukao/japanese/data/index.html) and Fukao and Yue (2000). The GDP data set
for the12 EU nations for the HP-filtered GDP cross-correlations in the bottom panel of
Figure 2 is taken from World Development Indicator (Edition September 2006, World
Bank). GDP is constant 2000 US dollars. The US GSP (gross state product) data sets forthe autocorrelation in the top and middle panels of Figure 2 are taken from Bureau of
Economic Analysis, US Department of Commerce(http://www.bea.gov/regional/index.htm#gsp). The unit of real GSP is millions ofchained 2000 dollars.

Definitions
The dependent variable

The bilateral cross-correlation of cyclical deviates from HP-filtered real GDPs in
two prefectures (i and j) in four sub-sample periods is transformed by Fisher’s z
transformation. The transformation is intended to expand the limited variation (from -1 to1) in the cross correlation measure. Fisher’s z transformation is a one-by-one mappingfrom a variable, ρ, to a variable , utilizing a uniformly increasing monotone function,
1  defined as   0.5 ln for -1<ρ<+1.
1  The independent variables

All the variables are related to prefectures i and j, corresponding to the correlationof the dependent variables. The variables are the average values in each sub-sample
period. In GMM estimations, variables lagged one period are used as instruments. Thus,
all dependent variables in period 1 (1955-1964) are only used as instruments.
GDP: GDP denotes the logarithm of the product of real GDPs in prefectures i and j.

Nominal GDP is taken from Fukao and Yue’s “Japanese prefecture data base”(Fukao andYue, 2000). Since there is no long-run GDP deflator correspondent to our sample, ourreal GDP is derived by using a national level GDP deflator (base year 1990). Here weassume each prefecture encounters a similar price index, alternatively we assume that15

intertemporal change in prices is much more relevant and more important than regional
price difference.
Gap: Absolute difference of log of GDP per capita between two prefectures.
KL: This is the variable of the logarithm of the capital-labor ratio gap between

prefectures i and j. This is private sector capital and labor denotes the work force. Bothcapital and labor are taken from the Fukao and Yue data sets.
Infra: This variable is the difference of the log of industrial infrastructure per capita

in two prefectures. Industrial infrastructure is a part of public capital formation, thesource is the Fukao and Yue data sets.
Human: this denotes the absolute difference of the human capital index calculated

by Fukao and Yue. The indices are derived from relative wages conditioned on genderand educational level. The index is normalized to one for male workers with less thanjunior high school levels of education. Higher values express greater human capitalendowments.
CL: this is the summation of the manufacturing ratios of two prefectures. The ratio

is defined as the manufacturing worker population of prefecture i as a proportion of thetotal number of Japanese manufacturing workers, CLi 

Manufacturing i. This Manufacturingirepresents prefecture i’s share of manufacturing in Japan. The data are taken from
Manufacturing Census.
CV: this denotes the summation of two prefectures’ manufacturing specializationindex. The index is defined as the deviation of manufacturing worker in all working force(e.g. agriculture, manufacturing and service sectors) in prefecture i from the average inJapan, i.e. CVi Manufacturing i
WorkForcei. When the value takes a higher
 Manufacturing WorkForceiipositive number, prefecture i is relatively specialized in manufacturing, otherwise
prefecture i is relatively specialized in service and agriculture, this reflects a comparativeadvantage in manufacturing. The data are taken from Manufacturing Census.
Area: Area is the logarithm of the product of two areas ( km 2 ).
Dist: Distance is the logarithm of the geographical distance between two

prefectures. The distance is measured between the capitals of the prefectures (km).
Neighbor: Dummy for shared border between two prefectures.

16

Trade: The data are taken from the Inter-regional IO Table published by METI.

The Table has been published every five years since 1960. The trade data of 1960 are forthe first period (1955-1964), which is solely used as an instrumental variable in thesecond period, the trade data of 1970, 1980 and 1990 are used for our sample period of
1965-1974, 1975-1984 and 1985-1995, respectively.

Appendix: Moran’s I statistic (Spatial Autocorrelation)
This statistic is aimed at studying (global) spatial autocorrelation in terms of GDPs
across prefectures (Moran, 1948, 1950). Figure A shows Moran’s I statistic to test spatial
autocorrelation statistic in HP filtered GDP for 47 prefectures from 1955 to 1995, which
is bounded in value between -1 and +1. The formula of Moran’s I is given as
n

I

n

W ( X  X )( X  X )i 1 j 1
n
n

ij

Wij
i 1 j 1

i

j

1 n
( X i  X )2
n i 1

where geographical distance is used as the weight matrix W. Values for the I-statistic
closer to 1 indicate clustered (spatially concentrated) data points with similar
characteristics, whilst values close to -1 imply gathering data points with totally different
characteristics. When the value is zero, it is randomly distributed in space, i.e. there is no
spatial pattern in the distribution of characteristics.

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20

Figure 1: GDP Cycles.

