COVID-19 High Frequency Phone Survey of Households 2020 - Ethiopia

Abstract
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The potential impacts of the COVID-19 pandemic in Ethiopia are expected to be severe on Ethiopian households' welfare. To monitor these impacts on households, the team selected a subsample of households that had been interviewed for the Living Standards Measurement Study (LSMS) in 2019, covering urban and rural areas in all regions of Ethiopia. The 15-minute questionnaire covers a series of topics, such as knowledge of COVID and mitigation measures, access to routine healthcare as public health systems are increasingly under stress, access to educational activities during school closures, employment dynamics, household income and livelihood, income loss and coping strategies, and external assistance.

The survey is implemented using Computer Assisted Telephone Interviewing, using a modular approach, which allows for modules to be dropped and/or added in different waves of the survey. Survey data collection started at the end of April 2020 and households are called back every three to four weeks for a total of seven survey rounds to track the impact of the pandemic as it unfolds and inform government action. This provides data to the government and development partners in near real-time, supporting an evidence-based response to the crisis.

The sample of households was drawn from the sample of households interviewed in the 2018/2019 round of the Ethiopia Socioeconomic Survey (ESS). The extensive information collected in the ESS, less than one year prior to the pandemic, provides a rich set of background information on the COVID-19 High Frequency Phone Survey of households which can be leveraged to assess the differential impacts of the pandemic in the country.

Geographic coverage
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National coverage - rural and urban

Analysis unit
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Individual and household

Universe
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The survey covered all de jure households excluding prisons, hospitals, military barracks, and school dormitories.

Kind of data
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Sample survey data [ssd]

Sampling procedure
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The sample of the HFPS-HH is a subsample of the 2018/19 Ethiopia Socioeconomic Survey (ESS). The ESS is built on a nationally and regionally representative sample of households in Ethiopia. ESS 2018/19 interviewed 6,770 households in urban and rural areas. In the ESS interview, households were asked to provide phone numbers either their own or that of a reference household (i.e. friends or neighbors) so that they can be contacted in the follow-up ESS surveys should they move from their sampled location. At least one valid phone number was obtained for 5,374 households (4,626 owning a phone and 995 with a reference phone number). These households established the sampling frame for the HFPS-HH.

To obtain representative strata at the national, urban, and rural level, the target sample size for the HFPS-HH is 3,300 households; 1,300 in rural and 2,000 households in urban areas. In rural areas, we attempt to call all phone numbers included in the ESS as only 1,413 households owned phones and another 771 households provided reference phone numbers. In urban areas, 3,213 households owned a phone and 224 households provided reference phone numbers. To account for non-response and attrition all the 5,374 households were called in round 1 of the HFPS-HH.

The total number of completed interviews in round one is 3,249 households (978 in rural areas, 2,271 in urban areas).
The total number of completed interviews in round two is 3,107 households (940 in rural areas, 2,167 in urban areas).
The total number of completed interviews in round three is 3,058 households (934 in rural areas, 2,124 in urban areas).
The total number of completed interviews in round four is 2,878 households (838 in rural areas, 2,040 in urban areas).
The total number of completed interviews in round five is 2,770 households (775 in rural areas, 1,995 in urban areas).
The total number of completed interviews in round six is 2,704 households (760 in rural areas, 1,944 in urban areas).
The total number of completed interviews in round seven is 2,537 households (716 in rural areas, 1,1821 in urban areas).
The total number of completed interviews in round eight is 2,222 households (576 in rural areas, 1,646 in urban areas).
The total number of completed interviews in round nine is 2,077 households (553 in rural areas, 1,524 in urban areas).
The total number of completed interviews in round ten is 2,178 households (537 in rural areas, 1,641 in urban areas).
The total number of completed interviews in round eleven is 1,982 households (442 in rural areas, 1,540 in urban areas).
The total number of completed interviews in round twelve is 888 households (204 in rural areas, 684 in urban areas).

Mode of data collection
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Computer Assisted Telephone Interview [cati]

Research instrument
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The survey questionnaires were administered to all the households in the sample. The questionnaires consisted of the following sections:

Baseline (Round 1)
- Household Identification
- Interview Information
- Household Roster
- Knowledge Regarding the Spread of Coronavirus
- Behavior and Social Distancing
- Access to Basic Services
- Employment
- Income Loss and Coping
- Food Security
- Aid and Support/ Social Safety Nets

Round 2
- Household Identification
- Household Roster
- Access to Basic Services
- Employment
- Income Loss and Coping
- Food Security
- Aid and Support/ Social Safety Nets

Round 3
- Household Identification
- Household Roster
- Behavior and social distancing
- Access to Basic Services
- Employment
- Income Loss and Coping
- Food Security
- Agriculture
- Aid and Support/ Social Safety Nets

Round 4
- Household Identification
- Household Roster
- Access to Basic Services
- Employment
- Income Loss and Coping
- Food Security
- Agriculture
- Aid and Support/ Social Safety Nets
- Locusts
- WASH

Round 5
- Household Identification
- Household Roster
- Access to Basic Services
- Employment
- Income Loss and Coping
- Aid and Support/ Social Safety Nets
- Agriculture
- Livestock

