Ethiopia - COVID-19 High Frequency Phone Survey of Households 2020, Round 2

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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.

Type: 
Microdata
Acronym: 
COVID-19 HFPS-R2 2020
Languages Supported: 
English
Topics: 
Topic not specified
Geographical Coverage: 
Ethiopia
Economy Coverage: 
Economy Coverage not specified
Release Date: 
July 7, 2020

Last Updated

Last Updated: 
July 7, 2020

Harvest System ID

Harvest System ID: 
Microdata

Harvest Source ID

Harvest Source ID: 
11573
Disclaimer: 
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Version Description: 
Version 01 (July 2020)
Publisher Name: 

Development Economics Data Group; The World Bank

Funding Name, Abbreviation, Role: 
United States Agency for International Development, World Bank
Study Type: 
Socio-Economic/Monitoring Survey [hh/sems]
Series Information: 
The World Bank is providing support to countries to help mitigate the spread and impact of the new coronavirus disease (COVID-19). One area of support is for data collection to inform evidence-based policies that may help mitigate the effects of this disease. Towards this end, the World Bank is leveraging the Living Standards Measurement Study - Integrated Survey on Agriculture (LSMS-ISA) program to implement high-frequency phone surveys on COVID-19 in 5 African countries – Nigeria, Ethiopia, Uganda, Tanzania, and Malawi. This effort is part of a broader first wave of World Bank-supported national longitudinal high frequency survey that can be used to help assess the economic and social implications of the COVID-19 pandemic on households and individuals.
Primary Investigator Name, Affiliation: 
World Bank; World Bank
Sampling Procedure: 
The sample of the HFPS 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. To obtain representative strata at the national, urban, and rural level, the target sample size for the HFPS 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. The total number of completed interviews in Round 2 is 3,107 households (940 in rural areas, 2,167 in urban areas).
Deviations from Sample Design: 
Round 2 interviewed 3, 107 households out of the baseline total of 3,249 households.
Weighting: 
To obtain unbiased estimates from the sample, the information reported by households needs to be adjusted by a sampling weight (or raising factor) w_h. To construct the sampling weights, we follow the steps outlined in Himelein, K. (2014), which outlines eight steps, of which we follow six, to construct the sampling weights for the HFPS-HH: 1. Begin with base weights from the Ethiopia Socioeconomic Survey ESS 2018/19 for each household 2. Incorporate probability of sub-selection of round 1 unit for each of the phone survey households. We calculate the probability of selection for each of the 20 strata in the ESS (urban and rural in each of the 11 regions except for Addis Ababa where we only have an urban stratum) by creating the numerators as the number of completed phone interviews and the denominator as the number of households in the ESS for each stratum. 3. Pool the weights in Steps 1 and 2. 4. Derive attrition-adjusted weights for all individuals by running a logistic response propensity model based on characteristics of the household head (i.e. education, labor force status, demographic characteristics), characteristics of the household (consumption, assets, financial characteristics), and characteristics of the dwelling (house ownership, overcrowding). 5. Trim weights by replacing the top two percent of observations with the 98th percentile cut-off point; and 6. Post-stratify weights to known population totals to correct for the imbalances across our urban and rural sample. In doing so, we ensure that the distribution in the survey matches the distribution in the ESS. * Additional technical details and explanations on each of the steps briefly outlined above can be found in Himelein, K. (2014).
Questionnaires: 
The Ethiopia COVID-19 High Frequency Phone Survey of Households, Round 2 questionnaire consists of the following sections: - Household Identification - Interview Information - Household Roster - Access to Basic Services - Employment - Income Loss - Coping/Shocks - Food Security - Aid and Support/ Social Safety Nets
Data Editing: 
The Ethiopia- COVID-19 High Frequency Phone Survey of Households (HFPS) was conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, SurveyCTO. Each enumerator was given a tablet which they used to implement the interviews, along with data bundles to be used on their own mobile phone devices. Data QUALITY MONITORING Data was sent to the server daily. Senior Field Supervisors served as the first step in ensuring data quality. Senior Field Supervisors reviewed completed surveys with enumerators twice daily via one-on-one calls and were always available to address any concerns that arose while performing an interview. At the same time, a Research Analyst was in charge of checking the uploaded data on a daily basis to correct errors and work to prevent them in future surveys. The following data quality checks were completed: (1) Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. (2) Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using Stata was used to monitor each module’s duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. (3) Daily Data Team reporting: The Field Supervisors and Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. (4) Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators’ side of the conversation only. The recordings were reviewed for any surveys that enumerators flagged as having data quality concerns and for an additional random sample of 2% of respondents for a total of 63 interviews. Consent recordings were similar to Round 1, with most being around one minute, with some outliers in situations where the respondents asked more about the survey, or had previously consented during an earlier call. All reviewed audio recordings were completed satisfactorily. (5) Back-check survey: Back-check enumerators made back-check calls to 5% of the households that completed a survey in Round 2, for a total of 166 households. 132 of these households were reached and interviewed. The back-check protocol and analysis are detailed below. DATA CLEANING At the end of data collection, the raw dataset was cleaned by Laterite’s Research team. This included formatting to World Bank standards, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning processes did not change from Round 1 (for a detailed description of the cleaning process, please refer to the Round 1 Field Report). Data corrections • In five cases, household operating a non-farm family business was incorrectly listed in Round 1. This led to several non-applicable questions being asked, which have been corrected to missing values. • In the section, Income Loss & Coping, some “Other, specify” write-in responses specified forms of livelihoods that would fit into one of the existing categories. Shoe polishing, for example, can be considered a non-farm family business (Q3 “Was a non-farm family business a means of livelihood for your household?”). These responses have been corrected accordingly during data cleaning. • Some responses to Aid & Support received from any institution (such as the government, international organizations, religious bodies) were actually remittances received from family and relatives (within Ethiopia). These responses have been corrected in accordance with Income Loss & Coping Q9 and Q10. • Other specifications were made consistent where possible. For example, for ‘other’ = forms of assistance related to soap or sanitizer, the responses were corrected to “soap and sanitizer”. • Relationship-to-Household-Head values and labels were changed to match the input roster format. • Household Head values have been updated for cases where the head has changed. • Individual IDs in the roster have been updated to align with the updated Round 1 values. • IDs for new respondents not in the previous household roster have been updated to their new Individual ID.
Other Processing: 
The Ethiopia COVID-19 High Frequency Phone Survey of Households, Round 2 covered the following topics: - Household Roster - Access to Basic Services - Employment - Income Loss - Coping/Shocks - Food Security - Aid and Support/ Social Safety Nets

