The 2007-2012 Outlook for Broadwoven Plain Weave Fabrics Made from at Least 85-Percent Manmade Spun Yarns Excluding Wool Blends and Pile in Japan
ICON Group International, September 2006, Pages: 175
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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile across the prefectures and cites of Japan, we used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, we 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile across the prefectures and cities of Japan. The smallest cities have few inhabitants. we 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile. So, latent demand in the long-run has a zero intercept. However, we 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, we will now describe the methodology used to create the latent demand estimates for broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile in Japan. Since this methodology has been applied 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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, we 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, we 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 “broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile” 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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 “broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile, please see below. The NAICS code for broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile is 313210H1. It is for this definition of broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile that the aggregate latent demand estimates are derived for the prefectures and cities of Japan. “Broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile” is specifically defined as follows:
313210H1
Manmade fiber broadwoven plain weave fabrics, of 85 percent or more spun yarns (excluding pile), excluding wool blends (gray goods)
313210H100
Manmade fiber broadwoven plain weave fabrics, of 85 percent or more spun yarns (excluding pile), excluding wool blends (gray goods)
313210H101
Broadwoven tobacco, cheese, and bandage cloth (gray goods), chiefly spun polyester mixed with cotton
313210H104
Broadwoven tobacco, cheese, and bandage cloth (gray goods), chiefly spun rayon and~or acetate
313210H107
Broadwoven plain weave tobacco, cheese, and bandage cloth fabric (gray goods), all other fabrics, 85 percent or more spun yarn
313210H111
Broadwoven plain weave print cloth fabric (gray goods), chiefly spun polyester mixed with cotton
313210H114
Broadwoven print cloth (gray goods), chiefly spun polyester mixed with rayon
313210H116
Broadwoven print cloth (gray goods), chiefly spun rayon and~or acetate
313210H119
Broadwoven plain weave print cloth fabric (gray goods), all other fabrics, 85 percent or more spun yarn
313210H121
Broadwoven plain weave poplin and broadcloth fabric (gray goods), chiefly spun polyester mixed with carded cotton
313210H123
Broadwoven plain weave poplin and broadcloth fabric (gray goods), chiefly spun polyester mixed with combed cotton
313210H125
Broadwoven poplin and broadcloth (gray goods), chiefly spun polyester mixed with carded cotton, weighing more than 5 oz per square yard
313210H127
Broadwoven poplin and broadcloth (gray goods), chiefly spun polyester mixed with combed cotton, weighing more than 5 oz per square yard
313210H131
Broadwoven poplin and broadcloth (gray goods), chiefly spun rayon and~or acetate
313210H136
Broadwoven plain weave poplin and broadcloth fabric (gray goods), all other fabrics, 85 percent or more spun yarn
313210H141
Broadwoven lawns, voiles, and batistes (gray goods), chiefly spun polyester mixed with cotton
313210H144
Broadwoven lawns, voiles, and batistes (gray goods), chiefly spun rayon and~or acetate
313210H146
Broadwoven plain weave lawn, voile, and batiste fabric (gray goods), all other fabrics, 85 percent or more spun yarn
313210H151
Broadwoven plain weave bedsheeting fabric (gray goods), chiefly spun polyester mixed with carded cotton
313210H154
Broadwoven plain weave bedsheeting fabric (gray goods), chiefly spun polyester mixed with combed cotton
313210H157
Broadwoven bedsheeting (gray goods), of chiefly spun rayon and~or acetate
313210H159
Broadwoven plain weave bedsheeting fabric (gray goods), all other fabrics, 85 percent or more spun yarn
313210H161
