The nature and prevalence of these issues of representativeness is often highly dependent on the type of sampling frame used. This choice affects the extent to which a representative sample can be achieved, and post-survey adjustments can improve representativeness ex-post. While in-person surveys in lowand middle-income countries often follow the common two-stage sampling approach, phone surveys adopt a more varied set of approaches. There are three predominant approaches to establishing a sampling frame and drawing a sample for a phone survey, each with distinct advantages and disadvantages when it comes to both implementation as well as representativeness (Lepkowski et al. 2007; Glazerman et al., 2020; Himelein et al., 2020; Gourlay et al., 2021):

**1. Recontact:** Respondents are drawn from a list of phone numbers from previous surveys or programs or from a recent listing of households/holdings conducted within selected EAs (or other groups).

**2. Random Digit Dialing (RDD):** A sample of randomly drawn numbers corresponding to the construction of phone numbers in the country is drawn and called.

**3. Telecom list:** Respondents are drawn from a list of active phone numbers obtained in most cases from network service providers.

When it comes to agricultural surveys, these three approaches will sometimes require specific recommendations and considerations, which will be noted in the sections to follow.

# _Mobile phone ownership and coverage_

One of the challenges in order for mobile phone surveys to yield data representative of the population of interest is at the first step tied to the level of mobile phone penetration in the population (Leo et al., 2015). Mobile phone ownership is not universal in low- and middle-income contexts: mobile penetration rates were at 67 percent of the general population globally in 2019, and at 45 percent in Sub-Saharan Africa (GSMA, 2020), though penetration continues to increase at a significant rate. Moreover, agricultural households are less likely to own phones than the general population (Ambel et al., 2021; Himelein et al., 2015; Leo et al., 2015) and adoption of mobile phones in low- and middle-income countries has been found to be associated with wealth, gender, remoteness, and education (Henderson and Rosenbaum, 2020; Himelein et al., 2020). Drawing on household survey evidence from 28 countries, **Error! Reference source not found.** Figure 2 illustrates the discrepancy in access to mobile phones, with access defined as mobile phone ownership within the household, for the population with no household income from agriculture and those with over 30% of household income coming from agriculture.<sup>5</sup> ￼ The majority of countries exhibit a pattern in which households that are reliant on agriculture, i.e., those with over 30% of their total income coming from agriculture, have lower rates of mobile phone access<sup>6</sup> ￼ This is especially pronounced in Ethiopia, Malawi, and Sierra Leone where the difference between the share of

> 5 Households earning between 0% and 30% of total income from agriculture are excluded for presentation purposes. The rates of phone access for this group of households generally falls between the rate of those with no income from agriculture and those earning more than 30% of income from agriculture.

> 6 Only countries with data on access to mobile phones in 2010 or later are included. The year of data collection by country is as follows: Armenia - 2013; Bangladesh - 2010; Burkina Faso - 2014; Cameroon - 2014; Ecuador - 2014; Ethiopia - 2016; Georgia - 2015; Ghana - 2013; Guatemala - 2014; India - 2012; Iraq - 2012; Kyrgyzstan - 2013; Malawi - 2017; Mali - 2017; Mexico - 2014; Mongolia - 2014; Nepal - 2011; Nicaragua - 2014; Niger - 2014; Nigeria -2019; Peru - 2019; Rwanda - 2014; Senegal - 2011; Sierra Leone - 2011; South Africa - 2015; Tanzania - 2015; Uganda - 2016; Vietnam - 2010. For more details on the country surveys and the RuLIS methodology, visit: <u>https://www.fao.org/in-action/rural-livelihoods-dataset-rulis/en/.</u>

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