No Visualizations Available.

Use of the dataset must be acknowledged using a citation which would include: - the Identification of the Primary Investigator - the title of the survey (including country, acronym and year of implementation) - the survey reference number - the source and date of download World Bank. Ethiopia - COVID-19 High Frequency Phone Survey of Households, Round 2. 2020. Dataset downloaded from www.microdata.worldbank.org on [date].

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.

FieldValue
Modified Date
2020-07-09
Release Date
Identifier
d70800c0-95fc-41f0-a8ec-98a224763de4
License
License Not Specified
Contact Email
Public Access Level
Public
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No votes yet
Acronym: 
COVID-19 HFPS-R2 2020
Type: 
Languages Supported: 
Disclaimer: 
The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Weighting: 
To obtain unbiased estimates from the sample, the information reported by households needs to be adjusted by a sampling weight (or raising factor) w_h. To construct the sampling weights, we follow the steps outlined in Himelein, K. (2014), which outlines eight steps, of which we follow six, to construct the sampling weights for the HFPS-HH: 1. Begin with base weights from the Ethiopia Socioeconomic Survey ESS 2018/19 for each household 2. Incorporate probability of sub-selection of round 1 unit for each of the phone survey households. We calculate the probability of selection for each of the 20 strata in the ESS (urban and rural in each of the 11 regions except for Addis Ababa where we only have an urban stratum) by creating the numerators as the number of completed phone interviews and the denominator as the number of households in the ESS for each stratum. 3. Pool the weights in Steps 1 and 2. 4. Derive attrition-adjusted weights for all individuals by running a logistic response propensity model based on characteristics of the household head (i.e. education, labor force status, demographic characteristics), characteristics of the household (consumption, assets, financial characteristics), and characteristics of the dwelling (house ownership, overcrowding). 5. Trim weights by replacing the top two percent of observations with the 98th percentile cut-off point; and 6. Post-stratify weights to known population totals to correct for the imbalances across our urban and rural sample. In doing so, we ensure that the distribution in the survey matches the distribution in the ESS. * Additional technical details and explanations on each of the steps briefly outlined above can be found in Himelein, K. (2014).
Economy Coverage: 
Primary Investigator Name, Affiliation: 
World Bank; World Bank
Publisher Name: 
Development Economics Data Group; The World Bank
Version Description: 
Version 01 (July 2020)
Subtitle: 
Round 2
Geographical Coverage: 
Data Classification of a Dataset: 
Series Information: 
The World Bank is providing support to countries to help mitigate the spread and impact of the new coronavirus disease (COVID-19). One area of support is for data collection to inform evidence-based policies that may help mitigate the effects of this disease. Towards this end, the World Bank is leveraging the Living Standards Measurement Study - Integrated Survey on Agriculture (LSMS-ISA) program to implement high-frequency phone surveys on COVID-19 in 5 African countries – Nigeria, Ethiopia, Uganda, Tanzania, and Malawi. This effort is part of a broader first wave of World Bank-supported national longitudinal high frequency survey that can be used to help assess the economic and social implications of the COVID-19 pandemic on households and individuals.
Sampling Procedure: 
The sample of the HFPS 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. To obtain representative strata at the national, urban, and rural level, the target sample size for the HFPS 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. The total number of completed interviews in Round 2 is 3,107 households (940 in rural areas, 2,167 in urban areas).
Deviations from Sample Design: 
Round 2 interviewed 3, 107 households out of the baseline total of 3,249 households.
Release Date: 
Tuesday, July 7, 2020
Last Updated Date: 
Tuesday, July 7, 2020
Questionnaires: 
The Ethiopia COVID-19 High Frequency Phone Survey of Households, Round 2 questionnaire consists of the following sections: - Household Identification - Interview Information - Household Roster - Access to Basic Services - Employment - Income Loss - Coping/Shocks - Food Security - Aid and Support/ Social Safety Nets
Data Editing: 
