Review: (Holland and Wessells 1998) Predicting Consumer Preferences for Fresh Salmon: The Influence of Safety Inspection and Production Method Attributes

Daniel Holland and Cathy R. Wessells

Agricultural and Resource Economics Review 27:1–14

Objectives

- Questions to answer:

1) How important to consumers is an inspection that verifies seafood products are safe and of high quality?

2) Does it matter to consumers which agency of government is in charge of inspection program?

- major foci:

* determine whether production method (farmed or wild) will influence consumer preferences

* assess consumer perceptions of seafood quality and safety

* determine economic value of mandatory quality inspection program

Methods/Approach

- based on conjoint analysis to determine average relative importance and value of three product attributes for fresh salmon:
* seafood inspection

* production method

* price

^ The three price levels chosen were consistent with prevailing retail prices at the time of the survey. The mid-level price was equal to the average price in supermarkets from Narragansett, RI., Newark, Del., and Durham, NH. The price levels were $4.49, $4.99, and $5.49/lb.

- data set from mail survey of randomly selected households in northeastern and mid-Atlantic states

- rank preferences for fresh salmon products with different price, inspection and production attributes using conjoint experiment

- "The attribute combinations for the nine products represent a full profile, orthogonal
 Addleman design that eliminates multi collinearity and resulting bias in parameter estimate (Addleman 1962)
- "The estimation of the parameters that model these preferences can be done in a variety of ways, but it is commonly referred to as conjoint analysis (Green and Srinivasan, 1978)." (p. 4)

- Conjoint analysis

* used to find relative importance and value

* used to design products and services to maximize consumer appeal or used to estimate market share for competing products

* originally used in fields of psychometrics and marketing, now widely used to evaluate consumer preferences and demand for market goods

* used in market studies when new or hypothetical products or product attributes are assessed and market data does not exist

* when used in combination with demographic data and perception questions can suggest market segmentation and potential marketing strategies based on heterogeneity in preferences among consumers (Swallow et al. 1994)

REPRESENTATIVE CONSUMER MODEL vs. INDIVIDUAL-LEVEL

- a single utility function is estimated and assumed to be representative of all consumers

* componential segmentation = adding terms to model (demographic variables or responses to perception of preference questions) that capture interactions between attribute preferences and individually specific variables

* accounts for heterogeneity based on observable characteristics of individuals

* useful in identifying market segments that differ in preferences and consumption patterns

- also estimated individual preference models, used information to improve performance of aggregate models where entire data set is used

* proved to be critical in avoiding misinterpretations of results of aggregate model

* "Individual-level estimations have the disadvantage of requiring enough information collected from each individual to estimate his or her utility function. This can require a large number of observations when the number of attributes and attribute levels is large." (p.5)

* Tests of statistical significance of attribute parameters or dependent variable predictions are cumbersome (Elrod, Louviere, and Davey 1992), sometimes can't be calculated

- cluster analysis = separates sample into groups of respondents based on similar preferences, accounts for diversity in preferences that cannot be tied to observed personal characteristics

- Predictive validity: individual level models > cluster analysis > componential model (Moore 1980, Green and Helsen 1989)

- Individual models in commercial conjoint applications (Wittink and Cattin 1981; Green and Srinivasan 1990)

- This study compares group differentiation between: 1) "representative consumer" model, 2) componential model (with interactive terms between product attributes, demographic and perception variables) and 3) individual-level models

- Ordinal ranking, as opposed to a rating scale, was chosen because it is believed to be more reliable (Green and Srinivasan 1978), particularly in mail surveys

^ "A referendum or paired comparison format would have the same advantages but requires a greater number of questions to derive the same amount of information." (p. 5)

^ rank-ordered logit estimation of conjoint question is based on random utility theory

^ Because the ranks are ordinal rather than cardinal, and because the nine ranks given by each respondent are not independent, neither an OLS, ordered probit nor logit specification would provide consistent estimates.

