Differences in selling mechanisms, differences in prices: the case of the Boulogne s/mer fish market

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1 Differences in selling mechanisms, differences in prices: the case of the Boulogne s/mer fish market Sylvain Mignot 1, Gabriele Tedeschi 2 and Annick Vignes 1 December 18, Ermes-CNRS, EAC 7181, Université Pantheon-Assas Paris II, 12, place du Pantheon, F Paris Cedex 05, France sy.mignot@gmail.com, avignes@u-paris2.fr 2 Universita delle Marche, Ancona, Italy Abstract The Boulogne s/ mer fish market is organizing through two different submarkets: a negotiated one and an auction one and people can freely choose where to sell their merchandize. First empirical results show that the same species of fish are sold in more or less same amount of quantities to the same people on both markets and that the distribution of the prices transactions slightly differs from a sub-market to the other. This paper then shows how agents adopt herding strategies to choose between one market or the other. keywords: fish market, price dispersion, micro data analysis J.E.L. codes: L1, L2, D8, G24 Acknowledgments: 1

2 1 Introduction J. Stiglitz has recently quoted that one of the recent revolutions in economics is an understanding that markets do not automatically work well. Design matters, and our paper seeks to understand the role and the influence of two different sale mechanisms, i.e auctions and pairwise, on the markets results in terms of prices and quantities exchanged. The influence of different trade mechanisms on the market outcome has been largely emphasized in the economic literature and two important questions are considered, one comparing different auctions mechanisms, an other one comparing auction mechanisms and pairwise exchanges. Concerning the comparison between different auction mechanism, the literature is quite huge, founded by Vickrey (1961) seminal result. Following this way, a famous theorem proposed by Myerson (1981) or Riley & Samuelson (1981) demonstrates the equivalence of revenue for sellers through four different auctions mechanisms. This theoretical result requires strong hypotheses concerning the behavior and the intrinsic characteristics of agents and these conditions are quite difficult to reach on real markets. This point is underlined by Maskin & Riley (1985) who show that if buyers have downward sloping demand curves and that more than one single unit of a same good is sold, the revenue equivalence theorem fails. The interactions between market organizations, agents behavior and goods characteristics will then be enlighten by the literature. Milgrom (1986) and Milgrom (2004) show that auctions are more favorable to sellers than to buyers in the sense that they absorb the whole buyers surplus. This result has been reinforced by Bulow & Klemperer (1996) who demonstrates that the auction is always preferable when bidders signals are independent. More recently, some results, mostly based on empirical or experimental evidence weaken the idea of auction dominance. Progrebna (2006) reports on a natural experiment in the British television show, Bargain Hunt, which offers a good opportunity to compare bilateral bargaining with auctions. It appears that auction prices are lower than negotiated prices. Kirman & Moulet (2009, forthcoming) compares two forms of market, a auction one and a negotiated one. They show, through simulation, that, if the auction is more interesting for "rich buyers", the one with the higher reservation prices, the negotiated market allows "poor buyers" to purchase. These evidences are more in line with the studies developed by the literature in financial markets. Viswanathan & Wang (2002) observe that on stock markets, small orders get executed via a limit-order book and large orders get executed via a dealership market. Considering the links between market architecture and behavioral ecology, Bottazzi, Dosi & Rebesco (February 2005) highlight the role of the institutional setting in the dynamic of prices determination but also suggest that this dynamic 2

3 can result of a complicated interaction between the trading protocol and the ecology of traders behaviors. The fish market has a long tradition in the economic literature. Thornton (1869) observed that the equilibrium price differs whether the fish is sold through English or Dutch auctions. Many authors refer to it when they want to investigate the organization of exchanges, no matter whether they are dealing with equilibrium or disequilibrium. This market constitutes a kind of economic paradox in the sense that, in a lot of cases, at first glance, one could conclude that these markets are pure competitive markets. They are constituted by a a large enough number of sellers to avoid collusion, a huge number of buyers, it always exists a collective place of transaction and exchanges are carrying out in a very short time-period. There are no barriers to enter this market (traditionally, fish boats can sell their merchandize where ever they want, they only have to pay taxes on the fish sold). But the empirical analyzes always reveal strong price dispersion for homogeneous or very similar goods. The problem is therefore to explain this price dispersion. A second glance lets appear some important differences with the usual representation of a Walrasian market. In particular, the fact that fish markets are regularly ones (generally they are daily markets), implies non anonymity between individuals (buyers and sellers). This article seeks to understand how the very particular architecture of the Boulogne s/mer (North of France) fish market can be a stable one. This market is organized both through an auction mechanism and a negotiated one. We propose to take into account the influence of social interactions in the way opened by Kranton & Minehart (2001). From our point of view, the main question here is to understand the sellers strategies or in other terms, understand how they decide the repartition of their merchandize and when they decide to switch from a market to the other. They are heterogeneous in terms of quantities caught and can choose to act on the two different sub-markets. Through an empirical analysis, our article seeks to characterize the sellers decisions and understand if it exists a kind of learning mechanism on this market. This paper is organized as follows: the section 2 of this paper presents the database, the section 3 emphasizes the network analysis. The section 4 presents the prices distribution analysis. The section 5 considers the question of agent strategic variables. The conclusion follows. 3

