The 2007-2012 Outlook for Rubber Medical, Surgical, and Household Gloves in the United States
ICON Group International, September 2006, Pages: 584
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 rubber medical, surgical, and household gloves 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 rubber medical, surgical, and household gloves 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 rubber medical, surgical, and household gloves across the states and cites of the United States, 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 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 rubber medical, surgical, and household gloves across the states and cities of the United States. The smallest cities have few inhabitants. we 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 rubber medical, surgical, and household gloves. So, latent demand in the long-run has a zero intercept. However, we 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, we will now describe the methodology used to create the latent demand estimates for rubber medical, surgical, and household gloves in the United States. 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 and geographic locations, not just rubber medical, surgical, and household gloves 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, we 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, we 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 “rubber medical, surgical, and household gloves” 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 rubber medical, surgical, and household gloves 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 “rubber medical, surgical, and household gloves” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of rubber medical, surgical, and household gloves, please see below. The NAICS code for rubber medical, surgical, and household gloves is 3391132669. It is for this definition of rubber medical, surgical, and household gloves 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 rubber medical, surgical, and household gloves 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 rubber medical, surgical, and household gloves 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 rubber medical, surgical, and household gloves 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 rubber medical, surgical, and household gloves). 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. we 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
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 12
1.3.5 Step 5. Fixed-Parameter Linear Estimation 13
1.3.6 Step 6. Aggregation and Benchmarking 13
2 SUMMARY OF FINDINGS 14
2.1 Latent Demand in The US 14
3 FAR WEST 16
3.1 Executive Summary 16
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 39
3.8 Latent Demand by Year - Hawaii 57
3.9 Cities Sorted by Rank - Hawaii 58
3.10 Cities Sorted by Zipcode - Hawaii 59
3.11 Latent Demand by Year - Nevada 62
3.12 Cities Sorted by Rank - Nevada 63
3.13 Cities Sorted by Zipcode - Nevada 64
3.14 Latent Demand by Year - Oregon 66
3.15 Cities Sorted by Rank - Oregon 67
3.16 Cities Sorted by Zipcode - Oregon 70
3.17 Latent Demand by Year - Washington 73
3.18 Cities Sorted by Rank - Washington 74
3.19 Cities Sorted by Zipcode - Washington 80
4 GREAT LAKES 87
4.1 Executive Summary 87
4.2 Latent Demand by Year - Illinois 89
4.3 Cities Sorted by Rank - Illinois 90
4.4 Cities Sorted by Zipcode - Illinois 101
4.5 Latent Demand by Year - Indiana 112
4.6 Cities Sorted by Rank - Indiana 113
4.7 Cities Sorted by Zipcode - Indiana 117
4.8 Latent Demand by Year - Michigan 122
4.9 Cities Sorted by Rank - Michigan 123
4.10 Cities Sorted by Zipcode - Michigan 129
4.11 Latent Demand by Year - Ohio 137
4.12 Cities Sorted by Rank - Ohio 138
4.13 Cities Sorted by Zipcode - Ohio 148
4.14 Latent Demand by Year - Wisconsin 158
4.15 Cities Sorted by Rank - Wisconsin 159
4.16 Cities Sorted by Zipcode - Wisconsin 167
5 MID-ATLANTIC 176
5.1 Executive Summary 176
5.2 Latent Demand by Year - Delaware 177
5.3 Cities Sorted by Rank - Delaware 178
5.4 Cities Sorted by Zipcode - Delaware 179
5.5 Latent Demand by Year - District of Columbia 180
5.6 Cities Sorted by Rank - District of Columbia 181
5.7 Cities Sorted by Zipcode - District of Columbia 181
5.8 Latent Demand by Year - Maryland 182
5.9 Cities Sorted by Rank - Maryland 183
5.10 Cities Sorted by Zipcode - Maryland 188
5.11 Latent Demand by Year - New Jersey 194
5.12 Cities Sorted by Rank - New Jersey 195
5.13 Cities Sorted by Zipcode - New Jersey 203
5.14 Latent Demand by Year - New York 213
5.15 Cities Sorted by Rank - New York 214
5.16 Cities Sorted by Zipcode - New York 235
5.17 Latent Demand by Year - Pennsylvania 257
5.18 Cities Sorted by Rank - Pennsylvania 258
5.19 Cities Sorted by Zipcode - Pennsylvania 270
