Although the complete dataset comprises a survey of almost 10,000 farms from 11 African countries, at several stages in the analysis the data were truncated. Farms that failed to provide an answer to questions regarding farm value were removed, since without this data the other information provided was of no value to this study, and farms that failed to provide plot size data were also removed for the same reason. In total the dependent variable was not observed for 2066 out of 9064 farmers.

The usual approach for dealing with incidental truncation is to include an explicit selection equation for the population of interest. Sample selection is only problematic from an econometric point of view if the error term from the sample selection equation is correlated with the error term from the equation of primary interest. Heckman’s method for correcting this source of bias relies on identifying a variable (or variables) that could be included as an explanatory variable in the selection equation but which does not appear as an explanatory variable in the primary equation (Heckman 1979).

Variables used to identify our model include among other things the age of the respondent in years, the education of the respondent in years, the respondent’s gender, whether the respondent was married, whether the respondent worked on the farm, whether the respondent also worked off-farm, whether the respondent was head of the household, and whether the respondent owned the primary plot. In particular, it seems likely that individuals who own land would be better able to answer a question regarding the value of their farm. Almost 63% of respondents claimed to have formal property rights to their primary plot of land.

For the reasons mentioned above the dependent variable was initially taken in terms of US dollars per hectare, while the structural characteristics of the land were divided by the size of the farm.<sup>14</sup> Attempts to perform a Box Cox transformation, however, suggested that a linear functional form was inappropriate and that, contrary to theoretical expectation, a logarithmic transformation of the dependent variable resulted in a substantial improvement in fit. The reason for this is perhaps that the values of environmental characteristics are interdependent. In what follows, therefore, the dependent variable was taken as the logarithm of perceived sale value per hectare and an additional variable was included measuring farm size.

If we examine the regression equation in Table 2 we see that the number of hectares is negatively signed and indeed statistically significant. Smaller farms are evidently perceived as being worth more on a per hectare basis. This suggests that the repackaging (i.e. the subdivision or combining) of plots is prohibitively expensive in the African context.

The number of farm buildings per hectare is positively signed and highly significant. The numbers of animals per hectare of various types are generally statistically insignificant, other than the number of steers and the number of chickens, both of which are highly statistically significant. The numbers of files, weeders, ploughs etc. and non-specified forms of animal power per hectare have a statistically significant impact on farm values. Curiously, the possession of a generator diminishes farm values. Possibly this serves as a marker for those farms not served by a reliable electricity supply.<sup>15</sup> Turning now to the environmental attributes we see that the climate variables are highly significant. The characteristic inverted U-shape is observed for both minimum and maximum temperatures. Higher temperatures are ultimately detrimental to agricultural production and

> 14 Note that the area of the farm excludes any communally owned grazing land used by the farmer. 15 The coefficient is only significant at the 5% level.

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