The 2009-2014 Outlook for Educational Services in Japan
ICON Group International, May 2008, Pages: 144
WHAT IS LATENT DEMAND AND THE P.I.E.?
The concept of latent demand is rather subtle. The term latent typically refers to something that is dormant, not observable, or not yet realized. Demand is the notion of an economic quantity that a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly defined by economists as the industry earnings of a market when that market becomes accessible and attractive to serve by competing firms. It is a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if Japan is served in an efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The “market” is defined at a given level in the value chain. There can be latent demand at the retail level, at the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of higher levels of the value chain being always smaller than the P.I.E. of levels at lower levels of the same value chain, assuming all levels maintain minimum profitability).
The latent demand for educational services in Japan is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be either lower or higher than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, including the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the part of firms. In general, however, latent demand is typically larger than actual sales in a market.
For reasons discussed later, this report does not consider the notion of “unit quantities”, only total latent revenues (i.e., a calculation of price times quantity is never made, though one is implied). The units used in this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends). If inflation rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (not adjusted for inflation). On the other hand, latent demand can be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the level of latent demand.
As mentioned in the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. In fact, all the current products or services on the market can cease to exist in their present form (i.e., at a brand-, R&D specification, or corporate-image level) and all the players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be latent demand for educational services at the aggregate level. Product and service offerings, and the actual identity of the players involved, while important for certain issues, are relatively unimportant for estimates of latent demand.
THE METHODOLOGY
In order to estimate the latent demand for educational services across the prefectures and cites of Japan, I used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, I heavily rely on the use of certain basic economic assumptions. In particular, there is an assumption governing the shape and type of aggregate latent demand functions. Latent demand functions relate the income of a prefecture, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level of the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be able to serve that demand at a minimal return. The single most important variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels of the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for the product in question.
Ignoring, for the moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption has been a central theme in economics. The figure below concisely summarizes one aspect of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the average propensity to consume would fall. The average propensity to consume is the level of consumption divided by the level of income, or the slope of the line from the origin to the consumption function. He estimated this relationship empirically and found it to be true in the short-run (mostly based on cross-sectional data). The higher the income, the lower the average propensity to consume. This type of consumption function is labeled "A" in the figure below (note the rather flat slope of the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated that the marginal propensity to consume was rather constant (using time series data). This type of consumption function is shown as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.
Is it declining or is it constant? A number of other economists, notably Franco Modigliani and Milton Friedman, in the 1950s (and Irving Fisher earlier), explained why the two functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can depend on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to consume is more constant. Similarly, in the long run, households with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related is interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for educational services across the prefectures and cities of Japan. The smallest cities have few inhabitants. I assume that all of these cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these prefectures having wealth; current income dominates the latent demand for educational services. So, latent demand in the long-run has a zero intercept. However, I allow different propensities to consume (including being on consumption functions with differing slopes, which can account for differences in industrial organization, and end-user preferences).
Given this overriding philosophy, I will now describe the methodology used to create the latent demand estimates for educational services in Japan. Since ICON Group has asked me to apply this methodology to a large number of categories, the rather academic discussion below is general and can be applied to a wide variety of categories and geographic locations, not just educational services in Japan.
Step 1. Product Definition and Data Collection
Any study of latent demand requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key indicators are more likely to reflect efficiency than others. These indicators are given greater weight than others in the estimation of latent demand compared to others for which no known data are available. Of the many alternatives, I have found the assumption that the highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e. some cities have high aggregate income, but low income per capita and can not assumed to be efficient). Aggregate income can be operationalized in a number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or number of households times average household income).
Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Official Chinese Agencies, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, and the World Bank). Depending on original data sources used, the definition of “educational services” is established. In the case of this report, the data were reported at the aggregate level, with no further breakdown or definition. In other words, any potential product or service that might be incorporated within educational services falls under this category. Public sources rarely report data at the disaggregated level in order to protect private information from individual firms that might dominate a specific product-market. These sources will therefore aggregate across components of a category and report only the aggregate to the public. While private data are certainly available, this report only relies on public data at the aggregate level without reliance on the summation of various category components. In other words, this report does not aggregate a number of components to arrive at the “whole”. Rather, it starts with the “whole”, and estimates the whole for all prefectures and cities in Japan (without needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “educational services” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of educational services, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for educational services is 61. It is for this definition of educational services that the aggregate latent demand estimates are derived for the prefectures and cities of Japan. “Educational services” is specifically defined as follows:
61
The Sector as a Whole
The Educational Services sector comprises establishments that provide instruction and training in a wide variety of subjects. This instruction and training is provided by specialized establishments, such as schools, colleges, universities, and training centers. These establishments may be privately owned and operated for profit or not for profit, or they may be publicly owned and operated. They may also offer food and accommodation services to their students.
Educational services are usually delivered by teachers or instructors that explain, tell, demonstrate, supervise, and direct learning. Instruction is imparted in diverse settings, such as educational institutions, the workplace, or the home through correspondence, television, or other means. It can be adapted to the particular needs of the students, for example sign language can replace verbal language for teaching students with hearing impairments. All industries in the sector share this commonality of process, namely, labor inputs of instructors with the requisite subject matter expertise and teaching ability.
611
Industries in the Educational Services subsector provide instruction and training in a wide variety of subjects. The instruction and training is provided by specialized establishments, such as schools, colleges, universities, and training centers.
The subsector is structured according to level and type of educational services. Elementary and secondary schools, junior colleges and colleges, universities, and professional schools correspond to a recognized series of formal levels of education designated by diplomas, associate degrees (including equivalent certificates), and degrees. The remaining industry groups are based more on the type of instruction or training offered and the levels are not always as formally defined. The establishments are often highly specialized, many offering instruction in a very limited subject matter, for example ski lessons or one specific computer software package. Within the sector, the level and types of training that are required of the instructors and teachers vary depending on the industry.
Establishments that manage schools and other educational establishments on a contractual basis are classified in this subsector if they both manage the operation and provide the operating staff. Such establishments are classified in the educational services subsector based on the type of facility managed and operated.
6111
Elementary and Secondary Schools
61111
See industry description for 611110 below.
611110
This industry comprises establishments primarily engaged in furnishing academic courses and associated course work that comprise a basic preparatory education. A basic preparatory education ordinarily constitutes kindergarten through 12th grade. This industry includes school boards and school districts.
6112
Junior Colleges
61121
See industry description for 611210 below.
611210
This industry comprises establishments primarily engaged in furnishing academic, or academic and technical, courses and granting associate degrees, certificates, or diplomas below the baccalaureate level. The requirement for admission to an associate or equivalent degree program is at least a high school diploma or equivalent general academic training. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
6113
Colleges, Universities, and Professional Schools
61131
See industry description for 611310 below.
611310
This industry comprises establishments primarily engaged in furnishing academic courses and granting degrees at baccalaureate or graduate levels. The requirement for admission is at least a high school diploma or equivalent general academic training. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
6114
Business Schools and Computer and Management Training
61141
See industry description for 611410 below.
611410
This industry comprises establishments primarily engaged in offering courses in office procedures and secretarial and stenographic skills and may offer courses in basic office skills, such as word processing. In addition, these establishments may offer such classes as office machine operation, reception, communications, and other skills designed for individuals pursuing a clerical or secretarial career. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
61142
See industry description for 611420 below.
611420
This industry comprises establishments primarily engaged in conducting computer training (except computer repair), such as computer programming, software packages, computerized business systems, computer electronics technology, computer operations, and local area network management. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
61143
See industry description for 611430 below.
611430
This industry comprises establishments primarily engaged in offering an array of short duration courses and seminars for management and professional development. Training for career development may be provided directly to individuals or through employers training programs; and courses may be customized or modified to meet the special needs of customers. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
6115
Technical and Trade Schools
61151
This industry comprises establishments primarily engaged in offering vocational and technical training in a variety of technical subjects and trades. The training often leads to job-specific certification. Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
611511
This U.S. industry comprises establishments primarily engaged in offering training in barbering, hair styling, or the cosmetic arts, such as makeup or skin care. These schools provide job-specific certification.
