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

The concept of latent demand is pretty subtle. The phrase latent typically describes a thing that is dormant, not observable, or otherwise yet realized. Demand is the notion associated with an economic quantity a target population or market requires under different assumptions of price, quality, and distribution, among other factors. Latent demand, therefore, is commonly based on economists as the industry earnings of your market when that market becomes accessible and attractive to serve by competing firms. It is really a measure, therefore, of potential industry earnings (P.I.E.) or total revenues (not profit) if Greater China is served in an efficient manner. It is normally expressed because the total revenues potentially extracted by firms. The "market" is scheduled at a given level in the value chain. There can be latent demand in the retail level, with the wholesale level, the manufacturing level, and the raw materials level (the P.I.E. of upper levels with the value chain being always smaller than the P.I.E. of levels at lower levels from the same value chain, assuming all levels maintain minimum profitability).

The latent need for bicycles and bicycle accessories in Greater China isn't actual or historic sales. Nor is latent demand future sales. In fact, latent demand may be either lower or maybe more than actual sales if a market is inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise from a number of factors, such as the lack of international openness, cultural barriers to consumption, regulations, and cartel-like behavior on the a part of firms. In general, however, latent demand is typically bigger 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 with 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 opposite hand, latent demand might be typically more than actual sales because there in many cases are distribution inefficiencies that reduce actual sales below the a higher level latent demand.

As mentioned within the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective in the players or products involved. In fact, all of the current products or services about the market can cease to exist of their present form (i.e., at the brand-, R&D specification, or corporate-image level) and players may be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there'll still be latent interest in bicycles and bicycle accessories in the aggregate level. Product and service offerings, and also the actual identity with the players involved, while very important to certain issues, are relatively unimportant for estimates of latent demand.

THE METHODOLOGY

In order to estimate the latent demand for bicycles and bicycle accessories over the regions and cites of Greater China, I used a multi-stage approach. Before using 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 exists an assumption governing the shape and kind of aggregate latent demand functions. Latent demand functions relate the income of the region, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an market is efficient), at any level with the value chain, occurs if an equilibrium is realized. For firms to serve a market, they must perceive a latent demand and stay in a posture to serve that demand in a minimal return. The only most important variable determining consumption, assuming latent demand exists, is income (or other savings at higher levels from the value chain). Other factors that may pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), and or changes in utility for that product in question.

Ignoring, for your moment, exogenous shocks and variations in utility across geographies, the aggregate relation between income and consumption continues to be a central theme in economics. The figure below concisely summarizes one part of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the common propensity to consume would fall. The typical propensity to eat could be the level of consumption divided with the amount of income, or slope with the line in the origin on the consumption function. He estimated this relationship empirically and discovered it to become true inside the short-run (mostly according to cross-sectional data). The bigger the income, the reduced the typical propensity to consume. This type of consumption function is labeled "A" within the figure below (note the rather flat slope with 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" within the figure below (note the higher slope and zero-zero intercept). The normal propensity to use is constant.





Is it declining or is it constant? A variety of other economists, notably Franco Modigliani and Milton Friedman, inside the 1950s (and Irving Fisher earlier), explained why both 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 consume is a whole lot more constant. Similarly, within the long run, households with no income eventually have zero consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related is interesting, in this study an extremely particular school of thought is adopted. In particular, were thinking about the latent need for bicycles and bicycle accessories through the regions and cities of Greater China. The smallest cities have few inhabitants. I assume that every of those cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these states having wealth; current income dominates the latent interest in bicycles and bicycle accessories. So, latent demand inside long-run includes a zero intercept. However, I allow different propensities to eat (including being 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 used to produce the latent demand estimates for bicycles and bicycle accessories in Greater China. Since ICON Group has asked me to use this methodology to your large amount of categories, the rather academic discussion below is general and could be applied to some wide selection of categories and geographic locations, not just bicycles and bicycle accessories in Greater China.

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 how the optimal approach would be to believe that certain key indicators are more probable to reflect efficiency than others. These indicators get greater weight than others inside the estimation of latent demand in comparison to others which is why no known data are available. Of the countless 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 may not assumed to become efficient). Aggregate income can be operationalized in the number of ways, including gross domestic product (for industrial categories), or total disposable income (for household categories; population times average income per capita, or variety 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... --This text refers towards the Digital edition.

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