0.1
0.05

-0.1
year

Tokyo
Aichi
Osaka
Japan

1995

1990

1985

1980

1975

1970

1965

1960

-0.05

1955

0

Figure 2: GDP Cross-correlations in the USA and Euro countries.

Frequency

US states (1990-1997)
180
160
140
120
100
80
60
40
20
0
-1 -0.8-0.6-0.4-0.2 0 0.2 0.4 0.6 0.8 1

USA (1997-2005)
160
140

Frequency

120
100
80
60
40
20
0

-1 -0.8-0.6-0.4-0.2 0 0.2 0.4 0.6 0.8 1

EU Currency Area(12 EU countries)
1975-1995

16
14
Frequency

12
10
8
6
4
2
0
-1

-0.7

-0.4

-0.1

0.2

0.5

0.8

Mor

Figure 3: Japanese Prefecture GDP Cross-correlations.
1955-1964

200
Frequency

150
100
50
0
-1 -0.8 -0.6 -0.4 -0.20

0.2 0.4 0.6 0.8

1

1965-1974

400
Frequency

300
200
100
0
-1

-0.8 -0.6 -0.4 -0.2

0

0.2

0.4

0.6

0.8

1

0.2 0.4 0.6 0.8

1

1975-1984

Frequency

200
150
100
50
0
-1 -0.8 -0.6 -0.4 -0.2

0

1985-1996
250
Frequency

200
150
100
50
0
-1

-0.8

-0.6

-0.4

-0.2

0

0.2

0.4

0.6

0.8

1

0.4

0.6

0.8

Ent ir e Per iod (1955-1995)
400
350

Frequency

300
250
200
150
100
50
0
-1

-0.8

-0.6

-0.4

0.2
-0.2 0
Correlations

1

Figure4: Consumption Risk Sharing.

1

Output Correlations

0.8
0.6
0.4
0.2
0
0

0.2

0.4

0.6

0.8

1

-0.2
Consumption Correlations

Figure A: Spatial Autocorrelations.

Index

-0.1

-0.2

-0.3
Year

Note: The detrended GDP data are used.

1993

1990

1987

1984

1981

1978

1975

1972

1969

1966

1963

1960

1957

0

Table 1: GMM Results

Classical Gravity ModelGDP
Neighbor
Dist
Sample Size
Underidentification
Weak Identification
Sargan Statistic
Centered R-sq
Uncentered R-sq
Root MSE

1965-1974
Coefficients z-value
8.25 **
0.0840034-1.31-0.06482-6.74 **
-0.119142
1035
1010.6443000
00.12740.90710.36981975-1984
Coefficientz-value
8.26 **
0.077148
2.05 **
0.093037-0.01577 -0.98
1035
1027.008
13000000.07880.91530.3382

1985-1995
Coefficientz-value
3.68 **
0.042957
0.53
0.031013
0.07
0.001381
1035
1028.28
16000000.01170.51830.4346

Gravity ModelGDP
GDPcapita
Area
Neighbor
Dist
Sample Size
Underidentification
Weak Identification
Sargan Statistic
Centered R-sq
Uncentered R-sq
Root MSE

1965-1974
Coefficients z-value
9.69 **
0.1078818-4.24 **
-0.335064-4.9 **-0.069792-0.54-0.026755-4.23 **
-0.079308
1035
793.055
1686.44200.16920.91160.36081975-1984
Coefficientz-value
9.12 **
0.09609-0.3385 -3.38 **5.73 **
0.076505
0.59
0.026524-0.05287 -3.14 **1035
591.645686.58600.13610.9206
0.3275

1985-1995
Coefficientz-value
6.89 **
0.084707-8.5 **-0.77129-0.01925 -1.16
0.29
0.016420.49
0.010645
1035
899.4633414.375
00.07750.55040.4199Augmented Gravity Model and Factor Endowments
GDP
KL
Human
Infra
Area
Neighbor
Dist
Sample Size
Underidentification
Weak Identification
Sargan Statistic
Centered R-sq
Uncentered R-sq
Root MSE

1965-1974
Coefficients z-value
6.39 **
0.1114197
1.23
0.002736-0.18-0.093917-2.03 **
-0.037291-3.09 **
-0.046838-0.77-0.039105-5.16 **
-0.097988
1035
388.799
154.478
00.13870.90830.36741975-1984
Coefficientz-value
6.9 **
0.091824-2.69 **
-0.00242-1.51-0.72558
0.52
0.007213
5.53 **
0.075612
0.62
0.028083-3.08 **
-0.05206
1035
541.381281.59300.13630.9206
0.3275

1985-1995
Coefficientz-value
4.44 **
0.070263-0.00118 -2.11 **-2.26307 -3.19 **0.24
0.003903-0.00389 -0.21
0.34
0.020003
0.4
0.008968
1035
622.751387.852
00.03690.53060.4291

Augmented Gravity Model with Industrial Structure 1
GDP
KL
Human
Infra
CL
Area
Neighbor
Dist
Sample Size
Underidentification
Weak Identification
Sargan Statistic
Centered R-sq
Uncentered R-sq
Root MSE

