populations to avoid biasing survey estimates, specifically underrepresenting the poorest and most vulnerable households and individuals. While reweighting techniques function to counteract these biases and improve representativeness in important ways, they cannot be an all-around remedy for systematic under-coverage and nonresponse. One option is to provide households and individuals who would normally not have access to one with a mobile phone, but that requires considering not only cost implications but also how having a phone may alter respondents behavior in areas the survey is actually aiming to investigate, such as access to market information or extension services. And if phones are given to households participating in the surveys, protocols will have to be devised on how many phones per household to hand out and who receives them, as informational content can be enhanced, and biases reduced or introduced, with different decisions in this domain.

Given the remote nature of phone interviewing, respondent burden and attrition deserve particular attention. Survey designers should make use of the tools at their disposal, especially questionnaire design, survey timing, contact protocols, respondent selection, and incentives, to minimize burden, curb nonresponse, and avoid attrition, within the limits dictated by the survey objectives. As for the frequency, the nature of data collected and the survey budget are key factors in determining the desired frequency. Arthi et al. (2018) and Gaddis et al. (2021) use weekly phone calls to collect agricultural labor data. Kilic et al. (2021) even called twice a week for extended harvest crop production data. In contrast, the LSMSsupported HFPS on COVID-19 collected monthly data, and the Georgia Survey of Agricultural Holdings, which converted to phone-based implementation in light of COVID-19 restrictions on in-person interviewing, is a quarterly survey. In practice, respondent burden and fatigue need to be considered, which very high interview frequencies could conceivably increase, risking higher attrition rates (see discussion in Section 3.1). The extent to which a sequence of short but more frequent phone interviews are less burdensome on respondents than a longer in-person sitting remains an empirical question, as there is no ready prior on the number of in-person visits and telephone interviews to optimize the use of survey resources and maximize data quality.

Having an in-person baseline visit makes possible the use of diaries or journals that can be facilitated by phone interviews. Kilic et al. (2021) asked respondents to keep diaries to document cassava harvest and twice weekly phone calls served, among other things, to supervise and support the diary-keeping. The authors conclude that the diary-keeping yields better cassava harvest data than the common end-ofseason recall module. Deininger et al. (2012) use harvest diaries for several different crops in Uganda, which is again deemed more reliable than end-of-season recall. Beegle et al. (2012b) compare personal diaries to several other common methods to measure household consumption and poverty, deeming diaries the most reliable method. These experiences indicate significant scope for improving data quality through diaries which can be readily combined with phone surveys, though survey designers need to consider cost and logistical implications of this method.

Finally, cost considerations and the possible improvements in data quality need to be also weighed against the loss of comparability stemming from possible mode effects. This is a preoccupation that concerns both the comparison with earlier survey years, but also the comparison of different interviews within the same survey round, to the extent that they are implemented using different data collection modes. Longitudinal surveys may attach a different weight to the loss of comparability than cross-sectional surveys (Cernat and Saskaug, 2021). Response burden and attrition patterns may also have features that vary across households with important characteristics of interest (e.g., income and opportunity cost of time, literacy).

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