6 NEW ENGLAND 283
6.1 Executive Summary 283
6.2 Latent Demand by Year - Connecticut 284
6.3 Cities Sorted by Rank - Connecticut 285
6.4 Cities Sorted by Zipcode - Connecticut 289
6.5 Latent Demand by Year - Maine 295
6.6 Cities Sorted by Rank - Maine 296
6.7 Cities Sorted by Zipcode - Maine 299
6.8 Latent Demand by Year - Massachusetts 304
6.9 Cities Sorted by Rank - Massachusetts 305
6.10 Cities Sorted by Zipcode - Massachusetts 313
6.11 Latent Demand by Year - New Hampshire 321
6.12 Cities Sorted by Rank - New Hampshire 322
6.13 Cities Sorted by Zipcode - New Hampshire 326
6.14 Latent Demand by Year - Rhode Island 330
6.15 Cities Sorted by Rank - Rhode Island 331
6.16 Cities Sorted by Zipcode - Rhode Island 332
6.17 Latent Demand by Year - Vermont 334
6.18 Cities Sorted by Rank - Vermont 335
6.19 Cities Sorted by Zipcode - Vermont 337
7 PLAINS 340
7.1 Executive Summary 340
7.2 Latent Demand by Year - Iowa 342
7.3 Cities Sorted by Rank - Iowa 343
7.4 Cities Sorted by Zipcode - Iowa 346
7.5 Latent Demand by Year - Kansas 350
7.6 Cities Sorted by Rank - Kansas 351
7.7 Cities Sorted by Zipcode - Kansas 353
7.8 Latent Demand by Year - Minnesota 356
7.9 Cities Sorted by Rank - Minnesota 357
7.10 Cities Sorted by Zipcode - Minnesota 362
7.11 Latent Demand by Year - Missouri 368
7.12 Cities Sorted by Rank - Missouri 369
7.13 Cities Sorted by Zipcode - Missouri 374
7.14 Latent Demand by Year - Nebraska 379
7.15 Cities Sorted by Rank - Nebraska 380
7.16 Cities Sorted by Zipcode - Nebraska 381
7.17 Latent Demand by Year - North Dakota 383
7.18 Cities Sorted by Rank - North Dakota 384
7.19 Cities Sorted by Zipcode - North Dakota 385
7.20 Latent Demand by Year - South Dakota 386
7.21 Cities Sorted by Rank - South Dakota 387
7.22 Cities Sorted by Zipcode - South Dakota 388
8 ROCKIES 389
8.1 Executive Summary 389
8.2 Latent Demand by Year - Colorado 390
8.3 Cities Sorted by Rank - Colorado 391
8.4 Cities Sorted by Zipcode - Colorado 394
8.5 Latent Demand by Year - Idaho 398
8.6 Cities Sorted by Rank - Idaho 399
8.7 Cities Sorted by Zipcode - Idaho 400
8.8 Latent Demand by Year - Montana 402
8.9 Cities Sorted by Rank - Montana 403
8.10 Cities Sorted by Zipcode - Montana 404
8.11 Latent Demand by Year - Utah 406
8.12 Cities Sorted by Rank - Utah 407
8.13 Cities Sorted by Zipcode - Utah 409
8.14 Latent Demand by Year - Wyoming 413
8.15 Cities Sorted by Rank - Wyoming 414
8.16 Cities Sorted by Zipcode - Wyoming 415
9 SOUTHEAST 416
9.1 Executive Summary 416
9.2 Latent Demand by Year - Alabama 417
9.3 Cities Sorted by Rank - Alabama 418
9.4 Cities Sorted by Zipcode - Alabama 422
9.5 Latent Demand by Year - Arkansas 426
9.6 Cities Sorted by Rank - Arkansas 427
9.7 Cities Sorted by Zipcode - Arkansas 429
9.8 Latent Demand by Year - Florida 433
9.9 Cities Sorted by Rank - Florida 434
9.10 Cities Sorted by Zipcode - Florida 447
9.11 Latent Demand by Year - Georgia 461
9.12 Cities Sorted by Rank - Georgia 462
9.13 Cities Sorted by Zipcode - Georgia 467
9.14 Latent Demand by Year - Kentucky 473
9.15 Cities Sorted by Rank - Kentucky 474
9.16 Cities Sorted by Zipcode - Kentucky 477
9.17 Latent Demand by Year - Louisiana 481
9.18 Cities Sorted by Rank - Louisiana 482
9.19 Cities Sorted by Zipcode - Louisiana 485
9.20 Latent Demand by Year - Mississippi 489
9.21 Cities Sorted by Rank - Mississippi 490
9.22 Cities Sorted by Zipcode - Mississippi 492
9.23 Latent Demand by Year - North Carolina 495
9.24 Cities Sorted by Rank - North Carolina 496
9.25 Cities Sorted by Zipcode - North Carolina 502
9.26 Latent Demand by Year - South Carolina 508
9.27 Cities Sorted by Rank - South Carolina 509
9.28 Cities Sorted by Zipcode - South Carolina 512
9.29 Latent Demand by Year - Tennessee 516
9.30 Cities Sorted by Rank - Tennessee 517
9.31 Cities Sorted by Zipcode - Tennessee 521
9.32 Latent Demand by Year - Virginia 525
9.33 Cities Sorted by Rank - Virginia 526
9.34 Cities Sorted by Zipcode - Virginia 530
9.35 Latent Demand by Year - West Virginia 534
9.36 Cities Sorted by Rank - West Virginia 535
9.37 Cities Sorted by Zipcode - West Virginia 536
10 SOUTHWEST 538
10.1 Executive Summary 538
10.2 Latent Demand by Year - Arizona 539
10.3 Cities Sorted by Rank - Arizona 540
10.4 Cities Sorted by Zipcode - Arizona 543
10.5 Latent Demand by Year - New Mexico 546
10.6 Cities Sorted by Rank - New Mexico 547
10.7 Cities Sorted by Zipcode - New Mexico 548
10.8 Latent Demand by Year - Oklahoma 550
10.9 Cities Sorted by Rank - Oklahoma 551
10.10 Cities Sorted by Zipcode - Oklahoma 553
10.11 Latent Demand by Year - Texas 556
10.12 Cities Sorted by Rank - Texas 557
10.13 Cities Sorted by Zipcode - Texas 570
11 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 583
11.1 Disclaimers & Safe Harbor 583
11.2 User Agreement Provisions 584
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