Japanese Journal of Human Geography 64―5（2012）
Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model
Éva Komlósi Graduate Research Assistant Faculty of Business and Economics, University of Pecs
FUJII Tadashi Tottori University
I Introduction
In the present study, we propose a possible method to ascertain the competitiveness of spatial units in a more objective manner, and thereby contribute to the underpinning and ex―post assessment of regional planning. Only in the last few decades has the concept of regional competitiveness received particular attention in regional development policy. Regional competitiveness especially has become a widely celebrated notion in policy―making circles : national, regional and city authorities have been propelled by an overwhelming urge to create indexes to measure and compare their positions with that of others and to invent strategies to enhance their competitiveness（ Gardiner― Martin―Tyler 2004）. Even though the concept has gained quick and ardent acceptance from practitioners, since its appearance, it has also induced a heated debate in academic circles, because it has raised serious doubts about interpretability. Perhaps the most critical opinion about regional competitiveness was expressed by Krugman, who called it a “dangerous obsession”. According to Krugman’s explanation, competitiveness is a feature of firms, not of nations. Krugman argues that the principle of comparative advantage assures that every nation can become a winner and, therefore, there is no meaning to talk about competitiveness（ Krugman 1996）. However, in his latter study, Krugman admitted that “at a regional level, however, the story changes drastically …” （Krugman 2003, 18）. What Krugman seems to affirm is that spatial units under the national level work on the basis of absolute rather than comparative advantage （Martin 2005）. A dozen spatial units under the national level do indeed compete to attract, or at least withhold, human capital and companies from other regions by offering a set of embedded region―specific givens （e. g. specialized knowledge, a skillful workforce, infrastructure, tax allowance, public services） which offer absolute advantage （i. e. higher productivity） for companies, and by this generate additional influx of people and capital （Boschma 2004 ; Bristow 2005 ; Bud―Hirmis 2004 ; Camagni 2002 ; Martin 2005）. Therefore, understanding regional competitiveness has become a matter of vital importance. It is clear that the earlier dismissive opinions have begun to recede or change. Nonetheless, the growing acceptance of regional competitiveness in academic circles has given rise to an array of questions : What is exactly regional competitiveness ? How can it be measured ? The full ― 54 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 435
consensus of researchers has clearly expressed that there is a need for a comprehensive th definition. The “standard concept of competitiveness”, published in the 6 periodical report of the European Commission, was partly developed to serve this purpose : ［“ Competitiveness is defined as］ the ability to produce goods and services which meet the test of international markets, while at the same time maintaining high and sustainable levels of income … and employment levels …” （European Commission 1999, 4）. Resilience, as one main feature of this definition, is readily adaptable to a broad range of economic units （e. g. nations, regions, sectors, companies）. On the other hand, this definition possesses the advantage of working with measurable economic categories（ Lengyel 2004）. The Pyramid Model of regional competitiveness published by Lengyel is built on this standard definition. This model provides a systematic account by identifying the possible factors responsible for competitiveness （Lengyel 2003, 2004）. Other models also exist for measuring regional competitiveness（ Kitson―Martin―Tyler 2004 ; Snieška―Bruneckienė 2009）. For two reasons, Lengyel’ s Pyramid Model was preferred : ⑴ its structure corresponds to the input―output―outcome logic in line with international recommendations, and ⑵ its elements can easily be converted into indicators （Lukovics 2007）. The model has gained wide academic consensus and has been employed in several studies of spatial analysis（ Gardiner―Martin―Tyler 2004 ; Garlick 2003 ; Parkinson et al. 2006 ; Sinabell et al. 2011）. The first comprehensive empirical test of the Pyramid Model was accomplished by Lukovics （Lukovics 2007, 2008）, who classified Hungarian micro―regions according to their competitiveness. To minimize subjectivity, he suggested useful methodological solutions and a multi ―stage process to denote the relevant set of variables for measuring competitiveness. Applicability, comparability in time and elimination of redundancy are the main advantages of his method. In this paper, the competitiveness of 141 Japanese Functional Urban Areas （JFUAs） was measured based on the methodological instruction of Lukovics. The following section explains about the method and results of the delineation of urban areas, which were used as spatial units in this analysis. Next, we discuss the selection method of relevant indicators which represented the Pyramid Model. Finally, we summarize the substantial outcomes of the cluster analysis and present the significance of the applying Pyramid Model into Japanese urban areas. The main objective of this analysis was to determine what differences could be observed among JFUAs regarding their competitiveness and find out which factors were responsible for their differentiation. The investigation indicated a detailed insight into the mechanism of urban competitiveness which can be used to ground and evaluate the effects of regional planning policies.
II Defining Japanese Functional Urban Areas（ JFUAs）
Finding the proper spatial unit is one of the most critical steps in spatial analysis. The city, as a local administrative unit, seems less and less able to serve in this role, because of intensive commuting. Therefore, the urban area, which consists of a central city and its hinterland, seemed a better choice. A symbiotic relationship holds the central city and its hinterland together. The attraction zone, in this case, connects to the core city through working commuting flows. Yet another question is what definition to use to designate urban areas. Various methods have been elaborated by researchers to provide a reliable solution to the delineation of Japanese urban ― 55 ― 436 Japanese Journal of Human Geography 64―5（2012）
Table 1. Four categories of JFUAs according to areas （Kanemoto ―Tokuoka 2002 ; Kawashima 1982 ; their population Osada 2003 ; Yamada―Tokuoka 1991 ; Yamagami 2006）. Name of JFUA Number Abbreviation Population Finding a proper index to express the urban category of JFUAs character of the central city is a decisive ― Small JFUA S 50 000 249 999 66 point. This is the crucial point in which the ― ― Medium sized JFUA M 250 000 499 999 33 various solutions differ most from each other. ― Large JFAU L 500 000 999 999 28 Yamagami （2006） criticized earlier methods ― Largest JFUA LS 1 000 000 14 because the population limit of the central Source : based on data edited by the authors city was too “loose”. One common point of the methods suggested by Kawashima （1982）, Yamada―Tokuoka（ 1991） and Yamagami（ 2006） is that, to express urban character, they used the ratio of daytime to night―time population. Osada（ 2003） emphasized that this indicator includes the number of students and, therefore, is not proper. Both Kanemoto―Tokuoka（ 2002） and Osada （2003） criticized earlier methods using the proportion of non―agricultural workers, because this type of criterion became redundant. To overcome these shortcomings, both authors developed their own respective methods. But their indexes, which represent urban character, differ significantly. Osada proposed the administratively defined urban area, i. e. “shi”, as a suitable index to denote the central city. However, the “shi” area, by itself, does not perfectly express urban character. Therefore, the value of the settlement size in population was employed as an additional criterion（ Osada 2003）. Kanemoto―Tokuoka（ 2002） proposed another metropolitan―area definition known as the Urban Employment Area（ UEA） and, as a qualification of central cities, they used Densely Inhabited District （DID）. Both indexes are equally appropriate to express urban character. Nevertheless, we preferred Osada’s approach, because in his method the “cut of points” of the central city and its suburban area were calculated, which increases the objectivity of the delineation. However, in spite of the conspicuous differences of the methods, attention should be paid to some similarities. In both cases, Tokyo JFUA （UEA） include Saitama, Yokohama, Chiba and Kawasaki cities, while Osaka JFUA（ UEA） does not include Kobe City and Nagoya JFUA（ UEA） does not include Toyota City. Since the main aim of the study was to examine the present competitiveness of JFUAs, therefore, we have endeavoured to use the most recent available dataset. All data for defining JFUAs were available for the municipal level and for 2005 provided by the Statistics Bureau of Japan（ e―Stat database）. In 2005, 141 JFUAs were determined by the following criteria : 1．） Each JFUA consists of a JFUA core and a ring area. 2．） The total of each JFUA contains 50,000 or more inhabitants. The JFUA core must satisfy the following two criteria : the JFUA core is a single “shi” area, and contains at least 20,000 1 workers who work in the same municipality as their residence in the 2005 Population Census of Japan. The number of workers commuting into the core must be greater than that of workers commuting out. 3．） Criteria for JFUA ring : The ring is composed of one or more municipalities where 7.5％ or more of the resident working population commute to the JFUA core. Each municipality of the JFUA ring should be contiguous with the JFUA core or another ring area of the same JFUA core. Each municipality can be classified in only one functional urban area.（ Osada 2003, 140）.
