sample dummies are jointly highly significant for the former and lack significance for the latter.<sup>14</sup> Also, as table 7 illustrates, the variable indicating the overlap between the period during which diary information was collected and the period covered by the recall survey is not significant in any of the specifications, most likely because where it existed, the overlap was quite short.

This evidence might also provide the basis for substantive conclusions regarding the types of households for which recall-based estimates of production might yield more or less reliable results.<sup>15</sup> Differences are estimated to consistently increase in household size, presumably because with larger size the probability of members managing their own plots is likely to increase. Regressions also suggest that differences decline in area cropped as well as yields plausibly because larger and more productive farmers will be better able to keep track of production outcomes. Finally, results in columns 1 and 2 also suggest that differences decline with the head‟s education and ownership of cattle as a major productive asset.

# **4.3 Implications for using diaries in practice**

Taking the results from Tables 7 and 3 together, it appears that, at least in some respects, well-supervised diaries can complement more traditional types of data collection methods. To see this, recall that in Table 3, non-response was estimated to increase with education and asset ownership and that the likelihood of diary completion increased with household size. Table 7 suggests that all of these variables are associated with larger differences between recall-based estimates of production and those obtained from diaries, so if the latter are closer to the true value, the potential for improvement would be considerable.

While the above results suggests that diaries can plausibly provide more accurate information on output quantities than recall-based surveys, a key argument against their more widespread use has been the supposedly high cost of conducting them. The Uganda experience reported here suggests otherwise: local monitors were paid some US$ 10 per month to assist in keeping of diaries in each ten-household cluster, something that would translate to approximately US$12 per household per year.<sup>16</sup> Even if this cost were doubled to provide adequate training for monitors and account for other expenses, the cost would only amount to slightly more than a tenth of the cost (US$ 200 per household) for the socio-economic survey.<sup>17</sup> This suggests not only that a focus on “low-hanging fruit” could have great benefits at relatively low cost but also that there may be scope for using harvest diaries more broadly to obtain reliable information on agricultural production in a more participatory way.

> 14 We lack detailed information on enumerator characteristics except gender which can be inferred from their name. Interestingly, however, the difference is higher for female enumerators than for male ones (not reported), a result that is consistent with the hypothesis that female enumerators exert more care and thus obtain higher figures for home consumption. Unfortunately, no information on respondents, which would have allowed further exploration of this line of reasoning, is available.

> 15 The weakly significant coefficient for the share of plots managed by females in column 3 provides some support for this interpretation. 16 A full costing will have to add expenses for training and supervision of ”diary monitors” and adding these is important because, as noted earlier, the experience reported here implies that failure to invest in these items can dramatically reduce the quality of the information obtained. 17 Although somewhat high by international standards, this cost is in line with past household surveys in Uganda. Costs seem to be increased by the need to visit households twice and the requirement to measure plots in the field.

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