The 2007-2012 Outlook for Processed and Cured Pork Produced in Slaughtering Plants 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants. 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants 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 “processed and cured pork produced in slaughtering plants” 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 processed and cured pork produced in slaughtering plants 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 “processed and cured pork produced in slaughtering plants” as defined by the NAICS coding system (pronounced “nakes”). For a complete definition of processed and cured pork produced in slaughtering plants, please see below. The NAICS code for processed and cured pork produced in slaughtering plants is 311611G. It is for this definition of processed and cured pork produced in slaughtering plants that the aggregate latent demand estimates are derived for the states and cities of the United States. “Processed and cured pork produced in slaughtering plants” is specifically defined as follows:

311611G
Pork, processed or cured, but not canned or made into sausage; mitse

311611G1
Pork, processed or cured, not canned or made into sausage, made in slaughtering plants

311611G111
Sweet_pickled or dry_cured pork (not smoked, cooked, canned, or made into sausage), made in slaughtering plants

311611G121
Dry salt pork, not canned or made into sausage, made in slaughtering plants

311611G131
Smoked hams and picnics, except canned, made in slaughtering plants

311611G141
Smoked slab bacon, made in slaughtering plants

311611G151
Smoked sliced bacon, made in slaughtering plants

311611G161
Other smoked pork, not canned or made into sausage, made in slaughtering plants

311611G171
Boiled ham, barbecue pork, and other cooked pork, except canned meats and sausage, made in slaughtering plants

