Market Potential Estimation Methodology
Overview
This study covers the world outlook for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes across more than 2000 cities. For the year reported, estimates are given for the latent demand, or potential industry earnings (P.I.E.), for the city in question (in millions of U.S. dollars), the percent share the city is of the region and of the globe. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others. Using econometric models which project fundamental economic dynamics within each country and across countries, latent demand estimates are created. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved.
This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all of the cities of the world). This
study gives, however, my estimates for the worldwide latent demand, or the P.I.E. for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes. It also shows how the P.I.E. is divided across the world’s cities. In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on international strategic planning at graduate schools of business.
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 a market 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 power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be lower 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 city market.
Another reason why sales do not equate to latent demand is exchange rates. In this report, all figures assume the long-run efficiency of currency markets. Figures, therefore, equate values based on purchasing power parities across countries. Short-run distortions in the value of the dollar, therefore, do not figure into the estimates. Purchasing power parity estimates of country income were collected from official sources, and extrapolated using standard econometric models. The report uses the dollar as the currency of comparison, but not as a measure of transaction volume. The units used in this report are: US $ mln.
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) and not adjusted for future dynamics in exchange rates (i.e., the figures reflect average exchange rates over recent history). If inflation rates or exchange rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (when expressed in U.S. dollars, 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 earlier, this study is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved. If 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 an international latent demand for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes at the aggregate level. Product and service offering details, 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 power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes on a city-by-city basis, 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 country, city, state, 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 in 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 countries, 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 across countries). This type of consumption function is show 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, industries or countries with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related and interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes across some 230 countries. The smallest have fewer than 10,000 inhabitants. I assume that all of these counties fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these countries having wealth, current income dominates the latent demand for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes. So, latent demand in the long-run has a zero intercept. However, I allow firms to have 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 power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes. 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, not just power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes.
Step 1. Product Definition and Data Collection
Any study of latent demand across countries 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 countries or cities are more likely to be at or near efficiency than others. These 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 world’s highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e., China has 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 per capita). Brunei, Nauru, Kuwait, and Lichtenstein are examples of countries with high income per capita, but not assumed to be efficient, given low aggregate level of income (or gross domestic product); these countries have, however, high incomes per capita but may not benefit from the efficiencies derived from economies of scale associated with large economies. Only countries with high income per capita and large aggregate income are assumed efficient. This greatly restricts the pool of countries to those in the OECD (Organization for Economic Cooperation and Development), like the United States, or the United Kingdom (which were earlier than other large OECD economies to liberalize their markets).
The selection of countries is further reduced by the fact that not all countries in the OECD report industry revenues at the category level. Countries that typically have ample data at the aggregate level that meet the efficiency criteria include the United States, the United Kingdom and in some cases France and Germany.
Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of “power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes” 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 power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes 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 cities and the world at large (without needing to know the specific parts that went into the whole in the first place).
Given this caveat, this study covers “power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes The NAICS code for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes is 3331201. It is for this definition of power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes that the aggregate latent demand estimates are derived. “Power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes” is specifically defined as follows:
3331201
Power cranes, dozers, tractors, off-hwy trucks, mixers, pavers, backhoes, etc.
33312011
Power cranes, draglines, shovels (including surface mining equip.)(exc parts)
3331201110
Power cranes, draglines, and shovels (excavators) (including surface mining equipment and attachments) (excluding parts)
