**Table 1: Overview of National Surveys Used in Analysis**

||**Ethiopia**|**Tanzania**|**Cambodia**|
|---|---|---|---|
|Survey|2018/2019<br>Ethiopia Socioeconomic<br>Survey|2019/2020<br>Tanzania National Panel<br>Survey|2019/2020<br>Cambodia LSMS+|
|Implementing Agency|Ethiopia Central<br>Statistical Agency|Tanzania National<br>Bureau of Statistics|National Institute of<br>Statistics of Cambodia|
|Fieldwork Period|9/2018 – 8/2019|1/2019 – 1/2020|10/2019 – 1/2020|
|Household Sample|6770 Households|1184 Households|1512 Households|
|Scope of Household<br>Questionnaire|8 modules|16 modules|10 modules|
|Adult Respondent<br>Sample for Individual<br>Questionnaire|7235 Men<br>8153 Women|1407 Men<br>1506 Women|1845 Men<br>2095 Women|
|Scope of Individual<br>Questionnaire|7 modules|8 modules|11 modules|
|Individual Questionnaire<br>Modules on Asset<br>Ownership|Non-residential (primarily<br>agricultural) and<br>residential land, financial<br>accounts, mobile phones,<br>livestock|Non-residential<br>(primarily agricultural)<br>and residential land,<br>financial accounts,<br>mobilephones|Non-residential (primarily<br>agricultural) and residential<br>land, financial accounts,<br>mobile phones, livestock,<br>consumer durables|
|Other Individual<br>Questionnaire Modules<br>Notes: LSMS+ data are pub|Employment, non-farm<br>enterprises, education,<br>health, savings<br>licly available. More informa|Employment, non-farm<br>enterprises, education,<br>health, subjective well-<br>being<br>tion on the data and question|Employment, non-farm<br>enterprises, education,<br>health, 24-hour time use<br>diary; domestic and<br>international migration<br>naire can be found in thislink.|

Notes: LSMS+ data are publicly available. More information on the data and questionnaire can be found in this link.

# **2.2 Paradata collection and cleaning**

The Cambodia, Ethiopia, and Tanzania surveys were conducted using the World Bank Survey Solutions CAPI software, which automatically includes a supplemental paradata that contains timestamps of all “events” associated with each interview. Table 2 provides an example of what a paradata file looks like. There is a row for each event followed by the timestamp of when the event occurred. The “parameters” column provides input associated with the event. For example, the first row shows an _AnswerSet_ event for the question hh_a01_1 where the person answered “53”.<sup>5</sup> Each row in the paradata follows the previous row sequentially in time which is shown in the column order.<sup>6</sup> Our analysis includes data on events initiated by the interviewer (variable role==1)

> 5 The variable “parameters” is important since it consists of key information concerning the event. For example, for an _AnswerSet_ event, the parameters value contains the question that is answered, what was the answer, and who answered the question. In some cases, there are multiple IDs in the paradata to uniquely distinguish specific assets or agricultural plots. Parsing these types of information allows for a richer set of analysis at the question-level or respondent-level.

> 6 Some events were deleted during the data cleaning process which explains the missing numbers in order.

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