The role of agriculture in development remains much debated. This paper takes an empirical perspective and focuses on poverty, as opposed to growth alone. The contribution of a sector to poverty reduction is shown to depend on its own growth performance, its indirect impact on growth in other sectors, the extent to which poor people participate in the sector, and the size of the sector in the overall economy. Bringing together these different effects using cross-country econometric evidence indicates that agriculture is significantly more effective in reducing poverty among the poorest of the poor (as reflected in the $1-day squared poverty gap). It is also up to 3.2 times better at reducing $1-day headcount poverty in low-income and resource rich countries (including those in Sub-Saharan Africa), at least when societies are not fundamentally unequal. However, when it comes to the better off poor (reflected in the $2-day measure), non-agriculture has the edge. These results are driven by the much larger participation of poorer households in growth from agriculture and the lower poverty reducing effect of non-agriculture in the presence of extractive industries.

The relative contribution of a sector to poverty reduction is shown to depend on its direct and indirect 'growth effects' as well as its 'participation effect'. The paper assesses how these effects compare between agriculture and non-agriculture by reviewing the literature and by analyzing cross-country national accounts and poverty data from household surveys. Special attention is given to Sub-Saharan Africa. While the direct growth effect of agriculture on poverty reduction is likely to be smaller than that of non-agriculture (though not because of inherently inferior productivity growth), the indirect growth effect of agriculture (through its linkages with nonagriculture) appears substantial and at least as large as the reverse feedback effect. The poor participate much more in growth in the agricultural sector, especially in low-income countries, resulting in much larger poverty reduction impact. Together, these findings support the overall premise that enhancing agricultural productivity is the critical entry-point in designing effective poverty reduction strategies, including in Sub-Saharan Africa. Yet, to maximize the poverty reducing effects, the right agricultural technology and investments must be pursued, underscoring the need for much more country specific analysis of the structure and institutional organization of the rural economy in designing poverty reduction strategies.

The PRUDENCE project has generated a set of spatially and temporally highresolution climate data, which provides new opportunities for assessing the impacts of climate variability and change on economic and human systems in Europe. In this context, we initiated the development of new approaches for linking climate change information and economic studies. We have considered a number of case studies that illustrate how linkages can be established between geographically detailed climate data and economic information. The case studies included wheat production in agriculture, where regional climate data has been linked to farm enterprise data in an integrated model of physical conditions, production inputs and outputs, and farm management practices. Similarly, temperature data were used to assess consequences of extreme heat and excess mortality in urban areas. We give an introduction of an analytical approach for assessing economic impacts of climate change and discuss how economic concepts and valuation paradigms can be applied to climate change impact evaluation. A number of methodological difficulties encountered in economic assessments of climate change impacts are described and a number of issues related to social and private aspects of costs are highlighted. It is argued that, in particular, detailed climate information matters in relation to understanding how private agents react to observed climate data.

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