location: household diaries significantly underreport consumption in urban areas (but not rural areas) suggesting the relative prevalence of personal consumption opportunities in urban areas; (3) the educational attainment of the household head: education had little relation to module performance except in the usual month approach, wherein inaccuracy was greater among less well educated households; and (4) household wealth as captured by a household asset index: the underreporting in recall modules is greatest among the poorest households and the deviation significantly declines with wealth. It is currently an open question whether these household characteristics, shown to be important mediators for consumption reporting accuracy, are affected to differing degrees by the various types of reporting error. This possibility is investigated in the error decomposition framework introduced in the next section.<sup>9</sup>

# **IV. Reporting error decomposition**

Earlier analyses of consumption reporting errors has focused on a net measure of total misreporting. This masks two aspects of consumption reporting: whether any consumption occurred and, if it did occur, the value of the consumption. Our main analytic approach in this paper is to examine these two aspects of misreporting in comparison with the benchmark module by modeling total food consumption as a product of two vectors whereby each ordered element of the two vectors corresponds to an individual food good _f_ . The first vector records, through an indicator function, whether the household reports any positive consumption of _f_ . The second vector records the stated consumption value of each element. More formally, total consumption _C_ recorded for household _h_ by survey module _m_ can be written as the following: where the first vector in the product is the consumption incidence vector, and the second vector is the consumption value.<sup>10</sup> This decomposition enables a separate analysis of survey design effects on consumption incidence and consumption value (or quantity). Different survey designs may differentially affect these two sources of error, and simple summary cross-module comparisons of total consumption can obscure these error patterns and, consequently, inhibit the lessons for improvement in consumption survey design. Furthermore, different research questions may not be as equally concerned about the errors in each of these vectors. For example, food diversity indicators are often based solely on incidence, rather than value or quantity.

> 9 <mark>Another important consideration is the effect of the characteristics of the enumerator on the interview quality and response error. Unfortunately, the measurement experiment cannot shed much light on this question. First, the survey modules were equally balanced across enumerators; so, any difference in relative module performance cannot be attributed to differential enumerator quality. Second, the characteristic distributions are much more uniform across the enumerators than across the general population; all the enumerators had completed secondary school, but none had yet entered university, were between 20 and 30 years of age, and were from urban areas. This narrow range severely limits an analysis of response heterogeneity by enumerator characteristics. While data quality is a function, in part, of enumerator effort and quality, these characteristics are not easily observable. Future work along these lines might consider prefieldwork cognitive testing of enumerators to supplement the inquiries of this nature.</mark>

> 10 If the two vectors are to have the same dimension and thus allow total consumption to equate to the inner-product, the consumption value vector needs to include the zero consumption values. Therefore, the depiction of the vector as consumption values conditional on positive consumption is purely stylistic to highlight the decomposition analysis to follow.

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