- The scarcity of observations per individual (9 observations per individual and 7 parameters, including an intercept dropping one level of each attribute to avoid perfect multicollinearity) limits our ability to construct individual-level preference models from our data

* could estimate individual OLS models, but with only two degrees of freedom and violations of assumptions of cardinal data and independence of observations (statistical reliability unclear)

* however, did uncover over 46% of individuals in sample appeared to prefer a salmon product priced at 4.99/lb (med price) over 4.49 (low price) when all other attributes were identical, somewhat smaller proportion still preferred higher-priced product when prices were 5.49/lb and 4.49/lb (irrational)

^ may be due to artificiality of data-gathering

^ may represent actual consumer behavior

^ may be that important quality attribute was missing from product descriptions and some respondents assumed this attribute was represented at least partially by price (respondents told appearance was similar, but many assumed quality correlated to price)

^ frequent seafood consumers more likely to fall in "irrational" category supports that an important attribute that is related to price may be missing (in future ask if consumers think price is an indicator of quality)

* chose to rely on aggregate componential models

- Rank-ordered logit

* developed by in Beggs, Cardell and Hausman (1981)

* accounts for ordinal nature of data and lack of independence between observations for each respondent

* maxmimum likelihood estimates are those with maximum probability of resulting in observed sets of ranks

* rationale: individuals compare all choices, select most preferred - independent of ranking of remaining choices- then make next choices out of remaining subset of choices

- Sample:

* over 1,500 consumers in north-eastern and mid-Atlantic states (30% response rate)

* provided information about: preferences and consumption patterns for fresh fish, beliefs about safety and quality of fish, individual demographic characteristics

* 5,000 surveys mailed to randomly selected households according to each state's share of total population for region

* asked individual chiefly responsible for retail purchase of seafood to answer questionnaire

* 64% were male

* 97% were consumers of fresh seafood (seafood consumers more likely to return survey

* 756 (out of 1,529) answered conjoint question and all demographic and perception questions; 968 answered all demographic and perception questions

- Survey administration: 

* between April and August 1993

* began with personalized, hand-signed letter with information on impending arrival of survey and importance of responding and thanked for cooperation

* survey and letter followed initial letter by one week

* one week later postcard was sent to remind household =s to complete and return survey

* two weeks later follow-up survey was sent followed by another postcard reminder

- Willingness to Pay (WTP) for changes in attribute level can be estimated for representative consumer

* inappropriate if respondents assume implied relationship between cost and quality of market product or public goods

Results and Conclusions

- market segmentations and potential marketing strategies based on heterogeneity in preferences among consumers

- When presented with two choices (for example farmed, USDA-inspected salmon priced at $4.49/lb. versus wild, USDA-inspected salmon priced at $4.49/lb.), 81% of the people within our sample are predicted to choose the farmed product over the wild product. Also, 63% of the people within our sample are predicted to choose fresh salmon that is FDA inspected and priced at $5.49/lb. (but has no production method information) over farmed fresh salmon that is priced at $4.49/lb. (but has no inspection information). Finally, 97% of the people within our sample are predicted to choose the inspected product with no information on production method priced at $4.99/lb. over the farmed salmon product that has the same price but no inspection information.

- According to this sample of seafood consumers, the strongest preferences are related to seafood inspection, products indicating an inspection by either the USDA or the FDA are preferred to products with no inspection.

* FDA is preferred to the USDA as an inspection agency

* However, when these coefficients are modified by the interaction terms of the attributes with demographics and beliefs, the total effect is that the USDA is the preferred agency over the FDA

- wild salmon is preferred to farmed, modifying these effects by the coefficients of the dummy interactions with farmed and wild results in farmed being the preferred method of production

* The salmon industry’s strategy of labeling product as to what method of production is used, farmed or wild, seems to favor farmed salmon more than wild salmon in the northeastern and mid-Atlantic states. This may be the result of the perception that farmed salmon is higher quality and safer than wild, possibly because a farmed product is connected with a product over which the harvester has some degree of control, unlike wild salmon

- Conjoint analysis is useful for evaluating marketing strategies for seafood

- Public policies (namely an inspection program) can provide benefits to seafood consumers

