southern, and some in the tropics. It would be inappropriate to expect the response to December temperatures to be the same in the northern and southern hemispheres. Instead the analysis in this paper is based on temperatures in the warmest and coolest seasons, and precipitation in the wettest and driest seasons. Quadratic terms are included to account for any nonlinearity in the relationship between climate and agricultural performance. In the African context we expect higher temperatures to have a negative effect on farm values but increased precipitation to be beneficial.

The ability to irrigate land increases land values, but whether irrigation is possible depends partly on the availability of surface runoff. Runoff is determined by, among other things, rainfall and evaporation in neighboring areas. Runoff data were provided by the University of Colorado and the International Water Management Institute as part of a broader study. Runoff is expected to have a positive impact on agricultural performance as a result of the increased availability of water for agricultural use, but may also have a negative impact if extreme rainfall causes flooding. Runoff is also represented by the maximum and minimum three-month averages and their squares.

Not all of the farms surveyed provided information on the distance to their nearest market for the purchase of their inputs or the sale of their outputs. Rather than discard observations with missing values distances were inferred using the average distance to market of other farms located in the same district. Note that distances are measured in terms of kilometers and do not make any allowance for the presence or absence of all-weather roads. Distance to market is expected to have a negative impact on land value. Farms located further from relevant markets are likely to have higher transportation costs, leading to lower profitability.

Country dummies are included to capture all the factors that cause land value to differ between countries and that are not otherwise explicitly controlled for in the regression. This could include the quality of institutions and infrastructure, impediments to trade or preferential trade links, and technology. Note that the technology currently employed by the farmer to cultivate his land does not explain differences in perceived sale value, but rather differences in the technological ability of potential buyers.

Soil data were obtained from the Food and Agriculture Organization (FAO 1996). These data contain information about the fraction of land in a particular area falling into one of 55 different categories. These categories contain information not only about soil type but also about soil texture (coarse, medium or fine) and the slope of the land (undulating, hilly or steep). This classification has been developed by soil scientists and is intended to be meaningful for determining the potential agricultural productivity of land.

Turning to the dependent variable, this study uses farmers’ own perception of the worth of their land, rather than actual sale values. More specifically, the survey asked farmers the following question: ‘If this farm (including land, buildings, equipment and livestock) were for sale, what would be its approximate value?’ Although early studies in the US also used such data to explore the value of non-climate attributes one may nevertheless wonder whether perceptions provide an accurate measure of market value. Some farmers were, moreover, reported to be suspicious about the interviewers’ motives and may have withheld or deliberately distorted their responses. Measurement errors on the dependent variable are therefore likely.<sup>12</sup>

> 12 Econometric problems arising from the non-random ability of farmers to offer estimates of the sale value of their land are discussed below.

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