This study applies the Ricardian technique to estimate the effect of climate change on the smallholder agriculture sector in Sri Lanka. The main contribution of the paper is the use of householdlevel data to analyze long-term climate impacts on farm profitability. Household-level data allows us to control for a host of factors such as human and physical capital available to farmers as well as adaptation mechanisms at the farm level. We find that non-climate variables explain about half the variation in net revenues. However, our results suggest that climate change will have a significant impact on smallholder profitability. In particular, reductions in precipitation during key agricultural months can be devastating. At the national level, a change in net revenues of between −23% and +22% is likely depending on the climate change scenario simulated. These impacts will vary considerably across geographic areas from losses of 67% to gains that more than double current net revenues. The largest adverse impacts are anticipated in the dry zones of the North Central region and the dry zones of the South Eastern regions of Sri Lanka. On the other hand, the intermediate and wet zones are likely to benefit, mostly due to the predicted increase in rainfall.
This paper reviews the literature of price linkages and examines the degree to which cotton prices are linked; it also tests whether such linkages have improved over the last decade. It concludes that the degree of linkage has improved over the last decade while the main source of this improvement appears to be a result of short-run price transmission and to a lesser extent long-run co-movement.
Books in this series are published to communicate the results of Bank research, analysis, and operational experience with the least possible delay. The extent of language editing varies from book to book.This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries.Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.
Some rights reserved This work is a product of the staff of The World Bank with external contributions. The findings, interpretations, and conclusions expressed in this work do not necessarily reflect the views of The World Bank, its Board of Executive Directors, or the governments they represent. The World Bank does not guarantee the accuracy of the data included in this work. The boundaries, colors, denominations, and other information shown on any map in this work do not imply any judgment on the part of The World Bank concerning the legal status of any territory or the endorsement or acceptance of such boundaries. Nothing herein shall constitute or be considered to be a limitation upon or waiver of the privileges and immunities of The World Bank, all of which are specifically reserved.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.