We can ignore <sup>2</sup> H<sup>since the focus of this paper is changes in climate, and not household</sup> specific characteristics. In this case, changes in the variances of yield are explained just by changes in the variance‐covariance matrix of yield variables. Under these circumstances, the changes in the variances of yield due to climate would be the same at the regional and household scales. Under these circumstances, it is reasonable to apply climate‐instrumented yield changes to the household micro‐simulation model to capture climate change impacts at the household level, as we do here.

# 3. POVERTY ANALYSIS

# 3.1 SIMULATION FRAMEWORK

We are now in a position to analyze the poverty impacts of the interannual productivity change distributions of the late 20<sup>th</sup> Century and the early 21<sup>st</sup> Century. In order to estimate the changes in consumer prices and earnings stemming from changes to agricultural productivity due to climate effects, we employ a widely used computable general equilibrium economic simulation model.

We begin with the GTAP Database Version 6 (Dimaranan, 2006) and use this with a modified version of the standard GTAP model (Hertel, 1997). We retain the empirically robust assumptions of constant returns to scale and perfect competition, and introduce factor market segmentation which is important in countries where the rural sector remains a dominant source of poverty following the methodology of Keeney and Hertel (2005). Farm and non‐farm mobility of factors are restricted by specifying a constant elasticity of transformation function which “transforms” farm employed versions of labor and capital into non‐farm uses and vice‐versa. This allows for persistent wage differences between the farm and non‐farm sectors, and is the foundation of the intersectoral distributional analysis. In order to parameterize these factor mobility functions we draw on the OECD’s (2001) survey of agricultural factor markets. We assume a constant aggregate level of land, labor, and capital employment reflecting the belief that the aggregate supply of factors is unaffected by climate change. International trade is potentially important, particularly since Tanzania is a small, open economy. However, empirical estimates suggest that international price transmission is imperfect. Here, we capture this through the use of the so‐called Armington product differentiation model of trade.

The model is also adjusted to distinguish between lands with different biophysical characteristics, following the approach of Hertel et al (2009a), distinguishing land by Agro‐ Ecological Zone (AEZ), based on the data of Lee et al (2009) and Monfreda et al (2009). The model is then calibrated such that simulations of estimated historical productivity volatility of Grains for the 1971‐2001 period replicate observed historical price volatility<sup>7</sup> .

In order to link price changes in the CGE model to poverty, we use the household model of Hertel et al (2004) to examine households in the neighborhood of the poverty line. That study used the AIDADS (An Implicitly Directly Additive Demand System) consumer demand system of Rimmer and Powell (1996) to determine household consumption and the household’s maximum possible utility for a given set of prices and income. For poverty analysis, the utility of the household at the poverty line is then defined as the poverty level of utility. If an adverse climate

> 7 Please see Appendix B for details of model calibration

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