Step 2. Filtering and Smoothing

Based on the aggregate view of processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants 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 processed and cured pork produced in slaughtering plants). 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 is used. 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 13
1.3.3 Step 3. Filling in Missing Values 13
1.3.4 Step 4. Varying Parameter, Non-linear Estimation 13
1.3.5 Step 5. Fixed-Parameter Linear Estimation 14
1.3.6 Step 6. Aggregation and Benchmarking 14
2 SUMMARY OF FINDINGS 15
2.1 Latent Demand in The US 15
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 22
3.6 Cities Sorted by Rank - California 23
3.7 Cities Sorted by Zipcode - California 42
3.8 Latent Demand by Year - Hawaii 62
3.9 Cities Sorted by Rank - Hawaii 63
3.10 Cities Sorted by Zipcode - Hawaii 65
3.11 Latent Demand by Year - Nevada 68
3.12 Cities Sorted by Rank - Nevada 69
3.13 Cities Sorted by Zipcode - Nevada 70
3.14 Latent Demand by Year - Oregon 72
3.15 Cities Sorted by Rank - Oregon 73
3.16 Cities Sorted by Zipcode - Oregon 77
3.17 Latent Demand by Year - Washington 81
3.18 Cities Sorted by Rank - Washington 82
3.19 Cities Sorted by Zipcode - Washington 89
4 GREAT LAKES 97
4.1 Executive Summary 97
4.2 Latent Demand by Year - Illinois 98
4.3 Cities Sorted by Rank - Illinois 99
4.4 Cities Sorted by Zipcode - Illinois 112
4.5 Latent Demand by Year - Indiana 126
4.6 Cities Sorted by Rank - Indiana 127
4.7 Cities Sorted by Zipcode - Indiana 133
4.8 Latent Demand by Year - Michigan 140
4.9 Cities Sorted by Rank - Michigan 141
4.10 Cities Sorted by Zipcode - Michigan 149
4.11 Latent Demand by Year - Ohio 158
4.12 Cities Sorted by Rank - Ohio 159
4.13 Cities Sorted by Zipcode - Ohio 171
4.14 Latent Demand by Year - Wisconsin 184
4.15 Cities Sorted by Rank - Wisconsin 185
4.16 Cities Sorted by Zipcode - Wisconsin 195
5 MID-ATLANTIC 206
5.1 Executive Summary 206
5.2 Latent Demand by Year - Delaware 207
5.3 Cities Sorted by Rank - Delaware 208
5.4 Cities Sorted by Zipcode - Delaware 209
5.5 Latent Demand by Year - District of Columbia 210
5.6 Cities Sorted by Rank - District of Columbia 212
5.7 Cities Sorted by Zipcode - District of Columbia 212
5.8 Latent Demand by Year - Maryland 213
5.9 Cities Sorted by Rank - Maryland 214
5.10 Cities Sorted by Zipcode - Maryland 220
5.11 Latent Demand by Year - New Jersey 227
5.12 Cities Sorted by Rank - New Jersey 228
5.13 Cities Sorted by Zipcode - New Jersey 237
5.14 Latent Demand by Year - New York 247
5.15 Cities Sorted by Rank - New York 248
5.16 Cities Sorted by Zipcode - New York 274
5.17 Latent Demand by Year - Pennsylvania 300
5.18 Cities Sorted by Rank - Pennsylvania 301
5.19 Cities Sorted by Zipcode - Pennsylvania 317
6 NEW ENGLAND 333
6.1 Executive Summary 333
6.2 Latent Demand by Year - Connecticut 335
6.3 Cities Sorted by Rank - Connecticut 336
6.4 Cities Sorted by Zipcode - Connecticut 341
6.5 Latent Demand by Year - Maine 346
6.6 Cities Sorted by Rank - Maine 347
6.7 Cities Sorted by Zipcode - Maine 352
6.8 Latent Demand by Year - Massachusetts 358
6.9 Cities Sorted by Rank - Massachusetts 359
6.10 Cities Sorted by Zipcode - Massachusetts 367
6.11 Latent Demand by Year - New Hampshire 376
6.12 Cities Sorted by Rank - New Hampshire 377
6.13 Cities Sorted by Zipcode - New Hampshire 381
6.14 Latent Demand by Year - Rhode Island 386
6.15 Cities Sorted by Rank - Rhode Island 387
6.16 Cities Sorted by Zipcode - Rhode Island 388
6.17 Latent Demand by Year - Vermont 390
6.18 Cities Sorted by Rank - Vermont 391
6.19 Cities Sorted by Zipcode - Vermont 394
7 PLAINS 398
7.1 Executive Summary 398
7.2 Latent Demand by Year - Iowa 399
7.3 Cities Sorted by Rank - Iowa 400
7.4 Cities Sorted by Zipcode - Iowa 405
7.5 Latent Demand by Year - Kansas 410
7.6 Cities Sorted by Rank - Kansas 411
7.7 Cities Sorted by Zipcode - Kansas 414
7.8 Latent Demand by Year - Minnesota 418
7.9 Cities Sorted by Rank - Minnesota 419
7.10 Cities Sorted by Zipcode - Minnesota 426
7.11 Latent Demand by Year - Missouri 433
7.12 Cities Sorted by Rank - Missouri 434
7.13 Cities Sorted by Zipcode - Missouri 440
7.14 Latent Demand by Year - Nebraska 447
7.15 Cities Sorted by Rank - Nebraska 448
7.16 Cities Sorted by Zipcode - Nebraska 450
7.17 Latent Demand by Year - North Dakota 452
7.18 Cities Sorted by Rank - North Dakota 453
7.19 Cities Sorted by Zipcode - North Dakota 454
7.20 Latent Demand by Year - South Dakota 455
7.21 Cities Sorted by Rank - South Dakota 456
7.22 Cities Sorted by Zipcode - South Dakota 457
8 ROCKIES 459
8.1 Executive Summary 459
8.2 Latent Demand by Year - Colorado 460
8.3 Cities Sorted by Rank - Colorado 461
8.4 Cities Sorted by Zipcode - Colorado 465
8.5 Latent Demand by Year - Idaho 470
8.6 Cities Sorted by Rank - Idaho 471
8.7 Cities Sorted by Zipcode - Idaho 473
8.8 Latent Demand by Year - Montana 475
8.9 Cities Sorted by Rank - Montana 476
8.10 Cities Sorted by Zipcode - Montana 478
8.11 Latent Demand by Year - Utah 480
8.12 Cities Sorted by Rank - Utah 481
8.13 Cities Sorted by Zipcode - Utah 484
8.14 Latent Demand by Year - Wyoming 488
8.15 Cities Sorted by Rank - Wyoming 489
8.16 Cities Sorted by Zipcode - Wyoming 490
9 SOUTHEAST 492
9.1 Executive Summary 492
9.2 Latent Demand by Year - Alabama 493
9.3 Cities Sorted by Rank - Alabama 494
9.4 Cities Sorted by Zipcode - Alabama 499
9.5 Latent Demand by Year - Arkansas 504
9.6 Cities Sorted by Rank - Arkansas 505
9.7 Cities Sorted by Zipcode - Arkansas 508
9.8 Latent Demand by Year - Florida 512
9.9 Cities Sorted by Rank - Florida 513
9.10 Cities Sorted by Zipcode - Florida 528
9.11 Latent Demand by Year - Georgia 543
9.12 Cities Sorted by Rank - Georgia 544
9.13 Cities Sorted by Zipcode - Georgia 550
9.14 Latent Demand by Year - Kentucky 557
9.15 Cities Sorted by Rank - Kentucky 558
9.16 Cities Sorted by Zipcode - Kentucky 562
9.17 Latent Demand by Year - Louisiana 567
9.18 Cities Sorted by Rank - Louisiana 568
9.19 Cities Sorted by Zipcode - Louisiana 573
9.20 Latent Demand by Year - Mississippi 578
9.21 Cities Sorted by Rank - Mississippi 579
9.22 Cities Sorted by Zipcode - Mississippi 582
9.23 Latent Demand by Year - North Carolina 585
9.24 Cities Sorted by Rank - North Carolina 586
9.25 Cities Sorted by Zipcode - North Carolina 593
9.26 Latent Demand by Year - South Carolina 601
9.27 Cities Sorted by Rank - South Carolina 602
9.28 Cities Sorted by Zipcode - South Carolina 606
9.29 Latent Demand by Year - Tennessee 611
9.30 Cities Sorted by Rank - Tennessee 612
9.31 Cities Sorted by Zipcode - Tennessee 617
9.32 Latent Demand by Year - Virginia 623
9.33 Cities Sorted by Rank - Virginia 624
9.34 Cities Sorted by Zipcode - Virginia 629
9.35 Latent Demand by Year - West Virginia 634
9.36 Cities Sorted by Rank - West Virginia 635
9.37 Cities Sorted by Zipcode - West Virginia 637
10 SOUTHWEST 640
10.1 Executive Summary 640
10.2 Latent Demand by Year - Arizona 641
10.3 Cities Sorted by Rank - Arizona 642
10.4 Cities Sorted by Zipcode - Arizona 645
10.5 Latent Demand by Year - New Mexico 649
10.6 Cities Sorted by Rank - New Mexico 650
10.7 Cities Sorted by Zipcode - New Mexico 652
10.8 Latent Demand by Year - Oklahoma 654
10.9 Cities Sorted by Rank - Oklahoma 655
10.10 Cities Sorted by Zipcode - Oklahoma 659
10.11 Latent Demand by Year - Texas 663
10.12 Cities Sorted by Rank - Texas 664
10.13 Cities Sorted by Zipcode - Texas 681
11 DISCLAIMERS, WARRANTEES, AND USER AGREEMENT PROVISIONS 698
11.1 Disclaimers & Safe Harbor 698
11.2 User Agreement Provisions 699