Cross-sectional analyses of climate
            change impacts
The authors explore the use of
            cross-sectional analysis to measure the impacts of climate
            change on agriculture. The impact literature, using
            experiments on crops in laboratory settings combined with
            simulation models, suggests that agriculture will be
            strongly affected by climate change. The extent of these
            effects varies by country and region. Therefore, local
            experiments are needed for policy purposes, which becomes
            expensive and difficult to implement for most developing
            countries. The cross-sectional technique, as an alternative
            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.
            However, the technique is not concern-free. The four
            chapters in this paper examine important potential concerns
            of the cross-sectional method and how they could be
            addressed, especially in developing countries. Data
            availability is a major concern in developing countries. The
            first chapter looks at whether estimating impacts using
            individual farm data can substitute using agricultural
            census data at the district level that is more difficult to
            obtain in developing countries. The study, conducted in Sri
            Lanka, finds that the individual farm data from surveys are
            ideal for cross-sectional analysis. Another anticipated
            problem with applying the cross-sectional approach to
            developing countries is the absence of weather stations, or
            discontinued weather data sets. Further, weather stations
            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. 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. Because satellite data are readily available for the
            entire planet, the availability of climate data will not be
            a constraint. A continuing debate is 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). Chapter 3 reveals
            that climate normals and climate variance are highly
            correlated. By adding climate variance, the studies can
            begin to measure the importance of weather extremes as well
            as normals. 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 in Chapter 4 and shown to be the
            case. The analysis reveals that local people in rural areas
            could be heavily affected by climate change even in
            circumstances when the aggregate agricultural sector in the
            country does fine.