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The 2009-2014 Outlook for Canned Beans and Pasta in the United States

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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 canned beans and pasta 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 canned beans and pasta 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 canned beans and pasta 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 canned beans and pasta 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 canned beans and pasta. 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 canned beans and pasta 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 canned beans and pasta 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 “canned beans and pasta” 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 canned beans and pasta 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, in this report we define the retail sales of "canned beans and pasta" as including all commonly understood products falling within this broad category, such as all types of canned readymade beans and pasta meals that are sold and stored at room temperature, irrespective of product packaging, formulation, size, or form (e.g. the retail sales of products or brands such as Chef Boyardee Beef Ravioli and Franco-American Spaghettios with Meatballs). All figures are in a common currency (U.S. dollars, millions) and are not adjusted for inflation (i.e., they are current values). Exchange rates used to convert to U.S. dollars are averages for the year in question. Future exchange rates are assumed to be constant in the future at the current level (the average of the year of this publication’s release in 2008). Step 2. Filtering and Smoothing Based on the aggregate view of canned beans and pasta 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 canned beans and pasta 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 canned beans and pasta 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 canned beans and pasta). 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.
 
Contents:
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 15 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 56 3.9 Cities Sorted by Rank - Hawaii 57 3.10 Cities Sorted by Zipcode - Hawaii 58 3.11 Latent Demand by Year - Nevada 60 3.12 Cities Sorted by Rank - Nevada 61 3.13 Cities Sorted by Zipcode - Nevada 62 3.14 Latent Demand by Year - Oregon 63 3.15 Cities Sorted by Rank - Oregon 64 3.16 Cities Sorted by Zipcode - Oregon 67 3.17 Latent Demand by Year - Washington 70 3.18 Cities Sorted by Rank - Washington 71 3.19 Cities Sorted by Zipcode - Washington 76 4 GREAT LAKES 83 4.1 Executive Summary 83 4.2 Latent Demand by Year - Illinois 85 4.3 Cities Sorted by Rank - Illinois 86 4.4 Cities Sorted by Zipcode - Illinois 96 4.5 Latent Demand by Year - Indiana 106 4.6 Cities Sorted by Rank - Indiana 107 4.7 Cities Sorted by Zipcode - Indiana 111 4.8 Latent Demand by Year - Michigan 115 4.9 Cities Sorted by Rank - Michigan 116 4.10 Cities Sorted by Zipcode - Michigan 122 4.11 Latent Demand by Year - Ohio 128 4.12 Cities Sorted by Rank - Ohio 129 4.13 Cities Sorted by Zipcode - Ohio 138 4.14 Latent Demand by Year - Wisconsin 147 4.15 Cities Sorted by Rank - Wisconsin 148 4.16 Cities Sorted by Zipcode - Wisconsin 154 5 MID-ATLANTIC 161 5.1 Executive Summary 161 5.2 Latent Demand by Year - Delaware 163 5.3 Cities Sorted by Rank - Delaware 164 5.4 Cities Sorted by Zipcode - Delaware 164 5.5 Latent Demand by Year - District of Columbia 165 5.6 Cities Sorted by Rank - District of Columbia 167 5.7 Cities Sorted by Zipcode - District of Columbia 167 5.8 Latent Demand by Year - Maryland 168 5.9 Cities Sorted by Rank - Maryland 169 5.10 Cities Sorted by Zipcode - Maryland 174 5.11 Latent Demand by Year - New Jersey 180 5.12 Cities Sorted by Rank - New Jersey 181 5.13 Cities Sorted by Zipcode - New Jersey 189 5.14 Latent Demand by Year - New York 198 5.15 Cities Sorted by Rank - New York 199 5.16 Cities Sorted by Zipcode - New York 218 5.17 Latent Demand by Year - Pennsylvania 237 5.18 Cities Sorted by Rank - Pennsylvania 238 5.19 Cities Sorted by Zipcode - Pennsylvania 247 6 NEW ENGLAND 258 6.1 Executive Summary 258 6.2 Latent Demand by Year - Connecticut 260 6.3 Cities Sorted by Rank - Connecticut 261 6.4 Cities Sorted by Zipcode - Connecticut 265 6.5 Latent Demand by Year - Maine 270 6.6 Cities Sorted by Rank - Maine 271 6.7 Cities Sorted by Zipcode - Maine 274 6.8 Latent Demand by