1965-1974
Coefficients z-value
3.48 **
0.0685275
2.54 **
0.005841
0.91
0.498021-2.56 **
-0.047121
4.81 **
8.454948
0.64
0.0124057-0.11-0.00573-3.4 **-0.068214
1035
389.662
123.90200.13180.9076
0.36881975-1984
Coefficientz-value
6.54 **
0.086918-1.66 *
-0.00156-0.77-0.37641
0.22
0.003032.85 **
2.60363
6.19 **
0.088293
0.86
0.038473-2.53 **
-0.04318
1035
538.641
222.6800.14770.92160.3253

1985-1995
Coefficientz-value
4.41 **
0.069823-0.00126 -2.12 **-2.33988 -3.14 **0.28
0.004604-0.37958 -0.39-0.00488 -0.27
0.32
0.018688
0.35
0.007916
1035
617.384
303.358
00.03680.53060.4291

Augmented Gravity Model with Industrial Structure 2
GDP
KL
Human
Infra
CV
Area
Neighbor
Dist
Sample Size
Underidentification
Weak Identification
Sargan Statistic
Centered R-sq
Uncentered R-sq
Root MSE

1965-1974
Coefficients z-value
1.2
0.0229844
2.84 **
0.0061484
1.39
0.7354466-2.31 **
-0.040983
10.35 **
1.899804
1.03
0.0164076
0.85
0.0421626-1.18-0.023269
1035
388.58123.351
00.19220.914
0.35581975-1984
Coefficientz-value
4.63 **
0.062653-1.48-0.00131-0.04-0.02021
0.22
0.002949
6.81 **
1.119015
7.39 **
0.101541
1.55
0.068099-0.83-0.01435
1035
540.359224.166
00.191
0.9256
0.31691985-1995
Coefficientz-value
4.43 **
0.071228-0.00122 -2.16 **-2.32725 -3.19 **0.24
0.003974-0.09323 -0.42-0.0049 -0.27
0.3
0.017607
0.28
0.006428
1035
622.649309.85100.03750.53090.4289Notes
Constant terms, prefecture dummies and time dummies are omitted.
* significant at the 10 per cent level.
** significant at the 5 per cent level.
Independent variables are Fisher z-transformed cross-correlations of HP filtered GDPs.
Instrumental variables are one period lags of all independent variables.
All variables in the period of 1955-1964 are used as instrumental variables for 1965-1974.
There are no estimations for the first period because no data is available for a lagged period (before 1955).

Table 2: Regional Panel Regressions
1
Coefficients t-value
0.1540276
3.3 **-0.1019627 -2.12 **-0.1032499 -1.34

GDP
GDPcapita
Dist
Trade
TradeIntensityArea-0.0753554
Number of Sampl
84
R-squared
0.515

2
Coefficients t-value

6.783852-0.83-0.0252396
84
0.48843
Coefficients t-value

4.54 **-0.36

0.4550325-0.100572
84
0.3481

Constant terms, prefecture dummies and time dummies are omitted.
* significant at the 10 per cent level.
** significant at the 5 per cent level.
Independent variables are Fisher z-transformed cross-correlations of HP filtered GDPs.
Instrumental variables are one period lags of all independent variables.

2.1 **-1.34

Table A: Japanese Prefectures and Regions.
Prefecture Name
1 Hokkaido
2 Aomori
3 Iwate4 Miyagi
5 Akita
6 Yamagata
7 Fukushima
8 Ibaraki
9 Tochigi
10 Gunma
11 Saitama
12 Chiba
13 Tokyo
14 Kanagawa
15 Niigata
16 Toyama
17 Ishikawa
18 Fukui
19 Yamanashi
20 Nagano
21 Gifu
22 Sizuoka
23 Aichi24 Mie25 Shiga
26 Kyoto
27 Osaka
28 Hyougo
29 Nara30 Wakayama
31 Tottori
32 Shimane
33 Okayama
34 Hiroshima
35 Yamaguchi
36 Tokushima
37 Kagawa
38 Ehime
39 Kouchi
40 Fukuoka
41 Saga
42 Nagasaki
43 Kumamoto
44 Oita
45 Miyazaki
46 Kagoshima
47 Okinawa

Code

Region Region name Prefectures
Hokkaido
1 Hokkaido
2 Tohoku
Aomori, Iwate, Miyagi, Akita, Yamagata, Fukushima
3 KantoIbaraki, Tochigi, Gunma, Saitama, Chiba, Tokyo
Kanagawa, Niigata, Yamanashi, Shizuoka, Nagano
4 Chubu
Aichi, Gifu, Ishikawa, Toyama, Mie
5 KinkiNara, Shiga, Kyoto, Osaka, Hyogo, Fukui, Wakayama
6 Chugoku
Tottori, Shimane, Okayama, Hiroshima, Yamaguchi
7 Shikoku
Kagawa, Tokushima, Kochi, Ehime
8 KyushuFukuoka, Saga, Oita,Nagasaki, Kumamoto, Miyazaki
Kagoshima