4 2 The market The Boulogne fish market is the most important fish market in France, in terms of quantities. It is situated in the North of France, near the Belgium frontier. Boats which lay down their fish come from France but also from other countries (mainly from Great Britain and Holland). 213 boats are registered in this market, that we will consider as sellers in what follows. 100 buyers purchase regularly, most of them present on both sub-markets. This paper deals with the offer side strategies. We do the assumption that the sellers are the first players in choosing the market, because they arrive first and then cannot move. This market is opened 6 days a week and functioning through two different sub-markets. Each day, sellers can decide if they sell all their quantity through one organization (auction or negotiated) or if they share their production (and in which amount) between the two places. Once they have decided, they can t change their strategy until the next market day. The following subsection presents the database we exploit for our analysis. 2.1 Descriptive Results The analysis of the database tells a story of heterogeneity. On this market, sellers are very different in terms of quantities offered as it is shown by figure 1 and in terms of quality of the products (the species proposed are sold at very different prices according to their membership). Figure 1: distribution of the log of the quantities sold per seller. 4

5 Our database concerns three years (from 2 nd of January 2005 to the 29 th of December 2007) of daily transactions in Boulogne s/mer. At the beginning of the period, the market was organized through one exclusively price mechanism, the auction one. From the 1 st of April 2006 the authority has allowed agents to trade both through auction and bilateral. For each transaction, we know the date, the type and characteristics (size, presentation, quality) of the fish exchanged, the buyers and sellers identity, the type of trade mechanism (auction or negotiated), the quantity exchanged and the price. The database represents more than half a million transactions. There are 213 sellers on the period considered: the sellers are mostly fishermen (or owners of fish boats) and not present every day. 200 sellers are present on both markets. The other 13 are solely present on the negotiated market or selling exclusively one type of fish in high quantities or selling several different species in non significant amounts. We remove them of our analysis. The analysis of the distribution of prices reveal that prices are lower in average on the auction market than on the negotiated one. This is not true anymore when we consider the different species of fish separately. These results are clearly posted further. The trades concern 80 species of fish. Between 37% and 54% of each of the four main fish species are sold on the auction market which suggests an equivalent distribution of the production between the two market mechanisms as it is shown in figure 2. Figure 2: The negotiated market: This graph shows that the 20 main species of fish are equivalently sold on both markets The two sub-markets (auctions and negotiated) have an equal importance: we 5

6 observe on figure (3) that significant percentages of the production are traded on both markets each day tons are sold through auctions while tons are exchanged through the negotiated market. Around 60% of the transactions are made on the negotiated market and this proportion is stable through the period considered. This proportion stays more or less the same when one considers the quantities 63% or the value 66%. If we exclude the six bigger boats (which exclusively sell a particular specie of fish, i.e. the coalfish, very cheap), we obtain 42% of the transactions on the auctions market. This organization gives the feeling to be a stable one and this looks like a paradox. The average quantity per transaction is slightly higher in auction (94 kilos) than in negotiated (87 kilos). Figure 3: The negotiated market: This graph exhibits the frequency of the part of the production sold on the negotiated market. Significant percentages of the production are sold on both markets each day. We drive our analysis on the whole market (and transactions) except the ones concerning the "big boats". When we consider all the daily transactions (without discerning between the different species of fish), we find that the prices distributions are not normal and differ from each other. This result is still true when we consider the returns distributions. This difference remains significant when we consider the monthly aggregated distribution. First analysis also reveal that the same people are transacting on these two "sub-markets" and that the same types of fish are sold through the both mechanisms. Considering the prices/ quantities relation, we find the usual negative one, but econometric evidence also reveals that quantities do not explain all the prices. Looking a bit further to sellers behavior, we observe that there exists a negative correlation between the quantities sold on one market one day and the revenue got on the other the day before. Choosing the price mechanism seems to be a strategic tool for sellers. Figure 4 reveals that among the biggest sellers, two main strategies dominate, one which consists to sell mostly on the negotiated market, another one which consists 6

