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The 2007-2012 Outlook for Wood Household Den, Family Room, Library, and Living Room Desks Excluding Custom Desks Sold at Retail Directly to the Customer in Japan

Description:
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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer. 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 “wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer” 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 “wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer, please see below. The NAICS code for wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer is 3371221221. It is for this definition of wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer that the aggregate latent demand estimates are derived for the prefectures and cities of Japan.

Step 2. Filtering and Smoothing

Based on the aggregate view of wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer 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 wood household den, family room, library, and living room desks excluding custom desks sold at retail directly to the customer). 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
 
Contents:
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 13 1.3.3 Step 3. Filling in Missing Values 13 1.3.4 Step 4. Varying Parameter, Non-linear Estimation 14 1.3.5 Step 5. Fixed-Parameter Linear Estimation 14 1.3.6 Step 6. Aggregation and Benchmarking 14 2 SUMMARY OF FINDINGS 16 2.1 The Latent Demand in Japan 16 2.2 Top 100 Cities Sorted by Rank 18 3 AICHI 21 3.1 Latent Demand by Year - Aichi 21 3.2 Cities Sorted by Rank - Aichi 22 3.3 Cities Sorted Alphabetically - Aichi 24 4 AKITA 26 4.1 Latent Demand by Year - Akita 26 4.2 Cities Sorted by Rank - Akita 27 4.3 Cities Sorted Alphabetically - Akita 28 5 AOMORI 29 5.1 Latent Demand by Year - Aomori 29 5.2 Cities Sorted by Rank - Aomori 30 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 36 7.1 Latent Demand by Year - Ehime 36 7.2 Cities Sorted by Rank - Ehime 37 7.3 Cities Sorted Alphabetically - Ehime 38 8 FUKUI 39 8.1 Latent Demand by Year - Fukui 39 8.2 Cities Sorted by Rank - Fukui 40 8.3 Cities Sorted Alphabetically - Fukui 41 9 FUKUOKA 42 9.1 Latent Demand by Year - Fukuoka 42 9.2 Cities Sorted by Rank - Fukuoka 43 9.3 Cities Sorted Alphabetically - Fukuoka 44 10 FUKUSHIMA 46 10.1 Latent Demand by Year - Fukushima 46 10.2 Cities Sorted by Rank - Fukushima 47 10.3 Cities Sorted Alphabetically - Fukushima 48 11 GIFU 49 11.1 Latent Demand by Year - Gifu 49 11.2 Cities Sorted by Rank - Gifu 50 11.3 Cities Sorted Alphabetically - Gifu 51 12 GUMMA 52 12.1 Latent Demand by Year - Gumma 52 12.2 Cities Sorted by Rank - Gumma 53 12.3 Cities Sorted Alphabetically - Gumma 54 13 HIROSHIMA 55 13.1 Latent Demand by Year - Hiroshima 55 13.2 Cities Sorted by Rank - Hiroshima 56 13.3 Cities Sorted Alphabetically - Hiroshima 57 14 HOKKAIDO 58 14.1 Latent Demand by Year - Hokkaido 58 14.2 Cities Sorted by Rank - Hokkaido 59 14.3 Cities Sorted Alphabetically - Hokkaido 60 15 HYOGO 62 15.1 Latent Demand by Year - Hyogo 62 15.2 Cities Sorted by Rank - Hyogo 63 15.3 Cities Sorted Alphabetically - Hyogo 64 16 IBARAKI 66 16.1 Latent Demand by Year - Ibaraki 66 16.2 Cities Sorted by Rank - Ibaraki 67 16.3 Cities Sorted Alphabetically - Ibaraki 68 17 ISHIKAWA 70 17.1 Latent Demand by Year - Ishikawa 70 17.2 Cities Sorted by Rank - Ishikawa 71 17.3 Cities Sorted Alphabetically - Ishikawa 72 18 IWATE 73 18.1 Latent Demand by Year - Iwate 73 18.2 Cities Sorted by Rank - Iwate 74 18.3 Cities Sorted Alphabetically - Iwate 75 19 KAGAWA 76 19.1 Latent Demand by Year - Kagawa 76 19.2 Cities Sorted by Rank - Kagawa 77 19.3 Cities Sorted Alphabetically - Kagawa 78 20 KAGOSHIMA 79 20.1 Latent Demand by Year - Kagoshima 79 20.2 Cities Sorted by Rank - Kagoshima 80 20.3 Cities Sorted Alphabetically - Kagoshima 81 21 KANAGAWA 82 21.1 Latent Demand by Year - Kanagawa 82 21.2 Cities Sorted by Rank - Kanagawa 83 21.3 Cities Sorted Alphabetically - Kanagawa 84 22 KOCHI 85 22.1 Latent Demand by Year - Kochi 85 22.2 