CHAPTER 6

URBAN AREAS: DEFINING JAPANESE METROPOLITAN AREAS AND CONSTRUCTING THE STATISTICAL DATABASE FOR THEM

Yoshitsugu Kanemoto

Graduate School of Public Policy and Graduate School of Economics, University of

Tokyo

Reiji Kurima

Project Researcher, Research Center for the Relationship Between Market Economy and Non-Market Institutions, Graduate School of Economics, University of Tokyo

6.1 INTRODUCTION

For those interested in analyzing urban activity, the first task should be to define urban areas. The legal definition of a is a natural starting point, but many urban activities extend beyond jurisdictional boundaries. For example, many workers in large metropolitan areas commute from suburban jurisdictions to central . We therefore need a definition of an within which most everyday activities are undertaken.

An urban area typically comprises a core area that has significant concentrations of employment, which is surrounded by densely settled areas that have close ties to the core.

1 In the United States, the Federal Government has defined metropolitan areas since 1947 and provides a variety of statistical data relating to them. There is no counterpart in

Japan, and the only definitions of metropolitan areas available are those proposed by a small number of researchers. Most of these adopt standards similar to the Standard

Metropolitan Statistical Area (SMSA), which was in use in the 1960s and 1970s.

However, in the U.S., two major changes have occurred since then, which reflect changes in the population distribution and activity patterns. First, in the 1980s, the

Consolidated Metropolitan Statistical Area (CMSA) was introduced, which connects metropolitan areas that have significant interactions. Second, a new definition known as the Core Based Statistical Area (CBSA), was introduced for the 2000 population census.

In , changes in metropolitan areas motivated a revision of the first generation of definitions. Kanemoto and Tokuoka (2002) proposed a new metropolitan area definition to deal with complicated interaction patterns in Japanese metropolitan areas. The newly defined metropolitan areas are known as Urban

Employment Areas (UEAs) because they are based on employment patterns. The UEAs are divided between Metropolitan Employment Areas (MEAs) and Micropolitan

Employment Areas (McEAs) according to their sizes. Researchers affiliated with the

Center for Spatial Information Science at the University of Tokyo have been constructing a database for UEAs. In this chapter, we explain the definition of the UEA and a method of constructing an economic database for them.

2 6.2 THE NEED FOR A NEW METROPOLITAN AREA DEFINITION

As noted above, a number of researchers have developed their own definitions of metropolitan areas. Examples are the Regional Economic Cluster (REC) of Glickman, the Functional Urban Core (FUC) of Kawashima, and the Standard Metropolitan

Employment Area (SMEA) of Yamada and Tokuoka. These SMSA-type definitions apply different standards to central cities and suburban areas. According to the SMEA, a central city requires a population of at least 50,000, a percentage of non-agricultural workers of at least 75%, at least as many daytime occupants as nighttime ones, no more than 30% of the population commuting out, and no more than 15% commuting to another central city. A suburban requires a percentage of non-agricultural workers of at least 75% and at least 10% of the population commuting to the central city.

The idea of defining central cities and suburban areas separately is attractive because of its simplicity. It first defines central cities and then finds suburban areas for each of them, and the process does not involve iteration. However, it has shortcomings. For example, a city with a high may not be included in a metropolitan area. For example, city, which is the capital of the Yamaguchi , did not belong to an SMEA until 1985. It was not classed as a central city because it had fewer daytime occupants than nighttime occupants and at the same time did not satisfy the conditions for being a of another city.

Recently, this problem has become increasingly serious because of the emergence of a large number of subcenters and because of increasingly complex commuting patterns.

If we use commuting ties to define a suburban area in relation to a particular central city,

3 a city that is close to more than one central city may not belong to a metropolitan area.

In the 1995 population census, there were 441 cities with populations of at least 50,000, of which 60 could not be classed as either central cities or of an SMEA. Of these 60 cities, 16 have populations of at least 100,000. Given that a single city with a population of 100,000 can itself be classed as an SMEA, excluding these cities from metropolitan areas is not consistent. Many cities that do not belong to an SMEA are suburban areas from which at least 30% of the population commute out. Typically, commuters have more than one city to commute to. Almost 50% of these cities are located on the periphery of the Tokyo SMEA.

To deal with these problems, we can relax either the requirements for central cities or those for suburban areas. For example, the core of a metropolitan area may include subcenters with sufficiently large concentrations of employment even if they satisfy the requirements for classification as suburban areas of a central city. In the Tokyo metropolitan area, , in which employment was about 1.4 million in 1995, could be included in the core area. Another possibility is to modify the requirements for suburban areas so that they take account of commuting to other suburban cities.