7 to sell less than 45% on this market. Figure 4: The negotiated market: distribution of quantities purchased by the 100 biggest sellers on each market. Two mains strategies dominates, selling mostly on the negotiated market, or selling less than 45% on this market. Considering that each seller proposes a diversified gathering, it seems important now to explore the revenues properties, looking at the dispersion (or concentration) of the revenues among the sellers. 2.2 The repartition of revenues through the two different markets The total revenue on the negotiated market is higher than on the auction one ( euro versus euro): this could suggest a kind of specialization in terms of quality (more expensive fishes would be sold on the negotiated market).we also observe that most of the species are sold on both markets. The graphs 5 and 6 establish that most of the boats have mixed strategies concerning the revenues. Very few of them exclusively sell on one market. It seems that smaller boats win the most important part of their revenue on the negotiated market while this is not true for the bigger boats. We therefore have to keep in mind that the 6 biggest boats solely sell through pairwise exchanges. We also observe that the heterogeneity in revenues reflect the heterogeneity in quantities underlined above. 7

8 Figure 5: Distribution of revenues on both markets for the 100 biggest boats in terms of revenues Figure 6: Distribution of revenues on both markets for the 100 smallest boats in terms of revenues 8

9 Table 1: Skewness, Kurtosis, Mean and Standard Deviation for the Revenues Distribution. Auction Market Negotiated Market kurtosis skewness M ean M edian St.Dev The graph 7 shows the density distribution function (DDF) of the revenues on both market. The curves have similar slopes and seem to follow an exponential law, or a mind tails distribution. The table 1 explores the revenues distribution properties. This distribution exhibits a small (but positive) kurtosis value with a right tail. The revenues are higher in average on the negotiated market than on the auction one and the difference between mean and median is significant for both distributions. We know now, that this market is a place where sellers of different importance are in competition. The following section seeks to understand if some collusion can emerge, or some leader-follower phenomena, which could help the boats to to pass their differences. 9

10 DDF revenues Figure 7: The DDF of the revenues on both markets: the auction revenues are in black and the negotiated revenues are in red. 10

11 3 The Network structure We have now understood that there exists a huge heterogeneity between sellers in terms of quantities. When they arrive in the morning, they have to choose the amount of quantities to let on each sub-market. They also have to choose the repartition of the different species, using the quantity they let on the two different markets as a strategy tool. These decisions do have an influence on the form of the competition which could be more or less dominated by some influent groups. One question of this paper is to understand which rules the boats follow to take their decision. Our hypothesis is, because the sellers meet regularly, know and observe each others, they socially interact. We then consider the structure of a particular network which would be driven by collusion strategies. We do the hypothesis that some sellers play regularly together, in other words, that it exists some coalitions between the sellers. To evaluate the existence and stability of social coalitions, we build a homogeneous seller-seller network, where two sellers are linked when they both decide to sell the majority of their merchandize on the negotiated market. In this network, we say there is a link between two agents (two sellers) when they are both purchasing the majority of their merchandize on the same market in the same day. To evaluate the weight of the link W between two sellers i and j, we use the measure proposed by Bonacich (1987) as presented below. q i N,t is the quantity of fish sold by the seller i on the negotiated market on day t. q i t is the total quantity of fish sold by the seller i on both market on day t. n is the number of days considered. with : W i,j = n W i,j,t (1) t=1 W i,j,t = 1 if qi N,t > 0.5 and qj N,t qt i q j t W i,j,t = 0 if qt i = 0 or q j t = 0 W i,j,t = 2 otherwise > 0.5 (2) A link between two sellers exists when the corresponding weight is strictly positive. l i,j = 1 W i,j > 0 (3) 11

12 The graph 8 exhibits that the main clusters are composed of sellers selling on average the majority of their fishes on the same market. In figure 9 we research communities in the whole group of sellers, by optimizing the modularity score, revealing the existence of five distinct groups. The two main ones being those of sellers selling in large majority on one market. If we do the same analysis monthly we always find two communities. One selling mainly on the negotiated sub-market and the other on the auctions one, there is never a community of sellers adopting a real mixed strategy. The sellers composing the two communities changing from one month to the other, it makes it difficult to calculate a stability for the graph. These figures reveal that the decision of going on one market or the other certainly result on the decision of others. Compared to the random graph, the whole network reveals a positive significant assortativity (0.607 against 0.001) and the transitivity (or clustering) is against These results are stable among the months. We now explore the properties of the prices distributions and the returns distributions in the following section. The idea is to better understand the formation of prices on these two sub-markets and verify if there exists some differences from one market to the other. 12