Year - Massachusetts 277 6.9 Cities Sorted by Rank - Massachusetts 278 6.10 Cities Sorted by Zipcode - Massachusetts 285 6.11 Latent Demand by Year - New Hampshire 293 6.12 Cities Sorted by Rank - New Hampshire 294 6.13 Cities Sorted by Zipcode - New Hampshire 297 6.14 Latent Demand by Year - Rhode Island 300 6.15 Cities Sorted by Rank - Rhode Island 301 6.16 Cities Sorted by Zipcode - Rhode Island 302 6.17 Latent Demand by Year - Vermont 303 6.18 Cities Sorted by Rank - Vermont 304 6.19 Cities Sorted by Zipcode - Vermont 305 7 PLAINS 307 7.1 Executive Summary 307 7.2 Latent Demand by Year - Iowa 309 7.3 Cities Sorted by Rank - Iowa 310 7.4 Cities Sorted by Zipcode - Iowa 312 7.5 Latent Demand by Year - Kansas 316 7.6 Cities Sorted by Rank - Kansas 317 7.7 Cities Sorted by Zipcode - Kansas 319 7.8 Latent Demand by Year - Minnesota 321 7.9 Cities Sorted by Rank - Minnesota 322 7.10 Cities Sorted by Zipcode - Minnesota 326 7.11 Latent Demand by Year - Missouri 331 7.12 Cities Sorted by Rank - Missouri 332 7.13 Cities Sorted by Zipcode - Missouri 336 7.14 Latent Demand by Year - Nebraska 341 7.15 Cities Sorted by Rank - Nebraska 342 7.16 Cities Sorted by Zipcode - Nebraska 343 7.17 Latent Demand by Year - North Dakota 344 7.18 Cities Sorted by Rank - North Dakota 345 7.19 Cities Sorted by Zipcode - North Dakota 345 7.20 Latent Demand by Year - South Dakota 346 7.21 Cities Sorted by Rank - South Dakota 347 7.22 Cities Sorted by Zipcode - South Dakota 347 8 ROCKIES 349 8.1 Executive Summary 349 8.2 Latent Demand by Year - Colorado 351 8.3 Cities Sorted by Rank - Colorado 352 8.4 Cities Sorted by Zipcode - Colorado 355 8.5 Latent Demand by Year - Idaho 358 8.6 Cities Sorted by Rank - Idaho 359 8.7 Cities Sorted by Zipcode - Idaho 360 8.8 Latent Demand by Year - Montana 361 8.9 Cities Sorted by Rank - Montana 362 8.10 Cities Sorted by Zipcode - Montana 363 8.11 Latent Demand by Year - Utah 364 8.12 Cities Sorted by Rank - Utah 365 8.13 Cities Sorted by Zipcode - Utah 367 8.14 Latent Demand by Year - Wyoming 371 8.15 Cities Sorted by Rank - Wyoming 372 8.16 Cities Sorted by Zipcode - Wyoming 372 9 SOUTHEAST 374 9.1 Executive Summary 374 9.2 Latent Demand by Year - Alabama 376 9.3 Cities Sorted by Rank - Alabama 377 9.4 Cities Sorted by Zipcode - Alabama 380 9.5 Latent Demand by Year - Arkansas 384 9.6 Cities Sorted by Rank - Arkansas 385 9.7 Cities Sorted by Zipcode - Arkansas 387 9.8 Latent Demand by Year - Florida 389 9.9 Cities Sorted by Rank - Florida 390 9.10 Cities Sorted by Zipcode - Florida 402 9.11 Latent Demand by Year - Georgia 416 9.12 Cities Sorted by Rank - Georgia 417 9.13 Cities Sorted by Zipcode - Georgia 421 9.14 Latent Demand by Year - Kentucky 426 9.15 Cities Sorted by Rank - Kentucky 427 9.16 Cities Sorted by Zipcode - Kentucky 429 9.17 Latent Demand by Year - Louisiana 433 9.18 Cities Sorted by Rank - Louisiana 434 9.19 Cities Sorted by Zipcode - Louisiana 437 9.20 Latent Demand by Year - Mississippi 440 9.21 Cities Sorted by Rank - Mississippi 441 9.22 Cities Sorted by Zipcode - Mississippi 443 9.23 Latent Demand by Year - North Carolina 445 9.24 Cities Sorted by Rank - North Carolina 446 9.25 Cities Sorted by Zipcode - North Carolina 450 9.26 Latent Demand by Year - South Carolina 455 9.27 Cities Sorted by Rank - South Carolina 456 9.28 Cities Sorted by Zipcode - South Carolina 458 9.29 Latent Demand by Year - Tennessee 461 9.30 Cities Sorted by Rank - Tennessee 462 9.31 Cities Sorted by Zipcode - Tennessee 465 9.32 Latent Demand by Year - Virginia 469 9.33 Cities Sorted by Rank - Virginia 470 9.34 Cities Sorted by Zipcode - Virginia 473 9.35 Latent Demand by Year - West Virginia 478 9.36 Cities Sorted by Rank - West Virginia 479 9.37 Cities Sorted by Zipcode - West Virginia 480 10 SOUTHWEST 481 10.1 Executive Summary 481 10.2 Latent Demand by Year - Arizona 482 10.3 Cities Sorted by Rank - Arizona 483 10.4 Cities Sorted by Zipcode - Arizona 485 10.5 Latent Demand by Year - New Mexico 488 10.6 Cities Sorted by Rank - New Mexico 489 10.7 Cities Sorted by Zipcode - New Mexico 490 10.8 Latent Demand by Year - Oklahoma 492 10.9 Cities Sorted by Rank - Oklahoma 493 10.10 Cities Sorted by Zipcode - Oklahoma 495 10.11 Latent Demand by Year - Texas 497 10.12 Cities Sorted by Rank - Texas 498 10.13 Cities Sorted by Zipcode - Texas 509 11 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 520 11.1 Disclaimers & Safe Harbor 520 11.2 ICON Group International, Inc. User Agreement Provisions 521
 
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