better education, higher incomes, and a higher likelihood of working than respondents to the mobile phone survey.<sup>16</sup><sup>_,_17</sup> These differences suggest that one of the most important caveats of analyses using internet-based surveys is the need to account for the overrepresentation of certain groups in the survey modality. Given the difficulty of trying to accurately measure the income and employment status of the population to properly re-weight the surveys to make them more representative of the general population, we focus on ensuring that all differences between the internet and mobile phone surveys are qualitatively identical even when restricting the samples of each survey to be more comparable. For example, in our analysis of estimating the difference in responses to sensitive questions between the two modalities, we further check whether that difference survives restricting both modalities only to lower income households, those located in less urban governorates, older respondents, and so on. Similarly, if one were to use the modality to estimate changes in response to large shocks, it is important to separately estimate the change for households that are over and underrepresented in internet-based surveys.

And lastly, we also have to address the possibility that the significant non-response rates in the internet-based surveys might result in significant non-observable sample selection that also limits the generalizability of the results. Although the response rates of random digit dialing mobile phone surveys similar to the one used here are also very low<sup>18</sup> , it is important to better understand and verify that the low response rates are not

> 16The largest difference in the table is in the share of non-working respondents who are not currently looking for work. Given the much smaller difference in the shares of households that are working, the difference is driven by the omitted category- a larger share of internet respondents who are not working and are not looking for work (e.g., students, retired, etc.). Although this is difficult to precisely interpret, it could be consistent with individuals who are not working or looking for work having more time to use the internet.

> 17Although there are no comparison questions in the 2014 HBS for the subjective income assessment and the question on employment, it is likely that the mobile phone respondents have higher incomes and are more likely to work than the general population. Although this is difficult to precisely establish, reports derived from the most recent household surveys in the Republic of Yemen suggest that mobile phone respondents have better food access than the general population (e.g., IPC 2020; Favari et al. 2022; IPC 2022). However, the face-to-face survey that was conducted with the least amount of political and conflict interference at the end of 2016 suggest that food access of mobile phone users was similar to that of a survey that was attempting to be nationally representative in a difficult environment (e.g., FAO 2017; WFP 2017; etc.). Only in later years as the political interference in the face-to-face food security assessments likely increased, did the wedge between food security assessments and the food access of mobile phone users become apparent (e.g., Favari et al. 2022).

> 18 Although we are unable to calculate the response rates exactly due to how the RDD survey was

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