― 56 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 437
Figure 1. The structure of the Pyramid Model Source : Lengyel 2004, 336.
III Defining indicators for competitiveness analysis
The Pyramid Model of regional competitiveness contains ex ―post variables to measure directly “revealed competitiveness”. These variables belong to the “basic categories” of the model. On the other hand, it incorporates those ex―ante variables on which the revealed competitiveness is based, as “source of competitiveness”. These variables can be classified into two groups as “development factors” and “success determinants” depending on whether they have a short― or long―term effect（ Lengyel 2004）. Regarding the Pyramid Model, a fundamental question which immediately presents itself is : How general is it ? The “development factors” correspond with the tools of regional policy, whereas the “success determinants” comprise those fields which are influenced by various sectorial policies. To counteract regional differences, countries employ different tools, and within a country, different tools can be preferred in each planning period. This matter calls attention to the importance of “weighting” of the indicators. Due to weighting, it becomes possible for a general model to reflect the singularity of the spatial unit. Nevertheless, what can be considered as adequate weights ? In the previous subsection, we referred to the sound methodological solution offered by Lukovics （2007）, which is worth considering. First, Lukovics reviewed the literature to determine a set of potential variables. As a next step, he tested the relevance of these potential variables with the help of principal component analysis which was carried out with the indicators of the basic, development and success categories of the Pyramid Model, respectively. The main rule was that each variable should belong to one or two principal components which altogether preserve at least 70％ of the information of the original variables. 78 relevant and also standardized variables were used in his analysis to examine the competitiveness of Hungarian micro ―regions. Lukovics underlined the importance of weighting, ― 57 ― 438 Japanese Journal of Human Geography 64―5（2012）
because the different variables did not have the same effect on competitiveness. To determine the weights, a principal component analysis was carried out again, but this time with all variables together. Through doing so, he received the communalities（ their square roots are known as multiple correlation coefficients）, which were simply and solely used as weights. These communalities do not represent merely the characteristics of the distribution of the collected variables, but they express their relation to the competitiveness itself. We intended to use these already tested relevant 78 variables as a sample set of indicators measuring competitiveness of Japanese urban areas. However, there were differences between this original set of variables and the set we finally used, due to the availability of data. If a variable was not available, we tried to replace it with a similar available one which directly or indirectly represented the categories of the model. Although the fact that we could not cover all 2 fields of the Pyramid Model is not favourable, it is not problematic. The year 2005 was chosen as 3 the year for investigation and the digital e―Stat and other databases for municipal level was used. Finally, in the light of the availability of data, we were constrained to use 56 variables which were the same or similar to those Lukovics identified as relevant variables of competitiveness. After 4 standardization, we also laid stress on the weighting of variables. Therefore, the principal component analysis was carried out only in order to receive multiple correlation coefficients using them as weights of the 56 variables. The 56 variables were compressed into 12 principal components （which preserved 78.8％ of the information of the original variables）. The final database contained 1122 municipalities. As a next step, the cluster analysis was conducted to measure the competitiveness of the 141 JFUAs with the help of the 56 relevant, standardized and also weighted variables（ not with the 12 principal components !）.
IV Cluster analysis
5 Since we had previous presumptions about the number of clusters, the K―means analysis was conducted with the selected 56 standardized and weighted relevant variables. The 141 JFUAs were classified into three clusters. Only the Tokyo JFUA belonged to Cluster1 with relatively strong competitiveness. Cluster2 with medium competitiveness contained 49 JFUAs and Cluster3 with relatively weak competitiveness consisted of 91 JFUAs. Cluster2 comprised most of the largest JFUAs （78.6％）, as well as half of the large （57.1％） and medium―sized JFUAs （42.4％）. Small JFUAs were overrepresented in Cluster3（ 87.9％）, but a considerable part of the large（ 42.9％） and medium― sized JFUAs（ 57.6％） were also found here（ see Table 2 and Table 4）. Before conducting the analysis, we presumed that the size of the JFUAs （namely population concentration） is one of the most significant factors which have effect on the scale of competitiveness of urban areas. The theory behind this is that the larger the population of the urban area, this is resulted in more entrepreneurship activities, consequently, an increasing number of （innovative） firms, would increase competition and thus regional economic growth （Sternberg 2009）. This proposition was confirmed by the fact that the Tokyo JFUA belonged to the relatively strong competitive cluster and the other major metropolitan areas （altogether 11 MMAs） belonged to the medium competitiveness category. However, surprisingly, the medium competitiveness cluster also contained some small and medium―sized JFUAs. This means that there are some other factors might also play a decisive role determining the competitiveness of urban areas, besides the population concentration. Such as, among other things, ― 58 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 439
Table 2. The three clusters according to the categories of JFUAs
Cluster 1＝relatively Cluster 2＝medium Cluster 3＝relatively weak Categories of JFUAs Sum strong competitiveness competitiveness competitiveness
Small JFUA ― ― 8 12.1％ 58 87.9％ 66 Medium―sized JFUA ― ― 14 42.4％ 19 57.6％ 33 Large JFUA ― ― 16 57.1％ 12 42.9％ 28 Largest JFUA １ 7.1％ 11 78.6％ 2 14.3％ 14 Sum 1 49 91 141
Source : Results of the cluster analysis, edited by the authors
⑴ the advantageous/disadvantageous location, Table 3. Distance between final cluster centres