SMEAs have three types of requirement for central cities: namely, population size, urban characteristics, and the employment core. Of these elements, the employment- core requirements should be re-examined first so that areas with significant population concentrations are not excluded from metropolitan areas. Another problem with the

SMEA relates to the requirements for urban characteristics. The percentage of non- agricultural workers represents this element, but it is no longer an effective index of .

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6.3 METROPOLITAN AREA DEFINITIONS IN THE U.S.

In revising the Japanese metropolitan area definition, it is useful to study other that have experienced a similar trend of increasingly complex metropolitan areas. In the

U.S., there was a major revision in 2000 with the introduction of a new metropolitan area definition known as the CBSA. According to the Office of Management and

Budget (2000),

A CBSA is a geographic entity associated with at least one core of 10,000 or

more population, plus adjacent that has a high degree of social and

economic integration with the core as measured by commuting ties. The

standards designate and define two categories of CBSA: Metropolitan

Statistical Areas and Micropolitan Statistical Areas. (Office of Management

and Budget (2000), p. 82,236.)

A Metropolitan Statistical Area is associated with at least one urbanized area that has a population of at least 50,000, and a Micropolitan Statistical Area is associated with at least one urban cluster that has a population of at least 10,000 but less than 50,000.

A CBSA is identified in four steps. First, a CBSA must contain sufficiently large urban

(densely settled) areas. Specifically, it must have an urbanized area, as defined by the

Census Bureau, of at least 50,000 people or an urban cluster, as defined by the Census

Bureau, of at least 10,000 people.

5 Second, the core of a CBSA comprises a central or associated with the urban areas. Specifically, a central county or counties must:

(a) have at least 50% of its population in urban areas of at least 10,000 people; or

(b) have within its boundaries a population of at least 5,000 located in a single urban

area of at least 10,000 people.

Third, outlying counties of a CBSA must satisfy the commuting requirement that:

(a) at least 25% of the employed residents of the county work in the central county or

counties of the CBSA; or

(b) at least 25% of the employment in the county is accounted for by workers who

reside in the central county or counties of the CBSA.

Fourth, closely connected CBSAs are merged into one CBSA. In particular, two adjacent CBSAs merge into one CBSA if the central county or counties (as a group) of one CBSA qualify as outlying counties to the central county or counties (as a group) of the other CBSA using the measures and thresholds stated in (a) and (b) above.

Because of institutional differences, we cannot apply the U.S. definitions to Japanese cities. The most important difference is that the Japanese government does not define urban areas that extend beyond jurisdictional boundaries. The nearest equivalent in

Japan is a Densely Inhabited (DID) defined within a local municipality. The

DID is defined by the Statistics Bureau as an area which is a group of contiguous Basic

Unit Blocks each of which has a population density of 4,000 inhabitants or more per square kilometer, or which has public, industrial, educational and recreational facilities, and whose total population is 5,000 or more within a local municipality.

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U.S. requirements for outlying areas have changed considerably. First, the measures of settlement structure, such as population density, that had been used to define outlying counties are no longer used; currently, commuting data are used. The reason for this change is that “as changes in settlement and commuting patterns as well as changes in communications technologies have occurred, settlement structure is no longer as reliable an indicator of metropolitan character as was previously the case” (Office of

Management and Budget (1999)). In Japan, metropolitan areas have expanded into rural areas, and the use of settlement structure may no longer be relevant. Second, the percentage commuting out was raised from 15% to 25% because the percentage of workers in the U.S. who commute to work outside their counties of residence increased from approximately 15% in 1960 to almost 25% in 1990. In Japan, we should also reconsider the commuting ratio. However, it is not clear whether the ratio should be raised because, as we explain later, commuting patterns in Japan are much more complicated than in the U.S.

6.4 THE STRUCTURE OF JAPANESE METROPOLITAN AREAS

According to the 1995 population census, 724 cities and have DID populations of at least 10,000, of which 440 have total populations of at least 50,000. The number of cities with DID populations of at least 50,000 is 297.

Many urban areas are dormitory towns, and relatively few of these are employment centers. Of the 724 (297) cities and towns with DID populations of at least 10,000

7 (50,000), only 281 (120) have larger commuter inflows than outflows. Some large cities such as Yokohama, , and Kawasaki have larger outflows than inflows.

However, the central wards of these cities are employment centers that have larger commuter inflows than outflows, and DID populations of at least 50,000.