- Componential models, which include interactive terms that capture the heterogeneity attributable to observable demographic characteristics, greatly improve model performance. Further improvements are made by including information derived from questions about individual perceptions. (However, much of heterogeneity is not correlated with observable characteristics)

- Individual-level analysis typically results in higher predictive ability and can identify important heterogeneity in preferences not revealed by aggregate model, but require large amount of data per individual and not applicable outside of sample

- many individuals apparently preferred to pay more rather than less for a given piece of fish, suggesting that they may associate price with quality (not apparent with aggregate model)

- looking at group and individual heterogeneity can improve performance of models in predictions as well as provide valuable added information to industry or policymakers

- amount and type of data collected (ordinal ranks vs. scaled rating) may limit acceptable estimation procedures

Errors in experimental design, statistical analyses or analytical approaches?
Assumptions

- Consumers unaware of regulatory changes in seafood safety, communicating changes is important so seafood industry can increase demand and raise prices to cover increased regulation costs

- salmon industry experienced 97% increase in world production from 1986 to 1995 (Johnson 1996), in large part due to increased production of farmed salmon 

- "Orthogonal Addleman design assumes that the attributes are completely separable. This could be problematic if, for example, an inspection has relatively more value to consumers when they know fish is wild as opposed to farmed" (p. 4)

- sample may be representative of seafood consumers in the northeastern and mid-Atlantic

United States, we suspect that results might vary considerably elsewhere (preference for wild versus farmed may be reversed on the West Coast given the dominance of the wild fisheries in the Pacific)

Opinion

According to Google Scholar this study has been cited 79 times since 1998 which is not a lot. Many of the articles referred to eco-labeling and food safety. I was mainly looking at this article to understand their methodology. This has been helpful as I have read the Elrod (1992) article and decided we may not have a large enough sample size for choice-based. Combining this with information from Sawtooth (Orme 2009) I think we can do a CVA analysis where cards are ranked given our small sample. Sawtooth has a CVA/HB module for $1000 which allows for a better estimation of an individual’s part worths by borrowing from the population. This is useful especially since individual-level classification is not very important to us.

Useful References

Elrod, T., J.J. Louviere, and K,S, Davey. 1992. “An Empirical Comparison of Ratings-Based and Choice-Based Conjoint Models.” Journal of Marketing Research 29:368-77.

Addleman, S. 1962, “Orthogonal Main-Effect Plans for Asymmetrical Factorial Experiments.” Technometrics 4(1):21-46.

Beggs, S., S. Cardell, and J. Hausman. 1981. “Assessing the Potential Demand for Electric Cars.” Journal of Econometrics 17:1-19.

Swallow, S.K., T.W. Weaver, J.J. Opaluch, and T.S. Michelman. 1994. “Heterogeneous Preferences and Aggregation in Environmental Policy Analysis: A Landfill Siting Case.” American Journal of Agricultural Economics 76:431-3.

Johnson, H.M. 1996.1996 Annual Report on the United States Seafood Industry. Bellevue, Wash.

Green, P.E,, and K. Helsen. 1989. “Cross-Validation Assessment to Individual-Level Conjoint Analysis: A Case Study.” Journal of Marketing Research 26:346-50.

Green, P.E., and V. Srinivasan. 1978. “Conjoint Analysis in Consumer Research: Issues and Outlook.” Journal of Consumer Research 5:103-23.

Green, P.E. 1990. “Conjoint Analysis in Marketing: New Developments with Implications for Research and Practice.” Journal of Marketing 54(4):3–19.

Manalo, A,B, 1990, “Assessing the Importance of Apple Attributes: An Agricultural Application of Conjoint Analysis,” Northern Journal of Agricultural and Resource Economics 

19:118–124.

Anderson, J. L., and S.U, Bettencourt. 1993. “A Conjoint Approach to Model Product Preferences: The New England Market for Fresh and Frozen Salmon. ” Marine Resource Economics 8:3149.

Stoker, T.M. 1993. “Empirical Approaches to the Problem of Aggregation Over Individuals.” Journal of Economic Literature 31: 1827–74.

Moore, W.L. 1980. “Levels of Aggregation in Conjoint Analysis: An Empirical Comparison.” Journal of Marketing Research 17:5 16-23.

