The 2009-2014 World Outlook for Manufacturing or Rebuilding Motor Vehicle Steering Mechanisms and Suspension Components Excluding Springs
ICON Group International, September 2008, Pages: 201
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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs 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 country 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) and not adjusted for future dynamics in exchange rates. 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 in the introduction, 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs on a worldwide 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 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 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs. 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs. 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs.
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 are more likely to be at or near efficiency than others. These countries are given greater weight than others in the estimation of latent demand compared to other countries 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 “manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs” 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 manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs 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 countries 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 “manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs is 336330. It is for this definition of manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs that the aggregate latent demand estimates are derived. “Manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs” is specifically defined as follows:
336330
This industry comprises establishments primarily engaged in manufacturing and/or rebuilding motor vehicle steering mechanisms and suspension components (except springs).
3363301
Motor vehicle steering and suspension components, new
33633011
Motor vehicle shock absorbers, new
3363301101
Motor vehicle shock absorbers, new
33633012
Motor vehicle tie rod ends, new
3363301204
Motor vehicle tie rod ends, new
33633013
Motor vehicle steering idler arms, drag links, and control arms, new
3363301307
Motor vehicle steering idler arms, drag links, and control arms, new
33633014
Motor vehicle steering wheels, columns and gearboxes, new
3363301417
Motor vehicle steering wheels, columns, and gearboxes, new
33633015
Other motor vehicle steering and suspension components, including motor vehicle ball joints, new
3363301511
Motor vehicle ball joints, new
3363301514
Motor vehicle struts, new
3363301521
Motor vehicle rack and pinion steering gears, new
3363301524
Motor vehicle integral and manual steering gears, new
3363301526
Motor vehicle power steering pumps, new
3363301528
Motor vehicle power steering hose assemblies, new
3363301531
Other motor vehicle steering and suspension components, new
33633016
Other motor vehicle steering & suspension components, new
3363303
Motor vehicle steering and suspension components, rebuilt
33633031
Motor vehicle steering and suspension components, rebuilt
3363303101
Motor vehicle power steering pumps, rebuilt
3363303104
Motor vehicle rack and pinion steering assemblies, rebuilt
3363303121
Other steering and suspension components, rebuilt
336330M
Miscellaneous receipts
336330P
Primary products
336330S
Secondary products
336330SM
Secondary and miscellaneous receipts
Furthermore, the definition of NAICS code 336330 includes the following:
Automotive, truck and bus steering assemblies and parts manufacturing
Automotive, truck and bus suspension assemblies and parts (except springs) manufa
Power steering hose assemblies manufacturing
Power steering pumps manufacturing
Rack and pinion steering assemblies manufacturing
Shock absorbers, automotive, truck, and bus, manufacturing
Steering boxes, manual and power assist, manufacturing
Steering columns, automotive, truck, and bus, manufacturing
Steering wheels, automotive, truck, and bus, manufacturing
Struts, automotive, truck, and bus, manufacturing
Wheels, steering, automotive, truck, and bus, manufacturing.
Step 2. Filtering and Smoothing
Based on the aggregate view of manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs as defined above, data were then collected for as many similar countries as possible for that same definition, at the same level of the value chain. This generates a convenience sample of countries 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 on a sporadic basis. In other cases, data from a country 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), countries 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 in additional countries 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 countries 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 countries along the aggregate consumption function, but also over time (i.e., not all countries are perceived to have the same income growth prospects over time and this effect can vary from country to country as well). Another way of looking at this is to say that latent demand for manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs is more likely to be similar across countries that have similar characteristics in terms of economic development (i.e., African countries will have similar latent demand structures controlling for the income variation across the pool of African countries).
This approach is useful across countries for which some notion of non-linearity exists in the aggregate cross-country consumption function. For some categories, however, the reader must realize that the numbers will reflect a country’s contribution to global latent demand and may never be realized in the form of local sales. For certain country-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 “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 countries 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 200 countries, there will always be those countries, especially toward the bottom of the consumption function, where non-linear estimation is simply not possible. For these countries, equilibrium latent demand is assumed to be perfectly parametric and not a function of wealth (i.e., a country’s stock of income), but a function of current income (a country’s flow of income). In the long run, if a country has no current income, the latent demand for manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs is assumed to approach zero. The assumption is that wealth stocks fall rapidly to zero if flow income falls to zero (i.e., countries which earn low levels of income will not use their savings, in the long run, to demand manufacturing or rebuilding motor vehicle steering mechanisms and suspension components excluding springs). In a graphical sense, for low income countries, latent demand approaches zero in a parametric linear fashion with a zero-zero intercept. In this stage of the estimation procedure, low-income countries are assumed to have a latent demand proportional to their income, based on the country 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 countries 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.
