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WHAT IS LATENT DEMAND AND THE P.I.E.?

The concept of latent demand is pretty subtle. The word latent typically describes something is dormant, not observable, or otherwise not yet realized. Demand could be the notion associated with an economic quantity that the target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is often based on economists because the industry earnings of the market when that market becomes accessible and attractive to offer by competing firms. It is often a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if Japan is served within an efficient manner. It is usually expressed since the total revenues potentially extracted by firms. The "market" is scheduled at the given level inside the value chain. There may be latent demand on the retail level, with the wholesale level, the manufacturing level, along with the raw materials level (the P.I.E. better levels with the value chain being always smaller as opposed to P.I.E. of levels at lower levels with the same value chain, assuming all levels maintain minimum profitability).

The latent demand for bicycles and bicycle accessories in Japan isn't actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be either lower or more than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise coming from a number of factors, such as the insufficient international openness, cultural barriers to consumption, regulations, and cartel-like behavior for the part of firms. In general, however, latent demand is normally larger than actual sales in the 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 isn't made, though one is implied). The units used within this report are U.S. dollars not adjusted for inflation (i.e., the figures incorporate inflationary trends). If inflation rates vary inside a substantial way compared to recent experience, actually sales can also exceed latent demand (not adjusted for inflation). On the opposite hand, latent demand may be typically higher than actual sales as there are often distribution inefficiencies that reduce actual sales below the degree of latent demand.

As mentioned inside introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective with the players or products involved. In fact, all the current services or products about the market can cease to exist within 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 probably be latent demand for bicycles and bicycle accessories in the aggregate level. Product and repair offerings, along with 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 need for bicycles and bicycle accessories throughout 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 around the utilization of certain basic economic assumptions. In particular, there's an assumption governing the shape and kind of aggregate latent demand functions. Latent demand functions relate the income of your prefecture, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an marketplace is efficient), at any level in the value chain, takes place if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and be in a position to serve that demand at a minimal return. The one most critical variable determining consumption, assuming latent demand exists, is income (or other financial resources at higher levels with the value chain). Other factors that can pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), or changes in utility for your product in question.

Ignoring, for that moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption may be 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 common propensity to consume would fall. The average propensity to eat is the amount of consumption divided through the degree of income, or even the slope with the line through the origin to the consumption function. He estimated this relationship empirically and found it to get true inside the short-run (mostly based on cross-sectional data). The higher the income, the reduced the typical propensity to consume. This kind of consumption function is labeled "A" inside figure below (note the rather flat slope in the curve). In the 1940s, another macroeconomist, Simon Kuznets, estimated long-run consumption functions which indicated how the marginal propensity to use was rather constant (using time series data). This kind of consumption function is shown as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to take is constant.





Is it declining or perhaps is it constant? A quantity of other economists, notably Franco Modigliani and Milton Friedman, within the 1950s (and Irving Fisher earlier), explained why both the functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter enough time horizon, the more consumption can rely on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to eat is more constant. Similarly, within the long run, households with no income eventually have zero consumption (wealth is depleted). Whilst the debate surrounding beliefs about how precisely income and consumption are related is interesting, with this study a really particular school of thought is adopted. In particular, we are thinking about the latent need for bicycles and bicycle accessories throughout the prefectures and cities of Japan. The smallest cities have few inhabitants. I assume that of those cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of the prefectures having wealth; current income dominates the latent need for bicycles and bicycle accessories. So, latent demand inside long-run includes a zero intercept. However, I allow different propensities to eat (including located on consumption functions with differing slopes, which could account for differences in industrial organization, and end-user preferences).

Given this overriding philosophy, I'll now describe the methodology utilized to create the latent demand estimates for bicycles and bicycle accessories in Japan. Since ICON Group has asked me to apply this methodology to a large amount of categories, the rather academic discussion below is general and could be applied to a wide number of categories and geographic locations, not just bicycles and bicycle accessories 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've found how the optimal approach is always to believe that certain key indicators are more probable to reflect efficiency than others. These indicators receive greater weight than others in the estimation of latent demand compared to others which is why no known data are available. Of the countless alternatives, I have found the assumption 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 may not assumed being efficient). Aggregate income might be operationalized in a quantity of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or amount 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... --This text refers for the Digital edition.

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