EUR USD GBP
+353-1-416-8900REST OF WORLD
1-800-526-8630U.S. (TOLL FREE)

The 2007-2012 World Outlook for Women’s, Misses’, and Girls’ below the Knee-Length Sheer Finished Hosiery

  • ID: 490822
  • Report
  • May 2006
  • Region: Global
  • 193 pages
  • ICON Group International
1 of 3
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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery 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 (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 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery on a worldwide basis, we used a multi-stage approach. Before applying the approach, one needs a basic theory from which such estimates are created. In this case, we 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery across some 230 countries. The smallest have fewer than 10,000 inhabitants. we 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery. So, latent demand in the long-run has a zero intercept. However, we 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, we will now describe the methodology used to create the latent demand estimates for women’s, misses’, and girls’ below the knee-length sheer finished hosiery. Since this methodology has been applied 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery.

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, we 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, we 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 “women’s, misses’, and girls’ below the knee-length sheer finished hosiery” 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery 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 “women’s, misses’, and girls’ below the knee-length sheer finished hosiery” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of women’s, misses’, and girls’ below the knee-length sheer finished hosiery, please see below. The NAICS code for women’s, misses’, and girls’ below the knee-length sheer finished hosiery is 3151111121. It is for this definition of women’s, misses’, and girls’ below the knee-length sheer finished hosiery that the aggregate latent demand estimates are derived.

