# **SUMMARY**

This working paper explores the use of cross-sectional analysis in order to measure the impacts of climate change on agriculture. Although these methods have been applied to study the United States and other developed countries, they have been only sparsely applied to developing countries. Before a major investment was made in using these methods in a host of low-latitude countries, a number of technical issues needed to be addressed. This working paper addresses specific questions about applying crosssectional methods to developing countries.

The impacts from climate change are difficult to measure. We have no direct experience with new future climates. Past climate change has been very slow and difficult to discern, and technological changes have obscured any possible signal historic climate could have given. Despite these difficulties, the impact literature has made many strides toward understanding and quantifying climate impacts. Experiments on crops in laboratory settings have confirmed that most crops are highly sensitive to climate. Although the ideal climate varies across crops, each crop tends to have an optimal climate setting where it grows best. Alternative experiments reveal that increased carbon dioxide will also help plants grow, increasing the yields of most crops substantially. Combining these experiments with simulation models suggests that agriculture will be strongly affected by climate change.

Although the experimental–simulation approach is an important methodology, it is expensive and difficult to implement for most developing countries. This working paper reports on another approach to measuring the impacts of climate change: crosssectional analysis. The cross-sectional approach examines farm performance across a broad range of climates. By seeing how farm performance changes with climate, one can estimate long-run impacts. The advantage of this approach is that it fully captures adaptation as each farmer adapts to the climate they have lived in. The technique measures the full net cost of climate change, including the costs as well as the benefits of adaptation.

The four chapters in this working paper examine important potential limitations of the cross-sectional method and how they could be addressed. The first chapter looks at estimating impacts using individual farm data. Past cross-sectional studies have focused on aggregate data for a county or district. The information at this level was available in secondary sources (agricultural census). However, many developing countries do not have adequate census data. This study addresses whether having individual farm level data from a survey would be an acceptable substitute for district level data. The study, conducted in Sri Lanka, finds that individual farm data from surveys is ideal for crosssectional analysis. By having more detailed information on each farm, one can control for many of the differences between one farm and another that are not related to climate. Uncontrolled variables are more likely to be captured in the analysis using individual farm data. The Sri Lankan study also illustrates the importance of precipitation in addition to temperature. At least in Sri Lanka, what happens to the monsoons may be more important than warming itself.

Another anticipated problem with applying the cross-sectional approach to developing countries is the absence of weather stations. Although there are weather 6