The 2009-2014 Outlook for Non-Extension, Extension, and Scaffolding Wood Rung Ladders in the United States

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 the United States 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 non-extension, extension, and scaffolding wood rung ladders in the United States is not actual or historic sales. Nor is latent demand future sales. In fact, latent demand can be 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 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). If inflation rates vary in a substantial way compared to recent experience, actually sales can also exceed latent demand (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. In 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 latent demand for non-extension, extension, and scaffolding wood rung ladders at the aggregate level. Product and service offerings, 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 non-extension, extension, and scaffolding wood rung ladders across the states and cites of the United States, 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 state, city, 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 geographies, 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). This type of consumption function is shown as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant.

Is it declining or 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 with no income eventually have no consumption (wealth is depleted). While the debate surrounding beliefs about how income and consumption are related is interesting, in this study a very particular school of thought is adopted. In particular, we are considering the latent demand for non-extension, extension, and scaffolding wood rung ladders across the states and cities of the United States. The smallest cities have few inhabitants. I assume that all of these cities fall along a "long-run" aggregate consumption function. This long-run function applies despite some of these states having wealth; current income dominates the latent demand for non-extension, extension, and scaffolding wood rung ladders. So, latent demand in the long-run has a zero intercept. However, I allow 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 non-extension, extension, and scaffolding wood rung ladders in the United States. 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 and geographic locations, not just non-extension, extension, and scaffolding wood rung ladders in the United States.

Step 1. Product Definition and Data Collection

Any study of latent demand requires that some standard be established to define “efficiently served”. Having implemented various alternatives and matched these with market outcomes, I have found that the optimal approach is to assume that certain key indicators are more likely to reflect efficiency than others. These indicators 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 highest aggregate income and highest income-per-capita markets reflect the best standards for “efficiency”. High aggregate income alone is not sufficient (i.e. some cities have high aggregate income, but low income per capita and 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).

Latent demand is therefore estimated using data collected for relatively efficient markets from independent data sources (e.g. Official Chinese Agencies, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, and the World Bank). Depending on original data sources used, the definition of “non-extension, extension, and scaffolding wood rung ladders” 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 non-extension, extension, and scaffolding wood rung ladders 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 states and cities in the United States (without needing to know the specific parts that went into the whole in the first place).

Given this caveat, this study covers “non-extension, extension, and scaffolding wood rung ladders” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of non-extension, extension, and scaffolding wood rung ladders, please refer to the Web site at http://www.icongrouponline.com/codes/NAICS.html. The NAICS code for non-extension, extension, and scaffolding wood rung ladders is 3219990164. It is for this definition of non-extension, extension, and scaffolding wood rung ladders that the aggregate latent demand estimates are derived for the states and cities of the United States.

Step 2. Filtering and Smoothing

Based on the aggregate view of non-extension, extension, and scaffolding wood rung ladders as defined above, data were then collected for as many geographic locations as possible for that same definition, at the same level of the value chain. This generates a convenience sample of indicators 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 geographic region, but these reflect short-run aberrations due to exogenous shocks (such as would be the case of beef sales in a state or city 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 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, state and city-level income. Based on the overriding philosophy of a long-run consumption function (defined earlier), states and cities which have missing data for any given year, are estimated based on historical dynamics of aggregate income for that geographic entity.

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 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 states or cities). This assumption applies along the aggregate consumption function, but also over time (i.e., not all states or cities in the United States are perceived to have the same income growth prospects over time). Another way of looking at this is to say that latent demand for non-extension, extension, and scaffolding wood rung ladders is more likely to be similar across states or cities that have similar characteristics in terms of economic development.

This approach is useful across geographic regions for which some notion of non-linearity exists in the aggregate cross-region consumption function. For some categories, however, the reader must realize that the numbers will reflect a state’s or city’s contribution to latent demand in the United States and may never be realized in the form of local sales.

Step 5. Fixed-Parameter Linear Estimation

Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the United States consists of more than 15,000 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 state has no current income, the latent demand for non-extension, extension, and scaffolding wood rung ladders 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 non-extension, extension, and scaffolding wood rung ladders). 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, a low-income city is assumed to have a latent demand proportional to its income, based on the cities 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 major cities in the United States. These are then aggregated to get state totals. This report considers a city as a part of the regional and national market. The purpose is to understand the density of demand within a state and the extent to which a city might be used as a point of distribution within its state. 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. I allocate latent demand across areas of dominant influence based on the relative economic importance of cities within its state. Not all cities (e.g. the smaller towns) are estimated within each state as demand may be allocated to adjacent areas of influence. Since some cities have higher economic wealth than others within the same state, 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 is used. Figures are rounded, so minor inconsistencies may exist across tables. SHOW LESS READ MORE >

