al, 2013). These techniques are particularly critical for agricultural phone survey samples where issues of coverage and nonresponse are likely to be magnified given a lower share of mobile phone ownership and likely less reliable mobile networks and electricity in rural areas. The availability and success of these different adjustment methods will also vary depending on the sampling method for the survey and the amount of reference information available on the general population.

One commonly used reweighting approach is weighting class adjustments in which the sample is divided into cells based on the characteristics of sampling units (households, farms, individuals, etc.) such as location, size, or sex that are demonstrated or assumed to be associated with coverage or nonresponse bias. Then, the sampling units in each cell are weighted proportional to the general population total of each cell (Little, 1986).

A second reweighting technique is response propensity adjustment (Valliant et al, 2013). This technique models the probability of response (i.e., selection and successful interview) for each sampling unit given a range of its observable characteristics. Then the inverse predicted probability of selection derived from this response probability model is used to adjust sampling weights and counteract the bias (see Himelein et al. (2020a) for an in-depth discussion). Response propensity adjustment techniques can accommodate many variables, allowing reweighting to be more fine-grained, while weighting class adjustment can only accommodate a handful of characteristics as otherwise the number of different cells becomes too large.

In addition, it is good practice to calibrate the adjusted sampling weights to match known population totals (e.g., number of households by urban, rural) which can help to reduce overall standard errors and also help to reduce bias due to coverage or nonresponse. There are several calibration methods that can be implemented, depending on the level of detail of information available for calibration (Valliant et al, 2013; <mark>Lundström and Särndal, 1999; Andersson and Särndal, 2016).</mark> With a previous in-person survey as a sampling frame (i.e., recontact surveys), there is often a wealth of information associated with each household (or individual or farm), which readily allows modeling the response probability. This was the approach taken in the LSMS-supported HFPS, for example (Gourlay et al., 2021). With sampling frames based on RDD or lists provided by mobile network operators, however, there is usually no or limited information available in addition to telephone numbers. Auxiliary data sets are required for post-survey adjustments to reduce selection biases. A recent census or a recent nationally representative survey may serve this function. The phone survey data and the auxiliary data need to have in common a set of variables, such as demographic and location variables, for weighting adjustments to be possible (Lepkowski et al. 2007; Himelein et al., 2020).

Post-survey adjustments such as reweighting can never fully correct coverage and non-response biases in phone survey data. Their relative success in reducing biases depends on how much of the population is left out, how different the selected sample is from the general population, and how much information is available for bias reduction methods. Ambel et al. (2021) examine how well response propensity adjustments perform at reducing coverage and non-response biases in LSMS-supported HFPS in four African countries. They find biases to be substantially reduced but not fully eradicated in all four surveys. In contrast to Ambel et al. (2021), Brubaker et al. (2021) investigate respondent selection biases in individual-level data from the same surveys. Selection biases are found to be more pronounced at the individual level than at the household level, such that reweighting is relatively less successful in overcoming these often more substantial biases. Obtaining more representative individual-level data would require a probability-based/randomized respondent selection protocol.

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