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the electric power, recreation, construction, and agriculture sectors of the economy due to the <br />restricted availability of water. These duect impacts, as well as indirect impacts resulting from <br />the reallocation of resources due to the direct impacts, occur at various points in time <br />throughout the time period of the study. <br />The time period defined for this study-1995 through 2040-is based on several factors: <br />biological projections for recovery of the fishes, availability of reliable data related to <br />economic development plans for the region, and recognition that economic impacts in the long <br />term are diminished due to discounting. The initial impacts of listing and critical habitat <br />designation are projected to appear in 1999, and the recovery of the fishes is expected to be <br />complete by 2020. Thus, the defined time period should capture all impacts of critical habitat <br />designation. <br />This study uses the input-output (I-O) method of economic modeling to investigate the <br />impacts of critical habitat designation on a defined region. I-O models areaway of describing <br />an economy by representing it as a series of linkages among various production sectors. Once <br />a model has been constructed for a particular economy, it can be used to investigate "what if' <br />scenarios such as the impact of exogenous shocks to that economy-in this case, shocks <br />associated with critical habitat designation. A shock will have direct impacts: for example, <br />production of a particular commodity will be curtailed because a basic input (such as water) <br />has become scarce. Because of the linkages in the economy, a shock will also have indirect <br />impacts: for example, lower production will affect employment and income in that sector, <br />while the higher cost of a scarce commodity means that less disposable income can be spent <br />on the products of other sectors of the economy. Ultimately, the shock is reflected in a change <br />in final demand for products of the regional economy. <br />The IlVIPLAN data sets, which permit construction of county and regional-level models, were <br />used as a basis for this study. These data sets were combined with Bureau of Economic <br />Analysis (LJ.S. Department of Commerce) county-level employment projections, which were <br />vi <br />