1 INTRODUCTION 9
1.1 Overview 9
1.2 What is Latent Demand and the P.I.E.? 9
1.3 The Methodology 10
1.3.1 Step 1. Product Definition and Data Collection 11
1.3.2 Step 2. Filtering and Smoothing 12
1.3.3 Step 3. Filling in Missing Values 12
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 13
1.3.5 Step 5. Fixed-Parameter Linear Estimation 13
1.3.6 Step 6. Aggregation and Benchmarking 13
2 SUMMARY OF FINDINGS 15
2.1 Latent Demand in The US 16
3 FAR WEST 17
3.1 Executive Summary 17
3.2 Latent Demand by Year - Alaska 18
3.3 Cities Sorted by Rank - Alaska 19
3.4 Cities Sorted by Zipcode - Alaska 20
3.5 Latent Demand by Year - California 21
3.6 Cities Sorted by Rank - California 22
3.7 Cities Sorted by Zipcode - California 35
3.8 Latent Demand by Year - Hawaii 48
3.9 Cities Sorted by Rank - Hawaii 49
3.10 Cities Sorted by Zipcode - Hawaii 50
3.11 Latent Demand by Year - Nevada 51
3.12 Cities Sorted by Rank - Nevada 52
3.13 Cities Sorted by Zipcode - Nevada 53
3.14 Latent Demand by Year - Oregon 54
3.15 Cities Sorted by Rank - Oregon 55
3.16 Cities Sorted by Zipcode - Oregon 57
3.17 Latent Demand by Year - Washington 59
3.18 Cities Sorted by Rank - Washington 60
3.19 Cities Sorted by Zipcode - Washington 64
4 GREAT LAKES 68
4.1 Executive Summary 68
4.2 Latent Demand by Year - Illinois 69
4.3 Cities Sorted by Rank - Illinois 70
4.4 Cities Sorted by Zipcode - Illinois 77
4.5 Latent Demand by Year - Indiana 85
4.6 Cities Sorted by Rank - Indiana 86
4.7 Cities Sorted by Zipcode - Indiana 88
4.8 Latent Demand by Year - Michigan 91
4.9 Cities Sorted by Rank - Michigan 92
4.10 Cities Sorted by Zipcode - Michigan 95
4.11 Latent Demand by Year - Ohio 99
4.12 Cities Sorted by Rank - Ohio 100
4.13 Cities Sorted by Zipcode - Ohio 105
4.14 Latent Demand by Year - Wisconsin 111
4.15 Cities Sorted by Rank - Wisconsin 112
4.16 Cities Sorted by Zipcode - Wisconsin 116
5 MID-ATLANTIC 120
5.1 Executive Summary 120
5.2 Latent Demand by Year - Delaware 121
5.3 Cities Sorted by Rank - Delaware 122
5.4 Cities Sorted by Zipcode - Delaware 123
5.5 Latent Demand by Year - District of Columbia 123
5.6 Cities Sorted by Rank - District of Columbia 125
5.7 Cities Sorted by Zipcode - District of Columbia 125
5.8 Latent Demand by Year - Maryland 126
5.9 Cities Sorted by Rank - Maryland 127
5.10 Cities Sorted by Zipcode - Maryland 131
5.11 Latent Demand by Year - New Jersey 135
5.12 Cities Sorted by Rank - New Jersey 136
5.13 Cities Sorted by Zipcode - New Jersey 142
5.14 Latent Demand by Year - New York 149
5.15 Cities Sorted by Rank - New York 150
5.16 Cities Sorted by Zipcode - New York 161
5.17 Latent Demand by Year - Pennsylvania 172
5.18 Cities Sorted by Rank - Pennsylvania 173
5.19 Cities Sorted by Zipcode - Pennsylvania 177
6 NEW ENGLAND 181
6.1 Executive Summary 181
6.2 Latent Demand by Year - Connecticut 182
6.3 Cities Sorted by Rank - Connecticut 183
6.4 Cities Sorted by Zipcode - Connecticut 187
6.5 Latent Demand by Year - Maine 190
6.6 Cities Sorted by Rank - Maine 191
6.7 Cities Sorted by Zipcode - Maine 192
6.8 Latent Demand by Year - Massachusetts 194
6.9 Cities Sorted by Rank - Massachusetts 195
6.10 Cities Sorted by Zipcode - Massachusetts 201
6.11 Latent Demand by Year - New Hampshire 208
6.12 Cities Sorted by Rank - New Hampshire 209
6.13 Cities Sorted by Zipcode - New Hampshire 211
6.14 Latent Demand by Year - Rhode Island 213
6.15 Cities Sorted by Rank - Rhode Island 214
6.16 Cities Sorted by Zipcode - Rhode Island 215
6.17 Latent Demand by Year - Vermont 217
6.18 Cities Sorted by Rank - Vermont 218
6.19 Cities Sorted by Zipcode - Vermont 219
7 PLAINS 220
7.1 Executive Summary 220
7.2 Latent Demand by Year - Iowa 221
7.3 Cities Sorted by Rank - Iowa 222
7.4 Cities Sorted by Zipcode - Iowa 223
7.5 Latent Demand by Year - Kansas 225