Step 2. Filtering and Smoothing

Based on the aggregate view of women’s, misses’, and girls’ below the knee-length sheer finished hosiery 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery 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 women’s, misses’, and girls’ below the knee-length sheer finished hosiery). 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. we 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.
READ MORE
Note: Product cover images may vary from those shown
2 of 3
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 13
1.3.3 Step 3. Filling in Missing Values 13
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 14
1.3.5 Step 5. Fixed-Parameter Linear Estimation 15
1.3.6 Step 6. Aggregation and Benchmarking 15
1.3.7 Step 7. Latent Demand Density: Allocating Across Cities 15
2 SUMMARY OF FINDINGS 16
2.1 The Worldwide Market Potential 16
3 AFRICA 18
3.1 Executive Summary 18
3.2 Algeria 19
3.3 Angola 20
3.4 Benin 21
3.5 Botswana 21
3.6 Burkina Faso 22
3.7 Burundi 23
3.8 Cameroon 23
3.9 Cape Verde 24
3.10 Central African Republic 25
3.11 Chad 25
3.12 Comoros 26
3.13 Congo (formerly Zaire) 27
3.14 Cote dIvoire 28
3.15 Djibouti 28
3.16 Egypt 29
3.17 Equatorial Guinea 30
3.18 Ethiopia 30
3.19 Gabon 31
3.20 Ghana 32
3.21 Guinea 32
3.22 Guinea-Bissau 33
3.23 Kenya 34
3.24 Lesotho 35
3.25 Liberia 35
3.26 Libya 36
3.27 Madagascar 37
3.28 Malawi 37
3.29 Mali 38
3.30 Mauritania 39
3.31 Mauritius 39
3.32 Morocco 40
3.33 Mozambique 41
3.34 Namibia 41
3.35 Niger 42
3.36 Nigeria 43
3.37 Republic of Congo 44
3.38 Reunion 44
3.39 Rwanda 45
3.40 Sao Tome E Principe 46
3.41 Senegal 46
3.42 Sierra Leone 47
3.43 Somalia 48
3.44 South Africa 48
3.45 Sudan 49
3.46 Swaziland 50
3.47 Tanzania 50
3.48 The Gambia 51
3.49 Togo 52
3.50 Tunisia 52
3.51 Uganda 53
3.52 Western Sahara 54
3.53 Zambia 54
3.54 Zimbabwe 55
4 ASIA 57
4.1 Executive Summary 57
4.2 Bangladesh 58
4.3 Bhutan 59
4.4 Brunei 60
4.5 Burma 60
4.6 Cambodia 61
4.7 China 62
4.8 Hong Kong 63
4.9 India 63
4.10 Indonesia 64
4.11 Japan 65
4.12 Laos 66
4.13 Macau 67
4.14 Malaysia 68
4.15 Maldives 69
4.16 Mongolia 69
4.17 Nepal 70
4.18 North Korea 71
4.19 Papua New Guinea 72
4.20 Philippines 72
4.21 Seychelles 73
4.22 Singapore 74
4.23 South Korea 74
4.24 Sri Lanka 75
4.25 Taiwan 76
4.26 Thailand 77
4.27 Vietnam 78
5 EUROPE 79
5.1 Executive Summary 79
5.2 Albania 80
5.3 Andorra 81
5.4 Austria 82
5.5 Belarus 83
5.6 Belgium 84
5.7 Bosnia and Herzegovina 85
5.8 Bulgaria 85
5.9 Croatia 86
5.10 Cyprus 87
5.11 Czech Republic 87
5.12 Denmark 88
5.13 Estonia 89
5.14 Finland 90
5.15 France 91
5.16 Georgia 92
5.17 Germany 92
5.18 Greece 93
5.19 Hungary 94
5.20 Iceland 95
5.21 Ireland 95
5.22 Italy 96
5.23 Kazakhstan 97
5.24 Latvia 98
5.25 Liechtenstein 98
5.26 Lithuania 99
5.27 Luxembourg 100
5.28 Malta 100
5.29 Moldova 101
5.30 Monaco 102
5.31 Netherlands 102
5.32 Norway 103
5.33 Poland 104
5.34 Portugal 105
5.35 Romania 106
5.36 Russia 107
5.37 San Marino 108
5.38 Slovakia 108
5.39 Slovenia 109
5.40 Spain 110
5.41 Sweden 111
5.42 Switzerland 112
5.43 Ukraine 113
5.44 United Kingdom 114
6 LATIN AMERICA 115
6.1 Executive Summary 115
6.2 Argentina 116
6.3 Belize 117
6.4 Bolivia 118
6.5 Brazil 118
6.6 Chile 119
6.7 Colombia 120
6.8 Costa Rica 121
6.9 Ecuador 122
6.10 El Salvador 123
6.11 Falkland Islands 123
6.12 French Guiana 124
6.13 Guatemala 125
6.14 Guyana 125
6.15 Honduras 126
6.16 Mexico 127
6.17 Nicaragua 128
6.18 Panama 129
6.19 Paraguay 129
6.20 Peru 130
6.21 Suriname 131
6.22 Uruguay 132
6.23 Venezuela 133
7 NORTH AMERICA & THE CARIBBEAN 134
7.1 Executive Summary 134
7.2 Antigua and Barbuda 135
7.3 Aruba 136
7.4 Bahamas 137
7.5 Barbados 137
7.6 Bermuda 138
7.7 British Virgin Islands 139
7.8 Canada 139
7.9 Cayman Islands 140
7.10 Cuba 141
7.11 Dominica 142
7.12 Dominican Republic 142
7.13 Greenland 143
7.14 Grenada 144
7.15 Guadeloupe 145
7.16 Haiti 146
7.17 Jamaica 146
7.18 Martinique 147
7.19 Netherlands Antilles 148
7.20 Puerto Rico 148
7.21 St. Kitts and Nevis 149
7.22 St. Lucia 150
7.23 St. Vincent and the Grenadines 150
7.24 Trinidad and Tobago 151
7.25 United States 152
7.26 Virgin Islands, US 153
8 OCEANA 154
8.1 Executive Summary 154
8.2 American Samoa 155
8.3 Australia 156
8.4 Christmas Island 157
8.5 Cook Islands 157
8.6 Fiji 158
8.7 French Polynesia 159
8.8 Guam 159
8.9 Kiribati 160
8.10 Marshall Islands 161
8.11 Micronesia Federation 161
8.12 Nauru 162
8.13 New Caledonia 163
8.14 New Zealand 163
8.15 Niue 164
8.16 Norfolk Island 165
8.17 Northern Mariana Island 165
8.18 Palau 166
8.19 Solomon Islands 167
8.20 Tokelau 167
8.21 Tonga 168
8.22 Tuvalu 168
8.23 Vanuatu 169
8.24 Wallis and Futuna 170
8.25 Western Samoa 170
9 THE MIDDLE EAST 172
9.1 Executive Summary 172
9.2 Afghanistan 173
9.3 Armenia 174
9.4 Azerbaijan 175
9.5 Bahrain 176
9.6 Iran 177
9.7 Iraq 178
9.8 Israel 179
9.9 Jordan 179
9.10 Kuwait 180
9.11 Kyrgyzstan 181
9.12 Lebanon 181
9.13 Oman 182
9.14 Pakistan 183
9.15 Palestine 184
9.16 Qatar 184
9.17 Saudi Arabia 185
9.18 Syrian Arab Republic 186
9.19 Tajikistan 187
9.20 Turkey 187
9.21 Turkmenistan 188
9.22 United Arab Emirates 189
9.23 Uzbekistan 189
9.24 Yemen 190
10 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 192
10.1 Disclaimers & Safe Harbor 192
10.2 User Agreement Provisions 193

Note: Product cover images may vary from those shown
3 of 3
Note: Product cover images may vary from those shown
Adroll
adroll