Step 7. Latent Demand Density: Allocating Across Cities
With the advent of a “borderless world”, cities become a more important criteria in prioritizing markets, as opposed to regions, continents, or countries. This report also covers the world’s top 2000 cities. The purpose is to understand the density of demand within a country and the extent to which a city might be used as a point of distribution within its region. 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.
Similar to country-level data, the reader needs to realize that latent demand allocated to a city may or may not represent real sales. For many items, latent demand is clearly observable in sales, as in the case for food or housing items. Consider, again, the category “satellite launch vehicles.” Clearly, there are no launch pads in most cities of the world. However, the core benefit of the vehicles (e.g. telecommunications, etc.) is "consumed" by residents or industries within the worlds cities. Without certain cities, in other words, the world market for satellite launch vehicles would be lower for the world in general. One needs to allocate, therefore, a portion of the worldwide economic demand for launch vehicles to regions, countries and cities. This report takes the broader definition and considers, therefore, a city as a part of the global market. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its home country, within its region and across the world total. Not all cities are estimated within each country as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same country, 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.
1 INTRODUCTION 10
1.1 Overview 10
1.2 What is Latent Demand and the P.I.E.? 10
1.3 The Methodology 11
1.3.1 Step 1. Product Definition and Data Collection 12
1.3.2 Step 2. Filtering and Smoothing 15
1.3.3 Step 3. Filling in Missing Values 15
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 15
1.3.5 Step 5. Fixed-Parameter Linear Estimation 16
1.3.6 Step 6. Aggregation and Benchmarking 16
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 16
2 SUMMARY OF FINDINGS 18
2.1 The Worldwide Market Potential 18
3 AFRICA 20
3.1 Executive Summary 20
3.2 Algeria 21
3.3 Angola 22
3.4 Benin 23
3.5 Botswana 24
3.6 Burkina Faso 25
3.7 Burundi 25
3.8 Cameroon 26
3.9 Cape Verde 27
3.10 Central African Republic 27
3.11 Chad 28
3.12 Comoros 29
3.13 Congo (formerly Zaire) 29
3.14 Cote dIvoire 30
3.15 Djibouti 31
3.16 Egypt 32
3.17 Equatorial Guinea 33
3.18 Ethiopia 33
3.19 Gabon 34
3.20 Ghana 35
3.21 Guinea 36
3.22 Guinea-Bissau 36
3.23 Kenya 37
3.24 Lesotho 38
3.25 Liberia 38
3.26 Libya 39
3.27 Madagascar 40
3.28 Malawi 40
3.29 Mali 41
3.30 Mauritania 42
3.31 Mauritius 42
3.32 Morocco 43
3.33 Mozambique 44
3.34 Namibia 44
3.35 Niger 45
3.36 Nigeria 46
3.37 Republic of Congo 47
3.38 Reunion 47
3.39 Rwanda 48
3.40 Sao Tome E Principe 49
3.41 Senegal 49
3.42 Sierra Leone 50
3.43 Somalia 51
3.44 South Africa 52
3.45 Sudan 53
3.46 Swaziland 54
3.47 Tanzania 54
3.48 The Gambia 55
3.49 Togo 56
3.50 Tunisia 57
3.51 Uganda 58
3.52 Western Sahara 59
3.53 Zambia 59
3.54 Zimbabwe 60
4 ASIA & OCEANA 62
4.1 Executive Summary 62
4.2 American Samoa 63
4.3 Australia 64
4.4 Bangladesh 65
4.5 Bhutan 66
4.6 Brunei 66
4.7 Burma 67
4.8 Cambodia 68
4.9 China 68
4.10 Christmas Island 69
