The Economics of Seafood Fraud

By Dr Sang-Hyun Kim

To those who remember the horse meat scandal in 2013 in Europe, a recent donkey meat scandal in Pakistan may not appear particularly surprising. Food adulterations are so prevalent that people may laugh rather than worry.

This is probably not because people do not care about what they eat. It is rather because there are few things they can do to avoid mislabelled foods. I believe, that laughing is a psychological defence scheme rather than a response to something really funny.

When you eat a beef burger, can you tell for sure that the patty is indeed made of beef? Can you tell the difference between tuna and albacore after tasting them? How many oils or chili powders can you discern by their tastes? You probably cannot discern all the ingredients of the dinner that you had yesterday. Even if you felt that something tasted funny, you would probably not have wanted to think that something was seriously wrong. Even if you felt quite confident that something was wrong with the meal, it could be very costly to prove it, say at the court. So it would be easier to persuade yourself that it was probably nothing serious.

The difficulty of detecting and verifying food adulterations is one of the main reasons why the fraud is so prevalent. In economics, an experience good is defined as a product of which quality and/or price are difficult to observe in advance, but these characteristics can be learned upon consumption. A credence good, on the other hand, is a good whose utility impact is difficult or impossible to ascertain even after the consumption. These are abstract concepts, and most products and services in the real world do not fit into these categorisations. One can regard foods as experience goods by focusing on that consumers cannot “see” the taste in advance but “feel” the taste upon consumption. To some extent, they are credence goods as well because more often than not it is difficult to discern ingredients with certainty.

The bottom line is that when the detection and verification of fraud are difficult, consumers are likely to face mislabelled products. The most obvious way to discourage the fraud is for the government to inspect sellers regularly and to impose fines when any dishonest behaviour is detected. Unfortunately, such an inspection has been rare. Worse is that punishments for the misdeeds have not been strong enough to discourage the fraud, partly because it is difficult to tell who among producers, wholesalers, and retailers is really responsible.

What is rather surprising in this aspect is that many, I wish I could say “most”, sellers do not cheat consumers. Oceana, an international non-profit organisation conducted one of the largest seafood fraud investigations in the U.S. from 2010 to 2012, which revealed that less desirable or cheaper fish are frequently mislabelled as more desirable or expensive fish in grocery stores and restaurants. The proportion of retailers who were reported to sell mislabelled seafood ranges from 18% for grocery stores to 74% for sushi venues (Warner et al. 2013). In a recent paper, Hao Lan of Norwich Business School and I investigated the factors that affect fish merchants’ mislabelling behaviour (Kim and Lan, 2015). This is, in a sense, to see why not every seller is cheating even though it is not very likely to be punished by the government for mislabelling seafood.

Wikimania_Sushi

In the paper, we modelled the reputation concern of sellers: if a seller mislabels a low quality product as a high quality one, some consumers will notice that the seller cheated, and will go to another seller. This process is facilitated by two factors: the number of sellers in the market and the average income level of the consumer group. It is rather obvious that if there are more sellers in business, consumers can more easily find another seller to visit. Also, because the cheating is detected by more consumers in regions with higher income level where more people consume more expensive foods, sellers in those regions have to manage their reputation with greater care.

Analysing the DNA-tests data of Oceana, we checked whether mislabelling is indeed more prevalent in regions with lower average income and fewer sellers. The patterns found in the data were consistent with what the theory predicts. We also found that labels are more deceiving in restaurants than in grocery stores. This might be because consumers can more easily identify fish species in groceries (where consumers can have a look at large chunks) than in restaurants (where cooked and disintegrated fish are served). In grocery stores, frozen seafood is less likely to be mislabelled.

Take-aways from this analysis are as follows.

  • Even when the government regulation is imperfect, sellers may be reluctant to cheat due to the possibility for consumers to go to another seller. So suspecting a food fraud whenever possible is a good attitude to discipline sellers.
  • It is not only good for disciplining sellers but also rational to suspect cheating, especially in sushi places, because food adulterations are indeed very prevalent.
  • Fortunately, there are a few things you can do (or shouldn’t do) to avoid mislabelled seafood: do not try an expensive food in a remote area with low average income; tuna and seabass are very often incorrectly labelled (about 60%), so you may want to avoid them; in our data, 86% of fish samples that were labelled as snapper were not a snapper, so do not buy a snapper.

Food fraud is not funny. And, we can do something about it; I believe that it can be almost completely eliminated if the regulator punish severely whoever sells a mislabelled food. In the meantime, we need to be wiser not to be a victim of the fraud.

Kim, Sang-Hyun and Hao Lan (2015) The Economics of Seafood Fraud, working paper.

Warner, Kimberly, Walker Timme, Beth Lowell and Michael Hirshfield (2013) Oceana Study Reveals Seafood Fraud Nationwide, Oceana.

The University of East Anglia are currently running a free course on Identifying Food Fraud as part of a range of interactive MOOCS (Massive Open Online Courses).

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