# **1. Introduction**

Even in the face of rapid global urbanization, agriculture continues to be the mainstay for the majority of households in the developing world. Rapid development of the agricultural sector is one of the most effective mechanisms for reducing poverty and food insecurity (Ligon and Sadoulet 2008, Ravallion and Chen 2007). Still, data on agricultural production in most developing countries are notoriously weak and unreliable. This can greatly undermine the scope to design and fine-tune policies that will fully realize agriculture‟s potential strengths. For example, without high quality data, it will be difficult to assess the effectiveness of interventions to improve access to technology, the extent to which these affect household welfare, and the ways in which gains are distributed among different types of households or members within the same household. All of this will reduce the ability for evidence-based policy making and quick scaling-up or adjustment of policies and other interventions in light of the outcomes they achieve.

To explore ways that could possibly help improve the quality of information on agricultural production, this paper draws on the large-scale implementation of diaries, aimed to be filled by a household member, assisted by a local person, at the time of harvest, in parallel to Uganda‟s 2005/06 National Household Survey (UNHS). Having the diary administered to households included in the regular survey allows us to compare production estimates by the two methods and allows identification of household characteristics that are associated with higher levels of participation and reporting of production in diaries.

The analysis reveals some useful findings. First, despite a very limited budget, with no pretest and neither central training nor any manual for local monitors, the diary produced encouraging results. Households remained in the sample for an average of 5 months during the reference period and made an average of 115 entries that are fairly evenly distributed over time and do not provide strong evidence of respondent fatigue. While some 20% failed to start filling the diary altogether (many of them because no qualified monitor could be found), the share of drop-outs, i.e. respondents who abandoned the diary before it was collected by UNHS field staff during their second visit, was below 10%. Location-specific attributes, including unobserved quality of supervision by the local monitor, rather than observable household characteristics, explain 70% of the variation in drop-outs.

Second, comparing incidence of reporting and value of output (computed applying the same price vector) points to large discrepancies across methods. Changes in the magnitude and direction of these differences across crops can help identify ways to improve the quality of production estimates. The fact that output value from diaries and estimates based on reported home consumption plus sales significantly differ from those based on recall (by 70% or 40%, respectively), suggests that, in the aggregate, values of production as estimated from the UNHS‟s recall-based production module are likely to be an underestimate. This could have far-reaching impact on estimates of agricultural production in Uganda.

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