reduce recall burden, but with limited depth in questionnaire content. Taken together, these findings point to promising potential for the combination of phone surveys and in-person surveys for agricultural data collection going forward.

Phone surveys are cheaper on a per-interview and per-question basis than in-person surveys, but what are the cost implications of combining the two in a mixed-mode survey? The baseline scenario is complementing in-person surveys with phone interviews to improve data quality, while leaving the inperson survey component unchanged. In this case, the phone survey rounds add costs to the overall survey operation. Based on data from their survey experiment in Tanzania, Arthi et al. (2018) estimate that adding one phone interview round to a two-visit in-person survey increases the total cost by 6 percent, adding ten phone interview rounds increases total costs by 54 percent, while adding 20 phone interview rounds more than doubles the total cost (+108 percent). Similarly, Dillon (2012) estimates the added costs of one phone survey round to be 7 percent. For Malawi, Kilic et al. (2021) estimate that an inperson survey with subsequent semi-weekly phone calls over the course of 12 months was 77 percent more costly than the baseline of two in-person visits over the same 12-month period. The exact cost is likely to differ significantly depending on the implementation details of the phone surveys. For instance, the cost calculations in Arthi et al. (2018) and Kilic et al. (2021) include mobile phones distributed to respondents (as well as harvest diaries in the case of Kilic et al., 2021). Overall, the additional cost of phone surveys ranges from marginal for a small number of rounds of interviews to substantial if many phone survey rounds are added to an existing in-person component that is left unchanged.

However, phone surveys can also substitute parts of in-person surveys. For instance, if agricultural labor data are collected via phone, these data may no longer need to be collected in-person, reducing the time and burden of in-person interviews. Especially if phone interviews can substitute an entire in-person visit, a substantial cost reduction could be achieved. The combination of in-person and phone modes will also necessitate a series of decisions to be taken with respect to the organizational machinery behind a survey. With mixed mode surveys there will be economies of scale in the creation of a call center for data collection if it can be used for several surveys, for example. Similarly, the organization of the interviewers’ training will have to adapt to the new structure and decisions will need to be made on whether to use the same interviewers for both modes or to use different interviewers for phone and in-person interviews.

As for which topics to include in the phone-based component of a mixed-mode survey, the discussion on survey length, response fatigue, data quality, and attrition indicates that phone interviews should not be too long, naturally limiting the number of topics that can be covered in any given phone call. Topical coverage could be increased by scheduling several phone survey rounds and spreading topics out across those rounds, a common strategy used in the World Bank’s L2A surveys, the LSMS-supported HFPS, and IPA’s RECOVR surveys (Etang and Himelein, 2020; Innovations for Poverty Action (IPA), 2020; Living Standards Measurement Study, 2022). Data items that require objective measures such as land area or crop cutting, or sensitive topics, are likely to be those where in-person remains the primary mode. Data that is more variable over time, less salient, or that can benefit from higher frequency data collection because they are more likely subject to recall error (agricultural labor inputs is one example) are on the contrary those where the promise for the use of phones appears to be higher.

A specific question regarding the adoption of phones for mixed mode surveys will be how to avoid or mitigate biases connected with phone access between and within households. Dealing with limited and uneven mobile phone coverage remains a design priority especially when targeting rural and agricultural 24