Green, P.E,, and K. Helsen. 1989. “Cross-Validation Assessment to Individual-Level Conjoint Analysis: A Case Study.” Journal of Marketing Research 26:346-50.

Wittink, D.R., and P. Cattin. 1981. “Alternative Estimation Methods for Conjoint Analysis: A Monte Carlo Study.” Journal of Marketing Research 18:101-6.

Louviere, J.J. 1988. “Conjoint Analysis Modeling of Stated Preferences.” Journal of Transport Economics and Policy 22 (1):93-119.

Vatn, A., and D. Bromley. 1994, “Choices without Prices without Apologies,” Journal of Environmental Economics and Management 26(2): 129-48.

Review: (Orme 2009): Which Conjoint Method Should I Use?

Bryan K. Orme, Sawtooth Software, Inc.
Copyright 2009

Objective

- provides greater depth in understanding issues involved with choosing a conjoint approach

Useful Information

- Ratings-Based Systems

* respondents are asked to rate (or rank) a series of concept cards describing product concepts using multiple attributes

* creator, Paul Green, suggested six attributes and 12 to 30 cards to avoid simplification strategies and that increased attributes meant increased cards

* early version referred to as "card-sort conjoint"

* Adaptive Conjoint Analysis

^ Sawtooth developed software to adapt cards to previous answers

^ possible to study a dozen to two-dozen attributes while keeping respondent engaged 

^ had varying sections of interview with one or few attributes presented at a time

^ led through systematic investigation over all attributes

^ resulted in full set of preference scores for levels of interest (part-worth utilities)

^ require computer administration

^ main-effects model

^ "all else equal," without inclusion of attribute interactions

^ understates importance of price, understatement increased with number of attributes

What were the key methods or approach?

- Choice-Based Conjoint (CBC)

* closely mimic purchase process in competitive contexts

* instead of rating or ranking, asked to choose from a set of products

* some products show products, about a dozen, on a screen as if on store shelves

* recommend researchers show more rather than fewer product concepts per choice task

* contain less information than rating per unit of respondent effort

^ do not learn degree of preference among products

^ do not learn relative preference among rejected alternatives

* Sawtooth can include up to 10 attributes, with 15 levels each (advanced design: 30 attributes with 254 levels)

* traditionally analyzed at aggregate level, but now individual level can be assessed using latent class and hierarchical Bayes (HB) estimation methods (majority of Sawtooth users use HB for final market simulation models)

^ Aggregate Choice Analysis

+ argued that it permits estimation of subtle interaction effects

+ market is not homogeneous, consumers have unique preferences

+ suffers from Independence from Irrelevant Alternatives (IIA) assumption (red bus/blue bus problem: very similar products in competitive scenarios receive too much net share), fail when there are differential cross-effects between brands

^ Latent Class Analysis

+ simultaneously detects homogeneous respondent segments and calculates segment-level part-worths

+ if market is truly segmented can reveal much about market structure and improve predictability over aggregate choice models

+ subtle interactions can be modeled

^ HB (Hierarchical Bayes Estimation)

+ "borrows" information from each respondent to improve accuracy and stability of each individual's part-worth

+ consistently proven successful in reducing IIA problem and improves predictive validity of individual-level model and market simulation share results

+ can employ main effects or models that include interaction terms

+ main effects models with HB are sufficient to model choice

+ HB outperforms aggregate logit for predicting shares for holdout choices and actual market shares even when there was very little heterogeneity in data

- Partial-Profile CBC

* used to increase number of attributes

* each choice question includes subset of total number of attributes which are randomly rotated into tasks so that each respondent considers all attributes and levels

* data are spread quite thin because each task has many attribute omissions

* require larger sample sizes to stabilize 

* individual-level estimation under HB does not always produce stable individua-level part-worths

* if main goal is to achieve accurate market simulations (and large enough samples are used), individual-level stability can be sacrificed

* subject to similar price bias as ACA (though not as pronouced)

* respondents can ignore omitted attributes and base choice solely on partial information presented in each task