the easy/uneasy accessibility of the area to Cluster 1 Cluster 2 Cluster3 metropolitan areas via good transportation Cluster 1 28.050 29.800 （i. e. exploitation of urban externalities） networks ; Cluster 2 28.050 5.209 ⑵ strong interconnectivity between small or Cluster 3 29.800 5.209 medium―sized areas and big cities based on Source : Results of the cluster analysis, edited by the authors specialization（ i. e. creation of clusters） ; ⑶ the role of an effective/ineffective development policy based on the promotion of local （endogen） resources can be mentioned. The distance of the cluster centres between Cluster1 and Cluster3 was the greatest （29.800）. This means the Tokyo JFUA in Cluster1 and the JFUAs in Cluster3 differ from each other the most. However, the distance was also considerable （28.050） between the Tokyo JFUA and the JFUAs in Cluster2. Between Cluster2 and Cluster3, the distance was relatively small（ 5.209）. This brief analysis of the final cluster centres unambiguously confirms the significant gap between the Tokyo JFUA and the other urban areas of the country（ see Table 3）. Table 5 shows the final cluster centres which clarify the effect of each factor one by one on competitiveness. Moreover, Table 6, 7 and 8 contain detailed and precise description about the meaning of each variable, as well as the average values of each variable of the three clusters are compared with each other. The first 13 variables in Table 5 represented the “basic categories” of the Pyramid Model and measured the revealed competitiveness. According to the variables of the basic categories, the Tokyo JFUA reached a better position in 8 variables. The JFUAs in Cluster2 took the lead over the Tokyo JFUA, but just within a “hair’s breadth” in 4 variables（ mainly related to local public finance）. Except for one variable, the JFUAs in Cluster3 did not seize a better position than other JFUAs in Cluster2. Thus according to the revealed competitiveness of JFUAs, there are serious differences among the clusters. For example, “LEGALPERSONSINC” is one of the very telling variables of the “basic categories”. It shows the revenues of companies with legal personality（ 2001 ―2005） per taxpayers. In the Tokyo JFUA, the value was 1,060 thousand yen per taxpayer. The average value was 548 thousand yen in Cluster2 and it was the lowest in Cluster3 with 176 thousand yen. This variable clearly represents the order of the three clusters regarding their competitiveness（ see further variables in Table 6）. Next 12 variables in Table 5 （14―25. variables） were related to the “development factors” of the model. 5 variables estimated the business sector, 4 were connected to human resources and 3 to accumulated social capital. In 9 variables, the Tokyo JFUA had a more favourable position than JFUAs in the other two clusters. The JFUAs in Cluster2 achieved a better position than the ― 59 ― 440 Japanese Journal of Human Geography 64―5（2012）
Table 4. List of JFUAs in terms of competitiveness
List of JFUAs in Cluster 1 Tokyo JFUA（ 110） LS 31121543 List of JFUAs in Cluster2 Sapporo JFUA（7） LS 2250468 Osaka JFUA（78） LS 12037841 Yuzawa JFUA（2） S 73557 Tottori JFUA（5） S 247469 Chitose JFUA（3） S 168182 Kobe JFUA（6） LS 2233370 Yamagata JFUA（9） L 503939 Yonago JFUA（8） S 240254 Sendai JFUA（19） LS 1576075 Himeji JFUA（10） L 887028 Yonezawa JFUA（4） S 173163 Kurayoshi JFUA（5） S 113177 Mito JFUA（8） L 649226 Okayama JFUA（10） LS 1464556 Tsuruoka JFUA（2） S 150387 Matsue JFUA（5） M 326482 Tsuchiura JFUA（5） M 336662 Hiroshima JFUA（12） LS 1694131 Sakata JFUA（3） S 159106 Hamada JFUA（2） S 90820 Tsukuba JUFA（3） M 317925 Mihara JFUA（2） S 134853 Fukushima JFUA（7） L 509591 Matsuda JFUA（2） S 61883 Utsunomiya JFUA（13） L 935143 Fukuyama JFUA（6） L 767699 Aizuwakamatsu JFUA（7） S 193238 Tsuyama JFUA（6） S 197112 Oyama JFUA（4） M 267330 Tokushima JFUA（14） L 597658 Koriyama JFUA（10） L 570938 Ube JFUA（2） S 245216 Maebashi JFUA（6） L 663038 Kochi JFUA（8） L 729263 Shirakawa JFUA（7） S 138152 Yamaguchi JFUA（3） M 316115 Ota JFUA（7） L 633665 Kitakyushu JUFA（13） LS 1373884 Hitachi JFUA（5） M 377047 Shunan JFUA（3） M 259867 Kumagaya JFUA（3） M 492298 Fukuoka JFUA（21） LS 2409904 Kashima JFUA（2） S 95959 Takamatsu JFUA（9） L 772650 Narita JFUA（9） M 348606 Oita JFUA（6） L 737202 Moka JFUA（2） S 108082 Matsuyama JFUA（5） L 642696 Atsugi JFUA（5） M 492298 Naha JFUA（13） L 822364 Otawara JFUA（3） S 213920 Uwajima JFUA（3） S 106566 Toyama JFUA（7） L 649504 List of JFUAs in Cluster 3 Takasaki JFUA（6） L 549867 Niihama JFUA（2） S 237323 Takaoka JFUA（4） M 318686 Hakadate JFUA（4） M 376768 Niigata JFUA（7） LS 1057891 Ozu JFUA（2） S 70406 Kanazawa JFUA（9） L 754919 Otaru JFUA（2） S 164895 Nagaoka JFUA（4） M 371081 Omuta JFUA（4） S 215634 Gifu JFUA（11） L 918603 Asahikawa JFUA（8） M 406340 Sanjo JFUA（3） S 219500 Kurume JFUA（10） L 557960 Ogaki JFUA（9） M 328396 Muroran JFUA（3） S 188573 Kashiwazaki JFUA（3） S 104792 Iizuka JFUA（4） S 203074 Shizuoka JFUA（3） LS 1008368 Kushiro JFUA（4） S 225402 Joetsu JFUA（2） S 245913 Saga JFUA（9） M 413320 Hamamatsu JFUA（3） L 865026 Obihiro JFUA（6） M 270376 Nanao JFUA（2） S 80830 Nagasaki JFUA（3） L 526988 Numazu JFUA（6） M 479737 Tomakomai JFUA（2） S 193506 Komatsu JFUA（3） S 231273 Sasebo JFUA（6） M 313796 Fuji JFUA（3） M 384773 Aomori JFUA（5） M 340427 Fukui JFUA（6） M 445538 Isahaya JFUA（3） M 282072 Iwata JFUA（3） M 274163 Hirosaki JFUA（8） M 317610 Tsuruga JFUA（3） S 96205 Kumamoto JFUA（16） LS 1089366 Kakegawa JFUA（3） S 200631 Hachinohe JFUA（6） M 345442 Echizen JFUA（3） S 103421 Yatsushiro JFUA（3） S 170958 Gotemba JFUA（3） S 160516 Goshogawara JFUA（4） S 131674 Kofu JFUA（13） L 612702 Nakatsu JFUA（4） S 180334 Nagoya JFUA（35） LS 4841953 Towada JFUA（4） S 117276 Nagano JFUA（11） L 595983 Hita JFUA（2） S 92441 Handa JFUA（4） S 207697 Morioka JFUA（7） M 481471 Matsumoto JFUA（10） M 441919 Miyazaki JFUA（7） M 499980 Hekinan JFUA（2） S 112759 Miyako JFUA（3） S 83730 Ueda JFUA（5） S 223463 Miyakonojo JFUA（3） S 237787 Toyota JFUA（4） L 898285 Kitakami JFUA（3） S 215745 Iida JFUA（11） S 172709 Hyuga JFUA（2） S 82762 Anjo JFUA（2） M 312384 Ichinoseki JFUA（3） S 144541 Ina JFUA（5） S 155069 Kagoshima JFUA（6） L 773809 Nishio JFUA（4） S 163232 Ichinomaki JFUA（3） S 221282 Takayama JFUA（2） S 125133 Kanoya JFUA（4） S 141652 Komaki JFUA（2） S 168784 Kesennuma JFUA（2） S 78011 Iga JFUA（4） S 131751 Satsumasendai JFUA（3） S 161051 Tsu JFUA（2） M 457511 Akita JFUA（6） M 429178 Nagahama JFUA（8） S 165507 Nago JFUA（7） S 104179 Yokkaichi JFUA（6） L 602553 Noshiro JFUA（4） S 96656 Toyooka JFUA（3） S 138953 Kusatsu JFUA（3） M 251851 Yokote JFUA（3） S 129870 Wakayama JFUA（6） L 595817 Kyoto JFUA（14） LS 2522937 Odate JFUA（3） S 129377 Tanabe JFUA（4） S 135116 The table contains the population data for each JFUA. The number in brackets represents the number of settlements in each ring area. Source : edited by the authors according to results of the cluster analysis
Tokyo JFUA in the case of only three variables. The JFUAs in Cluster3 showed an unfavourable position in all variables. This result confirmed that according to the business sector, the characteristics of human resources and social capital, there were relevant differences among the three clusters. One characteristic variable belong to the “development factors” of the model was picked up here expressing the order of the three clusters regarding their competitiveness : ”SMALLCOMPANY” : companies with 10 ―29 employees per 1,000 inhabitants. In the Tokyo JFUA, the value was 18. In Cluster 2, the average value was 7. Only 2 JFUAs（ 4.1％） belonged to Cluster 2 exceeded the value of the Tokyo JFUA. The average number of small companies was also 7 in Cluster 3, but 71.4％ of these JFUAs were under the average of their group.（ see further variables in Table 7）. The “success determinants” of the Pyramid Model were measured by 31 variables （26―56. variables in Table 5） and divided into six groups : industrial structure, innovation capacity, social structure, decision centres, quality of the environment and social cohesion. According to the industrial structure, innovation capacity, social structure, location of the decision centres and quality of the environment, there were conclusive differences among the clusters : the Tokyo ― 60 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 441