13 Figure 8: sellers relationships and strategies (blue sell mainly on negotiated market and red on auctions. Figure 9: sellers communities. 13

14 Figure 10: sellers communities for two months taken at random. 14

15 4 Prices and returns In what follows we analyze the forms of the distributions of prices and returns on the two markets. We first drive our analysis for the prices of all transactions, which means that the goods are heterogeneous. We calculate the kurtosis and the skewness and compute the Jarque bera test, which clearly indicate that for all the distributions, the skewness and the kurtosis are different from the ones of a normal distribution. In particular, high value of kurtosis indicate the presence of volatility. This effect is much more present on the negotiated market than on the auction ones. Surprisingly, it is also more present in the prices distributions than in the returns ones. In subsection 5 we analyze the prices distributions for each different species and show that the behaviors (shape of the distribution and average prices differences) differ from some species to the others. 4.1 The prices distributions analysis From Figure 11 and Figure 12, we observe that the prices times series reveal some "bubbles" formations with seasonal repetitions. The price index used in this paper corresponds to a classic Paasche index. For each day t we calculate : i=n q i ˆP t = (p i ( i=n i=1 (q i) )) i=1 p i being the unit price of one transaction, q i the quantity sold in this transaction, and N the number of transaction made on this day. shows the distribution of prices for the two markets. Clearly, the prices distribution are not normal with a high kurtosis value and a right asymmetry (or right-tail) representing low-frequency, large-gain events. The high kurtosis value suggests some volatility on this market. These phenomena are more important on the auction market than on the negotiated one which suggests that one market is more risky than the others. 15

16 15 auction prices days Figure 11: Times series of daily prices on Auction markets 15 negotiated prices days Figure 12: Times series of daily prices on Negotiated markets 16

17 Table 2: Jarque Bera Test, Skewness, Kurtosis, Mean and Standard Deviation for the Prices Distribution. Auction Market Negotiated Market JarqueBeraT est Chi 2 (2) kurtosis skewness M ean M edian St.Dev The returns analysis Going a little bit further in our risk analysis, we now calculate the returns on each market, for the whole studied period. The Figure 13 and Figure 14 plot the returns time series on both market for the studied period. The return for each day is defined as ˆP t R t = ln( ) P t 1 ˆ We then calculate the standard statistics on the returns on revenues. The results are null for all the distributions. 17

18 Table 3: Jarque Bera Test, Skewness, Kurtosis, mean, Median and Standard Deviation for the Prices Returns Distribution. Auction Market Negotiated Market JarqueBeraT est Chi 2 (2) kurtosis skewness M ean M edian St.Dev Table 4: Skewness and Kurtosis for the Revenues Returns Distribution. Auction Market Negotiated Market kurtosis skewness M ean M edian St.Dev

19 4 2 auction returns days Figure 13: Returns on the Auction market 4 2 negotiated returns days Figure 14: Returns on the Negotiated market 19

20 5 Different species, different prices behavior Considering the daily transactions for each types of prices, we observe that the types of fish which are significantly more expensive on the negotiated market are the one for which the unit price is higher in average: (6.83 against 3.87). Concerning the cheapest fishes (3, 56), we observe that there is no significant differences between the prices on the auction market and the prices on the negotiated one. A third group of fish is characterized by average daily prices which are significantly higher on the auction market than on the negotiated. The quantities are, without any surprise lower in the first group than in the two others. (4.2 millions of kilos for the first group, 19 millions of kilos more expensive in auctions than in negotiated, 20 million for which the prices are equivalent. We also observe that the quantities for the fish which are more expensive on the negotiated market are higher on this market: the same is true for the fish which are more expensive on the auction market. This result is obtained, compare to the "neutral" group, the one for which the prices are the same on both market. Building these three different families of fish will allow us to better classify the sellers production and better understand agent s strategies. 5.1 Strategic quantities The question of our paper is to understand the way the individuals take their decision (auctions or negotiated), i.e. enlightening the strategic variables. The distribution analysis in 4.1 suggests that the prices and returns are higher on the negotiated market than on the auctions one. The auction market is also the one where the kurtosis value is the most important, i.e. the market is riskier. When we look at the detailed distributions (distributions by species) we realize that these phenomena are not true anymore. For some species, the auction market is more profitable, at least in terms of average prices. Our intuition is that the repartition of species and quantities through the two markets will be used as strategic variables by the sellers. In what follows, we evaluate the correlation between the difference of quantities for two consecutive weeks and the difference between the two markets prices interval. (ˆq S t ˆq S t+1 ) ( P St A P St N P S t+1 A P S t+1 N ) A designs the auction market, N the negotiated one. S t and S t+1 two successive weeks. 20