611512
This U.S. industry comprises establishments primarily engaged in offering aviation and flight training. These establishments may offer vocational training, recreational training, or both.
611513
This U.S. industry comprises establishments primarily engaged in offering apprenticeship training programs. These programs involve applied training as well as course work.
611519
This U.S. industry comprises establishments primarily engaged in offering job or career vocational or technical courses (except cosmetology and barber training, aviation and flight training, and apprenticeship training). The curriculums offered by these schools are highly structured and specialized and lead to job-specific certification.
6116
This industry group comprises establishments primarily engaged in offering or providing instruction (except academic schools, colleges, and universities; and business, computer, management, technical, or trade instruction). Instruction may be provided in diverse settings, such as the establishments or clients training facilities, educational institutions, the workplace, or the home, and through correspondence, television, Internet, or other means.
61161
See industry description for 611610 below.
611610
This industry comprises establishments primarily engaged in offering instruction in the arts, including dance, art, drama, and music.
6116101
Establishments primarily engaged in teaching dance to children and adults.
6116102
Establishments primarily engaged in offering instruction in the arts, including art, drama, and music.
61162
See industry description for 611620 below.
611620
This industry comprises establishments, such as camps and schools, primarily engaged in offering instruction in athletic activities to groups of individuals. Overnight and day sports instruction camps are included in this industry.
61163
See industry description for 611630 below.
611630
This industry comprises establishments primarily engaged in offering foreign language instruction (including sign language). These establishments are designed to offer language instruction ranging from conversational skills for personal enrichment to intensive training courses for career or educational opportunities.
61169
This industry comprises establishments primarily engaged in offering instruction (except business, computer, management, technical, trade, fine arts, athletic, and language instruction). Also excluded from this industry are academic schools, colleges, and universities.
611691
This U.S. industry comprises establishments primarily engaged in offering preparation for standardized examinations and/or academic tutoring services.
611692
This U.S. industry comprises establishments primarily engaged in offering automobile driving instruction.
611699
This U.S. industry comprises establishments primarily engaged in offering instruction (except business, computer, management, technical, trade, fine arts, athletic, language instruction, tutoring, and automobile driving instruction). Also excluded from this industry are academic schools, colleges, and universities.
6117
Educational Support Services
61171
See industry description for 611710 below.
611710
This industry comprises establishments primarily engaged in providing noninstructional services that support educational processes or systems.
Step 2. Filtering and Smoothing
Based on the aggregate view of educational services as defined above, data were then collected for as many geographic locations as possible for that same definition, at the same level of the value chain. This generates a convenience sample of indicators from which comparable figures are available. If the series in question do not reflect the same accounting period, then adjustments are made. In order to eliminate short-term effects of business cycles, the series are smoothed using an 2 year moving average weighting scheme (longer weighting schemes do not substantially change the results). If data are available for a geographic region, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a prefecture or city stricken with foot and mouth disease), these observations were dropped or "filtered" from the analysis.
Step 3. Filling in Missing Values
In some cases, data are available on a sporadic basis. In other cases, data may be available for only one year. From a Bayesian perspective, these observations should be given greatest weight in estimating missing years. Assuming that other factors are held constant, the missing years are extrapolated using changes and growth in aggregate national, prefecture and city-level income. Based on the overriding philosophy of a long-run consumption function (defined earlier), prefectures and cities which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that geographic entity.
Step 4. Varying Parameter, Non-linear Estimation
Given the data available from the first three steps, the latent demand is estimated using a “varying-parameter cross-sectionally pooled time series model”. Simply stated, the effect of income on latent demand is assumed to be constant unless there is empirical evidence to suggest that this effect varies (i.e., . the slope of the income effect is not necessarily same for all prefectures or cities). This assumption applies along the aggregate consumption function, but also over time (i.e., not all prefectures or cities in Japan are perceived to have the same income growth prospects over time). Another way of looking at this is to say that latent demand for educational services is more likely to be similar across prefectures or cities that have similar characteristics in terms of economic development.
