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The 2019-2024 Outlook for Fresh and Frozen Seafood Processing in the United States

The 2019-2024 Outlook for Fresh and Frozen Seafood Processing in the United States

  • June 2018
  • 605 pages
  • ID: 5452336

Summary

Table of Contents

This study covers the latent demand outlook for fresh and frozen seafood processing across the states and cities of the United States. Latent demand (in millions of U.S. dollars), or potential industry earnings (P.I.E.) estimates are given across some 12,600 cities in the United States.

For each city in question, the percent share the city is of its state and of the United States as a whole is reported. These comparative benchmarks allow the reader to quickly gauge a city vis-à-vis others.

This statistical approach can prove very useful to distribution and/or sales force strategies. Using econometric models which project fundamental economic dynamics within each state and city, latent demand estimates are created for fresh and frozen seafood processing. This report does not discuss the specific players in the market serving the latent demand, nor specific details at the product level. The study also does not consider short-term cyclicalities that might affect realized sales. The study, therefore, is strategic in nature, taking an aggregate and long-run view, irrespective of the players or products involved.

This study does not report actual sales data (which are simply unavailable, in a comparable or consistent manner in virtually all cities in the United States). This study gives, however, my estimates for the latent demand, or potential industry earnings (P.I.E.), for fresh and frozen seafood processing in the United States. It also shows how the P.I.E. is divided and concentrated across the cities and regional markets of the United States. For each state, I also show my estimates of how the P.I.E. grows over time. In order to make these estimates, a multi-stage methodology was employed that is often taught in courses on strategic planning at graduate schools of business.

Another reason why sales do not equate to latent demand is exchange rates. In this report, all figures assume the long-run efficiency of currency markets.

Figures, therefore, equate values based on purchasing power parities across geographies. Short-run distortions in the value of the dollar, therefore, do not figure into the estimates. Purchasing power parity estimates were collected from official sources, and extrapolated using standard econometric models. The report uses the dollar as the currency of comparison, but not as a measure of transaction volume. The units used in this report are: US $ mln.

1.3 THE METHODOLOGY
In order to estimate the latent demand for fresh and frozen seafood processing across the states and cities 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 shown as "B" 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 show as "B" in the figure below (note the higher slope and zero-zero intercept). The average propensity to consume is constant. For a general overview of this subject area, see Principles of Macroeconomics by N. Gregory Mankiw, South-Western College Publishing; ISBN: 0030340594; 2nd edition (February 2002).

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 fresh and frozen seafood processing 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 fresh and frozen seafood processing. 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 fresh and frozen seafood processing. 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 fresh and frozen seafood processing in the United States.

1.3.1 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 cannot be 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. Euromonitor, Mintel, Thomson Financial Services, the U.S. Industrial Outlook, the World Resources Institute, the Organization for Economic Cooperation and Development, various agencies from the United Nations, industry trade associations, the International Monetary Fund, and the World Bank). Depending on original data sources used, the definition of fresh and frozen seafood processing 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 services that might be incorporated within fresh and frozen seafood processing fall 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 fresh and frozen seafood processing as defined by the North American Industrial Classification system or NAICS (pronounced "nakes").

The NAICS code for fresh and frozen seafood processing is 311712. It is for this definition that aggregate latent demand estimates are derived.

Fresh and frozen seafood processing is specifically defined as follows:
311712 This U.S. industry comprises establishments primarily engaged in one or more of the following: (1) eviscerating fresh fish by removing heads, fins, scales, bones, and entrails; (2) shucking and packing fresh shellfish; (3) manufacturing frozen seafood; and (4) processing fresh and frozen marine fats and oils.

3117121 Prepared fresh fish and other fresh seafood

31171211 Prepared fresh fish and other fresh seafood, surimi, and surimi based products

3117121111 Prepared fresh fish, ground fish (cod, cusk, haddock, etc.), fillets and steaks

3117121121 Prepared fresh fish, ground fish (cod, cusk, haddock, etc.), other

3117121131 Prepared fresh fish, flounder, halibut, and sole, fillets and steaks

3117121141 Prepared fresh fish, flounder, halibut, and sole, other

3117121151 Prepared fresh fish, Alaska pollock, fillets and steaks

3117121161 Prepared fresh fish, Alaska pollock, other

3117121171 Prepared fresh fish, catfish, fillets and steaks

3117121181 Prepared fresh fish, catfish, other

3117121191 Prepared fresh fish, other fish, fillets and steaks

31171211A1 Prepared fresh fish, other fish, other

31171211B1 Prepared fresh blue crab meat

31171211C1 Prepared fresh rock crab meat

31171211D1 Prepared fresh snow crab meat

31171211E1 Other prepared fresh crab meat

31171211F1 Prepared fresh shrimp

31171211G1 Prepared fresh oysters

31171211H1 Prepared fresh clams

31171211J1 Other prepared fresh shellfish (except surimi and surimi-based products)

31171211K1 Prepared fresh surimi, except surimi-based products

31171211L1 Prepared fresh surimi-based products

31171211M1 Other prepared fresh seafood (roe, squid, etc.)

