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The 2007-2012 World Outlook for Womens, Misses, Juniors, and Girls Knit Shirts and Blouses

  • ID: 490973
  • Report
  • May 2006
  • Region: Global
  • 189 pages
  • ICON Group International
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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 womens, misses, juniors, and girls knit shirts and blouses 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 womens, misses, juniors, and girls knit shirts and blouses 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.


In order to estimate the latent demand for womens, misses, juniors, and girls knit shirts and blouses 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 womens, misses, juniors, and girls knit shirts and blouses 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 womens, misses, juniors, and girls knit shirts and blouses. 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 womens, misses, juniors, and girls knit shirts and blouses. 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 womens, misses, juniors, and girls knit shirts and blouses.

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 “womens, misses, juniors, and girls knit shirts and blouses” 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 womens, misses, juniors, and girls knit shirts and blouses 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 “womens, misses, juniors, and girls knit shirts and blouses” as defined by the North American Industrial Classification system or NAICS (pronounced “nakes”). For a complete definition of womens, misses, juniors, and girls knit shirts and blouses, please see below. The NAICS code for womens, misses, juniors, and girls knit shirts and blouses is 3152321. It is for this definition of womens, misses, juniors, and girls knit shirts and blouses that the aggregate latent demand estimates are derived. “Womens, misses, juniors, and girls knit shirts and blouses” is specifically defined as follows:

womens, misses, juniors, and girls knit shirts and blouses

Women’s, misses’, and juniors’ knit shirts and blouses (polo, tennis, cowl, tank, T~shirts, sweat, etc.), made from purchased fabrics

Women’s, misses’, and juniors’ knit shirts and blouses (polo, tennis, cowal, tank, T~shirts, sweat, etc.), made from purchased fabrics

Women’s, misses’, and juniors’ knit T~shirts and tank tops made for outerwear

Women’s, misses’, and juniors’ sweatshirts

Women’s, misses’, and juniors’ other knit blouses and shirts

Girls’, knit shirts and blouses (polo, tennis, tank, sweat, T~shirts for outerwear, etc.), made from purchased fabrics

Girls’ knit shirts and blouses (polo, tennis, tank, sweat, T~shirts for outerwear, etc.), made from purchased fabrics

