# **I. Introduction**

Consumption or income, valued at prevailing market prices, is the workhorse metric of human welfare in economic analysis; poverty is almost universally defined in these terms. In low- and middle-income countries, these measures of household resource availability are typically assessed through household surveys. The global diversity in survey approaches is vast, with little rigorous evidence concerning which particular approach, in conjunction with which context, yields the most accurate resource estimate. Many other key dimensions of welfare, such as nutrition intake and hunger, are also widely assessed through household consumption surveys (Fiedler et al. 2008). While levels of hunger and nutrition covary with household resource availability, the role of resources relative to other driving forces is debated (Deaton 1997). The evidence cited in this debate has been influenced by the reliability of measures of food consumption and economic resources (Bouis, Haddad, and Kennedy 1992; Gibson and Kim 2013).

This paper focuses on the measurement of food consumption. It leverages a recent survey experiment to study the performance of commonly used consumption survey modules to shed light on the nature of reporting errors in consumption data. The experiment involved randomly allocating one of eight consumption survey modules to a nationally representative sample of Tanzanian households. An individual diary supervised on a daily basis has been taken as the benchmark, or gold standard, survey approach. This approach was adopted because of the scope of the resources and the care teams devoted to the survey (see below). The accuracy of the other modules is assessed with respect to this benchmark. Previous work associated with the same experiment has explored the relative performance of the eight modules in terms of mean consumption, inequality, poverty, and the prevalence of hunger (Beegle et al. 2012; De Weerdt et al. 2016; Gibson et al. 2015). These studies concentrate on total household-level consumption aggregates and do not consider variations in performance among individual items, as is done here. Moreover, variations in mean consumption by module, which represents up to 27 percent of the total value in these studies, convey the net effect of all possible types of reporting error, including the opposing impacts of recall and telescoping errors, as well as the difficulty of fully capturing individual consumption opportunities outside the home.

This paper extends previous findings through a more careful focus on the nature of survey reporting errors (relative to the benchmark). We accomplish this by decomposing the sum of reported consumption into a product of two vectors: (1) a vector of binary indicators recording whether the household reports any positive value consumed for each food subgroup or individual food item captured by the survey and (2) a real value vector of the subgroup or item-specific value consumed. This framework, akin to a separate analysis of the extensive and intensive margin of reporting food consumption, allows for an exploration of the relative importance of the different types of reporting error in the seven survey designs. Furthermore, it can relate the relative importance of these error types to individual commodity characteristics.

The next section briefly reviews the types of error in food consumption measurement captured by household surveys. The third section describes the Tanzania survey experiment. The fourth section presents the analytic methods we employ, and the fifth discusses the results. The final section summarizes the findings and discusses the consequent implications for improved survey design.

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