areas. Since agricultural households predominantly reside in rural areas where poor reliability of the mobile network and electricity is more common, they likewise may be underrepresented.

The level and nature of nonresponse can vary substantially depending on the context as well as the sampling approach adopted for the phone survey. Recontact surveys generally have higher response rates relative to RDD and telecom list-based phone surveys. One reason for this is that sampled units in recontact phone surveys have already been previously successfully contacted and interviewed and have an established relationship with the surveying agency as well as familiarity with being interviewed (relative to RDD and telecom list-based respondents). Furthermore, with RDD generated numbers are often unassigned or non-working, and survey practitioners are unable to tell in advance whether a number belongs to a household or a firm. For example, in an Ebola survey in Liberia, 214,823 calls to RDDgenerated numbers were made with only 24,000 of the numbers connecting; while in Ghana over 1 million RDD numbers were needed to get 16,003 connections (a connection rate of only 1.5%) (Himelein et al., 2020; Maffioli, 2020). Once a frame of phone numbers known to be assigned and working is established, response rates in RDD-based phone surveys are much higher, though usually still lower than recontact surveys (Henderson and Rosenbaum, 2020). IPA’s Recovr surveys, which relied on random digit dialing of phone numbers achieved response rates of between 4 percent in Mexico and 59 percent in Burkina Faso, with an average response rate of 28 percent (IPA, 2020). By contrast, some of the World Bank HFPS (recontact) surveys relied on phone numbers from previous LSMS-ISA surveys and achieved response rates ranging from 60 to 93 percent at an average of 74 percent (Gourlay et al., 2021;Table 1).

_Table 1. Response rates in round 1 of IPA Recovr surveys based on RDD (top panel) and HFPS surveys based on contact information from previous surveys (bottom panel)_

|**_Country_**|**_Respondents_**|**_Attempted_**<br>**_contacts_**|**_Response rate_**|
|---|---|---|---|
|**_Recovr - Rando_**|**_m Digit Dialing (RDD)_**|||
|Burkina Faso|1,356|2,284|59%|
|Colombia|1,507|6,984|22%|
|Côte d'Ivoire|1,329|3,022|44%|
|Ghana|1,357|10,781|13%|
|Mexico|1,330|29,876|4%|
|Philippines|1,389|8,378|17%|
|Rwanda|1,482|4,234|35%|
|Sierra Leone|1,304|3,831|34%|
|**_HFPS - Existing _**|**_phone contactfrompre_**|**_vious survey_**||
|Burkina Faso|1,968|2,500|79%|
|Ethiopia|3,249|5,374|60%|
|Malawi|1,729|2,337|74%|
|Nigeria|1,950|3,000|65%|
|Uganda|2,227|2,386|93%|

_Sources: Innovation for Poverty Action (IPA, 2020) and Gourlay et al. (2021). Based on survey round 1 in each country. The IPA Recovr surveys used random digit dialing of a nationally representative sample of phone numbers._ For the HFPS surveys, we can further diagnose the observed patterns of nonresponse. Figure 3 displays response rates from five countries broken down by urban/rural and agricultural/nonagricultural status. The response rates in Uganda, which are very high, benefitted from the relatively recent pre-COVID inperson survey which allowed for an updating of contact information shortly before implementation of the 8