From Thurlow and Wobst (2003), we know that grains are among the most important crops for impoverished Tanzanian households, both from an earnings and a consumption perspective. Volatility in the productivity of the grains sector will thus have different poverty implications for each of the seven strata of Tanzania’s poor. For example, a drought will reduce agricultural productivity, and push up food prices. To a first‐order approximation, whether a particular household gains or loses real income from this change depends on whether it is a net buyer or seller of the commodity. Higher prices will clearly push up the cost of living at the poverty line for non‐agricultural households. However, the degree to which this will occur depends on what happens to the wages earned by these households. Given the labor intensity of agriculture in Tanzania, any shock to agriculture is likely to have an impact on unskilled wages in the economy.

It is thus difficult to ascertain, in the absence of more specific knowledge of the situation, how climate volatility affects poverty, and empirical methods are necessary. For a comprehensive analysis of the poverty implications of prospective climate volatility changes over the course of the 21<sup>st</sup> Century, we have developed an analytical framework that incorporates climate variables, analyses of crop production, and economy‐wide, market equilibria, as described in the following section.

# 2. CLIMATE VOLATILITY AND AGRICULTURAL PRODUCTIVITY

The analytical framework used in this paper relies on several empirical methods implemented in sequence in order to shed light on the sensitivity of poverty in Tanzania to changing climate volatility. The first step in this process involves understanding how the distributions of key climate variables – temperature and precipitation – are likely to change in the future, and what those changes imply for the distribution of interannual agricultural productivity changes. In this study, we are particularly interested in climate volatility as reflected in the magnitude of year‐on‐year changes in productivity.

We draw on Phase 3 of the Coupled Model Intercomparison Project (CMIP3) archive of Global Circulation Model (GCM) experiments (Meehl et al, 2005) to obtain Tanzania’s nationally averaged precipitation (in mm/day) and temperature (in °C) data by month, for the years between 1971 and 2031. These data are drawn from an ensemble of 22 different GCMs. The period 1971‐2001 characterizes the late 20<sup>th</sup> Century, while the period 2001‐2031 characterizes the early 21<sup>st</sup> Century (under the SRES A2 emissions scenario). These data are aggregated to provide monthly average precipitation and temperature data series over the January‐June growing season for grains, which are then recalibrated so that their mean and standard deviations in the historical period match those of the observed data<sup>3</sup> .

Several important insights may be obtained by analyzing the bias‐corrected growing season temperature and precipitation data for Tanzania between the two time periods and across the 22 GCMs. All the models agree that the average January‐June growing season temperatures in the early 21<sup>st</sup> Century are going to be higher than in the 20<sup>th</sup> Century should greenhouse gas concentrations continue to rise (column III of Table 2), with the growing season average temperature increasing by 0.2 to 1.11 °C across the 22 GCMs (°C differences in parentheses). In a similar vein, most models agree that the average growing season

> 3 Please see Appendix A for details on bias‐correction.

4