Table 5. Final cluster centres of variables
BASIC CATEGORIES DEVELOPMENT FACTORS Cluster 1 Cluster 2 Cluster 3 Cluster 1 Cluster 2 Cluster 3 INCOME SMEs, BUSINESS SECTOR TAXINC 2.864330 0.831451 －0.479181 COMPANY 1.001511 0.108188 －0.069261 WAGE 0.630135 0.266702 －0.150533 SMALLCOMPANY 2.808659 －0.019509 －0.020360 FINPOW 2.064445 0.765922 －0.435106 LEGALPERS 0.531515 －0.082070 0.03835 PUBDEBT －0.138414 －0.431904 0.234085 SMALLEGALPERS 2.800807 －0.002685 －0.028267 LOCTAX 0.207819 0.511159 －0.277523 LEGALPENTERPRISE 1.703117 0.361884 －0.213576 LTAXREVN 0.982938 0.853472 －0.470363 HUMAN CAPITAL AND SKILL OF WORKFORCE LREVEXP －0.025848 0.245137 －0.131713 WCOLLARWORKER 0273462 0.46618 －0.254025 INCOME 0.435712 0.200253 －0.108057 SECSCHOOLGRAD 1.068627 0.507381 －0.214702 LEGALPERSONSINC 1.527796 0.482003 －0.276329 UNIVGRAD 2.399047 0.695164 －0.299977 PRODUCTIVITY GRADUNIVEMPLY 0.801961 0.534672 －0.296713 TAXINCTAXPAYER 3.01419 0.893856 －0.514430 SOCIAL CAPITAL EMPLOYMENT INSURED －0.287731 －0.518174 0.282179 EMPLOYMENT 0.149094 0.194965 －0.106620 UNIVSTUDPOP 1.392880 0.636276 －0.357916 TAXPAYER 1.404356 0.472731 －0.317428 INOUT 0.168417 0.460791 －0.249969 OPENNESS TOURIST 0.715512 －0,24757 0,155004 SUCCESS DETERMINANTS Cluster 1 Cluster 2 Cluster 3 Cluster 1 Cluster 2 Cluster 3 INDUSTRIAL STRUCTURE DECISION CENTRES BRWORKER 1.24478 0.387054 －0.222093 ENTMEDJAP 10.750620 0.091308 －0.167305 BRCOMPANY －0.03967 0.303847 －0.163174 ENTBIGJAP 11.039880 0.056620 －0.151805 PRIMARY －0.90016 －0.467394 0.281071 HQJAP 10.675115 0.090544 －0.166063 TERTIARY －0.11682 0.042209 －0.017598 BIGCAPTIALJFUA 3.203960 0.492057 －0.300162 INNOVATION CAPACITY BIGCAPITALJAP 11.148532 0.017267 －0.131809 LIBRARY －0.29777 －0.298851 0.164192 QUALITY OF THE ENVIRONMENT PROFESSOR 1.062362 0.257250 －0.150193 CRIME 0.875952 0.766033 －0.422105 FOREIGNSTUD 0.792597 0.286795 －0.163138 HOMEOLD －0.946527 －0.454810 0.255298 SOCIAL STRUCTURE PUBHALL －0.713568 －0.414730 0.231158 POP65 －1.22317 －0.796640 0.442402 DEPARTSTORE 9.462344 0.128040 －0.172926 POP15 －1.24492 0.304860 －0.150475 RETAILSTORE 9.491176 0.091896 －0.153781 LIVEDEATH 0.787706 0.769544 －0.423026 GENHOSPITAL 8.872241 0.096210 －0.149302 VITALITY 1.680499 0.859274 －0.481153 PHYSICIAN 0.041816 0.015453 －0.008780 ONEPERSON 2.192610 0.198314 －0.130879 NPO 1.656157 －0.009260 －0.013214 DIDPOP 1.643410 0.323562 －0.192285 SOCIAL COHESION CFPOP －1.74469 －0.333624 0.198816 OUTMIG 1.688482 0.103181 －0.074113 INMIG 1.738175 0.518020 －0.298035 WORKSAME －0.906880 －0.496800 0.277475 COMFROMCOMTO 0.123998 0.144868 －0.079369 Source : based on results of the cluster analysis, edited by the authors
JFUA had an edge on 22 variables, while JFUAs in Cluster2 and Cluster3 had slightly a favourable position merely in 3―3 variables. Two typical variables of the “success determinants” were denoted here which can evidently declare the order of the three clusters regarding their competitiveness : ― VITALITY : population between 20―39 years old per population between 20 ―59 years old. The ratio was 53.4％ in the Tokyo JFUA. In Cluster 2, the ratio was 50.1％ and only 44.8％ in Cluster 3. ― ENTBIGJAPAN : companies with more than 300 employees per companies in Japan. In the Tokyo JFUA, the ratio was 34.3％. In Cluster 2, the ratio was on the average 0.8％, and it was only 0.2％ in Cluster 3.（ see further variables in Table 8）. Additionally, the 49 JFUAs in Cluster2 shed light on some meaningful facts regarding the
― 61 ― 442 Japanese Journal of Human Geography 64―5（2012）
Table 6. Analysis of variables which belong to the basic categories
INCOME TAXINC : taxable individual incomes per 1,000 inhabitants. In the Tokyo JFUA, the value was 1,848,255 thousand yen（ fcc＝ 2.864）. In Cluster 2, the average value was 1,390,549 thousand yen（ fcc＝0.832）. It was 1,095,458 thousand yen in Cluster 3（ fcc＝ －0.479）.
WAGE : earnings from the main activity per taxpayers. In the Tokyo JFUA, the value was 358,492 yen（ fcc＝0.630）. In Cluster 2, the average value was 347,545 yen（ fcc＝0.267） and it was 334,977 yen in Cluster 3（ －0.151）.
FINPOW : financial strength index. The index was 1.11 in the Tokyo JFUA（ fcc＝2.064）. In Cluster 2, the average value of the index was only 0.77（ fcc＝0.766） and it was the most moderate with a value of 0.46 in Cluster 3（ fcc＝－0.435）.
PUBDEBT : ratio of public debt. In the Tokyo JFUA, the ratio was 14.5％（ fcc＝－0.138）. In Cluster 2, the average debt was 13.2％ （fcc＝－0.432） and it was 16.1％ in Cluster 3（ fcc＝0.234）. But in the case of 19 JFUAs in Cluster 2（ 38.8％）, the debt index was much higher than in the Tokyo JFUA（ around 16.7％）. 47 out of 91 JFUAs（ 51.6％） in Cluster 3 also exceeded the average debt ratio of the whole group.
LOCTAX : local tax revenues per 1,000 inhabitants. In Cluster 2, the average value was 162,170 yen（ fcc＝0.511） and it was 142,246 yen in the Tokyo JFUA（ fcc＝0.208）. The variable was the lowest in Cluster 3 with an average 110,369 yen（ fcc＝－0.278）.
LTAXREVN : local tax revenues per local revenues. In the Tokyo JFUA, the ratio was 44.6％. In Cluster 2, the average of the index was nearly the same（ 43.0％）, while in Cluster 3, it was merely 26.6％.
LREVEXP : local revenues per local expenditures. In the Tokyo JFUA 1.03 yen local revenue was covered by 1 yen local expenditure （fcc＝－0.026）, the ratio was 1.04 in Cluster 2（ fcc＝－0.026） and it was 1.027 in Cluster 3（ fcc＝－0.132）.
INCOME : incomes per inhabitants measuring local GDP. In the Tokyo JFUA, the value was 3,598 thousand yen. In Cluster 2, the average value was 3,188 thousand yen in Cluster 2 and it was only 2,651 thousand yen in Cluster 3.
LEGALPERSONSINC : revenues of companies with legal personality（ 2001―2005） per taxpayers. In the Tokyo JFUA, the value was 1,060 thousand yen. The average value was 548 thousand yen in Cluster 2 and it was the lowest in Cluster 3 with 176 thousand yen.
PRODUCTIVITY TAXINCTAXPAYER : taxable individual incomes per taxpayers. The value was the highest in the Tokyo JFUA with 4,071,803 yen （fcc＝3.014）. In Cluster 2, the average value was 3,376,637 yen（ fcc＝0.894） and it was 2,914,921 yen in Cluster 3（ fcc＝－0.514）.
EMPLOYMENT EMPLOYMENT : employment rate. In the Tokyo JFUA, the rate was 94.4％. The average rate was nearly the same in Cluster 2（ 94.5 ％） and in Cluster 3（ 93.9％）. But 42.9％ of JFUAs in Cluster 2 and also 40.7％ of JFUAs in Cluster 3 did not reach the average rate of their own group.
TAXPAYER : taxpayers per 1,000 inhabitants. The Tokyo JFUA had 454 taxpayers per 1,000 inhabitants. Cluster 2 had on the average 411 taxpayers per 1000 inhabitants and Cluster 3 had on the average 375. But 48.4％ of JFUAs in Cluster 3 did not reach the average.