7.6 Cities Sorted by Rank - Kansas 226
7.7 Cities Sorted by Zipcode - Kansas 227
7.8 Latent Demand by Year - Minnesota 229
7.9 Cities Sorted by Rank - Minnesota 230
7.10 Cities Sorted by Zipcode - Minnesota 233
7.11 Latent Demand by Year - Missouri 237
7.12 Cities Sorted by Rank - Missouri 238
7.13 Cities Sorted by Zipcode - Missouri 241
7.14 Latent Demand by Year - Nebraska 244
7.15 Cities Sorted by Rank - Nebraska 245
7.16 Cities Sorted by Zipcode - Nebraska 246
7.17 Latent Demand by Year - North Dakota 247
7.18 Cities Sorted by Rank - North Dakota 248
7.19 Cities Sorted by Zipcode - North Dakota 249
7.20 Latent Demand by Year - South Dakota 250
7.21 Cities Sorted by Rank - South Dakota 251
7.22 Cities Sorted by Zipcode - South Dakota 252
8 ROCKIES 253
8.1 Executive Summary 253
8.2 Latent Demand by Year - Colorado 254
8.3 Cities Sorted by Rank - Colorado 255
8.4 Cities Sorted by Zipcode - Colorado 257
8.5 Latent Demand by Year - Idaho 260
8.6 Cities Sorted by Rank - Idaho 261
8.7 Cities Sorted by Zipcode - Idaho 262
8.8 Latent Demand by Year - Montana 263
8.9 Cities Sorted by Rank - Montana 264
8.10 Cities Sorted by Zipcode - Montana 264
8.11 Latent Demand by Year - Utah 265
8.12 Cities Sorted by Rank - Utah 266
8.13 Cities Sorted by Zipcode - Utah 268
8.14 Latent Demand by Year - Wyoming 271
8.15 Cities Sorted by Rank - Wyoming 272
8.16 Cities Sorted by Zipcode - Wyoming 273
9 SOUTHEAST 274
9.1 Executive Summary 274
9.2 Latent Demand by Year - Alabama 275
9.3 Cities Sorted by Rank - Alabama 276
9.4 Cities Sorted by Zipcode - Alabama 278
9.5 Latent Demand by Year - Arkansas 280
9.6 Cities Sorted by Rank - Arkansas 281
9.7 Cities Sorted by Zipcode - Arkansas 282
9.8 Latent Demand by Year - Florida 284
9.9 Cities Sorted by Rank - Florida 285
9.10 Cities Sorted by Zipcode - Florida 293
9.11 Latent Demand by Year - Georgia 301
9.12 Cities Sorted by Rank - Georgia 302
9.13 Cities Sorted by Zipcode - Georgia 305
9.14 Latent Demand by Year - Kentucky 308
9.15 Cities Sorted by Rank - Kentucky 309
9.16 Cities Sorted by Zipcode - Kentucky 311
9.17 Latent Demand by Year - Louisiana 313
9.18 Cities Sorted by Rank - Louisiana 314
9.19 Cities Sorted by Zipcode - Louisiana 316
9.20 Latent Demand by Year - Mississippi 317
9.21 Cities Sorted by Rank - Mississippi 318
9.22 Cities Sorted by Zipcode - Mississippi 319
9.23 Latent Demand by Year - North Carolina 320
9.24 Cities Sorted by Rank - North Carolina 321
9.25 Cities Sorted by Zipcode - North Carolina 323
9.26 Latent Demand by Year - South Carolina 326
9.27 Cities Sorted by Rank - South Carolina 327
9.28 Cities Sorted by Zipcode - South Carolina 328
9.29 Latent Demand by Year - Tennessee 330
9.30 Cities Sorted by Rank - Tennessee 331
9.31 Cities Sorted by Zipcode - Tennessee 333
9.32 Latent Demand by Year - Virginia 335
9.33 Cities Sorted by Rank - Virginia 336
9.34 Cities Sorted by Zipcode - Virginia 339
9.35 Latent Demand by Year - West Virginia 341
9.36 Cities Sorted by Rank - West Virginia 343
9.37 Cities Sorted by Zipcode - West Virginia 344
10 SOUTHWEST 345
10.1 Executive Summary 345
10.2 Latent Demand by Year - Arizona 346
10.3 Cities Sorted by Rank - Arizona 347
10.4 Cities Sorted by Zipcode - Arizona 349
10.5 Latent Demand by Year - New Mexico 351
10.6 Cities Sorted by Rank - New Mexico 352
10.7 Cities Sorted by Zipcode - New Mexico 353
10.8 Latent Demand by Year - Oklahoma 354
10.9 Cities Sorted by Rank - Oklahoma 355
10.10 Cities Sorted by Zipcode - Oklahoma 356
10.11 Latent Demand by Year - Texas 358
10.12 Cities Sorted by Rank - Texas 359
10.13 Cities Sorted by Zipcode - Texas 366
11 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 373
11.1 Disclaimers & Safe Harbor 373
11.2 ICON Group International, Inc. User Agreement Provisions 374