* researchers and academics prefer full-profile conjoint techniques that display all attributes within each choice task to avoid bias of final part-worth utilities

- Adaptive CBC (ACBC)

* respondents first identify ideal product using configurator

* software builds a couple dozen similar product concepts for respondent to indicate which they would consider

* considered products are taken to a choice tournament to identify overall best concept where the choice tasks look like standard CBC tasks

* respondents find more engaging and realistic, but takes longer than CBC

* sample size is smaller than standard CBC because more information is captured from each individual

* more information at the individual level leads to better segmentation

* validity is slightly better than CBC

* capture percent of respondents find each attribute level to be "must have" or "unacceptable"

* not as useful for packaged goods with only a few attributes

* appropriate for problems involving more complex products and services with five attributes of more

- Sample size

* if dealing with relatively small sample sizes (especially less than 100), be cautious about using CBC unless respondents answer more than the usual number of choice tasks

* ACBC and ratings-based approaches

* If interview must be done on paper and small sample size is norm, consider CVA

Review: (Wessells, 2002) The Economics of Information: Markets for Seafood Attributes

Cathy Roheim Wessells, University of Rhode Island

Marine Resource Economics, Volume 17, pp. 153-162

Objectives

- Presents economic theory of information reflected by differing demand for and supply of seafood products in the presence or absence of certain attributes

- present theoretical framework for economics of market for information regarding product attributes

- reveal use of framework in empirical literature related to seafood

Methods/Approach
- This paper was a literature review of:

* the economics of attributes, specifically for seafood

* empirical literature of studies that broke seafood into attributes specifically using hedonic analysis, conjoint analysis and conjoint choice experiment

Results/Conclusions

- demand for attributes and information about those attributes exist, specific attributes are valuable to consumer

- there is a demand for information on product quality, seafood safety, environmental friendliness, and even the name of broker in the case of bluefin tunas sold in Tsukiji market

- demand is subject to nature of attribute, for cases where there is no demand there is no market for information

- if the government perceives there is a social benefit to the provision of product attributes or information, then there may be regulatory steps taken that force producers to expend resources to do so

- if consumers continue to demand attributes there is a potential for confusion if there are too many labels

* an extreme case of confusion is a lack of interest on the part of the consumer which could possibly lead to the disappearance for the market for attributes or information

Assumptions

- currently a competitive market for seafood

- consumers' interest in knowing more about seafood products will evolve and people will want desired attributes

- fishery sector only recently began paying attention to quality vs. aquaculture sector which has historically focused on quality attributes (ex. Pacific wild-caught salmon market has declined)

- seafood producers (farmed and wild) differentiate products with country of origin, brand, nutritional or environmental  labels

- producers will supply information as long as marginal cost to provide is less than marginal benefit

- some consumers concerned with dolphin-safe practices, but not shark-safe practices

- the difficulty of determining product quality in the market is even greater than that of determining price levels, since information about quality is usually more difficult to obtain.

- producers will make explicit claims for all positive aspects of goods which makes consumers suspicious of goods without claims (Aldrich 1999)

Opinion

According to Google Scholar this article has been cited 15 times, 3 are about seafood supply chains. The author never stated a methodology for how the empirical literature was chosen, so I am not certain the literature review is exhaustive. The stated objectives of discussing the economics of information about seafood attributes was concisely discussed, and the literature was informative as it discussed hedonic, conjoint and conjoint choice methodologies. The conclusions were not directly tied to or supported by the information in the paper, but it  still contributed to the general discussion. Overall the article had a lot of useful information for me regarding terminology. Specifically the breaking down of good or attributes into search, experience and credence will come in handy for choosing attributes for a conjoint analysis. The discussion of attributes in economic terms can contribute to the framing of our work.


One point of confusions is the last line of of the paper:

- " The extreme case of confusion is a lack of interest on the part of the consumer. Then, the framework presented above tells us that there will no longer be a market for attributes or information. Based on the empirical analysis discussed above, this would truly be unfortunate. "


I guess I just don't think that it will be "unfortunate" based on the empirical analysis of the discussion. I can see it would be unfortunate if industry and government could not align themselves with consumer information demand and therefore confusing the entire population to the point of not caring. The gap may arise in that industry and government do not know what information the consumer demands which is why papers such as this need to deliver this information to the proper recipients.