This approach is useful across geographic regions for which some notion of non-linearity exists in the aggregate cross-region consumption function. For some categories, however, the reader must realize that the numbers will reflect a prefecture’s or city’s contribution to latent demand in Japan and may never be realized in the form of local sales.
Step 5. Fixed-Parameter Linear Estimation
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because Japan consists of more than 1,000 cities, there will always be those cities, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these cities, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a city’s stock of income), but a function of current income (a city’s flow of income). In the long run, if a prefecture has no current income, the latent demand for educational services is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., cities which earn low levels of income will not use their savings, in the long run, to demand educational services). In a graphical sense, for low income cities, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, a low-income city is assumed to have a latent demand proportional to its income, based on the cities closest to it on the aggregate consumption function.
Step 6. Aggregation and Benchmarking
Based on the models described above, latent demand figures are estimated for all major cities in Japan. These are then aggregated to get prefecture totals. This report considers a city as a part of the regional and national market. The purpose is to understand the density of demand within a prefecture and the extent to which a city might be used as a point of distribution within its prefecture. From an economic perspective, however, a city does not represent a population within rigid geographical boundaries. To an economist or strategic planner, a city represents an area of dominant influence over markets in adjacent areas. This influence varies from one industry to another, but also from one period of time to another. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its prefecture. Not all cities (e.g. the smaller towns) are estimated within each prefecture as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same prefecture, a city’s population is not generally used to allocate latent demand. Rather, the level of economic activity of the city vis-à-vis others is used. Figures are rounded, so minor inconsistencies may exist across tables.
1 INTRODUCTION 10
1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 17
1.3.3 Step 3. Filling in Missing Values 17
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 17
1.3.5 Step 5. Fixed-Parameter Linear Estimation 18
1.3.6 Step 6. Aggregation and Benchmarking 18
2 SUMMARY OF FINDINGS 19
2.1 The Latent Demand in Japan 19
2.2 Top 100 Cities Sorted by Rank 21
3 AICHI 24
3.1 Latent Demand by Year - Aichi 24
3.2 Cities Sorted by Rank - Aichi 25
3.3 Cities Sorted Alphabetically - Aichi 26
4 AKITA 28
4.1 Latent Demand by Year - Akita 28
4.2 Cities Sorted by Rank - Akita 29
4.3 Cities Sorted Alphabetically - Akita 29
5 AOMORI 30
5.1 Latent Demand by Year - Aomori 30
5.2 Cities Sorted by Rank - Aomori 31
5.3 Cities Sorted Alphabetically - Aomori 31
6 CHIBA 32
6.1 Latent Demand by Year - Chiba 32
