Cross-Sectional Analyses of Climate Change Impacts

Abstract

This working paper explores the use of cross-sectional analysis in order 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 working 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 is readily available for the entire planet, the availability of climate data will not be a constraint. An ever continued 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 impacted by climate change even in circumstances when the aggregate agricultural sector in the country does fine.

This paper - a product of the Agriculture and Rural Development Department - is the result of the first phase of the study Climate and Rural Poverty: Incorporating Climate into Rural Development Strategies, funded by the Bank's Research Support Budget and the Agriculture and Rural Development Department.