Working Paper: Welfare Analysis in a Two-Stage Inverse Demand Model: An Application to Harvest Changes in the Chesapeake Bay
Paper Number: 2017-04
Document Date: 7/2017
Author(s): Chris Moore; Charles Griffiths
Subject Area(s): Fisheries, Modeling, Water Pollution
JEL Classification:
D12 - Consumer Economics: Empirical Analysis
D61 - Allocative Efficiency; Cost–Benefit Analysis
Q11 - Aggregate Supply and Demand Analysis; Prices
Q22 - Fishery; Aquaculture
Keywords: Two-stage budgeting, Inverse demands, Compensating surplus
Abstract: Like many agricultural commodities, fish and shellfish are highly perishable and producers cannot easily adjust supply in the short run to respond to changes in demand. In these cases, it is more appropriate to conduct welfare analysis using inverse demand models that take quantities as given and allow prices to adjust to clear the market. One challenge faced by economists conducting demand analysis is how to limit the number of commodities in the analysis while accounting for the relevant substitutability and complementarity among goods. A common approach in direct demand modeling is to assume weak separability of the utility function and apply a multi-stage budgeting approach. This approach has not, however, been applied to an inverse demand system or the associated welfare analysis. This paper develops a two-stage inverse demand model and derives the total quantity flexibilities which describe how market clearing prices respond to supply changes in other commodity groups. The model provides the means to estimate consumer welfare impacts of an increase in finfish and shellfish harvest from the Chesapeake Bay while recognizing that harvests from other regions are potential substitutes. Comparing the two-stage results with single-stage analysis of the same data shows that ignoring differentiation of harvests from different regions, or the availability of substitutes not affected by a supply shock, can bias welfare estimates.
This paper is part of the Environmental Economics Working Paper Series.