region. Such assumption include those of voluntary transactions in a level playing field of differentially endowed households in a risky world, where all agents have identical, complete information (Binswanger et al., 1993). The results derived from this analysis must be viewed with these underlying assumptions in mind and the knowledge that often the theoretical results obtained are generalizations of the entire economy and not specific micro-level occurrences.

In the context of water pricing, the difference between these two concepts is illustrated using a simple example. A _partial equilibrium analysis_ of the provision of irrigation services would try to set the price such that the marginal cost of supplying the water equaled the marginal benefit to the farmer of receiving that water. A _general equilibrium analysis_ of the same provision of irrigation services would examine the effects of setting this price on other sectors such as the urban sector or industrial sector. Meinzen-Dick and Bakker (1999) illustrate the need to incorporate other sectors in analysis when defining rights to water and choosing appropriate irrigation systems. Modeling (theoretically or empirically) additional sectors or regions is by necessity a difficult undertaking, which requires modeling sophistication and/or large data sets. As a result there is not as much literature regarding irrigation pricing issues.

One GE methodology revolves around computable general equilibrium models that calibrate equilibrium conditions using existing empirical data. Berck, Robinson and Goldman (1991) describe how computable general equilibrium (CGE) models can be used to evaluate policies. They summarize the contributions of _general equilibrium analysis_ over _partial equilibrium analysis._ In the calculations of a project's direct impact they conclude that CGE models suffer from the same limitations (i.e., definitions of costs and benefits), as does standard cost-benefit analysis. However, for large irrigation projects (e.g., Aswan high dam and California Central Valley Project) where it is conceivable that impacts of the project will have sequential effects on commodity prices, CGE will allow estimates of those endogenously determined variables. In addition evaluations of project alternatives (e.g., fallowing of land or trade alternatives) is facilitated under CGE modeling. Similarly alternative policies outside of water policy can be evaluated for its contribution to the impact of a project (e.g., optimal commodity taxes). In the LDC environment, prices for missing and distorted markets (e.g., labor) can be evaluated in the CGE format, which yields shadow values for those prices. Lastly, the CGE format is useful for generating the potential secondary benefits (costs) for the other sectors in the economy.

# **3.2.1 First-Best**

The definition of first-best allocations and Pareto optimality are as they were for _partial equilibrium._ It should be noted, however, that _general equilibrium_ expressly incorporates prices for other goods and sectors and therefore the focus is not necessarily on determining the Pareto optimal water price/allocation. For environmental policy, Hurwicz (1998) derives the optimality conditions for general equilibrium treatments of market failure and second best policies.

# **3.2.2 Second-Best**

Literature concerning second-best GE can be categorized similarly as was partial equilibrium analysis. Binswanger et al. (1993) discuss how GE analysis begins with perfect markets and perfect information (or first best), and as assumptions are relaxed enters the world of second-best (e.g., credit market imperfections, asymmetric information, moral hazard, income risks, and rentseeking distortions). There are few empirical GE studies examining the environment (Robinson 16