Consider commuting patterns. The average commuter outflow proportion is 32%, but employed residents commute to a wide variety of urban areas. Some cities have highly concentrated commuting patterns: more than 50% of the employed residents of 16 cities and towns (Tomiya, Wako, Urayasu, Fuchu, Komae, Hoya, Nagayo, Kokufu, Uchinada,

Ichikawa, Sanwa, Kouyagi, Ishikari, Musashino, Hashikami, and Yakumo) commute to one city. Another extreme is Zama, more than 5% of the employed residents of which commute to one of six other .

6.5 DEFINING URBAN EMPLOYMENT AREAS

Kanemoto and Tokuoka (2002) proposed a new metropolitan area definition known as the Urban Employment Area (UEA). UEAs are divided between Metropolitan

Employment Areas (MEAs) and Micropolitan Employment Areas (McEAs) according to their sizes. These are similar to the CBSA for U.S. cities, but there are substantial differences in specific requirements to reflect the higher densities and more complicated commuting patterns in Japanese cities.

In defining metropolitan areas for Japan, we must take the following four conditions as given.

8 1. The building blocks of metropolitan areas are municipalities (cities, towns, and

) because most statistical data are only available up to the municipality level.

2. The prevalence of commuting by car in smaller metropolitan areas has increased

the numbers commuting to cities from areas of low population density. The use of

population density standards in defining outlying areas is no longer practical.

3. In identifying densely inhabited urban areas, we use DID populations.

4. The inclusion of outlying suburban areas is determined by commuting patterns

between municipalities.

After examining many alternatives, Kanemoto and Tokuoka (2002) proposed the following definition of a UEA. First, local municipalities (cities, towns, and villages) are the building blocks of UEAs. The core of a UEA is a collection of densely settled municipalities (i.e., those with DID populations of at least 10,000) that do not constitute the “outlying municipalities” (suburbs) of any other core. The outlying municipalities of a UEA are defined mainly by the requirement that at least 10% of employed workers commute to the core. An MEA is a UEA whose core has a DID population of at least

50,000. An McEA is a UEA whose core has a DID population of at least 10,000 and less than 50,000. More specifically, they use the following standards.

6.5.1 Requirements for a core

1. In the first round, potential cores are municipalities with DID populations of at

least 10,000.

9 2. A municipality that is an outlying area of another central city is excluded from

being a core.

3. For a pair of municipalities that each satisfy the requirement for being in a core

and also satisfy the commuting-ratio requirement for being an outlying area of the

other municipality, the one with the lowest commuting ratio is in the core and the

other is its outlying area.

4. An outlying municipality is included in the core if the following two requirements

are satisfied. A 'major' city (known as a Seirei Shitei Toshi) for which data on its

wards are available is included in the core if at least one of its wards satisfies the

following requirements.

a. The employees-to-residents ratio (i.e., the ratio of the number of employees to

the number of residents) is at least unity.

b. The DID population is at least 100,000 or one third of the core.

Condition 4 implies that a core may contain more than one municipality. The reason for adding the condition on the size of the DID in 4b is that the largest metropolitan areas, such as Tokyo and , have multiple central cities, and some very small municipalities satisfy the employees-to-residents ratio. It is inappropriate to include a municipality of 5,000 in the core alongside the central city of Tokyo, which has more than 7 million employees.

6.5.2 Requirements for an outlying area

1. A municipality is an outlying area of a core if at least 10% of its employed

residents work in the core.

10 2. If a municipality satisfies condition (a) for more than one core, it is included in the

outlying area of the core with which it has the strongest commuting ties.

3. A second-order outlying municipality that is an outlying area of another outlying

municipality is included in a UEA. Higher-order outlying municipalities (i.e., third-

order, fourth-order, etc.) are also included in UEAs. The criterion for a second-order

municipality is that, of all the target municipalities, its commuting ratio to a first

order outlying municipality is the highest and satisfies the 10% criterion. Higher-

order outlying municipalities are defined analogously.

4. If a municipality simultaneously satisfies the requirement for being an outlying

area of a core and the requirement for being another outlying municipality, it is

classified as an outlying area of the one with which it has the highest commuting

ratio. That is, if 16% of the employed residents in city A work in core B and 17% of

them work in city C, which is an outlying municipality of core B, then city A is an

outlying area of city C.

6.5.3 The iterative procedure for defining UEAs

UEAs are determined by the following iterative procedure.

6.5.3.1 The First Iteration

1. Choose municipalities with DID populations of at least 10,000 as potential central

cities.

2. Exclude as potential central cities defined by (a) those municipalities that are

outlying areas of other potential central cities.