Broadwoven plain weave sheeting (except bedsheeting) fabric (including osnaburgs) (gray goods), 85 percent or more spun polyester
313210H164
Broadwoven other sheeting fabric including osnaburgs, except pile (gray goods), 85 percent or more spun polyester
313210H167
Broadwoven plain weave sheeting (except bedsheeting) fabric (including osnaburgs) (gray goods), chiefly spun polyester mixed with cotton, weighing 5.0 oz or less per square yard
313210H169
Broadwoven plain weave sheeting (except bedsheeting) fabric (including osnaburgs) (gray goods), chiefly spun polyester mixed with cotton, weighing more than 5.0 oz per square yard
313210H171
Broadwoven plain weave sheeting (except bedsheeting) fabric (including osnaburgs) (gray goods), chiefly spun acrylic
313210H174
Broadwoven other sheeting fabric including osnaburgs, except pile (gray goods), chiefly spun acrylic
313210H177
Broadwoven plain weave sheeting (except bedsheeting) fabric (including osnaburgs) (gray goods), chiefly spun rayon and/or acetate
313210H179
Broadwoven plain weave sheeting (except bedsheeting) fabric (including osnaburgs) (gray goods), all other fabrics, 85 percent or more spun yarn
313210H181
Broadwoven duck, including taped warp duck (gray goods), of chiefly spun polyester mixed with cotton
313210H184
Broadwoven plain weave duck (including taped warp) fabric (gray goods), all other fabrics, 85 percent or more spun yarn
Step 2. Filtering and Smoothing
Based on the aggregate view of broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile 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 broadwoven plain weave fabrics made from at least 85-percent manmade spun yarns excluding wool blends and pile). 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. we 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
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 15
1.3.3 Step 3. Filling in Missing Values 15
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 16
1.3.5 Step 5. Fixed-Parameter Linear Estimation 16
1.3.6 Step 6. Aggregation and Benchmarking 16
2 SUMMARY OF FINDINGS 18
2.1 The Latent Demand in Japan 18
2.2 Top 100 Cities Sorted by Rank 20
3 AICHI 23
3.1 Latent Demand by Year - Aichi 23
3.2 Cities Sorted by Rank - Aichi 24
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 30
5 AOMORI 31
5.1 Latent Demand by Year - Aomori 31
5.2 Cities Sorted by Rank - Aomori 32
5.3 Cities Sorted Alphabetically - Aomori 33
6 CHIBA 34
6.1 Latent Demand by Year - Chiba 34
6.2 Cities Sorted by Rank - Chiba 35
6.3 Cities Sorted Alphabetically - Chiba 36
7 EHIME 38
7.1 Latent Demand by Year - Ehime 38
7.2 Cities Sorted by Rank - Ehime 39
7.3 Cities Sorted Alphabetically - Ehime 40
8 FUKUI 41
8.1 Latent Demand by Year - Fukui 41
8.2 Cities Sorted by Rank - Fukui 42
8.3 Cities Sorted Alphabetically - Fukui 43
9 FUKUOKA 44
9.1 Latent Demand by Year - Fukuoka 44
9.2 Cities Sorted by Rank - Fukuoka 45
9.3 Cities Sorted Alphabetically - Fukuoka 46
10 FUKUSHIMA 48
10.1 Latent Demand by Year - Fukushima 48
10.2 Cities Sorted by Rank - Fukushima 49
10.3 Cities Sorted Alphabetically - Fukushima 50
11 GIFU 51
11.1 Latent Demand by Year - Gifu 51
11.2 Cities Sorted by Rank - Gifu 52
11.3 Cities Sorted Alphabetically - Gifu 53
12 GUMMA 54
12.1 Latent Demand by Year - Gumma 54
12.2 Cities Sorted by Rank - Gumma 55
12.3 Cities Sorted Alphabetically - Gumma 56
13 HIROSHIMA 57
13.1 Latent Demand by Year - Hiroshima 57
13.2 Cities Sorted by Rank - Hiroshima 58
13.3 Cities Sorted Alphabetically - Hiroshima 59
14 HOKKAIDO 60
14.1 Latent Demand by Year - Hokkaido 60
14.2 Cities Sorted by Rank - Hokkaido 61
14.3 Cities Sorted Alphabetically - Hokkaido 62
15 HYOGO 64
15.1 Latent Demand by Year - Hyogo 64
15.2 Cities Sorted by Rank - Hyogo 65
15.3 Cities Sorted Alphabetically - Hyogo 66
16 IBARAKI 68
16.1 Latent Demand by Year - Ibaraki 68
16.2 Cities Sorted by Rank - Ibaraki 69
16.3 Cities Sorted Alphabetically - Ibaraki 70
17 ISHIKAWA 72
17.1 Latent Demand by Year - Ishikawa 72
17.2 Cities Sorted by Rank - Ishikawa 73
17.3 Cities Sorted Alphabetically - Ishikawa 74
18 IWATE 75
18.1 Latent Demand by Year - Iwate 75
18.2 Cities Sorted by Rank - Iwate 76
18.3 Cities Sorted Alphabetically - Iwate 77
19 KAGAWA 78
19.1 Latent Demand by Year - Kagawa 78
19.2 Cities Sorted by Rank - Kagawa 79
19.3 Cities Sorted Alphabetically - Kagawa 80
20 KAGOSHIMA 81
20.1 Latent Demand by Year - Kagoshima 81