6.2 Cities Sorted by Rank - Chiba 33
6.3 Cities Sorted Alphabetically - Chiba 34
7 EHIME 35
7.1 Latent Demand by Year - Ehime 35
7.2 Cities Sorted by Rank - Ehime 36
7.3 Cities Sorted Alphabetically - Ehime 36
8 FUKUI 37
8.1 Latent Demand by Year - Fukui 37
8.2 Cities Sorted by Rank - Fukui 38
8.3 Cities Sorted Alphabetically - Fukui 38
9 FUKUOKA 39
9.1 Latent Demand by Year - Fukuoka 39
9.2 Cities Sorted by Rank - Fukuoka 40
9.3 Cities Sorted Alphabetically - Fukuoka 41
10 FUKUSHIMA 42
10.1 Latent Demand by Year - Fukushima 42
10.2 Cities Sorted by Rank - Fukushima 43
10.3 Cities Sorted Alphabetically - Fukushima 43
11 GIFU 44
11.1 Latent Demand by Year - Gifu 44
11.2 Cities Sorted by Rank - Gifu 45
11.3 Cities Sorted Alphabetically - Gifu 46
12 GUMMA 47
12.1 Latent Demand by Year - Gumma 47
12.2 Cities Sorted by Rank - Gumma 48
12.3 Cities Sorted Alphabetically - Gumma 48
13 HIROSHIMA 50
13.1 Latent Demand by Year - Hiroshima 50
13.2 Cities Sorted by Rank - Hiroshima 51
13.3 Cities Sorted Alphabetically - Hiroshima 51
14 HOKKAIDO 53
14.1 Latent Demand by Year - Hokkaido 53
14.2 Cities Sorted by Rank - Hokkaido 54
14.3 Cities Sorted Alphabetically - Hokkaido 55
15 HYOGO 56
15.1 Latent Demand by Year - Hyogo 56
15.2 Cities Sorted by Rank - Hyogo 57
15.3 Cities Sorted Alphabetically - Hyogo 57
16 IBARAKI 59
16.1 Latent Demand by Year - Ibaraki 59
16.2 Cities Sorted by Rank - Ibaraki 60
16.3 Cities Sorted Alphabetically - Ibaraki 61
17 ISHIKAWA 62
17.1 Latent Demand by Year - Ishikawa 62
17.2 Cities Sorted by Rank - Ishikawa 63
17.3 Cities Sorted Alphabetically - Ishikawa 63
18 IWATE 64
18.1 Latent Demand by Year - Iwate 64
18.2 Cities Sorted by Rank - Iwate 65
18.3 Cities Sorted Alphabetically - Iwate 65
19 KAGAWA 67
19.1 Latent Demand by Year - Kagawa 67
19.2 Cities Sorted by Rank - Kagawa 68
19.3 Cities Sorted Alphabetically - Kagawa 68
20 KAGOSHIMA 69
20.1 Latent Demand by Year - Kagoshima 69
20.2 Cities Sorted by Rank - Kagoshima 70
20.3 Cities Sorted Alphabetically - Kagoshima 70
21 KANAGAWA 72
21.1 Latent Demand by Year - Kanagawa 72
21.2 Cities Sorted by Rank - Kanagawa 73
21.3 Cities Sorted Alphabetically - Kanagawa 74
22 KOCHI 75
22.1 Latent Demand by Year - Kochi 75
22.2 Cities Sorted by Rank - Kochi 76
22.3 Cities Sorted Alphabetically - Kochi 76
23 KUMAMOTO 77
23.1 Latent Demand by Year - Kumamoto 77
23.2 Cities Sorted by Rank - Kumamoto 78
23.3 Cities Sorted Alphabetically - Kumamoto 78
24 KYOTO 80
24.1 Latent Demand by Year - Kyoto 80
24.2 Cities Sorted by Rank - Kyoto 81
24.3 Cities Sorted Alphabetically - Kyoto 81
25 MIE 82
25.1 Latent Demand by Year - Mie 82
25.2 Cities Sorted by Rank - Mie 83
25.3 Cities Sorted Alphabetically - Mie 83
26 MIYAGI 85
26.1 Latent Demand by Year - Miyagi 85
26.2 Cities Sorted by Rank - Miyagi 86
26.3 Cities Sorted Alphabetically - Miyagi 86
27 MIYAZAKI 88
27.1 Latent Demand by Year - Miyazaki 88
27.2 Cities Sorted by Rank - Miyazaki 89
27.3 Cities Sorted Alphabetically - Miyazaki 89
28 NAGANO 90
28.1 Latent Demand by Year - Nagano 90
28.2 Cities Sorted by Rank - Nagano 91
28.3 Cities Sorted Alphabetically - Nagano 91
29 NAGASAKI 93
29.1 Latent Demand by Year - Nagasaki 93
29.2 Cities Sorted by Rank - Nagasaki 94
29.3 Cities Sorted Alphabetically - Nagasaki 94
