(Rolstad et al., 2011; Zezza et al., 2017).<sup>3</sup> Aggregating items also yields cost savings and reduces the length of the survey.

While the appropriate level of disaggregation for FAFH is unclear, asking on a single line to account for all FAFH consumption for the entire household may result in high levels of underreported consumption, as well as an increase in measurement error (Fiedler & Yadav, 2017). One-line questions often give lower overall estimates of consumption and can be difficult for respondents to answer (Browning & Crossley, 2001; Gray et al., 2008). Still, around 24% of nationally representative household surveys collect FAFH data with just a single aggregated line (Smith et al., 2014). This extreme level of aggregation for a growing segment of household expenditures could likely lead to further measurement error.

The purpose of the current study is not to test the impact of each of these dimensions separately, but to identify scalable and cost-effective solutions that improve the accuracy of measures coming from modern surveys and data collection methods. The design of the experiment draws from pre-existing evidence and provides further insight on the measurement effects resulting from the level of reporting (household or individual), bounding methods, and the level of aggregation in order to identify the alternatives that work best when collecting FAFH consumption, and why they work best.

Our benchmark measure of ‘true FAFH consumption’ is that captured through an individual-level daily diary rather than asking about consumption in a given period (which is the recall method). With this approach, respondents are asked to keep a diary which allows them to record all FAFH consumption as it happens (or at least on a daily basis). Personal diaries have often been used as a benchmark for collecting information on food consumption (Beegle et al., 2012). Despite the “gold standard” status of diary methods, discrepancies can occur due to compliance or supervision issues, diary fatigue,<sup>4</sup> education, and even changes in spending behaviors<sup>5</sup> (Beegle et al., 2012; Browning et al., 2014; Burke et al., 2011). These drawbacks are significant enough that there is some disagreement about the accuracy of diary measurements.<sup>6</sup> Because of this, diary methods need strict protocols to increase compliance and reduce response burden to yield accurate consumption data (Browning et al, 2014). Greater interviewer

> 3 In addition, the impacts of disaggregation on measures of inequality and welfare are still unclear (Beegle et al., 2012; Browning et al., 2014; Hurd & Rohwedder, 2013).

> 4 For example, Ahmed et al. (2006) find a drop off in diary records of around 10 percent between the first and second week of the diary. In this and other studies, respondents stopped measuring their consumption as time went on due to the burden.

> 5 In other contexts (weight loss, financial and health habits and instrument take-up), surveys have been found to have impacts on behaviors (Burke et al., 2011; Crossley et al., 2017; Zwane et al., 2011).

> 6 In national budget surveys, recall and interview survey measurements can sometimes work as well as or better than diary measurements (Bee et al., 2014; Browning et al., 2014).

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