A Cartel's response to cheating: an empirical investigation of the De Beers diamond empire.

• Author: Bergenstock, Donna J.

1. Introduction

   Cartel theory suggests that serious punishment in the form of price competition is the predicted response to cheating by a cartel member, but recent work on cartel operations suggests that some cartels might use a richer set of more measured responses to support their arrangements. Levenstein (1997) and Genesove and Mullin (2001) describe cartels that used limited retaliation, as well as communication, to maintain collusion. However, even these cartels reacted to massive public cheating with the retaliatory response predicted by traditional theory.

   The record of the diamond cartel, headed by De Beers, suggests that in some circumstances a cartel might be flexible in its response to cheating even when that cheating is massive. In this paper, we study a particular episode of such cheating by one of the diamond cartel's principal members, namely, Russia. Our findings suggest that De Beers did not respond with aggressive price competition, but rather used a policy that combined accommodation and negotiation. Key to this result were the importance to De Beers of preventing diamond prices from falling and the firm's willingness and ability to absorb excess diamonds into inventory.

   De Beers, as head of the diamond cartel, influences the entire diamond trade from mining to retail. The bulk of its direct involvement, however, aside from mining, is in the purchase and sale of just-mined diamonds, or "rough." The Central Selling Organization (CSO) (1) buys the rough from mines owned or controlled by De Beers, from cartel members, and also from independent producers. As a result, much of world production flows through the CSO to be sorted, valued, and ultimately resold. (2) On the other hand, the CSO's sales downstream are carefully monitored to keep prices from falling. Consequently, the CSO holds a fluctuating stockpile of diamonds, which rises when growth in production exceeds growth in final diamond demand.

   De Beers supports this "supply management" strategy with legendary advertising campaigns that create and nurture final demand for diamonds while also reinforcing the image of diamonds as rare and valuable gems. It is important to keep prices from falling so as to perpetuate consumers' belief that diamonds remain as scarce as they were before the South African discoveries in the 19th century began a large expansion in their supply. (3) Promoting demand allows De Beers to maintain diamond prices, despite growth in diamond production, while keeping the size of the CSO inventory under control. Together, the supply management strategy and the cartel-funded advertising that encourages final demand have convinced cartel members of the value of cooperation: diamond prices are maintained and members have a sure flow of funds from sales of rough to the CSO (Johnson, Marriott, and von Saldern 1989).

   The result of De Beers' strategy has been a long-lived cartel that continues to sell diamonds at prices far above the marginal cost of mining them (Ariovich 1985), despite sometimes rapid growth in world production. Nevertheless, cartel members have occasionally cheated, and De Beers has a reputation for aggressive action against anyone threatening the long-run stability of the diamond market. For instance, in the early 1980s, De Beers is alleged to have punished Zaire for attempting to leave the cartel by flooding the market with the low-quality industrial diamonds that were Zaire's principal product (Spar 1994). Similarly, De Beers greatly increased its sales of low-quality rough diamonds to Indian diamond cutters in 1996 when Argyle, an Australian company, left the cartel (Hart 2001). (4)

   In both cases, De Beers was willing to drive down prices and take the short-term losses to punish defectors. However, because the grade of diamonds involved was low, price reductions at this level did not threaten the popular image of diamonds as a luxury item. In contrast, De Beers has always treated the controllers of the Siberian mines, first the government of the Soviet Union and more recently of Russia, with more caution. The Siberian finds of 1954 developed into some of the richest sources of diamonds in the world, producing roughly 20% of the world's gemstone-quality diamonds during the period of our analysis. As such, the output of these mines could have seriously disrupted the diamond markets, and, despite periodic difficulties, De Beers worked hard to negotiate contracts that would keep Soviet and Russian governments as members of the diamond cartel. (5)

