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

The notion of latent demand is quite subtle. The word latent typically identifies something which is dormant, not observable, or otherwise yet realized. Demand could be 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 often defined by economists because the industry earnings of the 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 a efficient manner. It is typically expressed as the total revenues potentially extracted by firms. The "market" is scheduled at a given level inside value chain. There may be latent demand on the retail level, with the wholesale level, the manufacturing level, and also the raw materials level (the P.I.E. of upper levels with the value chain being always smaller compared to P.I.E. of levels at lower levels from the same value chain, assuming all levels maintain minimum profitability).

The latent interest in bicycles and bicycle accessories in Greater China 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 companies are inefficient (i.e., not representative of relatively competitive levels). Inefficiencies arise coming from a quantity of factors, like the insufficient international openness, cultural barriers to consumption, regulations, and cartel-like behavior for the section of firms. In general, however, latent demand is typically greater than actual sales inside 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 isn't 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 when compared with recent experience, actually sales also can exceed latent demand (not adjusted for inflation). On the other hand, latent demand might be typically greater than actual sales as there tend to be distribution inefficiencies that reduce actual sales below the amount of latent demand.

As mentioned within the introduction, this study is strategic in nature, taking an aggregate and long-run view, irrespective from the players or products involved. In fact, every among the current products about the market can cease to exist inside their present form (i.e., with a brand-, R&D specification, or corporate-image level) and all sorts of players can be replaced by other firms (i.e., via exits, entries, mergers, bankruptcies, etc.), and there will still be latent demand for bicycles and bicycle accessories on the aggregate level. Product and service offerings, and the actual identity of 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 interest in bicycles and bicycle accessories across the regions and cites of Greater China, 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 usage 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 an region, city, household, or individual to realized consumption. Latent demand (often realized as consumption when an industry is efficient), at any level from the value chain, takes place if an equilibrium is realized. For firms for everyone a market, they must perceive a latent demand and stay capable of serve that demand in a minimal return. The one most critical variable determining consumption, assuming latent demand exists, is income (or other money at higher levels of the value chain). Other factors that will pivot or shape demand curves include external or exogenous shocks (i.e., business cycles), or changes in utility for that 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 part of problem. In the 1930s, John Meynard Keynes conjectured that as incomes rise, the common propensity to eat would fall. The average propensity to take will be the a higher level consumption divided from the amount of income, or the slope of the line in the origin on the consumption function. He estimated this relationship empirically and located it to become true inside short-run (mostly depending on cross-sectional data). The higher the income, the reduced the common propensity to consume. This sort of consumption function is labeled "A" within 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 this marginal propensity to eat 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 perhaps is it constant? A amount of other economists, notably Franco Modigliani and Milton Friedman, inside 1950s (and Irving Fisher earlier), explained why both the functions were different using various assumptions on intertemporal budget constraints, savings, and wealth. The shorter the time horizon, the more consumption can rely on wealth (earned in previous years) and business cycles. In the long-run, however, the propensity to take is a lot more constant. Similarly, inside long run, households without income eventually haven't any consumption (wealth is depleted). Even though the debate surrounding beliefs about how income and consumption are related is interesting, within this study a very particular school of thought is adopted. In particular, we're taking into consideration the latent demand for bicycles and bicycle accessories through the regions and cities of Greater China. The smallest cities have few inhabitants. I assume that all of the 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 the long-run has a zero intercept. However, I allow different propensities to eat (including located on consumption functions with differing slopes, which can account for variations in industrial organization, and end-user preferences).

Given this overriding philosophy, I am going to now describe the methodology utilized to create the latent demand estimates for bicycles and bicycle accessories in Greater China. Since ICON Group has asked me to apply this methodology to some large quantity of categories, the rather academic discussion below is general and may be applied to a 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, I have found how the optimal approach is to think that certain key indicators are more likely to reflect efficiency than others. These indicators are given greater weight than the others inside the estimation of latent demand in comparison to others for which no known data are available. Of the many alternatives, We've 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 get efficient). Aggregate income could 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 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... --This text refers for the Digital edition.

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