Useful Information

-  Another example of consumer demand for information on product attributes was the nationwide attempt by consumer advocate groups in the U.S. requesting that the federal government require labeling milk as to whether it came from hormone-treated dairy cows. In this case, the consumers wished for a regulatory solution, rather than a voluntary solution, by the milk industry. However, the U.S. Food and Drug Administration (FDA) decided that there was no public benefit in providing that information on milk cartons, as there are no known health risks of drinking milk from bST-treated cows. Thus the initiative failed, even though there was consumer demand for this information." (p. 154)

- search cost (Stigler 1961) = time and energy expended by consumer in determining seller with lower price (or any other attribute), marginal benefit of additional information is equal to marginal cost of obtaining additional information; there exists a willingness to pay for information and a marginal cost for obtaining it

- Types of products

* Search goods: can be determined by quality of searching price, color and smell

* Experience goods: quality discerned by experience of taste, durability or preparation

* Credence goods:  (Darby and Karni 1973)

_ imperfect market because it is not practical for consumers to assess quality of product

_ asymmetry of information between producer and consumer, ex. safety-approved or recyclable

_ labeling can transform credence attributes to search attributes that allow consumer to judge quality of good before purchase

- Quality Attributes of Food Products (Hooker and Caswell, 1996): 

* Food Safety: Foodborne pathogens, Heavy metals, Chemical residues, Food additives, Naturally occurring toxins, Veterinary residues

* Nutrition: Fat, Calories, Fiber, Sodium, Vitamins, Minerals

* Value: Purity, Compositional integrity, Size, Appearance, Taste, Convenience of Preparation

* Package: Package materials, Labeling, Other information such as recipes

* Process: Animal welfare, Biotechnology, Environmental impact, Chemical use, Worker safety

- "Recognizing that attributes like these have value to consumers, Lancaster (1971) characterized consumer demand for products instead as consumer demand for a bundle of attributes, where each product has one or more attributes. The essence of Lancaster’s framework is that a good by itself does not yield utility, but it possesses characteristics (attributes) that create it. Therefore, there is a demand for attributes in the marketplace. There is also a supply of attributes." (p. 156-157)

- An ecolabel such as the Marine Stewardship Council's (MSC) program creates a vertical supply curve

*only demand determines price

* the cost of certifying a fishery is fixed

* one level of environmental friendliness (fishery is well managed by MSC's standards and criteria)

* no gradation of management

* if size affects cost of supplying a well-managed fishery then supply can shift left or right

* food safety is a classic example of producers choosing not to supply product attributes because private marginal cost may outweigh private marginal benefits even though social benefit of information about attributes outweigh social cost

- freshness, fat content, color and shape have largest influence on price for bluefin tuna sold in Tsukiji Central Wholesale Market in Tokyo

* fat content is experience and search attribute

* determined in Carroll, Anderson , and Martinez-Garmendia (2001) using hedonic analysis, limited to success of data set in ability to differentiate various pieces of information pertinent to determination of product price

- Conjoint analysis vs. contingent choice

* used when market does not exist, product with certain characteristic has not reached the market

* Conjoint analysis
_ Holland and Wessells (1998): 9 hypothetical fresh salmon products presented, respondents asked to rank products, attributes that changed were price and production information such as farmed vs wild vs. no information, seafood safety inspection (USDA, FDA or no information)

^ used rank-ordered logit model (Beggs, Cardell and Hausman 1981)

^ credence goods: inspection, wild vs. farmed

^ search good: price

* Contingent Choice

_ Johnston et al. (2001): consumer tradeoff between products that are and are not ecolabelled in U.S. and Norway

^ using logit model, choice of certified is regressed against price, demographic attributes, preference attributes and environmental purchasing behavior

^ directly asked which agency would be trusted to provide sustainability claim (WWF, MSC or National Marine Fisheries Service (NMFS) in U.S. and WWF, MSC or Norwegian Fisheries Directorate (NFD))