The Ethiopia- COVID-19 High Frequency Phone Survey of Households (HFPS) was conducted using Computer Assisted Telephone Interview (CATI) techniques. The household questionnaire was implemented using the CATI software, SurveyCTO. Each enumerator was given a tablet which they used to implement the interviews, along with data bundles to be used on their own mobile phone devices. Data QUALITY MONITORING Data was sent to the server daily. Senior Field Supervisors served as the first step in ensuring data quality. Senior Field Supervisors reviewed completed surveys with enumerators twice daily via one-on-one calls and were always available to address any concerns that arose while performing an interview. At the same time, a Research Analyst was in charge of checking the uploaded data on a daily basis to correct errors and work to prevent them in future surveys. The following data quality checks were completed: (1) Daily SurveyCTO monitoring: This included outlier checks, skipped questions, a review of “Other, specify”, other text responses, and enumerator comments. Enumerator comments were used to suggest new response options or to highlight situations where existing options should be used instead. Monitoring also included a review of variable relationship logic checks and checks of the logic of answers. Finally, outliers in phone variables such as survey duration or the percentage of time audio was at a conversational level were monitored. A survey duration close to 15 minutes and a conversation-level audio percentage of around 40% was considered normal. (2) Dashboard review: This included monitoring individual enumerator performance, such as the number of calls logged, duration of calls, percentage of calls responded to and percentage of non-consents. Non-consent reason rates and attempts per household were monitored as well. Duration analysis using Stata was used to monitor each module’s duration and estimate the time required for subsequent rounds. The dashboard was also used to track overall survey completion and preview the results of key questions. (3) Daily Data Team reporting: The Field Supervisors and Data Manager reported daily feedback on call progress, enumerator feedback on the survey, and any suggestions to improve the instrument, such as adding options to multiple choice questions or adjusting translations. (4) Audio audits: Audio recordings were captured during the consent portion of the interview for all completed interviews, for the enumerators’ side of the conversation only. The recordings were reviewed for any surveys that enumerators flagged as having data quality concerns and for an additional random sample of 2% of respondents for a total of 63 interviews. Consent recordings were similar to Round 1, with most being around one minute, with some outliers in situations where the respondents asked more about the survey, or had previously consented during an earlier call. All reviewed audio recordings were completed satisfactorily. (5) Back-check survey: Back-check enumerators made back-check calls to 5% of the households that completed a survey in Round 2, for a total of 166 households. 132 of these households were reached and interviewed. The back-check protocol and analysis are detailed below. DATA CLEANING At the end of data collection, the raw dataset was cleaned by Laterite’s Research team. This included formatting to World Bank standards, and correcting results based on monitoring issues, enumerator feedback and survey changes. Data cleaning processes did not change from Round 1 (for a detailed description of the cleaning process, please refer to the Round 1 Field Report). Data corrections • In five cases, household operating a non-farm family business was incorrectly listed in Round 1. This led to several non-applicable questions being asked, which have been corrected to missing values. • In the section, Income Loss & Coping, some “Other, specify” write-in responses specified forms of livelihoods that would fit into one of the existing categories. Shoe polishing, for example, can be considered a non-farm family business (Q3 “Was a non-farm family business a means of livelihood for your household?”). These responses have been corrected accordingly during data cleaning. • Some responses to Aid & Support received from any institution (such as the government, international organizations, religious bodies) were actually remittances received from family and relatives (within Ethiopia). These responses have been corrected in accordance with Income Loss & Coping Q9 and Q10. • Other specifications were made consistent where possible. For example, for ‘other’ = forms of assistance related to soap or sanitizer, the responses were corrected to “soap and sanitizer”. • Relationship-to-Household-Head values and labels were changed to match the input roster format. • Household Head values have been updated for cases where the head has changed. • Individual IDs in the roster have been updated to align with the updated Round 1 values. • IDs for new respondents not in the previous household roster have been updated to their new Individual ID.
Other Processing: 
The Ethiopia COVID-19 High Frequency Phone Survey of Households, Round 2 covered the following topics: - Household Roster - Access to Basic Services - Employment - Income Loss - Coping/Shocks - Food Security - Aid and Support/ Social Safety Nets
Harvest Source: 
Harvest System ID: 
11573
Citation Text: 
Use of the dataset must be acknowledged using a citation which would include: - the Identification of the Primary Investigator - the title of the survey (including country, acronym and year of implementation) - the survey reference number - the source and date of download World Bank. Ethiopia - COVID-19 High Frequency Phone Survey of Households, Round 2. 2020. Dataset downloaded from www.microdata.worldbank.org on [date].
Modified date: 
18450
Study Type: 
Socio-Economic/Monitoring Survey [hh/sems]
Primary Dataset: 
Yes
Funding Name, Abbreviation, Role: 

United States Agency for International Development, World Bank

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