OPENNESS TOURIST : visitors per 1,000 inhabitants（ foreigners and daytime visitors included）. The Tokyo JFUA had 38 visitors per 1,000 inhabitants. Cluster 2 had on the average 20 visitors per 1,000 inhabitants and Cluster 3 had on the average 27 visitors.
Source : based on results of the cluster analysis, edited by the authors
features of competitiveness of urban areas in Japan. In this cluster, large leading regional capital cities can be found and other 13 prefectural centres（ Figure 2）. These cities are prefectural centres and/or designated cities, or central cities of major metropolitan areas, and thus deserve a special position. It was not an implausible outcome of the analysis that the most of the largest JFUAs （78,7％） would belong to the Cluster2 with medium competitiveness. These 11 JFUAs are central cities of major metropolitan areas （MMAs） which means that their size guarantee them a high level of competitiveness, to a certain extent. However, Cluster2 contains 8 small（ 12.1％） and 14 medium―sized JFUAs（ 42.4％） as well. This means that some other factors must play also a decisive role determining the competitiveness of these urban areas, besides the population concentration. In the case of the 8 small JFUAs in Cluster2, the values of variables belong to the “basic categories” were by far the average of the variables of the relatively weak cluster. In the case of the values of the variables measuring the ― 62 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 443
Table 7. Analysis of variables which belong to the development factors
SMEs, BUSINESS SECTOR COMPANY : companies per 1,000 inhabitants. In the Tokyo JFUA, the number of companies per 1,000 inhabitants was 23. In Cluster 2 the average number was 20. Only 6 JFUAs（ 12.2％） belonged to Cluster 2 exceeded the value of the Tokyo JFUA. In Cluster 3 the average number was 19. Nonetheless, 47.3％ of JFUAs in Cluster 3 did not reach the average of their group.
SMALLCOMPANY : companies with 10 ―29 employees per 1,000 inhabitants. In the Tokyo JFUA, the value was 18. In Cluster 2, the average value was 7. Only 2 JFUAs（ 4.1％） belonged to Cluster 2 exceeded the value of the Tokyo JFUA. The average number of small companies was also 7 in Cluster 3, but 71.4％ of these JFUAs were under the average of their group.
LEGALPERS : establishments with legal personality per 1,000 inhabitants. In the Tokyo JFUA, the value was 25. In Cluster 2, the average number was 22. Only 9 JFUAs（ 18.4％） belonged to Cluster 2 exceeded the value of the Tokyo JFUA. The average number was 23 in Cluster 3, but the 43.9％ of these JFUAs did not reach the average of their group.
SMALLEGALPERS : establishments with legal personality and with 10―29 employees per 1,000 inhabitants. In the Tokyo JFUA, the value was 18 and it was only on the average 8―8 establishments in Cluster 2 and Cluster 3. But only 3 JFUAs in Cluster 2 exceeded the value of the Tokyo JFUA and 64.8％ of JFUAs in Cluster 3 did not exceed the average of their group.
LEGALPENTERPRISE : companies with legal person per establishments. In the Tokyo JFUA, the 60.8％ of the establishments were companies with legal person. In Cluster 2, the average was only 51.3％, but the 57.14％ of these JFUAs did not reach the average of their group. In Cluster 3, on the average 47.1％ of the companies had legal personality, but 21.98％ of these JFUAs did not reach the average of their group.
HUMAN CAPTIAL AND SKILL OF WORKFORCE
WCOLLARWORKER : ratio of white ―collar workers per 1,000 inhabitants. In the Tokyo JFUA, the ratio was 36.2％. In Cluster 2, the ratio was 38.7％ and it was on the average only 29.3％ in Cluster 3.
SECSCHOOLGRAD : persons who finished secondary school and older than 20 years old per person above age 20. In the Tokyo JFUA, the ratio was 13.5％, and it was on the average 10.9％ in Cluster 2. Cluster 3 had the smallest ratio, 7.5％.
UNIVGRAD : persons who finished college or university and older than 25 years old per person above age 25. In the Tokyo JFUA, the ratio was 21.7％ and it was on the average only 13.0％ in Cluster 2. Cluster 3 had a ratio with 7.9％.
GRADUNIVEMPLY : ratio of persons who graduated from college or university per employed persons. In the Tokyo JFUA, the ratio was 6.3％, it was on the average 5.2％ in Cluster 2 and only 1.8％ in Cluster 3.
SOCIAL CAPITAL INSURE : insured persons per 1,000 inhabitants. The Tokyo JFUA had 363 insured persons from 1,000 persons. On the average 349 persons were insured in Cluster 2 and 396 persons in Cluster 3.
UNIVSTUDPOP : students in higher education per 1,000 inhabitants. In the Tokyo JFUA, the ratio was 30 students. In Cluster 2, the ratio was on the average 21 students and it was only 9 students in Cluster 3.
INOUT : internal net migration per 1,000 inhabitants. JFUAs which belong to Cluster 2 enjoyed a favourable position with 3.478 persons per 1,000 inhabitants. In the Tokyo JFUA, the number of out―migrants exceeded the number of in―migrants（ －0.029 person per 1,000 persons）. JFUAs which belong to the relatively weak competitiveness category had an unfavourable position （－5.048 persons per 1,000 person）.
Source : based on results of the cluster analysis, edited by the authors
“source of competitiveness” （e. g. the variables related to the business sector, the human and social capital, the innovation capacity, the quality of the environment or social cohesion） were all above the average values of the same variables in the medium competitiveness cluster. In the case of the 14 medium― sized JFUAs belonged to Cluster2, the values of the variables of revealed competitiveness unambiguously exceeded the average values of their own cluster. For example, the average value of “LEGALPERSONINC” variable was 528.18 thousand yen per capita for these 14 medium― sized JFUAs, however, the average of this variable was 190.18 thousand yen per capita in the whole medium competitiveness cluster and only 176.40 thousand yen per capita in the weak competitiveness category. Figure 2 displays the location of these small and medium ―sized JFUAs which belong to Cluster2. First conspicuous thing, that the majority of them can be found in the south ―west part of the country and belong to the so called Pacific Industrial Belt, which traditionally has had a better geographical location. Second, these JFUAs are not only suburban “dormitory towns”, because the number of commuters from other cities （especially from the nearest ― 63 ― 444 Japanese Journal of Human Geography 64―5（2012）
Table 8. Analysis of variables which belong to the success determinants
INDUSTRIAL STURCTURE BRWORKER : number of workers in the bank―finance and real estate sectors per employed persons. In the Tokyo JFUA, 4.2％ of workers were employed in these sectors. In Cluster 2, the ratio was on the average 2.9％ and it was 1.93％ in Cluster 3.
PRIMARY : workers in primary sector per employed persons. In the Tokyo JFUA, the ratio was the lowest（ 1.1％）. In Cluster 2, the ratio was on the average 3.5％ and it was on the average 7.7％ in Cluster 3.
TERTIARY : workers in tertiary sector per employed persons. In the Tokyo JFUA, the ratio was 53.9％. In Cluster 2, the ratio was the highest, on the average 74.1％ and it was on the average 66.5％ in Cluster 3. High level of tertiary sector could be a sign of weak industrial base（ tertiarisation as replacement of the earlier industrial base）.
INNOVATION CAPACITY LIBRARY : libraries per 1,000 persons. There was no significant difference between the three clusters（ Cluster1 : 0.021 institution, Cluster 2 : 0.021 institution, Cluster 3 : 0.032 institution）.
PROFESSOR : professors per 1,000 inhabitants. In the Tokyo JFUA, the value was 0.810 professors per 1,000 persons. In Cluster 2, the value was on the average 0.684 professor and it was 0.406 professors in Cluster 3.
FOREIGNSTUD : foreign students per 1,000 inhabitants. In the Tokyo JFUA, the value was 1.232 foreign students per 1,000 persons. In Custer 2, the value was on the average 0.473 foreign student it was 0.173 foreign student in Cluster 3.
SOCIAL STRUCTURE POP65 : persons above age 65 per persons. In the Tokyo JFUA, the ratio was 17.0％. In Cluster 2, the ratio was on the average 18.8％ and it was on the average 24.0％ in Cluster 3.
