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The 2011-2016 Outlook for Household Window Cleaning Products in Japan

  • ID: 1670953
  • Report
  • January 2011
  • Region: Japan
  • 141 pages
  • ICON Group International
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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 household window cleaning products 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 household window cleaning products 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.


In order to estimate the latent demand for household window cleaning products 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 household window cleaning products 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 household window cleaning products. 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 household window cleaning products 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 household window cleaning products 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 “household window cleaning products” 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 household window cleaning products 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, in this report we define the retail sales of "household window cleaning products" as including all commonly understood products falling within this broad category, such as household cleaning products specifically designed to clean windows, mirrors, and other glass surfaces without streaking which usually contain ammonia, irrespective of product packaging, formulation, size, or form (e.g. the retail sales of products or brands such as Glass Plus Cleaner with Trigger and Windex Glass and Surface Wipes). All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2010).

Step 2. Filtering and Smoothing

Based on the aggregate view of household window cleaning products 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 household window cleaning products 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 household window cleaning products 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 household window cleaning products). 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.
Note: Product cover images may vary from those shown
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1.1 Overview
1.2 What is Latent Demand and the P.I.E.?
1.3 The Methodology
1.3.1 Step 1. Product Definition and Data Collection
1.3.2 Step 2. Filtering and Smoothing
1.3.3 Step 3. Filling in Missing Values
1.3.4 Step 4. Varying Parameter, Non-linear Estimation
1.3.5 Step 5. Fixed-Parameter Linear Estimation
1.3.6 Step 6. Aggregation and Benchmarking
2.1 The Latent Demand in Japan
2.2 Top 100 Cities Sorted by Rank
3.1 Latent Demand by Year - Aichi
3.2 Cities Sorted by Rank - Aichi
3.3 Cities Sorted Alphabetically - Aichi
4.1 Latent Demand by Year - Akita
4.2 Cities Sorted by Rank - Akita
4.3 Cities Sorted Alphabetically - Akita
5.1 Latent Demand by Year - Aomori
5.2 Cities Sorted by Rank - Aomori
5.3 Cities Sorted Alphabetically - Aomori
6.1 Latent Demand by Year - Chiba
6.2 Cities Sorted by Rank - Chiba
6.3 Cities Sorted Alphabetically - Chiba
7.1 Latent Demand by Year - Ehime
7.2 Cities Sorted by Rank - Ehime
7.3 Cities Sorted Alphabetically - Ehime
8.1 Latent Demand by Year - Fukui
8.2 Cities Sorted by Rank - Fukui
8.3 Cities Sorted Alphabetically - Fukui
9.1 Latent Demand by Year - Fukuoka
9.2 Cities Sorted by Rank - Fukuoka
9.3 Cities Sorted Alphabetically - Fukuoka
10.1 Latent Demand by Year - Fukushima
10.2 Cities Sorted by Rank - Fukushima
10.3 Cities Sorted Alphabetically - Fukushima
11.1 Latent Demand by Year - Gifu
11.2 Cities Sorted by Rank - Gifu
11.3 Cities Sorted Alphabetically - Gifu
12.1 Latent Demand by Year - Gumma
12.2 Cities Sorted by Rank - Gumma
12.3 Cities Sorted Alphabetically - Gumma
13.1 Latent Demand by Year - Hiroshima
13.2 Cities Sorted by Rank - Hiroshima
13.3 Cities Sorted Alphabetically - Hiroshima
14.1 Latent Demand by Year - Hokkaido
14.2 Cities Sorted by Rank - Hokkaido
14.3 Cities Sorted Alphabetically - Hokkaido
15.1 Latent Demand by Year - Hyogo
15.2 Cities Sorted by Rank - Hyogo
15.3 Cities Sorted Alphabetically - Hyogo
16.1 Latent Demand by Year - Ibaraki