3117122 Prepared frozen fish, excluding shellfish

31171221 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), fillets and steaks, breaded or battered

3117122111 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), fillets and steaks, breaded or battered

31171222 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), fillets and steaks, plain

3117122221 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), fillets and steaks, plain

31171223 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), sticks and portions, breaded or battered

3117122331 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), sticks and portions, breaded or battered

31171224 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), sticks and portions, plain

3117122441 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), sticks and portions, plain

3117122451 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), other forms

3117122461 Prepared frozen flounder, halibut, and sole, fillets and steaks

3117122471 Prepared frozen flounder, halibut, and sole, other forms

31171225 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), other forms

3117122551 Prepared frozen groundfish (cod, cusk, haddock, hake, perch, pollock, and whiting), other forms

3117122581 Prepared frozen fish, Alaska pollock, fillets and steaks, breaded or battered

31171226 Prepared frozen flounder, halibut, and sole, fillets, steaks, and other forms

3117122661 Prepared frozen flounder, halibut, and sole, fillets and steaks

3117122671 Prepared frozen flounder, halibut, and sole, other forms

3117122691 Prepared frozen fish, Alaska pollock, fillets and steaks, plain

31171226A1 Prepared frozen fish, Alaska pollock, other forms

31171227 Prepared frozen fish, Alaska pollock, fillets and steaks, breaded or battered

3117122771 Prepared frozen fish, Alaska pollock, fillets and steaks, breaded or battered

31171227B1 Prepared frozen fish, catfish, fillets and steaks, breaded or battered

31171228 Prepared frozen fish, Alaska pollock, fillets and steaks, plain, and other forms

3117122881 Prepared frozen fish, Alaska pollock, fillets and steaks, plain

3117122891 Prepared frozen fish, Alaska pollock, other forms

31171228C1 Prepared frozen fish, catfish, fillets and steaks, plain or seasoned

31171228D1 Prepared frozen fish, catfish, other forms

31171229 Prepared frozen fish, catfish, fillets and steaks, breaded or battered

3117122991 Prepared frozen fish, catfish, fillets and steaks, breaded or battered

31171229E1 All other prepared frozen fish, fillets and steaks, breaded or battered

3117122A Prepared frozen fish, catfish, fillets and steaks, plain or seasoned, and other forms

3117122AA1 Prepared frozen fish, catfish, fillets and steaks, plain or seasoned

3117122AB1 Prepared frozen fish, catfish, other forms

3117122AF1 All other prepared frozen fish, fillets and steaks, plain

3117122AG1 All other prepared frozen fish, other forms

3117122B All other prepared frozen fish, fillets and steaks, breaded or battered

3117122BC1 All other prepared frozen fish, fillets and steaks, breaded or battered

3117122C All other prepared frozen fish, fillets and steaks, plain, and other forms

3117122CD1 All other prepared frozen fish, fillets and steaks, plain

3117122CE1 All other prepared frozen fish, other forms

3117123 Prepared frozen shellfish

31171231 Prepared frozen shrimp

3117123111 Prepared frozen headless shrimp, raw

3117123121 Prepared frozen peeled shrimp, raw

3117123131 Prepared frozen peeled shrimp, cooked

3117123141 Prepared frozen shrimp, breaded

31171232 Other prepared frozen shellfish, incl crabs, lobster tails, oysters & clams

3117123251 Other 100 percent prepared frozen shrimp products

3117123261 Prepared frozen lobster tails

3117123271 Prepared frozen blue crab meat

3117123281 Prepared frozen rock crab meat

3117123291 Prepared frozen snow crab meat

31171232A1 Prepared frozen dungeness crab meat

31171232B1 Prepared frozen king crab meat, cooked

31171232C1 Prepared frozen king crab sections

31171232D1 Other prepared frozen crabs and parts of crabs

31171232E1 Other prepared frozen shellfish (including oysters, clams, and parts of lobsters except tails)

3117124 Other prepared frozen seafoods, nec

31171241 Prepared frozen surimi and other prepared frozen seafoods

3117124111 Prepared frozen surimi, except surimi-based products

3117124121 Prepared frozen surimi-based products

3117124131 Other prepared frozen seafoods (soups, stews, chowders, pies, fishcakes, crabcakes, shrimpcakes, etc.), except surimi

31171242 Fresh or frozen fish and marine animal oil, scrap, and meal

3117124211 Fish and marine animal oil, fresh or frozen

3117124215 Fish oil (except cod or liver) and marine mammal oil, including refined and hydrogenated (excluding sperm)

3117124221 Fish scrap and meal, fresh or frozen

3117124231 Other fish and marine animal oil products, fresh or frozen

31171243 Foots, marine oil (fish, etc.)

3117124311 Foots, marine oil (fish, etc.)

311712M Miscellaneous receipts

311712P Primary products

311712S Secondary products

311712SM Secondary products and miscellaneous receipts

This report was prepared from a variety of sources including excerpts from documents and official reports or databases published by the World Bank, the U.S. Department of Commerce, the U.S. State Department, various national agencies, the International Monetary Fund, the Central Intelligence Agency, various agencies from the United Nations (e.g. ILO, ITU, UNDP, etc.), and non-governmental sources, including ICON Group Ltd., Euromonitor, the World Resources Institute, Mintel, the U.S. Industrial Outlook, and various public sources cited in the trade press.

1.3.2 STEP 2. FILTERING AND SMOOTHING
Based on the aggregate view of fresh and frozen seafood processing 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 a 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.

1.3.3 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.

1.3.4 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 crosssectionally pooled time series model". The interested reader can find longer discussions of this type of modeling in Studies in Global Econometrics (Advanced Studies in Theoretical and Applied Econometrics V. 30) , by Henri Theil, et al., Kluwer Academic Publishers; ISBN: 0792336607; (June 1996), and in Principles of Econometrics, by Henri Theil John Wiley & Sons; ISBN: 0471858455; (December 1971), and in Econometric Models and Economic Forecasts by Robert S. Pindyck, Daniel L. Rubinfeld McGraw Hill Text; ISBN: 0070500983; 3rd edition (December 1991). 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 fresh and frozen seafood processing 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.

1.3.5 STEP 5. FIXED-PARAMETER LINEAR ESTIMATION
Nonlinearities are assumed in cases where filtered data exist along the aggregate consumption function. Because the 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 fresh and frozen seafood processing 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 fresh and frozen seafood processing). 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.

1.3.6 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.

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