3331201111
Excavators, hydraulic operated, crawler_mounted, rated size not more than 24 metric tons
3331201114
Excavators, hydraulic operated, crawler_mounted, rated size more than 24 metric tons
3331201117
Excavators, hydraulic operated, crawler_mounted, rated size more than 40 metric tons
3331201129
Excavators, hydraulic operated, wheel (rubber) mounted, all sizes
3331201132
Cranes, lattice boom, crawler_mounted, maximum working load not more than 110 metric tons (121.25 short tons)
3331201134
Cranes, lattice boom, crawler_mounted, maximum working load more than 110 metric tons
3331201145
Cranes, lattice boom, wheel (rubber) mounted, all sizes
3331201152
Cranes, hydraulic_operated, telescopic boom, wheel (integral), multiple control stations, rubber_mounted, maximum working load of not more than 18 metric tons (19.84 short tons)
3331201154
Cranes, hydraulic_operated, telescopic boom, wheel (integral), multiple control stations, rubber_mounted, maximum working load of more than 18 but not more than 27 metric tons (29.76 short tons)
3331201156
Cranes, hydraulic_operated, telescopic boom, wheel (integral), multiple control stations, rubber_mounted, maximum working load of more than 27 but not more than 50 metric tons (55.12 short tons)
3331201158
Cranes, hydraulic_operated, telescopic boom, wheel (integral), multiple control stations, rubber_mounted, maximum working load of more than 50 metric tons
3331201167
Cranes, hydraulic_operated, telescopic boom, pinned_on type, telescopic and articulated
3331201171
Cranes, hydraulic_operated, telescopic boom, wheel, one control station, self_propelled, rubber_mounted, maximum working load of not more than 16 metric tons (17.64 short tons)
3331201174
Cranes, hydraulic_operated, telescopic boom, wheel, one control station, self_propelled, rubber_mounted, maximum working load of more than 16 but not more than 22.7 metric tons (25.02 short tons)
3331201177
Cranes, hydraulic_operated, telescopic boom, wheel, one control station, self_propelled, rubber_mounted, maximum working load of more than 22.7 metric tons
3331201182
Cranes, hydraulic_operated, telescopic boom, all_terrain, maximum working loads of not more than 20 metric tons (22.05 short tons)
3331201184
Cranes, hydraulic_operated, telescopic boom, all_terrain, maximum working loads more than 20 metric tons but not more than 35 metric tons (38.58 short tons)
3331201186
Cranes, hydraulic_operated, telescopic boom, all_terrain, maximum working loads more than 35 metric tons but not more than 110 metric tons (121.25 short tons)
3331201188
Cranes, hydraulic_operated, telescopic boom, all_terrain, maximum working loads more than 110 metric tons
3331201194
Pedestal or shipmounted marine cranes
3331201199
All other cranes (including locomotive wrecking, cable_operated excavators, and draglines)
33312011A4
Attachments (sold separately) for power cranes, draglines, and excavators, hoes
33312011A5
Attachments (sold separately) for power cranes, draglines, and excavators, dragline buckets, all sizes
33312011A7
Attachments (sold separately) for power cranes, draglines, and excavators, clamshells/grapples
33312011AA
Attachments (sold separately) for power cranes, draglines, and excavators, all other
33312012
Mixers, pavers, and related equipment (excluding parts)
3331201220
Mixers, pavers, and related equipment (excluding parts)
3331201222
Concrete mixers (except plaster and mortar), portable, all sizes (truck_ mixer, agitator, etc.)
3331201231
Plaster and mortar mixers, all sizes
3331201233
Slipform concrete paving machines (including multipurpose, automated curb and gutter, and concrete slipform pavers up to and including 34 ft wide)
3331201239
Concrete trowels
3331201241
Concrete vibrators (electric motor, gasoline engine, structural high_cycle, pneumatic, etc.)
3331201244
Concrete screeds, hand_propelled or winch type
3331201247
Concrete batching plants, bin and batch (for concrete aggregate only and bulk cement)
3331201251
Concrete pumps, mobile
3331201256
Other concrete, plaster, and mortar mixing and paving machinery
3331201266
Bituminous distributors
3331201271
Bituminous pavers, self_propelled, 21,999 lb gross weight and under (basic unit)
3331201275
Bituminous pavers, self_propelled, 22,000 lb gross weight and over (basic unit)
3331201282
Asphalt plants (including cold mix central plants), less than 7,500 lb (239 tons per hour)
3331201284
Asphalt plants (including cold mix central plants), 7,500 lb and over (240 tons per hour and over)
3331201286
Bituminous stabilization mixing equipment, including central mixing plants and mix_in_place
3331201289
Bituminous cold planers/milling machines (self_propelled)
3331201291
Other asphalt and bituminous mixing and paving machinery
3331201295
Other mixers, pavers, and related equipment
33312013
Off-highway trucks, haulers, truck-type tractor chassis, trailers, excl. parts
3331201330
Off_highway trucks, coal haulers, truck_type tractor chassis, trailers and wagons (excluding parts)
3331201371
Off_highway rear dump haulers
3331201376
Off_highway trucks, coal haulers, truck_type tractor chassis, trailers, and wagons
33312014
Tractor shovel loaders (skid steer, wheel, crawler, & integral design backhoe)
3331201440
Tractor shovel loaders (skid steer, wheel, and crawler, and integral_design loader_backhoes)
3331201460
Skid steer loaders, 4_wheel drive skid steer, all sizes
3331201471
Wheel loaders, rear engine mount, integral design, 4_wheel drive, non_skid steer, under 79 net engine horsepower (NEHP)
3331201475
Wheel loaders, rear engine mount, integral design, 4_wheel drive, non_skid steer, 80 to 149 NEHP
3331201479
Wheel loaders, rear engine mount, integral design, 4_wheel drive, non_skid steer, 150 to 249 NEHP
3331201483
Wheel loaders, rear engine mount, integral design, 4_wheel drive, non_skid steer, 250 NEHP and over
3331201488
Crawler loaders
3331201495
Integral_design tractor shovel loaders/backhoes (wheel tractor_chassis shipped as part of front engine mount contractor tractor)
33312015
Construction wheel & crawler tractors, dozers, & self-propelled log skidders
3331201550
Construction wheel and crawler tractors, dozers, and self_propelled log skidders
3331201577
Wheeled log skidders, self_propelled
3331201599
Crawler tractors (except crawler loaders) and tracklaying and other contractors’ off_highway_type wheel tractors and dozers
33312016