21 We obtain a significant negative correlation between these two measures. When the global quantity increases, the prices difference between the two markets decrease. In other terms, going on one market or the other is a strategic choice when the quantities are small. 5.2 Looking for herding behavior To assess the reciprocal influences among sellers and the coordinations of their actions we implement the herding coefficient used by Markose, Alentorn & Krause (2004) Since herding is the consequence of mimetic responses by agents interacting on a communication network, we measure, for each day t, the number of agents T r a,t taking the same decision to sell on auction market. So the herding phenomena can be captured at each t by a simple time varying herding function. H t = T r a,t T r t [0, 1], where T r a,t is the number of transactions that have been done at day t on the auction market and T r t is the total number of transactions per day. When this function is close to a half, the market show no herding since sellers play without no coordination between auction and negotiated market. When H tk is close to zero, the market is herding in the direction of negotiated and vice versa when H tk =1. The figure 15 exhibits the daily time series of herding coefficient for the period studied. 21

22 1 0.8 hearding coefficient days 6 Conclusion Figure 15: Herding behavior This paper seeks to understand the functioning of a very particular market. On this market, different species of fish are sold by sellers of very different importance in terms of presence, quantities and quality. The stability of the coexistence between two parallel sub-markets has given us the intuition that social relationships and coalitions may influence the market outcome. In this first step, we have explored the main statistic properties of a particular network (the one driven by the collusion behavior) on one hand and the statistic properties of prices and returns distributions on the other hand. We then have shown that social interactions do matter and that the prices and returns distributions follow some specific properties: they both exhibit positive kurtosis and right tail coefficient but are also concentrate around null annual average. A next step of our work will consist in driving an agent based model to better explain the strategic decision of the agents. 22

23 7 Annexes The figure reveals that most of the buyers buy on both markets. Figure 16: Distribution of the quantities bought by each buyers on the negotiated market. The figures 17 and 18 exhibit the distribution of prices returns on both markets. Figure 17: distribution of the returns for the auction market 23

24 References Figure 18: distribution of the returns for the negotiated market Bonacich, P. (1987), Power and centrality: a family of measures., American Journal of Sociology 92, Bottazzi, G., Dosi, G. & Rebesco, I. (February 2005), Institutional architectures and behavioral ecologies in the dynamics of financial markets, Journal of Mathematical Economics Special Issue on Evolutionary Finance Volume 41, Issues 1-2, pp Bulow, J. & Klemperer, P. (1996), Auctions versus negotiations, The American Economic Review Vol. 86, pp Kirman, A. & Moulet, S. (2009, forthcoming), Systèmes Complexes et Sciences Sociales, Vuibert., chapter Impact de l organisation du marché : Comparaison de la négociation de gré à gré et des enchères descendantes. Kranton, R. & Minehart, D. (2001), A theory of buyer-seller networks, American. Economic Review 91(3), Markose, S., Alentorn, A. & Krause, A. (2004), Dynamic learning, herding and guru effects in networks, Working Paper 582. Maskin, E. & Riley, J. (1985), Auction theory with private values, American Economic Review vol. 75, pp Milgrom, P. (1986), Advances in Economic theory :fifth world congress of the Econometric Society, Cambridge University Press, London: Mac Millan and Cř, chapter Auction Theory, pp. pp

25 Milgrom, P. (2004), Putting auction theory to work., Cambridge. Myerson, R. (1981), Optimal auction design, Mathematics of Operations Research vol 6(1), pp Progrebna, G. (2006), Auctions, versus bilateral bargaining : Evidence from a natural experiment, Working Paper, available at SSRN. Riley, J. & Samuelson, W. (1981), Optimal auctions, The American Economic Review vol 71(3), pp Thornton (1869), On Labour, Mac Millan. Vickrey, W. (1961), Couterspeculation, auctions, and competitive sealed tenders, Journal of Finance, 16, Viswanathan, S. & Wang, J. (2002), Market architecture: limit-order books versus dealership markets,, Journal of Financial Markets, Elsevier, vol. 5(2),, pages , April. 25