POP15 : persons above age 15 per persons. In the Tokyo JFUA the ratio was 12.7％. In Custer 2, the ratio was on the average 14.5％ and it was14.0％ in Cluster 3.
LIVEDEATH : live births per deaths. In the Tokyo JFUA and in Cluster 2, the ratio was favourable, the number of births exceeded the number of death（ Cluster 1 : 1.163, Cluster 2 : on the average 1.158）. In Cluster 3, the ratio was disadvantageous（ on the average 0.791）.
VITALITY : population between 20―39 years old per population between 20―59 years old. The ratio was 53.4％ in the Tokyo JFUA. In Cluster 2, the ratio was 50.1％ and only 44.8％ in Cluster 3.
ONEPERSON : one―person households per persons. The Tokyo JFUA had 147 on―person households per 1,000 persons. The difference was not considerable between Cluster 2 and Cluster 3（ on the average 99 and 91 households per 1,000 persons）.
2 DIDPOP : ratio of persons who live in an area where the density is higher than 4,000 persons per km . As bigger the value of the variable is, as stronger the urban character of the examined area. The 91.5％ of the whole population of the Tokyo JFUA lived in DID areas. In Cluster 2, the average ratio of DID was 45.7％ and it was 27.8 ％ in Cluster 3.
CFPOP : JFUA core’s population per population of the whole JFUA. Tokyo City as the core city of the Tokyo JFUA concentrated only 28.2％ of inhabitants of the whole JFUA. In Cluster 2, the average ratio was 56.1％ and it was 67.2％ in Cluster 3.
DECISION CENTRES ENTMEDJAPAN : private companies with 50 ―299 employees per companies in Japan. In the Tokyo JFUA, the ratio was 25.4％. In Cluster 2, the ratio was on the average 0.78％ and it was only 0.19％ in Cluster 3.
ENTBIGJAPAN : companies with more than 300 employees per companies in Japan. In the Tokyo JFUA, the ratio was 34.3％. In Cluster 2, the ratio was on the average 0.8％, and it was only 0.2％ in Cluster 3.
HQJAP : headquarters in the JFUA per all headquarters in Japan. In the Tokyo JFUA, the ratio was 20.8％. In Cluster 2, the ratio was on the average 0.65％ and only 0.16％ of HQs were found in JFUAs which belong to Cluster 3.
BIGCAPITALJAP : companies with more than 100 million yen capital per companies within Japan. In the Tokyo JFUA, the ratio was 0.00223. In Cluster 2, the ratio was on the average 0.000031 and it was only 0.000001 in Cluster 3.
QUALITIY OF ENVIRONMENT CRIME : recognised crime offences per 1,000 persons. The Tokyo JFUA and JFUAs in Cluster 2 had approximately 19 cases per 1,000 persons. JFUAs in Cluster 3 had only on the average 11 cases per 1,000 persons.
HOMEOLD（ homes for old people per 1,000 persons）, PUBHALL（ public halls per 1000 persons） variables confirmed the relatively better position of JFUAs in the Cluster 3, but the difference among the 3 clusters was not considerable.
DEPARTSOTRE（ department stores per 1,000 persons） and RETAILSTORE（ retail stores per 1,000 persons）. The Tokyo JFUA had 300 department stores and 1,247 retail stores per 1,000 persons. In Cluster 2, 18 department stores and 90 retail stores per 1,000 persons were the average and it was 8 department stores and 50 retail stores in Cluster 3.
GENHOSPITAL : general hospitals per 1,000 persons. The Tokyo JFUA had 6.35 hospitals per 1,000 inhabitants. In Cluster 2, the value was 0.478 and it was only 0.323 in Cluster 3.
― 64 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 445
PHYSICIAN : physicians per 1,000 persons. All three clusters were characterised by the same value : around 2 physicians per 1,000 persons.
NPO : non―profit organisations per 1,000 persons. In the Tokyo JFUA, the value of NPOs was 0.446 per 1,000 persons. In Cluster 2 and Cluster 3, the value was around 0.250 NPO per 1,000 persons.
SOCIAL COHESION
OUTMIG : out ―migrants per 1,000 inhabitants. In the Tokyo JFUA, the value was 59 out―migrants per 1,000 persons. In Cluster 2, the value was 37 out―migrants per 1,000 persons, and it was 35 out―migrants per 1000 persons in JFUAs in Cluster 3.
INMIG : in ―migrants per 1,000 inhabitants. In the Tokyo JFUA, the value was 59 in―migrants per 1,000 persons. In Cluster 2, the average value was 42 in―migrants and it was 30 in―migrants per 1,000 in Cluster 3.
WORKSAME : employed persons who works and resides in the same municipality per commuters to other municipalities. In the Tokyo JFUA, the ratio was 0,669 which meant the number of commuters to other municipality was bigger. In Cluster 2, the average value was 1,954 and it was 4,38 in Cluster 3. In these JFUAs the ratio of people who works in the same municipality of their residence is bigger than the ratio of those who commutes to other places.
COMFROMCOMTO : commuters from other settlements per commuters to other settlements（ outside the JFUA）. In the Tokyo JFUA, the ratio was 1.031 which meant the number of commuters from other settlements was much bigger than the number of commuters to other municipalities. In Cluster 2, the average ratio was almost the same（ 1.035） and it was 0.993 in Cluster 3 which meant the number of commuter to other settlements exceeded the number of commuter from other settlements.
Source : based on results of the cluster analysis, edited by the authors
Relatively strong competitiveness (Cluster 1)
Medium competitiveness (Cluster 2) Small and medium-sized JFUAs of Cluster 2 Relatively weak competitiveness (Cluster 3)
Figure 2. Types of JFUAs in terms of competitiveness Source : edited by the authors according to results of the cluster analysis. large city） is higher than the number of commuter to other cities. The favourable location, the easy accessibility to metropolitan areas, the interconnectivity with big cities based on substantial industrial specialization or the effect of positive local development strategies etc. might create a strong and fruitful symbiosis between these small and medium ―sized cities and their neighbouring metropolitan areas. We tried to collect available additional information about the ― 65 ― 446 Japanese Journal of Human Geography 64―5（2012）
character of these small and medium―sized JFUAs’ central cities in Cluster2 to understand their favourable position. Two main explanations could be distinguished based on their different social ―economic characteristics and location. Group1. Favourable location ― easy accessibility to metropolitan areas : ― Atsugi, Kumagaya, Narita, Tsuchiura, Tsukuba, Oyama（ Tokyo JFUA）, ― Himeji（ Kobe JFUA）, Kusatsu（ Kyoto JFUA, Osaka JFUA ― especially to Otsu city） ― Komaki（ Nagoya JFUA） ― Chitose（ Sapporo JFUA） ― Numazu, Gotemba, Kakegawa（ Shizuoka JFUA） ― Ogaki（ Gifu JFUA） In our research, we have special interest regarding the competitiveness of small and medium― sized JFUAs. Therefore to gain a better understanding of JFUA’s competitiveness, questionnaires 6 were sent. The answers, received from Numazu, Komaki, Kusatsu, Ogaki, Narita, Tsukuba, Tsuchiura and Kumagaya JFUAs’ central cities, confirmed the above ―mentioned statement : as their strong point, they mentioned about their good transportation network and easy accessibility to big neighbouring cities. Gotemba JFUA, connected to Shizuoka JFUA, is noted for a number of golf courses and spacious outlet shopping centres, therefore, it has an important recreational function for residents of big neighbouring cities. Kakegawa City serves as a good 7 example for underpinning the effectiveness of good local development policies. Group2. Specialized areas with an embedded industrial base : ― Toyota, Yokkaichi, Anjo, Handa, Nishio, Hekinan, Tsu（ Nagoya JFUA） ― Fuji, Takaoka, Iwata（ Hamamatsu JFUA） ― Mihara（ Hiroshima JFUA） The “Group2” contains those JFUAs which are characterised by an industrial specialisation. For example, Nagoya city’s dominant industry is the automotive business. Toyota Motor Corporation has headquarters in Nagoya and Toyota cities. Anjo City, due to its proximity to the various factories of Toyota company in neighbouring Toyota City, therefore, it is a host to many factories supplying components into the automotive industry. Also numerous suppliers to the automotive industry concentrated and have production plans in Nishio City. Hekinan City’s main industry is creating car parts. Toyota Industries is the biggest company for the industry in the city. It is about 10％ of the total industrial workers in Hekinan. Yokkaichi is the closest major city to Nagoya City. It is a manufacturing centre that produces automobiles, cotton textiles, chemicals, cement and computer parts （Yokkaichi Toshiba Electronics）. Fuji City is also one of the major industrial centres of Shizuoka Prefecture and the city hosts numerous paper factories including Nippon Paper Industries. Takaoka City has a water power plant, the electricity is cheaper, therefore, there is a base of aluminium manufactured production. Hamamatsu and Iwata cities have been famous as an industrial city, especially for musical instruments and motorcycles. Iwata is known for being the headquarters of the Yamaha Motor Corporation.