16.2 Cities Sorted by Rank - Ibaraki
16.3 Cities Sorted Alphabetically - Ibaraki
17.1 Latent Demand by Year - Ishikawa
17.2 Cities Sorted by Rank - Ishikawa
17.3 Cities Sorted Alphabetically - Ishikawa
18.1 Latent Demand by Year - Iwate
18.2 Cities Sorted by Rank - Iwate
18.3 Cities Sorted Alphabetically - Iwate
19.1 Latent Demand by Year - Kagawa
19.2 Cities Sorted by Rank - Kagawa
19.3 Cities Sorted Alphabetically - Kagawa
20.1 Latent Demand by Year - Kagoshima
20.2 Cities Sorted by Rank - Kagoshima
20.3 Cities Sorted Alphabetically - Kagoshima
21.1 Latent Demand by Year - Kanagawa
21.2 Cities Sorted by Rank - Kanagawa
21.3 Cities Sorted Alphabetically - Kanagawa
22.1 Latent Demand by Year - Kochi
22.2 Cities Sorted by Rank - Kochi
22.3 Cities Sorted Alphabetically - Kochi
23.1 Latent Demand by Year - Kumamoto
23.2 Cities Sorted by Rank - Kumamoto
23.3 Cities Sorted Alphabetically - Kumamoto
24.1 Latent Demand by Year - Kyoto
24.2 Cities Sorted by Rank - Kyoto
24.3 Cities Sorted Alphabetically - Kyoto
25 MIE
25.1 Latent Demand by Year - Mie
25.2 Cities Sorted by Rank - Mie
25.3 Cities Sorted Alphabetically - Mie
26.1 Latent Demand by Year - Miyagi
26.2 Cities Sorted by Rank - Miyagi
26.3 Cities Sorted Alphabetically - Miyagi
27.1 Latent Demand by Year - Miyazaki
27.2 Cities Sorted by Rank - Miyazaki
27.3 Cities Sorted Alphabetically - Miyazaki
28.1 Latent Demand by Year - Nagano
28.2 Cities Sorted by Rank - Nagano
28.3 Cities Sorted Alphabetically - Nagano
29.1 Latent Demand by Year - Nagasaki
29.2 Cities Sorted by Rank - Nagasaki
29.3 Cities Sorted Alphabetically - Nagasaki
30.1 Latent Demand by Year - Nara
30.2 Cities Sorted by Rank - Nara
30.3 Cities Sorted Alphabetically - Nara
31.1 Latent Demand by Year - Niigata
31.2 Cities Sorted by Rank - Niigata
31.3 Cities Sorted Alphabetically - Niigata
32.1 Latent Demand by Year - Oita
32.2 Cities Sorted by Rank - Oita
32.3 Cities Sorted Alphabetically - Oita
33.1 Latent Demand by Year - Okayama
33.2 Cities Sorted by Rank - Okayama
33.3 Cities Sorted Alphabetically - Okayama
34.1 Latent Demand by Year - Okinawa
34.2 Cities Sorted by Rank - Okinawa
34.3 Cities Sorted Alphabetically - Okinawa
35.1 Latent Demand by Year - Osaka
35.2 Cities Sorted by Rank - Osaka
35.3 Cities Sorted Alphabetically - Osaka
36.1 Latent Demand by Year - Saga
36.2 Cities Sorted by Rank - Saga
36.3 Cities Sorted Alphabetically - Saga
37.1 Latent Demand by Year - Saitama
37.2 Cities Sorted by Rank - Saitama
37.3 Cities Sorted Alphabetically - Saitama
38.1 Latent Demand by Year - Shiga
38.2 Cities Sorted by Rank - Shiga
38.3 Cities Sorted Alphabetically - Shiga
39.1 Latent Demand by Year - Shimane
39.2 Cities Sorted by Rank - Shimane
39.3 Cities Sorted Alphabetically - Shimane
40.1 Latent Demand by Year - Shizuoka
40.2 Cities Sorted by Rank - Shizuoka
40.3 Cities Sorted Alphabetically - Shizuoka
41.1 Latent Demand by Year - Tochigi
41.2 Cities Sorted by Rank - Tochigi
41.3 Cities Sorted Alphabetically - Tochigi
42.1 Latent Demand by Year - Tokushima
42.2 Cities Sorted by Rank - Tokushima
42.3 Cities Sorted Alphabetically - Tokushima
43.1 Latent Demand by Year - Tokyo
43.2 Cities Sorted by Rank - Tokyo
43.3 Cities Sorted Alphabetically - Tokyo
44.1 Latent Demand by Year - Tottori
44.2 Cities Sorted by Rank - Tottori
44.3 Cities Sorted Alphabetically - Tottori
45.1 Latent Demand by Year - Toyama
45.2 Cities Sorted by Rank - Toyama
45.3 Cities Sorted Alphabetically - Toyama
46.1 Latent Demand by Year - Wakayama
46.2 Cities Sorted by Rank - Wakayama
46.3 Cities Sorted Alphabetically - Wakayama
47.1 Latent Demand by Year - Yamagata
47.2 Cities Sorted by Rank - Yamagata
47.3 Cities Sorted Alphabetically - Yamagata
48.1 Latent Demand by Year - Yamaguchi
48.2 Cities Sorted by Rank - Yamaguchi
48.3 Cities Sorted Alphabetically - Yamaguchi
49.1 Latent Demand by Year - Yamanashi
49.2 Cities Sorted by Rank - Yamanashi
49.3 Cities Sorted Alphabetically - Yamanashi
50.1 Disclaimers & Safe Harbor
50.2 ICON Group International, Inc. User Agreement Provisions

Note: Product cover images may vary from those shown
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Note: Product cover images may vary from those shown