9 prices<sup>13</sup> was still among the highest of all agricultural commodities (Gilbert 2003) and by 2003, coffee prices had remained below the cost of production for most of the world’s 25 million coffee producers for the third harvest in a row (TechnoServe 2003).

# _The ‘New’ Commodity Policy Context_

The apparent unsustainability of the ICAs, the observed inefficiency of many marketing boards and the limited success of domestic interventions in raising producer prices and reducing their exposure to volatility led commodity policy in new directions. Increasingly, in the place of traditional commodity policies, came a new generation of policy prescriptions that accepted the failure of efforts to support or increase the size of coffee returns and to stabilize its volatility. The policy focus shifted instead to market-based coping strategies for its persistent price volatility and to the distribution of existing revenues. In this emphasis on redistribution, the ‘new’ commodity policy is supported by a burgeoning literature from the ‘value chain’ perspective.

Value chain analysis has been widely applied to coffee (de Graff 1986;Fafchamps, Vargas Hill, Kaudha, and Nsibirwa 2002;Fitter and Kaplinsky 2001;Kaplinsky 2003;Losch 1999;Mendoza 1996;Pelupessy 1999;Pelupessy and van Tilburg 1994;Ponte 2002a;Ponte 2002b;Talbot 1997a;Talbot 1997b). Broadly speaking, value chain analysis concerns itself not only with a description of the input-output structure and geographical coverage along a (productive) chain, but also with the general institutional and governance context in which the chain exists (Gereffi 1994). As part of this wider analysis, value chain studies of coffee will often break down the final (retail) price of a unit of coffee into ‘shares’ of either income or profit, depending on the data available. Since the most readily available data pertains to income as opposed to profit (due to the sensitivity of cost data), most value chain analyses draw policy conclusions about the distribution of _income_ along the chain. With few exceptions, these analyses of the coffee chain find that the farmer’s share of total income generated along the chain has been falling over time.<sup>14</sup> As long as income data is used, it is not possible to empirically distinguish costs and therefore, to identify factors such as market power or changing cost structures. This is probably why value chain analyses largely rely on the correlation or coincidence of these income shares with factors such as market structure (usually in the form of concentration figures). This has led much of the value chain literature on coffee to single out the rise of large, vertically integrated multinational firms as the primary cause for the decline in producer shares of coffee income.

In addition to this preoccupation with distribution and shares of coffee rent, the new policy initiatives aim at coping with _volatility_ by drawing on an existing menu of price risk management tools such as swaps, forward contracts and options on the

> 13 Volatility is measured in Gilbert (2003) as the standard deviation of the annual changes in the log of the deflated prices for coffee.

> 14  This conclusion is mostly drawn from deterministic measures of income, not profit, and can be derived either over many years  from data averaged across importing and exporting countries (so that what is obtained is necessarily an average distribution across many, potentially different, actors) or within a specific country, but over a shorter period of time (see, for example, Fafchamps _, et al_ . (2002), Pelupessy 1999, de Graff 1986,  Mendoza 1996).