V Conclusions
The prime objective of the regional development policy is to moderate spatial imbalances and to keep inequalities, at least partly, at a tolerable level. It gives an account of the efficiency of regional development policy, for that reason, the examination of spatial inequalities with the employment of comprehensive and objective methods has accentuated importance. In Japan, as ― 66 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 447 in case of any other countries, the success of regional planning policies can be determined by the appraisement of the regional disparities. Therefore, the significance of such kind of survey is out of question, but finding the adequate method for the evaluation is crucial. As a matter of course, there are numerous studies in which researchers have made serious efforts to offer methodological solutions for investigating regional structures or policy impacts. However, in the majority of them, it remains partially unexplained why a given variable was selected or rejected. On the contrary, in the present study, we endeavoured to ease this problem : the Pyramid Model offers a logical framework which considers all important fields of competitiveness and, in addition, the already tested methodological solutions of Lukovics help to identify the relevant indicators of competitiveness, as well as help to reflect the special character of the examined spatial unit. Therefore, this method has a clear advantage in measuring regional inequalities. In the present paper, we have made an attempt to scrutinize the efficiency of the Japanese regional planning policy with the measurement of the inequalities among Japanese subnational units regarding their competitiveness. This is the first application of this method to evaluate the competitiveness of functional urban areas in Japan. The significance of the results of the applying of the Pyramid Model for Japanese urban areas is the following : ⑴ In the present paper, the relative position of each identified JFUA was determined in terms of their revealed and source of competitiveness. Moreover, the investigation contributed to find out which factors were responsible for the differentiation in competitiveness among urban areas. These useful information can be used by local policy makers helping them to receive overall and unbiased insight into the performance of their urban area in order to evaluate the efficiency of their former development strategies and/or underpin new ones to improve their competitiveness （i. e. economic performance of their region） via the elimination of the shortcomings related to the identified factors. ⑵ According to the outcome of our investigation, the significant gap between the Tokyo JFUA and other areas was confirmed. Addressing this gap has been a long―standing objective of Japanese regional planning policy. During the 1970s, a decrease in income disparity was realized between metropolitan and local areas, partly because of successful regional planning. During the 1980s, the mono―polarization of Tokyo began, although it eased temporarily after the collapse of the bubble economy. However, since the mid―1990s, the re―centralization of Tokyo has continued（ Matsubara 2007）. Our analysis here attested that the regional policy cannot effectively counteract the negative effects of the changes ; it remained ineffective. This result shed light on the fact that there is a need to make further steps to enhance competitiveness of the country, however, if it is possible in a more balanced manner. As a common knowledge, regional development is not a zero sum game （the development of one area does not delay or cause a setback of any other areas）. This is the central motion of the polycentric regional development policy. “Decentralisation” and “reform” have been often used as catchphrases in Japanese policy making and academic circles since the second half of the 1980’s. Although, mainly after 2000 （due to the measurements of the Koizumi Cabinet）, some significant changes have occurred in order to enhance decentralisation and above all fiscal independence （several changes in the tax system） from central government, however, the outcomes were either ambiguous or simply moderated. The results of the analysis obviously confirmed that. ⑶ Nevertheless, it is a good point, in the light of the results, that although the size is the most prevalent determinant of Japanese urban competitiveness, however, other factors or ― 67 ― 448 Japanese Journal of Human Geography 64―5（2012）
circumstances seems to be able to mobilize local resources to attain successful development of an urban area. The small and medium―sized JFUAs in Cluster2 with medium competitiveness served as a good example. These relatively competitive small and medium―sized cities can hold a key role in achieving a more balanced regional development, therefore, identifying them and understanding their special character and potential should be of high priority. ⑷ The former Hungarian application of the Pyramid Model has determined very similar ― also less favourable ― outcomes : namely the very centralized character of the country. The majority of the micro―regions of the country lagged seriously behind the performance of the capital city （Budapest）. The analysis also confirmed the traditionally better position of the micro― regions being situated close to the western part of the country or to the agglomeration of the capital city, as well as to important transportation nodes（ highways）. In the case of Hungary, we can state that the size of the micro ―regions was the most decisive factor regarding competitiveness as well, but location and accessibility to big cites （in this case only Budapest） played an important role too.
＊ An earlier version of this paper was presented at Applied Regional Science Conference （Annual Meeting of ARSC）, Toyama December 3―4, 2011 December. Éva Komlósi was a research student from April 2010 to March 2012 at the Department of Regional Policy, Graduate School of Regional Sciences, Tottori University.
References Abe, K. （2004） Major cities and the urban system of Japan from the standpoint of large private firms’ head and branch offices. Annals of Japan Association of Economic Geographers, 50 : 139― 161. Bristow, G. （2005） Everyone’s a “winner” : problematizing the discourse of regional competitiveness. Journal of Economic Geography, 5 : 285―304. Budd, L., Hirmis, A.（ 2004） Conceptual framework for regional competitiveness. Regional Studies, 38 : 1015―1028. Boschma, R. （2004） Competitiveness of regions from an evolutionary perspective. Regional Studies, 38 : 1001―1014. Camagni, R. （2002） On the concept of territorial competitiveness : sound or misleading ? Urban Studies, 39 : 2395―2411. European Commission（ 1999） Sixth periodic report on the social and economic situation of regions in the EU. Brussels : European Commission. http://ec.europa.eu/regional_policy （last accessed December 14, 2011）. Gardiner, B., Martin, R., Tyler, P. （2004） Competitiveness, productivity and economic. Growth across the European regions. Regional Studies, 9 : 1045―1067. Garlick, S. （2003） Growth and competitiveness in the Gippsland region : performance and determinants 1984 ―2000. Sydney : Regional Knowledge Works. Kanemoto, Y., Tokuoka, K. （2002） Proposal for the Standards of Metropolitan Areas of Japan. Journal of Applied Regional Science, 7 : 1―15. Kawashima, T. （1982） Recent urban trends in Japan : analysis of functional urban regions. In Human Settlements System : Spatial Patterns and Trends, ed. T. Kawashima and P. Korcelli, 21 ―40. Laxenburg : IIASA. Kitson, M., Martin, R., Tyler, P. （2004） Regional competitiveness : an elusive yet key concept ? ― 68 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 449
Regional Studies, 38 : 991―999. Krugman, P. （1996） Making sense of the competitiveness debate. Oxford Review of Economic Policy, 12 : 17―35. Krugman, P. （2003） Growth on the Periphery : Second Wind for Industrial Regions ? Scotland : Fraser Allander Institute. Lengyel, I. （2003） Competitiveness and regional development : the competitiveness of the Hungarian’s regions. Szeged : JATEPress. （In Hungarian language : Verseny és területi fejlődés : térségek versenyképessége Magyarországon）. Lengyel, I. （2004） The pyramid model : enhancing regional competitiveness in Hungary. Acta Oeconomica, 54 : 323―342. Lukovics, M.（ 2007） Statistical analysis on the competitiveness of the Hungarian sub―regions. http://www.cers.tuke.sk/cers2007/PDF/Lukovics.pdf（ last accessed February 4, 2012）. Lukovics, M. （2008） Measurement of the regional competitiveness. Szeged : JATEPress. （In Hungarian : Térségek versenyképességének mérése）. Martin, R. （2005） Thinking about regional competitiveness : critical issues. Nottingham : East Midlands Development Agency. Matsubara, H. （2007） Reorganisation of Japanese urban system and internal structures of urban areas in a globalized economy and a declining population society. Annals of the Japan Association of Economic Geographers, 53 : 443―460. Okada, H.（ 1994） Features and economic and social effects of the shinkansen. Japan Railway and Transport Review, 3 : 9―16. Osada, S.（ 2003） The Japanese urban system 1970―1990. Progress in Planning, 59 : 151―231. Parkinson, M., Champion, T., Dorling, D., Parks, A., Simmie, J. and Turok, I.（ 2005） State of the English cities. London : ODPM. Snieška, V., Bruneckienė, J. （2009） Measurement of Lithuanian regions by Regional Competitiveness Index. Inžinerinė Ekonomika―Engineering Economics 61 : 45―57. Sinabell, F., Bock―Schappelwein, J., Mayer, C., Kniepert, M., Schmid, E., Schönhart, M., Streicher, G. （2011） Indicators of Effects of the 2007―2013 Rural Development Programme in Austria. Vienna : Austrian Institute of Economic Research. Sternberg, R. （2009） Regional dimension of entrepreneurship. Foundations and Trends in Entrepreneurship, 5 : 211―340. Yamada, H., Tokuoka, K.（ 1991） : A study of the urbanization process in post war Japan. Review of Urban & Regional Development Studies 3 : 152―169. Yamagami, T. （2006） The relationship between metropolitan size and the population redistribution pattern within metropolitan areas in Japan from the perspective of diversity among suburban municipalities. Japanese Journal of Human Geography, 58 : 56―72.