11 3. Determine the outlying municipalities for the potential central cities by using the

following procedure.

a. Select municipalities for which the percentage of employed residents who work

in a central municipality is at least 10%; and for each of them, determine the

central municipality that has the highest commuting ratio. This identifies

potential first-order outlying municipalities.

b. Determine potential second-order outlying municipalities by choosing

municipalities that satisfy the commuting-ratio criterion.

c. Determine potential second-order outlying municipalities.

d. Determine potential third-order outlying municipalities.

e. Check for fourth-order outlying municipalities. (Currently, there are none.)

f. If a municipality is simultaneously a first-order, second-order, and/or third-order

outlying area, identify the target municipality with the highest commuting ratio.

g. List the outlying municipalities for each central city.

6.5.3.2 The Second Iteration

1. Of the potential outlying municipalities identified in the first iteration, those that

satisfy the following two requirements are included in the cores of the UEAs to

which they belong. If a candidate is a 'major' city, it is included in the core if at least

one of its wards satisfies the following requirements.

a. The employees-to-residents ratio is at least unity.

b. The DID population is at least 100,000 or at least one third of that of the central

municipality.

2. Potential first-order outlying municipalities are those in which at least 10% of

employed residents work in the central municipality. For each of these, choose the

12 central municipality that has the highest commuting ratio. The procedures applied in

the first iteration are applied to determine higher-order outlying municipalities.

6.5.3.3 Other Iterations

The procedures used in the second iteration are used for subsequent iterations. Step 1b in the second iteration for adding an outlying municipality to the core remains the same.

In particular, the central municipality is the one identified in the first iteration and does not include those added in the second iteration.

6.6 URBAN EMPLOYMENT AREAS FOR THE 1995 POPULATION CENSUS

For the 1995 population census, three iterations were needed to define the UEAs. The total number of UEAs is 278, of which 118 are MEAs and 160 are McEAs. Figure 1 maps Japan's MEAs. Table 1 presents a rough outline of the requirements of UEAs.

Figure 2 shows MEAs, McEAs, DIDs and municipality boundaries in Ibaraki Prefecture.

The darkest gray areas within MEAs and McEAs are DIDs. The second darkest areas are the cores of MEAs and McEAs and light gray areas are their outlying areas. Light gray curves show boundaries of municipalities (cities, towns and villages).

13 Fig. 1. The Metropolitan Employment Areas for the 1995 Population Census

14 Table 1. Requirements of Urban Employment Areas

Requirement Urban Employment Area Categories Metropolitan Employment Area: The DID population of the core is at least 50,000 Micropolitan Employment Area: The DID population of the core is at least 10,000 and less than 50,000 Qualification of Areas City of at least 10,000 DID people Qualification of central Municipalities that satisfy either of the following two municipalities (Cores) requirements are included in the core. (The core may include more than one municipality.) (a) The DID population is at least 10,000 and the municipality is not an outlying area of another core. (b) The requirements for an outlying area are satisfied and the following two requirements are also satisfied. (i) The employees-to-residents ratio is at least unity. (ii) The DID population is at least 100,000 or one third of the core. For a pair of municipalities each of which satisfies the requirement for being in a core and also satisfies the commuting-ratio requirement for being an outlying area of the other municipality, the one with the lowest commuting ratio is in the core and the other is its outlying area. Qualification of outlying (a) A municipality is a first-order outlying area of a core if at municipalities least 10% of its employed residents work in the core. (b) A municipality is a second-order outlying area of a core if at least 10% of its employed residents work in an outlying municipality and the commuting ratio to the municipality is the highest among all target municipalities. Higher-order (i.e., third-order, fourth-order, etc.) outlying municipalities are defined analogously. (c) If a municipality satisfies requirement (a) for more than one core, it is included in the outlying area of the core with which it has the strongest commuting ties. (d) If a municipality satisfies the requirement for an outlying area of a core as well as for another outlying municipality, it is an outlying area of the one with which it has the highest commuting ratio.

15 Fig. 2. The Relationship among MEAs, McEAs, DIDs and Municipalities: An Example of Ibaraki Prefecture

6.7 THE CONSTRUCTION OF THE MEA ECONOMIC DATABASE

Because the building blocks of the UEAs are local municipalities, the UEA data can be obtained by summing the municipality-level data. However, data on many important economic indicators are not available at the municipality level. For example, most production data are available only at the prefectural level. Kurima and Ohkawara

(2001) constructed MEA data for total production (value added), private capital, and social overhead capital.

The Annual Report on the Prefectural Accounts includes detailed production data for 47 and major cities (11 cities up to 1988 and 12 from 1989). Private capital

(for manufacturing and non-manufacturing industries) and social overhead capital (for

16 12 industries) are available for prefectures (but not for major cities). Kurima and

Ohkawara (2001) construct MEA data by combining these data with the employment data for local municipalities.

