surveys, prior survey information may help in identifying households that are active in agriculture or reside in rural areas (with some limitations, as these features are not time invariant), whereas in the case of RDD no prior information is available to inform the selection prior to attempting to contact the respondent.

There are drawbacks and limitations to using contact information from previous surveys as sampling frames for agricultural phone surveys, however. The sample size of the previous in-person survey inevitably constrains the sample size of the phone survey, which may be problematic especially when contact phone numbers are available only for a smaller share of in-person households. Moreover, depending on the time passed since the previous in-person survey, the list of phone numbers may not be current. This could mean that many phone numbers have been disconnected in the meantime which could also mean the list of households is no longer representative of the target population. Another drawback of selecting a phone survey sample from previous in-person surveys is that there is some loss in sampling efficiency due to the multi-stage, clustered design of in-person survey samples. These design effects from clustering the in-person sample will be imported into the phone survey sample and therefore increase the variance of estimates obtained from the phone survey sample relative to a simple random sample of the same size. An additional consideration when using phone numbers from previous in-person surveys is the purpose and target population of the previous in-person survey. It is important to make sure that the population of interest of the phone survey aligns with the previous in-person survey (Glazerman et al., 2020) or contains the population of interest. For example, an in-person survey whose population of interest was farming households would not be a suitable frame for a phone survey targeting the general population.

A sampling strategy based on a list of phone numbers generated by random digit dialing (RDD) or obtained from a mobile network provider is an alternative (or the only feasible option) especially when no previous contact information is available. These strategies also eliminate the need for sample clustering, which is generally required for cost-efficiency in in-person survey operations, and do not constrain ex ante the size of the sample that can potentially be contacted. However, both strategies have drawbacks in terms of sample representativeness. The lack of _a priori_ information on the potential respondents associated with each phone number impedes the analysis of non-responders and limits post-survey reweighting to counteract selection biases (Himelein et al., 2020). Further, a list obtained from a mobile network operator is unlikely to represent even the phone-owning population if it is from one specific operator in a country where there are multiple operators.

A mixture of these methods could also be implemented to arrive at a representative sample. For example, in a situation where a recent previous survey is available, but the sample of phone numbers is small, survey designers can supplement the list with additional phone numbers generated through RDD. This was the approach taken in the Kenya high-frequency phone survey on the socio-economic impacts of COVID-19 in which the World Bank is collaborating with the Kenyan National Bureau of Statistics (Pape et al., 2020).

_Increasing coverage, reducing non-response, and improving representativeness_ Given the above issues, survey designers can draw on several approaches to attempt to reduce the level and impact of these sources of bias in phone survey samples. In-person survey operations should invest in the first place in the collection of phone contact information of surveyed households and individuals 10