+ NMFS had highest U.S. rating

+ 23% unsure

+ certifying agency is only important to Norwegians

* primary reason for use of conjoint choice is no market data exist from which to compare consumer demand

- seafood consumers place relatively little value on guarantees offered solely by industry groups (Wessells and Anderson 1995)

Further Research

- "There have not been any studies determining the cost structure of supplying seafood safety, environmental friendliness, or other attributes, nor have there been studies of the costs of supplying this information to the consumers." (p. 160)

Useful references

Homans, F.R. and J.E. Wilen. 1997. A model of Regulated Open Access Resource Use. Journal of Environmental Economics and Management 32:1-21.

Mariojouls and Wessels, 2002. ???. Journal of Environmental Economics and Management 32.

Hooker, N.H., and J.A. Caswell. 1996. Regulatory Targets and Regimes for Food Safety: A Comparison of North American and European Approaches. The Economics of Reducing Health Risk from Food, J.A. Caswell, ed., pp. 1–17. Storrs, CT: Food Marketing Policy Center.

Holland, D., and C.R. Wessells. 1998. Predicting Consumer Preferences for Fresh Salmon: The Influence of Safety Inspection and Production Method Attributes. Agricultural and Resource Economics Review 27:1–14.

Caswell, J.A. 1998. Valuing the Benefits and Costs of Improved Food and Safety and Nutrition. The Australian Journal of Agricultural and Resource Economics 42: 409-24.

Darby, M.R. and E. Karni. 1973. Free Competition and the Optimal Amount of Fraud. Journal of Law and Economics 16:67-88.

Lancaster, K.J. 1971. A New Approach to Consumer Theory. The Journal of Political Economy 74: 132-57.

Stigler, G. 1961. The Economics of Information. Journal of Political Economy 69:213-25.

Aldrich, L. 1999. Consumer Use of Information: Implications for Food Policy, Food and Rural Economics Division, Economic Research Service, U.S. Department of Agriculture. Agricultural Handbook No. 715.

Nelson, P. 1970. Information and Consumer Behavior. Journal of Political Economy 78: 311-29.

Nelson, P. 1974. Advertising as Information. Journal of Political Economy 81: 729-54.

Beggs, S., S. Cardell, and J. Hausman. 1981. Assessing the Potential Demand for Electric Cars. Journal of Econometrics 17:19–20.

Akerlof, G.A. 1970. The Market for ‘Lemons’: Quality Uncertainty and the Market Mechanism. Quarterly Journal of Economics 84:488–500.

Wessells, C.R., and J.G. Anderson. 1995. Consumer Willingness to Pay for Seafood Safety Assurances. Journal of Consumer Affairs 29:85–107.

Wessells, C.R., J. Kline, and J.G. Anderson. 1996. Seafood Safety Perceptions and their Effects on Consumption Choices under Varying Information Treatments. Agricultural and Resource Economics Review 25:12–21.

Johnston, R., C.R. Wessells, H. Donath, and F. Asche. 2001. A Contingent Choice Analysis of Ecolabelled Seafood: Comparing Consumer Preferences in the United States and Norway. Journal of Agricultural and Resource Economics 26:20–39.

Holland, D., and C.R. Wessells. 1998. Predicting Consumer Preferences for Fresh Salmon: The Influence of Safety Inspection and Production Method Attributes. Agricultural and Resource Economics Review 27:1–14.

Carroll, M., J.L. Anderson, and J. Martinez-Garmendia. 2001. Pricing U.S. North Atlantic Bluefin Tuna and Implications for Management. Agribusiness 17:243– 54.
Anderson, J.L. and S. Bettencourt. 1993. A Conjoint Approach to Model Product Preferences: The New England Market for Fresh and Frozen Salmon. Marine Resource Economics 8(1):31–49.

Wirth, F.F., C.K. Halbrendt, and G.F. Vaughn. 1991. Conjoint Analysis of the Mid- Atlantic Food-Fish Market for Farm-Raised Hybrid Striped Bass. Southern Journal of Agricultural Economics 23:155–64.