Graders , rollers & compactors, forklifts, scrapers, trenchers, excl parts
3331201660
Motor graders and light maintainers, including rollers and compactors, rough_ terrain forklifts, scraper bowls, and self_propelled continuous ditchers and trenchers (except parts)
3331201661
Motor graders and light maintainers, under 144 net engine horsepower (NEHP)
3331201667
Motor graders and light maintainers, 145 NEHP and over
3331201671
Rollers and compactors, static smooth steel wheel rollers, tandem, under 3 metric tons (3.31 short tons)
3331201673
Rollers and compactors, static smooth steel wheel rollers, tandem, at least 3 metric tons but less than 5.5 metric tons (6.06 short tons)
3331201676
Rollers and compactors, static smooth steel wheel rollers, tandem, 5.5 metric tons and over
3331201682
Rollers and compactors, pneumatic tire rollers
3331201687
Rollers and compactors, vibratory single drum with 1 or 2 drive wheels, under 5 metric tons (5.51 short tons)
3331201689
Rollers and compactors, vibratory single drum with 1 or 2 drive wheels, at least 5 metric tons but less than 8 metric tons (8.82 short tons)
3331201692
Rollers and compactors, vibratory single drum with 1 or 2 drive wheels, 8 metric tons and over
3331201696
Rollers and compactors, vibratory double drum, two drums vibrating, under 5 metric tons (5.51 short tons)
3331201699
Rollers and compactors, vibratory double drum, two drums vibrating, at least 5 metric tons but less than 8 metric tons (8.82 short tons)
33312016A2
Rollers and compactors, vibratory double drum, two drums vibrating, 8 metric tons and over
33312016A6
All other compaction equipment (including embankment and landfill compactors, towed roller and double drum compactors with one drum vibrating), except handheld
33312016D1
Rough_terrain forklifts (integral units only), vertical mast, 2_wheel drive
33312016D3
Rough_terrain forklifts (integral units only), vertical mast, 4_wheel drive
33312016D5
Rough_terrain forklifts (integral units only), telescopic handlers, 2_ and 4_ wheel drive
33312016E5
Scraper bowls
33312016J3
Ditchers and trenchers, self_propelled (integral units only), ladder_type digging element, under 2,000 lb gross weight
33312016J5
Ditchers and trenchers, self_propelled (integral units only), ladder_type digging element, 2,000 to 4,999 lb gross weight
33312016J7
Ditchers and trenchers, self_propelled (integral units only), ladder_type digging element, 5,000 lb gross weight and over
33312016JJ
Ditchers and trenchers, self_propelled (integral units only), wheel_type digging element
33312017
Attach. for tractors & other equip. (exc parts, winches, snow clearing attach.)
3331201770
Construction machinery for mounting on tractors and other prime movers (excluding parts, winches, and snow clearing attachments)
3331201782
Backhoes for mounting on tractors, trucks, and other prime movers (either shipped separately or already mounted on integral units)
3331201799
Other construction machinery for mounting (sidebrooms, pipehandlers, dozers, front_end loaders, shovel loaders, logging arches, etc.), except winches and materials_handling equipment
Step 2. Filtering and Smoothing
Based on the aggregate view of power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes as defined above, data were then collected for as many similar countries and cities as possible for that same definition, at the same level of the value chain. This generates a convenience sample 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 country, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a country 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 for countries or cities 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 income. Based on the overriding philosophy of a long-run consumption function (defined earlier), cities which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that country.
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 across cities 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 countries). This assumption applies across cities along the aggregate consumption function, but also over time (i.e., not all cities are perceived to have the same income growth prospects over time and this effect can vary from city to city as well). Another way of looking at this is to say that latent demand for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes is more likely to be similar across cities that have similar characteristics in terms of economic development (i.e., African cities will have similar latent demand structures controlling for the income variation across the pool of African cities).
This approach is useful across cities for which some notion of non-linearity exists in the aggregate consumption function. For some categories, however, the reader must realize that the numbers will reflect a city’s contribution to global latent demand and may never be realized in the form of local sales. For certain category combinations this will result in what at first glance will be odd results. For example, the latent demand for the category “space vehicles” will exist for cities in “Togo” even though they have no space program. The assumption is that if the economies in these countries did not exist, the world aggregate for these categories would be lower. The share attributed to these cities is based on a proportion of their income (however small) being used to consume the category in question (i.e., perhaps via resellers).
Step 5. Fixed-Parameter Linear Estimation
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the world consists of more than 2000 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 city has no current income, the latent demand for power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes 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 power cranes, dozers, tractors, off-highway trucks, mixers, pavers, and backhoes). 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, low-income cities are assumed to have a latent demand proportional to their income, based on the city 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 cities of the world, including for the smallest economies. These are then aggregated to get world totals and regional totals. To make the numbers more meaningful, regional and global demand averages are presented. Figures are rounded, so minor inconsistencies may exist across tables.
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