   Yet De Beers faced a serious challenge in the mid 1990s when Russia began leaking diamonds onto the market. The financial crisis in the Russian Federation apparently encouraged clandestine sales from the Russian diamond stockpile, which was of unknown, but reportedly large, size. These sales, most notably those from 1993 to 1996 (when, according to press reports, close to $1 billion in rough diamonds was leaked from Russia) threatened the pricing structure carefully developed by De Beers over the previous 100 years. (6)

   De Beers' public stance, according to a statement by Gary Ralfe, the Managing Director of De Beers, was that the cartel tried to avoid purchasing leaked diamonds so as not to bankroll those doing the leaking (Gooding 1997). However, the CSO is popularly supposed to have increased its purchases of rough diamonds to absorb some of the extra diamonds and, thus, maintain diamond prices. Our goal in this paper is to identify and then separate long-run changes in CSO inventory and in the quantity of CSO diamond sales from short-run movements in these two variables that occurred in response to the Russian dumping. We find that the cointegration techniques that were developed to identify long-run common movements among major macroeconomic time series variables are well suited to our task.

   If a set of time series variables can be shown to be cointegrated (i.e., they tend to move together over the long term), then current changes in the variables can be separated into those changes that bring variables back toward the long-run relationship, and short-run changes in response to other influences. We present evidence suggesting that a long-run relationship between diamond production, CSO inventory, and final diamond demand does exist, as would be suggested by the supply management strategy. We then use an error correction model to search for any evidence that the Russian leaks can be related to short-run changes in CSO decisions about inventory, the quantity of carats sold, or the diamond price and find that only inventory appears to have been affected by the leaks.

   Although applying cointegration techniques to a microeconomic problem is uncommon, we believe it has some advantages in this case. We are principally interested in separating long-run from short-run movements in a single time series, namely, the CSO inventory. This approach allows us to do so without having to specify a model of De Beers' buying and selling decisions that incorporates their calculations about how different possible responses to cheating might affect their future relationship with Russia or with other cartel members or might threaten the popular image of diamonds in the eyes of consumers.

   In the next section, we briefly review the steps involved in establishing cointegration among variables, of estimating the long-run, cointegrated relationship(s), and finally of estimating the associated short-run relationships, known as the error correction model. We then return to the diamond industry, describing the variables we expect to be cointegrated, and why. A section describing the data follows, and we then present our empirical analysis. We end with a short discussion of the results and some information about recent developments in this industry.

2. Methodology (7)

   The first step in a cointegration study is to identify variables that are potentially cointegrated, that is, that move together over time. To be part of a cointegrated relationship, a variable must have the property of being nonstationary, in particular, the variable must be stochastically trended. Consider, for example, the variable [x.sub.t], where

   [x.sub.t] = [alpha] [x.sub.t-1] + [[epsilon].sub.t]

   and [[epsilon].sub.t] is white noise with zero mean. If [absolute value of [alpha]]

   Once nonstationarity is established for each variable of interest, we check statistically for the existence of relationships among them, that is, for evidence that the variables move together over the long term. Such a relationship is called a cointegrated vector of the variables involved and is a special case: cointegration implies that there is a weighted average of the nonstationary variables that is itself stationary, that is, that has no stochastic trend. Suppose, for example, that in the long run [x.sub.1], and [y.sub.t] are related so that [y.sub.t] = b[x.sub.t] + [v.sub.t], where [v.sub.t] is stationary. Then the weighted average [y.sub.t] - b[x.sub.t] is stationary and the two variables are cointegrated.

   Because the weighted average series is stationary, the relationship among the variables in a cointegrated vector remains stable over the long run, although at any particular time period variables might deviate from it. In economic terms, the idea is that a set of cointegrated variables can depart from a common trend in the short and intermediate run but, in the long run, will return to an equilibrium path. (9)

   Statistical techniques only allow us to identify whether (and how many) such relationships exist within the data. We then use our knowledge of the industry and of economic theory to suggest restrictions that allow identification of the individual vectors. After testing for the acceptability of these restrictions, we can then find numerical estimates of the vectors, which show how different sets of variables move together in a long-run relationship.

   Once any long-term relationships have been identified, we estimate an...