20.2 Cities Sorted by Rank - Kagoshima 82
20.3 Cities Sorted Alphabetically - Kagoshima 83
21 KANAGAWA 84
21.1 Latent Demand by Year - Kanagawa 84
21.2 Cities Sorted by Rank - Kanagawa 85
21.3 Cities Sorted Alphabetically - Kanagawa 86
22 KOCHI 87
22.1 Latent Demand by Year - Kochi 87
22.2 Cities Sorted by Rank - Kochi 88
22.3 Cities Sorted Alphabetically - Kochi 89
23 KUMAMOTO 90
23.1 Latent Demand by Year - Kumamoto 90
23.2 Cities Sorted by Rank - Kumamoto 91
23.3 Cities Sorted Alphabetically - Kumamoto 92
24 KYOTO 93
24.1 Latent Demand by Year - Kyoto 93
24.2 Cities Sorted by Rank - Kyoto 94
24.3 Cities Sorted Alphabetically - Kyoto 95
25 MIE 96
25.1 Latent Demand by Year - Mie 96
25.2 Cities Sorted by Rank - Mie 97
25.3 Cities Sorted Alphabetically - Mie 98
26 MIYAGI 99
26.1 Latent Demand by Year - Miyagi 99
26.2 Cities Sorted by Rank - Miyagi 100
26.3 Cities Sorted Alphabetically - Miyagi 101
27 MIYAZAKI 102
27.1 Latent Demand by Year - Miyazaki 102
27.2 Cities Sorted by Rank - Miyazaki 103
27.3 Cities Sorted Alphabetically - Miyazaki 104
28 NAGANO 105
28.1 Latent Demand by Year - Nagano 105
28.2 Cities Sorted by Rank - Nagano 106
28.3 Cities Sorted Alphabetically - Nagano 107
29 NAGASAKI 108
29.1 Latent Demand by Year - Nagasaki 108
29.2 Cities Sorted by Rank - Nagasaki 109
29.3 Cities Sorted Alphabetically - Nagasaki 110
30 NARA 111
30.1 Latent Demand by Year - Nara 111
30.2 Cities Sorted by Rank - Nara 112
30.3 Cities Sorted Alphabetically - Nara 113
31 NIIGATA 114
31.1 Latent Demand by Year - Niigata 114
31.2 Cities Sorted by Rank - Niigata 115
31.3 Cities Sorted Alphabetically - Niigata 116
32 OITA 117
32.1 Latent Demand by Year - Oita 117
32.2 Cities Sorted by Rank - Oita 118
32.3 Cities Sorted Alphabetically - Oita 119
33 OKAYAMA 120
33.1 Latent Demand by Year - Okayama 120
33.2 Cities Sorted by Rank - Okayama 121
33.3 Cities Sorted Alphabetically - Okayama 122
34 OKINAWA 123
34.1 Latent Demand by Year - Okinawa 123
34.2 Cities Sorted by Rank - Okinawa 124
34.3 Cities Sorted Alphabetically - Okinawa 125
35 OSAKA 126
35.1 Latent Demand by Year - Osaka 126
35.2 Cities Sorted by Rank - Osaka 127
35.3 Cities Sorted Alphabetically - Osaka 128
36 SAGA 130
36.1 Latent Demand by Year - Saga 130
36.2 Cities Sorted by Rank - Saga 131
36.3 Cities Sorted Alphabetically - Saga 132
37 SAITAMA 133
37.1 Latent Demand by Year - Saitama 133
37.2 Cities Sorted by Rank - Saitama 134
37.3 Cities Sorted Alphabetically - Saitama 136
38 SHIGA 138
38.1 Latent Demand by Year - Shiga 138
38.2 Cities Sorted by Rank - Shiga 139
38.3 Cities Sorted Alphabetically - Shiga 140
39 SHIMANE 141
39.1 Latent Demand by Year - Shimane 141
39.2 Cities Sorted by Rank - Shimane 142
39.3 Cities Sorted Alphabetically - Shimane 143
40 SHIZUOKA 144
40.1 Latent Demand by Year - Shizuoka 144
40.2 Cities Sorted by Rank - Shizuoka 145
40.3 Cities Sorted Alphabetically - Shizuoka 146
41 TOCHIGI 148
41.1 Latent Demand by Year - Tochigi 148
41.2 Cities Sorted by Rank - Tochigi 149
41.3 Cities Sorted Alphabetically - Tochigi 150
42 TOKUSHIMA 151
42.1 Latent Demand by Year - Tokushima 151
42.2 Cities Sorted by Rank - Tokushima 152
42.3 Cities Sorted Alphabetically - Tokushima 153
43 TOKYO 154
43.1 Latent Demand by Year - Tokyo 154
43.2 Cities Sorted by Rank - Tokyo 155
43.3 Cities Sorted Alphabetically - Tokyo 156
44 TOTTORI 157
44.1 Latent Demand by Year - Tottori 157
44.2 Cities Sorted by Rank - Tottori 158
44.3 Cities Sorted Alphabetically - Tottori 158
45 TOYAMA 159
45.1 Latent Demand by Year - Toyama 159
45.2 Cities Sorted by Rank - Toyama 160
45.3 Cities Sorted Alphabetically - Toyama 161
46 WAKAYAMA 162
46.1 Latent Demand by Year - Wakayama 162
46.2 Cities Sorted by Rank - Wakayama 163
46.3 Cities Sorted Alphabetically - Wakayama 164
47 YAMAGATA 165
47.1 Latent Demand by Year - Yamagata 165
47.2 Cities Sorted by Rank - Yamagata 166
47.3 Cities Sorted Alphabetically - Yamagata 167
48 YAMAGUCHI 168
48.1 Latent Demand by Year - Yamaguchi 168
48.2 Cities Sorted by Rank - Yamaguchi 169
48.3 Cities Sorted Alphabetically - Yamaguchi 170
49 YAMANASHI 171
49.1 Latent Demand by Year - Yamanashi 171
49.2 Cities Sorted by Rank - Yamanashi 172
49.3 Cities Sorted Alphabetically - Yamanashi 173
50 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 174
50.1 Disclaimers & Safe Harbor 174
50.2 User Agreement Provisions 175
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