30 NARA 95
30.1 Latent Demand by Year - Nara 95
30.2 Cities Sorted by Rank - Nara 96
30.3 Cities Sorted Alphabetically - Nara 96
31 NIIGATA 98
31.1 Latent Demand by Year - Niigata 98
31.2 Cities Sorted by Rank - Niigata 99
31.3 Cities Sorted Alphabetically - Niigata 99
32 OITA 101
32.1 Latent Demand by Year - Oita 101
32.2 Cities Sorted by Rank - Oita 102
32.3 Cities Sorted Alphabetically - Oita 102
33 OKAYAMA 103
33.1 Latent Demand by Year - Okayama 103
33.2 Cities Sorted by Rank - Okayama 104
33.3 Cities Sorted Alphabetically - Okayama 104
34 OKINAWA 105
34.1 Latent Demand by Year - Okinawa 105
34.2 Cities Sorted by Rank - Okinawa 106
34.3 Cities Sorted Alphabetically - Okinawa 106
35 OSAKA 107
35.1 Latent Demand by Year - Osaka 107
35.2 Cities Sorted by Rank - Osaka 108
35.3 Cities Sorted Alphabetically - Osaka 109
36 SAGA 110
36.1 Latent Demand by Year - Saga 110
36.2 Cities Sorted by Rank - Saga 111
36.3 Cities Sorted Alphabetically - Saga 111
37 SAITAMA 112
37.1 Latent Demand by Year - Saitama 112
37.2 Cities Sorted by Rank - Saitama 113
37.3 Cities Sorted Alphabetically - Saitama 114
38 SHIGA 116
38.1 Latent Demand by Year - Shiga 116
38.2 Cities Sorted by Rank - Shiga 117
38.3 Cities Sorted Alphabetically - Shiga 117
39 SHIMANE 118
39.1 Latent Demand by Year - Shimane 118
39.2 Cities Sorted by Rank - Shimane 119
39.3 Cities Sorted Alphabetically - Shimane 119
40 SHIZUOKA 120
40.1 Latent Demand by Year - Shizuoka 120
40.2 Cities Sorted by Rank - Shizuoka 121
40.3 Cities Sorted Alphabetically - Shizuoka 122
41 TOCHIGI 123
41.1 Latent Demand by Year - Tochigi 123
41.2 Cities Sorted by Rank - Tochigi 124
41.3 Cities Sorted Alphabetically - Tochigi 125
42 TOKUSHIMA 126
42.1 Latent Demand by Year - Tokushima 126
42.2 Cities Sorted by Rank - Tokushima 127
42.3 Cities Sorted Alphabetically - Tokushima 127
43 TOKYO 128
43.1 Latent Demand by Year - Tokyo 128
43.2 Cities Sorted by Rank - Tokyo 129
43.3 Cities Sorted Alphabetically - Tokyo 130
44 TOTTORI 131
44.1 Latent Demand by Year - Tottori 131
44.2 Cities Sorted by Rank - Tottori 132
44.3 Cities Sorted Alphabetically - Tottori 132
45 TOYAMA 133
45.1 Latent Demand by Year - Toyama 133
45.2 Cities Sorted by Rank - Toyama 134
45.3 Cities Sorted Alphabetically - Toyama 134
46 WAKAYAMA 135
46.1 Latent Demand by Year - Wakayama 135
46.2 Cities Sorted by Rank - Wakayama 136
46.3 Cities Sorted Alphabetically - Wakayama 136
47 YAMAGATA 137
47.1 Latent Demand by Year - Yamagata 137
47.2 Cities Sorted by Rank - Yamagata 138
47.3 Cities Sorted Alphabetically - Yamagata 138
48 YAMAGUCHI 139
48.1 Latent Demand by Year - Yamaguchi 139
48.2 Cities Sorted by Rank - Yamaguchi 140
48.3 Cities Sorted Alphabetically - Yamaguchi 140
49 YAMANASHI 141
49.1 Latent Demand by Year - Yamanashi 141
49.2 Cities Sorted by Rank - Yamanashi 142
49.3 Cities Sorted Alphabetically - Yamanashi 142
50 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 143
50.1 Disclaimers & Safe Harbor 143
50.2 ICON Group International, Inc. User Agreement Provisions 144
Customers who bought this item also bought
All rights reserved. © Copyright 2013 Research and Markets WWW5
Terms and Conditions Privacy Policy Publishers Employment Opportunities Site Map Link to us Webmaster Affiliate Network