Round 6
- Household Identification
- Household Roster
- Behavior and Social Distancing
- Access to Basic Services
- Employment
- Income Loss and Coping
- Aid and Support/ Social Safety Nets
- Agriculture
- Locusts

Round 7
- Household Identification
- Household Roster
- Behavior and Social Distancing
- Access to Basic Services
- Employment
- Income Loss and Coping
- Aid and Support/ Social Safety Nets
- Agriculture
- Locusts

Round 8
- Household Identification
- Household Roster
- Access to Basic Services
- Employment
- Education and Childcaring
- Credit
- Migration
- Return Migration

Round 9
- Household Identification
- Household Roster Update
- Access to Basic Services
- Employment
- Aid and Support/ Social Safety Nets
- Agriculture
- WASH
- Tax

Round 10
- Household Identification
- Household Roster Update
- Access to Basic Services
- Employment

Round 11
- Household Identification
- Household Roster Update
- Access to Basic Services
- Employment
- Education and Childcaring
- Food Insecurity Experience Scale
- SWIFT

Round 12
- Household Identification
- Household Roster Update
- Youth Aspirations and Employment

Cleaning operations
---------------------------

DATA CLEANING
At the end of data collection, the raw dataset was cleaned by the Research team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes. The details are as follows.

Variable naming and labeling:
• Variable names were changed to reflect the lowercase question name in the paper survey copy, and a word or two related to the question.

• Variables were labeled with longer descriptions of their contents and the full question text was stored in Notes for each variable.

• “Other, specify” variables were named similarly to their related question, with “_other” appended to the name.

• Value labels were assigned where relevant, with options shown in English for all variables, unless preloaded from the roster in Amharic.

Variable formatting:
• Variables were formatted as their object type (string, integer, decimal, time, date, or datetime).

• Multi-select variables were saved both in space-separated single-variables and as multiple binary variables showing the yes/no value of each possible response.

• Time and date variables were stored as POSIX timestamp values and formatted to show Gregorian dates.

• Location information was left in separate ID and Name variables, following the format of the incoming roster. IDs were formatted to include only the variable level digits, and not the higher-level prefixes (2-3 digits only.)

• Full Household and Enumeration Area ID variables were given leading 0s to match incoming roster format.

Observation and variable arrangement:

• Only consented surveys were kept in the dataset, and all personal information and internal survey variables were dropped from the clean dataset.

• Roster data is separated from the main data set and kept in long-form but can be merged on the key variable (key can also be used to merge with the raw data).

• In the main dataset, ii4_resp_id and cs7_hhh_id are the roster IDs of the respondent and household head respectively, and can be merged with individual_id in the roster.

• The variables were arranged in the same order as the paper instrument, with observations arranged according to their submission time.

Backcheck data review: Results of the backcheck survey are compared against the originally captured survey results using the bcstats command in Stata. This function delivers a comparison of variables and identifies any discrepancies. Any discrepancies identified are then examined individually to determine if they are within reason.

摘要
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COVID-19疫情对埃塞俄比亚家庭福祉的潜在影响预计将极为严重。为监测这些影响对家庭的影响，研究团队选取了2019年曾接受生活标准测量研究（LSMS）访谈的户样本，涵盖埃塞俄比亚所有地区的城市和农村地区。该15分钟问卷涵盖了一系列主题，如对COVID-19和缓解措施的了解、公共卫生体系日益承受压力时的常规医疗服务获取、学校关闭期间的学业活动获取、就业动态、家庭收入和生计、收入损失和应对策略，以及外部援助。

调查采用计算机辅助电话访谈的方式实施，采用模块化方法，允许在调查的不同阶段删除和/或添加模块。调查数据收集始于2020年4月底，每三到四周对家庭进行一次回访，共计七轮调查，以追踪疫情的发展并指导政府采取行动。这为政府和开发伙伴提供了近乎实时的数据，支持基于证据的危机应对。

家庭样本是从2018/2019轮埃塞俄比亚社会经济调查（ESS）的户样本中抽取的。ESS在埃塞俄比亚全国及各地区的家庭样本基础上构建，2018/19年ESS在城市和农村地区共访谈了6,770户家庭。在ESS访谈中，家庭被要求提供自己的电话号码或参考家庭的电话号码（即朋友或邻居），以便在后续的ESS调查中联系他们，如果他们从抽样地点迁移。5,374户家庭（4,626户拥有电话，995户提供了参考电话号码）至少获得了一个有效的电话号码。这些家庭构成了HFPS-HH的抽样框架。

为了在全国、城市和农村层面获得具有代表性的层级，HFPS-HH的目标样本量为3,300户，其中农村地区1,300户，城市地区2,000户。在农村地区，我们尝试联系ESS中包含的所有电话号码，因为只有1,413户拥有电话，另外771户提供了参考电话号码。在城市地区，3,213户拥有电话，224户提供了参考电话号码。为了解决非响应和流失，HFPS-HH的第一轮中联系了所有5,374户家庭。