Girls’ knit T~shirts and tank tops for outerwear

Girls’ sweatshirts

Girls’ other knit blouses and shirts

women’s and girls’ knit shirts and blouses made from purchased fabrics

women’s and girls’ knit shirts and blouses made from purchased fabrics

Step 2. Filtering and Smoothing

Based on the aggregate view of womens, misses, juniors, and girls knit shirts and blouses 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 womens, misses, juniors, and girls knit shirts and blouses 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 womens, misses, juniors, and girls knit shirts and blouses 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 womens, misses, juniors, and girls knit shirts and blouses). 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.
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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 14
1.3.3 Step 3. Filling in Missing Values 14
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
2.1 The Worldwide Market Potential 17
3.1 Executive Summary 19
3.2 Algeria 20
3.3 Angola 21
3.4 Benin 22
3.5 Botswana 22
3.6 Burkina Faso 23
3.7 Burundi 24
3.8 Cameroon 24
3.9 Cape Verde 25
3.10 Central African Republic 26
3.11 Chad 26
3.12 Comoros 27
3.13 Congo (formerly Zaire) 28
3.14 Cote dIvoire 29
3.15 Djibouti 29
3.16 Egypt 30
3.17 Equatorial Guinea 31
3.18 Ethiopia 31
3.19 Gabon 32
3.20 Ghana 33
3.21 Guinea 33
3.22 Guinea-Bissau 34
3.23 Kenya 35
3.24 Lesotho 36
3.25 Liberia 36
3.26 Libya 37
3.27 Madagascar 38
3.28 Malawi 38
3.29 Mali 39
3.30 Mauritania 40
3.31 Mauritius 40
3.32 Morocco 41
3.33 Mozambique 42
3.34 Namibia 42
3.35 Niger 43
3.36 Nigeria 44
3.37 Republic of Congo 45
3.38 Reunion 45
3.39 Rwanda 46
3.40 Sao Tome E Principe 47
3.41 Senegal 47
3.42 Sierra Leone 48
3.43 Somalia 49
3.44 South Africa 49
3.45 Sudan 50
3.46 Swaziland 51
3.47 Tanzania 51
3.48 The Gambia 52
3.49 Togo 53
3.50 Tunisia 53
3.51 Uganda 54
3.52 Western Sahara 55
3.53 Zambia 55
3.54 Zimbabwe 56
4.1 Executive Summary 58
4.2 Afghanistan 59
4.3 Armenia 60
4.4 Azerbaijan 61
4.5 Bahrain 62
4.6 Bangladesh 63
4.7 Bhutan 64
4.8 Brunei 64
4.9 Burma 65
4.10 Cambodia 66
4.11 China 66
4.12 Hong Kong 67
4.13 India 68
4.14 Indonesia 69
4.15 Iran 70
4.16 Iraq 71
4.17 Israel 72
4.18 Japan 72
4.19 Jordan 73
4.20 Kuwait 74
4.21 Kyrgyzstan 75
4.22 Laos 75
4.23 Lebanon 76
4.24 Macau 77
4.25 Malaysia 77
4.26 Maldives 78
4.27 Mongolia 79
4.28 Nepal 79
4.29 North Korea 80
4.30 Oman 81
4.31 Pakistan 81
4.32 Palestine 82
4.33 Papua New Guinea 83
4.34 Philippines 83
4.35 Qatar 84
4.36 Saudi Arabia 85
4.37 Seychelles 86
4.38 Singapore 86
4.39 South Korea 87
4.40 Sri Lanka 88
4.41 Syrian Arab Republic 88
4.42 Taiwan 89
4.43 Tajikistan 90
4.44 Thailand 91
4.45 Turkey 92
4.46 Turkmenistan 93
4.47 United Arab Emirates 93
4.48 Uzbekistan 94
4.49 Vietnam 95
4.50 Yemen 95
5.1 Executive Summary 97
5.2 Albania 98
5.3 Andorra 99
5.4 Austria 100
5.5 Belarus 101
5.6 Belgium 102
5.7 Bosnia and Herzegovina 103
5.8 Bulgaria 103
5.9 Croatia 104
5.10 Cyprus 105
5.11 Czech Republic 105
5.12 Denmark 106
5.13 Estonia 107
5.14 Finland 108
5.15 France 109
5.16 Georgia 110
5.17 Germany 110
5.18 Greece 111
5.19 Hungary 112
5.20 Iceland 113
5.21 Ireland 113
5.22 Italy 114
5.23 Kazakhstan 115
5.24 Latvia 116
5.25 Liechtenstein 116
5.26 Lithuania 117
5.27 Luxembourg 118
5.28 Malta 118
5.29 Moldova 119
5.30 Monaco 120
5.31 Netherlands 120
5.32 Norway 121
5.33 Poland 122
5.34 Portugal 123
5.35 Romania 123
5.36 Russia 124
5.37 San Marino 125
5.38 Slovakia 126
5.39 Slovenia 126
5.40 Spain 127
5.41 Sweden 128
5.42 Switzerland 129
5.43 Ukraine 130
5.44 United Kingdom 131
6.1 Executive Summary 133
6.2 Argentina 134
6.3 Belize 135
6.4 Bolivia 136
6.5 Brazil 136
6.6 Chile 137
6.7 Colombia 138
6.8 Costa Rica 139
6.9 Ecuador 140
6.10 El Salvador 141
6.11 Falkland Islands 141
6.12 French Guiana 142
6.13 Guatemala 143
6.14 Guyana 143
6.15 Honduras 144
6.16 Mexico 145
6.17 Nicaragua 146
6.18 Panama 146
6.19 Paraguay 147
6.20 Peru 148
6.21 Suriname 149
6.22 Uruguay 149
6.23 Venezuela 150
7.1 Executive Summary 152
7.2 Antigua and Barbuda 153
7.3 Aruba 154
7.4 Bahamas 155
7.5 Barbados 155
7.6 Bermuda 156
7.7 British Virgin Islands 157
7.8 Canada 157
7.9 Cayman Islands 158
7.10 Cuba 159
7.11 Dominica 160
7.12 Dominican Republic 160
7.13 Greenland 161
7.14 Grenada 162
7.15 Guadeloupe 163
7.16 Haiti 163
7.17 Jamaica 164
7.18 Martinique 165
7.19 Netherlands Antilles 165
7.20 Puerto Rico 166
7.21 St. Kitts and Nevis 167
7.22 St. Lucia 167
7.23 St. Vincent and the Grenadines 168
7.24 Trinidad and Tobago 169
7.25 United States 169
7.26 Virgin Islands, US 170
8 OCEANA 172
8.1 Executive Summary 172
8.2 American Samoa 173
8.3 Australia 174
8.4 Christmas Island 175
8.5 Cook Islands 175
8.6 Fiji 176
8.7 French Polynesia 176
8.8 Guam 177
8.9 Kiribati 178
8.10 Marshall Islands 178
8.11 Micronesia Federation 179
8.12 Nauru 179
8.13 New Caledonia 180
8.14 New Zealand 181
8.15 Niue 182
8.16 Norfolk Island 182
8.17 Northern Mariana Island 183
8.18 Palau 183
8.19 Solomon Islands 184
8.20 Tokelau 184
8.21 Tonga 185
8.22 Tuvalu 185
8.23 Vanuatu 186
8.24 Wallis and Futuna 186
8.25 Western Samoa 187
9.1 Disclaimers & Safe Harbor 188
9.2 User Agreement Provisions 189

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