First, note that an MEA is a collection of local municipalities that may belong to different prefectures. Kurima and Ohkawara (2001) allocate the prefecture-level data to municipalities by using proportional allotment and then aggregate them to obtain MEA data. In prefectures that include the major cities, they construct the data for the remaining areas and allocate them to municipalities outside those cities. The production data are available for 14 industrial categories (12 industries, the public sector, and others). The output of each industry is allocated to municipalities on the basis of the industry employment shares. For example, consider an MEA denoted by A. Denote the number of workers in industry i in municipality j by N(i, j) , and denote the total production and employment of the prefecture (denoted by I) that contains the MEA by

Y(I,i) and N(I,i ) , respectively. Total production in the MEA is then

N(i, j) Y(.A) = ∑∑Y(I,i) ji N(I, j)

Second, the private capital in each MEA is obtained by using proportional allotment on the basis of the production shares in manufacturing and non-manufacturing industries

(rather than employment shares).

Third, different allotment procedures are used for each of the four types of social overhead capital. In agriculture, , and fishing, social overhead capital is allocated on the basis of production shares in the agricultural sector. In industrial

17 , allocation is based on the production shares in the manufacturing industry.

In telecommunications and railways, allocation is based on total production. Allocation of infrastructure for residents, such as parks and neighborhood , is based on population.

We have annual data on production and the capital stock, but census population and employment data are collected only every five years. Annual population data are available from the Basic Resident Registers, but their definitions differ slightly from those of the more detailed census data. The annual data are constructed by using the

Resident Registers data to modify the simple linear interpolations of the census data.

Let P denote the census population data and let Q denote the Resident Registers data. ˆ The annual data that we use, Pn+i , are then

i Q Pˆ = P + (P − P ) n+i n+i n n+5 n ˆ 5 Qn+i where n is the census year, i = 1, 2,3, 4 , and

i Qˆ = Q + (Q − Q ) . n+i n 5 n+5 n

The Annual Report on Prefecture Accounts contains annual employment data for three industry categories—namely, primary, secondary, and tertiary—but these are only available for prefectures and 12 large cities. We use these data for Q in the procedure described above.

The original data sources for the MEA economic database are as follows.

Number of employees and population: Population Censuses of 1980, 1985, 1990, and

18 1995, and Basic Resident Registers (Jumin Kihon Daicho) 1980–1995.

Production (Value Added): Annual Report on the Prefecture Accounts.

Private Capital Stock and Social Overhead Capital: Estimates by the Central

Research Institute of the Electric Power Industry (CRIEPI). (The method of

estimation is explained in Ohkawara et al. (1985).)

6.8 CONCLUDING REMARKS

We have developed an urban area definition called the UEA for Japanese cities. The

UEAs are divided between MEAs (large UEAs) and McEAs (small UEAs) according to their sizes. We have also constructed an economic database for the MEAs. The UEAs for the population censuses of 1980, 1990, 1995, and 2000 and the MEA economic database for the 1995 definition can be found on the UEA website

(http://www.urban.e.u-tokyo.ac.jp/UEA/index_e.htm). A number of researchers and government agencies have already used the UEA. Chapter 8 of this book contains an example of empirical studies that use the MEA economic database.

ACKNOWLEDGMENT

This research was supported by Grant-in-Aid for Scientific Research No. 10202202 and

No. 1661002 from the Ministry of Education, Culture, Sports, Science and Technology.

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Kanemoto, Y. and K. Tokuoka, (2002), “Proposal for the Standards of Metropolitan

Areas of Japan,” Journal of Applied No.7, 1–15, (in Japanese).

Kurima, R. and T. Ohkawara, (2001), Construction of MEA-based Economic Data (in

Japanese), mimeo

Office of Management and Budget, (1999), “Recommendations From the Metropolitan

Area Standards Review Committee to the Office of Management and Budget

Concerning Changes to the Standards for Defining Metropolitan Areas,” Federal

Register Vol. 64, No. 202, October 20.

Office of Management and Budget, (2000), “Standards for Defining Metropolitan and

Micropolitan Statistical Areas,” Federal Register Vol.65, No.249, December 27.

Ohkawara, T., Y. Matsu-ura, and M. Chuma (1985), “Chiiki Keizai Data no Kaihatsu

Sono 1—Seizougyou Shihon Sutokku to Shakai Shihon Sutokku no Suikei

(Estimation of Regional Economic Data Part 1: Manufacturing Capital and Social

Overhead Capital),” CRIEPI (Central Research Institute of Electric Power

Industry) Report No. 585005 (in Japanese).

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