第一轮完成访谈的总数为3,249户（农村地区978户，城市地区2,271户）。第二轮完成访谈的总数为3,107户（农村地区940户，城市地区2,167户）。第三轮完成访谈的总数为3,058户（农村地区934户，城市地区2,124户）。第四轮完成访谈的总数为2,878户（农村地区838户，城市地区2,040户）。第五轮完成访谈的总数为2,770户（农村地区775户，城市地区1,995户）。第六轮完成访谈的总数为2,704户（农村地区760户，城市地区1,944户）。第七轮完成访谈的总数为2,537户（农村地区716户，城市地区1,1821户）。第八轮完成访谈的总数为2,222户（农村地区576户，城市地区1,646户）。第九轮完成访谈的总数为2,077户（农村地区553户，城市地区1,524户）。第十轮完成访谈的总数为2,178户（农村地区537户，城市地区1,641户）。第十一轮完成访谈的总数为1,982户（农村地区442户，城市地区1,540户）。第十二轮完成访谈的总数为888户（农村地区204户，城市地区684户）。

数据收集方式
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计算机辅助电话访谈 [cati]

研究工具
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调查问卷被分发给所有样本家庭。问卷包括以下部分：

基线（第1轮）
- 家庭识别
- 访谈信息
- 家庭名单
- 对冠状病毒传播的了解
- 行为和社交距离
- 基本服务的获取
- 就业
- 收入损失和应对
- 食品安全
- 援助和支持/社会安全网

第2轮
- 家庭识别
- 家庭名单
- 基本服务的获取
- 就业
- 收入损失和应对
- 食品安全
- 援助和支持/社会安全网

第3轮
- 家庭识别
- 家庭名单
- 行为和社交距离
- 基本服务的获取
- 就业
- 收入损失和应对
- 食品安全
- 农业
- 援助和支持/社会安全网

第4轮
- 家庭识别
- 家庭名单
- 基本服务的获取
- 就业
- 收入损失和应对
- 食品安全
- 农业
- 援助和支持/社会安全网
- 蝗虫
- WASH

第5轮
- 家庭识别
- 家庭名单
- 基本服务的获取
- 就业
- 收入损失和应对
- 援助和支持/社会安全网
- 农业
- 畜牧业

第6轮
- 家庭识别
- 家庭名单
- 行为和社交距离
- 基本服务的获取
- 就业
- 收入损失和应对
- 援助和支持/社会安全网
- 农业
- 蝗虫

第7轮
- 家庭识别
- 家庭名单
- 行为和社交距离
- 基本服务的获取
- 就业
- 收入损失和应对
- 援助和支持/社会安全网
- 农业
- 蝗虫

第8轮
- 家庭识别
- 家庭名单
- 基本服务的获取
- 就业
- 教育和儿童照料
- 信贷
- 移民
- 返回移民

第9轮
- 家庭识别
- 家庭名单更新
- 基本服务的获取
- 就业
- 援助和支持/社会安全网
- 农业
- WASH
- 税收

第10轮
- 家庭识别
- 家庭名单更新
- 基本服务的获取
- 就业

第11轮
- 家庭识别
- 家庭名单更新
- 基本服务的获取
- 就业
- 教育和儿童照料
- 食品不安全经验量表
- SWIFT

第12轮
- 家庭识别
- 家庭名单更新
- 青年抱负和就业

数据清洗操作
---------------------------
数据清洗
在数据收集结束后，研究团队对原始数据集进行了清洗。这包括格式化，并根据监控问题、调查员反馈和调查变更纠正结果。具体细节如下。

变量命名和标签：
• 变量名称已更改，以反映纸质调查副本中的小写问题名称，并附加与问题相关的单词或短语。
• 变量标签带有更长的内容描述，并存储每个变量的完整问题文本在注释中。
• “其他，请注明”变量名称与相关问题的名称相似，名称后附加“_other”。
• 在相关的情况下分配了值标签，所有变量均以英语显示选项，除非从名单中预加载了阿姆哈拉语的选项。

变量格式化：
• 变量格式化为它们的对象类型（字符串、整数、十进制、时间、日期或日期时间）。
• 多选变量以空格分隔的单变量和显示每个可能响应的是/否值的多个二进制变量保存。
• 时间和日期变量存储为POSIX时间戳值，并格式化为显示格里历日期。
• 位置信息保留在单独的ID和名称变量中，遵循传入名单的格式。ID格式化仅包括变量级别的数字，不包括高级前缀（仅2-3位数字）。
• 完整的家庭和普查区ID变量在前面添加了0，以匹配传入名单的格式。

观察和变量排列：
• 仅保留同意的调查，并从清洗后的数据集中删除所有个人信息和内部调查变量。
• 名单数据与主要数据集分开，并保留为长格式，但可以根据关键变量合并（键也可以用于与原始数据合并）。
• 在主要数据集中，ii4_resp_id和cs7_hhh_id分别是受访者和户主名单ID，可以与名单中的individual_id合并。
• 变量按照纸质工具的顺序排列，观察结果按照提交时间排列。

回查数据审查：使用Stata中的bcstats命令比较回查调查结果与最初捕获的调查结果。该功能提供了变量的比较，并识别任何差异。然后，单独检查识别的差异，以确定它们是否在合理范围内。