stations throughout the world, they tend to be concentrated in urban settings. Measures of climate across the landscape, especially where farms are located, are difficult to acquire. The second chapter compares the use of satellite data with ground weather stations. The weather stations provide more accurate measures of ground level conditions in their vicinity. However, climate between weather stations must be inferred using sophisticated interpolation methods. Alternatively, satellite measures of climate directly observe the surface pattern of climate. Although the satellites have trouble measuring some relevant phenomenon such as precipitation, they have substitute measures such as soil moisture that can be used instead. Analyzing these two sources of information, the study reveals that satellite data can explain more of the observed variation in farm performance than ground station data. Only in Brazil where precipitation is key, do ground measurements outperform satellites. The results support the use of satellite data for cross-sectional analysis. Because satellite data are readily available for the entire planet, the availability of climate data will not be a constraint.

Chapter 3 examines how climate should be measured—it addresses the role of climate versus weather. The analysis tests whether farm performance depends on just climate normals- the average weather over a long period of time- or on climate variance (variations away from the climate normal). The study reveals that climate normals and climate variance are highly correlated. Either set of variables can explain a great deal of the variation in farm performance. However, when they are introduced together, the climate normals explain the bulk of the variation in farm performance and the variance terms explain only a little more. The results imply that it would be attractive to include both climate normals and climate variance to the extent possible. The satellite data can support measures of both sets of climate variables. By capturing climate variance, the studies can begin to measure the importance of weather extremes.

Chapter 4 explores the role of climate in rural income. A host of studies have revealed that climate affects agricultural performance. Since agriculture is a primary source of income in rural areas, it follows that climate might explain variations in rural income. This is tested in the analysis and shown to be the case. The very same variables that explain farm performance also explain why some rural districts and counties have higher income per capita than others. The results demonstrate the importance of climate to rural livelihood. The results also reveal that even if aggregate country-wide outcomes in agriculture are minimal, there may still be local distributional effects from climate change that are quite severe. That is, the study reveals that local people in rural areas could be heavily impacted by climate change even in circumstances when the aggregate agricultural sector in the country does fine.

The working paper reveals that the cross-sectional approach can be applied to developing countries to explore the impact of climate on agricultural performance. Combining an agricultural survey conducted across a range of climate zones with satellite climate data will yield a rich data set to study climate impacts. The approach will be able to study the effect of both climate normals and climate variance. The working paper thus provides strong support for continued work in this area.

The papers also suggest that climate will have a large impact on developing countries. Agriculture is a large fraction of GDP in many low-latitude countries. Laborintensive agriculture appears to be especially sensitive to warming. Many low-latitude 7