4.11 Cook Islands 70
4.12 Fiji 70
4.13 French Polynesia 71
4.14 Guam 72
4.15 Hong Kong 72
4.16 India 73
4.17 Indonesia 74
4.18 Japan 75
4.19 Kiribati 76
4.20 Laos 76
4.21 Macau 77
4.22 Malaysia 78
4.23 Maldives 79
4.24 Marshall Islands 79
4.25 Micronesia Federation 80
4.26 Mongolia 81
4.27 Nauru 81
4.28 Nepal 82
4.29 New Caledonia 83
4.30 New Zealand 83
4.31 Niue 84
4.32 Norfolk Island 85
4.33 North Korea 85
4.34 Palau 86
4.35 Papua New Guinea 87
4.36 Philippines 87
4.37 Seychelles 88
4.38 Singapore 89
4.39 Solomon Islands 90
4.40 South Korea 90
4.41 Sri Lanka 91
4.42 Taiwan 92
4.43 Thailand 93
4.44 The Northern Mariana Island 94
4.45 Tokelau 95
4.46 Tonga 95
4.47 Tuvalu 96
4.48 Vanuatu 97
4.49 Vietnam 97
4.50 Wallis and Futuna 98
4.51 Western Samoa 99
5 EUROPE 100
5.1 Executive Summary 100
5.2 Albania 101
5.3 Andorra 102
5.4 Austria 103
5.5 Belarus 104
5.6 Belgium 105
5.7 Bosnia and Herzegovina 106
5.8 Bulgaria 106
5.9 Croatia 107
5.10 Cyprus 108
5.11 Czech Republic 109
5.12 Denmark 110
5.13 Estonia 111
5.14 Finland 111
5.15 France 112
5.16 Georgia 113
5.17 Germany 114
5.18 Greece 115
5.19 Hungary 116
5.20 Iceland 117
5.21 Ireland 118
5.22 Italy 118
5.23 Kazakhstan 119
5.24 Latvia 120
5.25 Liechtenstein 121
5.26 Lithuania 122
5.27 Luxembourg 122
5.28 Malta 123
5.29 Moldova 124
5.30 Monaco 124
5.31 Norway 125
5.32 Poland 126
5.33 Portugal 127
5.34 Romania 128
5.35 Russia 129
5.36 San Marino 130
5.37 Slovakia 130
5.38 Slovenia 131
5.39 Spain 132
5.40 Sweden 133
5.41 Switzerland 134
5.42 The Netherlands 135
5.43 The United Kingdom 136
5.44 Ukraine 137
6 LATIN AMERICA 138
6.1 Executive Summary 138
6.2 Argentina 139
6.3 Belize 140
6.4 Bolivia 141
6.5 Brazil 142
6.6 Chile 143
6.7 Colombia 144
6.8 Costa Rica 145
6.9 Ecuador 145
6.10 El Salvador 146
6.11 French Guiana 147
6.12 Guatemala 147
6.13 Guyana 148
6.14 Honduras 149
6.15 Mexico 150
6.16 Nicaragua 151
6.17 Panama 152
6.18 Paraguay 153
6.19 Peru 154
6.20 Suriname 155
6.21 The Falkland Islands 155
6.22 Uruguay 156
6.23 Venezuela 157
7 NORTH AMERICA & THE CARIBBEAN 158
7.1 Executive Summary 158
7.2 Antigua and Barbuda 159
7.3 Aruba 160
7.4 Barbados 161
7.5 Bermuda 161
7.6 Canada 162
7.7 Cuba 163
7.8 Dominica 164
7.9 Dominican Republic 164
7.10 Greenland 165
7.11 Grenada 166
7.12 Guadeloupe 167
7.13 Haiti 168
7.14 Jamaica 168
7.15 Martinique 169
7.16 Puerto Rico 170
7.17 St. Kitts and Nevis 171
7.18 St. Lucia 171
7.19 St. Vincent and the Grenadines 172
7.20 The Bahamas 173
7.21 The British Virgin Islands 173
7.22 The Cayman Islands 174
7.23 The Netherlands Antilles 175
7.24 The U.S. Virgin Islands 175
7.25 The United States 176
7.26 Trinidad and Tobago 177
8 THE MIDDLE EAST 179
8.1 Executive Summary 179
8.2 Afghanistan 180
8.3 Armenia 181
8.4 Azerbaijan 182
8.5 Bahrain 183
8.6 Iran 184
8.7 Iraq 185
8.8 Israel 186
8.9 Jordan 187
8.10 Kuwait 187
8.11 Kyrgyzstan 188
8.12 Lebanon 189
8.13 Oman 189
8.14 Pakistan 190
8.15 Palestine 191
8.16 Qatar 191
8.17 Saudi Arabia 192
8.18 Syrian Arab Republic 193
8.19 Tajikistan 194
8.20 The United Arab Emirates 194
8.21 Turkey 195
8.22 Turkmenistan 196
8.23 Uzbekistan 197
8.24 Yemen 198
9 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 200
9.1 Disclaimers & Safe Harbor 200
9.2 ICON Group International, Inc. User Agreement Provisions 201
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