Notes 1. Osada identified a 30,000 working population as the cut―off point for cores in 1990. However, during the 15 years examined here, numerous changes have occurred, which were reflected in our new cut―off point. 2. Lukovics used several available variables to describe each field of competitiveness（ e. g. 3 variables from tourism, 4 from foreign companies）. Obviously, there are differences among countries regarding their data gathering（ e. g. domestic and foreign tourists’ data are only available in aggregated form in Japan）. Lukovics set was a “possible sample set” of variables, but our main aim was to find “at least one relevant variable” for every category of the Pyr- amid Model. However, we could not find any variables connected to R&D（ only aggregated data） or accessibility fields. However, if we examine available ― though aggregated ― R&D data, they also confirm the unique position of Tokyo and the unfavourable situation of the other areas. For instance, the number of patent applications in 2005 : it
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was 179,653 in Tokyo Prefecture, only 58,175 in Osaka Prefecture, 9,422 in Kyoto Prefecture and only 143 in Tot- tori Prefecture etc.（ http://www.jpo.go.jp）. The journal NATURE, one of the most prestigious journals in natural science, publishes a list of Japanese universities with regard to their corrected publication index. The total publica- tion index of universities which belong to Tokyo JFUA was 54.3 points. Universities belonging to JFUAs in Cluster2 reached 1.58 points as their total index. This was 0.025 point in total for JFUAs in Cluster3（ http://www.natureas- ia.com）. Regarding accessibility, no calculated accessibility index was available. However, the commuting variable ― although indirectly ― can represent accessibility. 3. Tourism data received from prefectures（ municipal level data）. Last finished school data were derived from the 2000 Population Census, establishment data from “Establishment and Enterprise Census” for 2006, NPO data from NPO Hiroba database（ http://www.npo―hiroba.or.jp） and the number of professors and foreign students from JASSO database（ http://www.g―studyinjapan.jasso.go.jp）. 4. Tokyo JFUA included Saitama, Yokohama, Chiba and Kawasaki cities. Therefore, compared with Osaka or Nagoya JFUA, the area of Tokyo JFUA was quite large. To eliminate the problem caused by the different “size” of the spatial units, per capita data and standardization was used. 5. ⑴ Based on studies which used different methods to identify the main groups of Japanese cities. For instance, Abe examined the Japanese urban system from the standpoint of large private firms’ head and branch offices be- tween 1950―2000. That study used a rank―size method to demonstrate the relative position of Japanese cities（ Abe 2004）. ⑵ Most competitiveness studies which deal with the classification of regions identified mainly 3 types of regions （e. g. Classification of WEF, Types of regions ― Cambridge, Types of regions ― OECD）（ see Lukovics 2008）. There- fore, we had a hypothesis about 3 clusters. ⑶ The hierarchical cluster analysis was conducted to determine the number of clusters : 3, 4 and 5 as number of clusters were considered. In the case of 4 and 5 clusters, we could not observe clearly the differences among the clusters（ identification problem）. Therefore, the solution of 3 clusters was chosen. 6. Questionnaires were sent to the identified core cities of 96 small and medium―sized JFUAs addressed directly to the planning department of the municipalities. The questionnaire consisted of 32 questions which were divided into four sections（ competitiveness, business planning practice and civil society and financial situation）. 40 core cities filled out and sent back the questionnaires（ 41.5％）. 7. ”When the Tokaido Shinkansen was opened in 1964, trains passed behind Kakegawa Station on the conventional Tokaido Line, and the city enjoyed no benefit … the local people believed that stopping shinkansen trains at Kakega- wa Station would surely revitalise both Kakegawa City and all the areas along the conventional Tokaido Line and lo- cal lines. Consequently a new station was built in 1988 … The new shinkansen station has had a great effect on the economy, lifestyle and culture of Kakegawa city”（ Okada 1994, 14）.
Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model
Éva Komlósi graduate research assistant Faculty of Business and Economics, University of Pecs FUJII Tadashi professor Faculty of Regional Sciences, Tottori University
In the present study, we propose a method to determine the competitiveness of spatial units in a more objective manner, which aims at improving the underpinning as well as ex―post assessment of regional planning. 141 JFUAs were created as spatial units based on the method introduced by Osada（ 2003）. These JFUAs were classified into three clusters in terms of their competitiveness with the help of the Pyramid Model （Lengyel, 2004）. The analysis of the ― 70 ― Competitiveness of Japanese Functional Urban Areas（ JFUAs） : Empirical Testing of the Pyramid Model（ Komlósi and FUJII） 451
clusters confirmed a significant gap between the Tokyo JFUA and the other urban areas of the country and highlighted the uneven nature of the distribution of economic, human and institutional resources in the country. On the other hand, as one of the most important contributions of our paper, we point out the existence of 22 small and medium ―sized JFUAs with medium competitiveness which can have an important role in the creation of a more balanced urban development. The investigation also contributed to finding out which factors were responsible for the differentiation in competitiveness among urban areas. Although size is the most prevalent determinant of Japanese urban competitiveness, other factors or circumstances seem to be able to mobilize local resources to attain successful development of an urban area. The small and medium―sized JFUAs in Cluster 2 with medium competitiveness serve as a good example.
Key words : competitiveness, Japanese Functional Urban Areas（ JFUAs）, Pyramid Model
日本の都市圏の競争力：ピラミッドモデルの実証的検定
コムローシ・エーヴァ ペーチ大学経営経済学部 藤井 正 鳥取大学地域学部
本研究では，まず長田進の方法によって，日本の141都市圏（Japanese Functional Urban Area）を 設定した（Osada 2003）。次にこれら都市圏を，経済競争力に関するピラミッドモデル（Lengyel 2004）にもとづくクラスター分析により，三つのクラスターに分類した。三つのクラスターは，ピラミ ッドモデルにおける基本カテゴリー・発展要因・成功要因の差違により構成されたものである。また最 終的なクラスター間の距離は，第１クラスターを単独で構成する東京大都市圏と他の都市圏との間の競 争力の大きな格差を示し，経済資源と人的資源の不均等分布を明らかにしている。一方，中位の競争力 を示す第２クラスターは，多くが太平洋ベルト地帯の都市圏群であるが，富山や那覇などの地方都市圏 や郊外中心，あるいは小規模な都市圏も含め分類されており，これは今後の均衡ある地域発展を考える 重要な鍵となる。このように都市圏の競争力に影響を与える要因が，日本では都市規模以外にも見られ る点もここで見いだされた新たな知見である。
キーワード：競争力，都市圏，ピラミッドモデル
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