Cities Sorted by Rank - Kochi 86 22.3 Cities Sorted Alphabetically - Kochi 87 23 KUMAMOTO 88 23.1 Latent Demand by Year - Kumamoto 88 23.2 Cities Sorted by Rank - Kumamoto 89 23.3 Cities Sorted Alphabetically - Kumamoto 90 24 KYOTO 91 24.1 Latent Demand by Year - Kyoto 91 24.2 Cities Sorted by Rank - Kyoto 92 24.3 Cities Sorted Alphabetically - Kyoto 93 25 MIE 94 25.1 Latent Demand by Year - Mie 94 25.2 Cities Sorted by Rank - Mie 95 25.3 Cities Sorted Alphabetically - Mie 96 26 MIYAGI 97 26.1 Latent Demand by Year - Miyagi 97 26.2 Cities Sorted by Rank - Miyagi 98 26.3 Cities Sorted Alphabetically - Miyagi 99 27 MIYAZAKI 100 27.1 Latent Demand by Year - Miyazaki 100 27.2 Cities Sorted by Rank - Miyazaki 101 27.3 Cities Sorted Alphabetically - Miyazaki 102 28 NAGANO 103 28.1 Latent Demand by Year - Nagano 103 28.2 Cities Sorted by Rank - Nagano 104 28.3 Cities Sorted Alphabetically - Nagano 105 29 NAGASAKI 106 29.1 Latent Demand by Year - Nagasaki 106 29.2 Cities Sorted by Rank - Nagasaki 107 29.3 Cities Sorted Alphabetically - Nagasaki 108 30 NARA 109 30.1 Latent Demand by Year - Nara 109 30.2 Cities Sorted by Rank - Nara 110 30.3 Cities Sorted Alphabetically - Nara 111 31 NIIGATA 112 31.1 Latent Demand by Year - Niigata 112 31.2 Cities Sorted by Rank - Niigata 113 31.3 Cities Sorted Alphabetically - Niigata 114 32 OITA 115 32.1 Latent Demand by Year - Oita 115 32.2 Cities Sorted by Rank - Oita 116 32.3 Cities Sorted Alphabetically - Oita 117 33 OKAYAMA 118 33.1 Latent Demand by Year - Okayama 118 33.2 Cities Sorted by Rank - Okayama 119 33.3 Cities Sorted Alphabetically - Okayama 120 34 OKINAWA 121 34.1 Latent Demand by Year - Okinawa 121 34.2 Cities Sorted by Rank - Okinawa 122 34.3 Cities Sorted Alphabetically - Okinawa 123 35 OSAKA 124 35.1 Latent Demand by Year - Osaka 124 35.2 Cities Sorted by Rank - Osaka 125 35.3 Cities Sorted Alphabetically - Osaka 126 36 SAGA 128 36.1 Latent Demand by Year - Saga 128 36.2 Cities Sorted by Rank - Saga 129 36.3 Cities Sorted Alphabetically - Saga 130 37 SAITAMA 131 37.1 Latent Demand by Year - Saitama 131 37.2 Cities Sorted by Rank - Saitama 132 37.3 Cities Sorted Alphabetically - Saitama 134 38 SHIGA 136 38.1 Latent Demand by Year - Shiga 136 38.2 Cities Sorted by Rank - Shiga 137 38.3 Cities Sorted Alphabetically - Shiga 138 39 SHIMANE 139 39.1 Latent Demand by Year - Shimane 139 39.2 Cities Sorted by Rank - Shimane 140 39.3 Cities Sorted Alphabetically - Shimane 141 40 SHIZUOKA 142 40.1 Latent Demand by Year - Shizuoka 142 40.2 Cities Sorted by Rank - Shizuoka 143 40.3 Cities Sorted Alphabetically - Shizuoka 144 41 TOCHIGI 146 41.1 Latent Demand by Year - Tochigi 146 41.2 Cities Sorted by Rank - Tochigi 147 41.3 Cities Sorted Alphabetically - Tochigi 148 42 TOKUSHIMA 149 42.1 Latent Demand by Year - Tokushima 149 42.2 Cities Sorted by Rank - Tokushima 150 42.3 Cities Sorted Alphabetically - Tokushima 151 43 TOKYO 152 43.1 Latent Demand by Year - Tokyo 152 43.2 Cities Sorted by Rank - Tokyo 153 43.3 Cities Sorted Alphabetically - Tokyo 154 44 TOTTORI 155 44.1 Latent Demand by Year - Tottori 155 44.2 Cities Sorted by Rank - Tottori 156 44.3 Cities Sorted Alphabetically - Tottori 156 45 TOYAMA 157 45.1 Latent Demand by Year - Toyama 157 45.2 Cities Sorted by Rank - Toyama 158 45.3 Cities Sorted Alphabetically - Toyama 159 46 WAKAYAMA 160 46.1 Latent Demand by Year - Wakayama 160 46.2 Cities Sorted by Rank - Wakayama 161 46.3 Cities Sorted Alphabetically - Wakayama 162 47 YAMAGATA 163 47.1 Latent Demand by Year - Yamagata 163 47.2 Cities Sorted by Rank - Yamagata 164 47.3 Cities Sorted Alphabetically - Yamagata 165 48 YAMAGUCHI 166 48.1 Latent Demand by Year - Yamaguchi 166 48.2 Cities Sorted by Rank - Yamaguchi 167 48.3 Cities Sorted Alphabetically - Yamaguchi 168 49 YAMANASHI 169 49.1 Latent Demand by Year - Yamanashi 169 49.2 Cities Sorted by Rank - Yamanashi 170 49.3 Cities Sorted Alphabetically - Yamanashi 171 50 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 172 50.1 Disclaimers & Safe Harbor 172 50.2 User Agreement Provisions 173
 
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