“simulation laboratory” for quantitatively examining how the individual impact channels of climate change influence the performance and structure of the whole economy (see Lofgren et al., 2004 for a detailed exposition of the base modeling framework adapted for this analysis).

Economic decision-making in the DCGE model is the outcome of decentralized optimization by producers and consumers within a coherent economy-wide framework. A variety of substitution mechanisms occur in response to variations in relative prices, including substitution between factors, between imports and domestic goods, and between exports and domestic sales. Production and trade function elasticities were drawn from Dimaranan (2006). The Tanzania model contains 28 activities or sectors, including 12 agricultural subsectors (see Pauw and Thurlow, 2010). Six factors of production are identified: three types of labor (unskilled, semi-skilled and skilled), agricultural land, livestock, and capital. Agricultural activities and land are distributed across the 20 administrative regions of mainland Tanzania. This sectoral and regional detail captures Tanzania’s economic structure and influences model results. Table A2 in the appendix outlines the disaggregation and regional characteristics of the model while Figure A1 provides a map of agro-climatic zones.

Climate change affects agricultural production, economic growth and household incomes in the DCGE model via predicted annual yield deviations for rain-fed crops estimated by CLICROP. The DCGE then determines how much resources should be devoted to each crop given their profitability relative to other activities. This reallocation of resources permits some autonomous adaptation by farmers and nonagricultural producers. For example, representative farmers in each region within the DCGE model allocate their land and capital between crops based on long-run rates of technical change and climate change. However, farmers are unable to anticipate weather conditions for a particular season, and so once planted, land cannot be reallocated even if weather patterns are not as expected. The representative producer in our model therefore corresponds to a “typical farmer” (see Füssel and Klein, 2006), who does not assume that historical weather patterns will persist indefinitely, but also does not have perfect foresight of future climate change. Rather they adapt their behavior based on the gradual realization of climate change.

The long timeframe over which climate change will unfold implies that dynamic processes are important (Arndt et al., forthcoming). The recursive dynamic specification of our CGE model allows it to capture annual changes in the rate of physical and human capital accumulation and technical change. So, for example, if climate change reduces agricultural production in a given year, it also reduces income and hence savings. This reduction in savings displaces investment and lowers production potential and economic growth. Given our long-run focus, our macroeconomic “closure” assumes that changes in aggregate absorption are proportionally distributed across nominal private and public consumption and investment via distribution neutral changes in savings rates. Government savings are flexible, tax rates are fixed, and the real exchange rate adjusts to maintain an exogenously determined current account 8