COVID-19 Rapid Response Phone Survey With Households 2020, Panel
Kenya, 2020
Utz J. Pape (World Bank)
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December 01, 2020
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December 01, 2020
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Identification
Survey ID Number
KEN_2020_COVIDRS_v01_M
Title
COVID-19 Rapid Response Phone Survey With Households 2020, Panel
Subtitle
Panel
Country
| Name | Country code |
|---|---|
| Kenya | KEN |
Study type
1-2-3 Survey, phase 1 [hh/123-1]
Series Information
The COVID-19 Rapid Response Phone Survey With Households 2020 is a three-wave bi-monthly panel survey that targets Kenyan nationals. This first two waves are documented here.
Abstract
The World Bank in collaboration with the Kenya National Bureau of Statistics and the University of California Berkeley are conducting the Kenya COVID-19 Rapid Response Phone Survey to track the socioeconomic impacts of the COVID-19 pandemic and provide timely data to inform a targeted response. This dataset contains information from the first and second waves of the COVID-19 RRPS Household Survey which is part of a three-wave bi-monthly panel survey that targets Kenyan nationals. The same households are interviewed every two months, between May and October 2020 and it is conducted using Computer Assisted Telephone Interviewing (CATI) techniques. Households that were not reached in wave 1 were also contacted along with households that were interviewed in wave 1. The wave 1 status of these households can be checked from the variable "surveystatus_rd1".
The dataset contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot. It covers urban and rural areas and is designed to be representative of the population of Kenya using cell phones. The second is made up of households whose members were randomly dialed from a list of active numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority.
The Random Digit Dialing method was used for this sample. The households previously reached by RDD were all reached again and additional households were added to the sample as well. It includes information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge for 4,457 households, of which 3156 were also interviewed in round 1 and 1301 are new.
The dataset contains information from two samples of Kenyan households. The first sample is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) Computer-Assisted Personal Interviewing (CAPI) pilot. It covers urban and rural areas and is designed to be representative of the population of Kenya using cell phones. The second is made up of households whose members were randomly dialed from a list of active numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority.
The Random Digit Dialing method was used for this sample. The households previously reached by RDD were all reached again and additional households were added to the sample as well. It includes information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge for 4,457 households, of which 3156 were also interviewed in round 1 and 1301 are new.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Household, Individual
Version
Version Description
Version 01
Version Date
2020-09-21
Scope
Notes
The Kenya COVID-19 RRPS survey 2020 covered the following topics:
• Household Roster
• Travel Patterns & Interactions
• Employment
• Food security
• Income Loss
• Transfers
• Subjective welfare (50% of sample)
• Health
• COVID-19 Knowledge
• Household and Social Relations (50% of sample)
• Household Roster
• Travel Patterns & Interactions
• Employment
• Food security
• Income Loss
• Transfers
• Subjective welfare (50% of sample)
• Health
• COVID-19 Knowledge
• Household and Social Relations (50% of sample)
Coverage
Geographic Coverage
National Coverage covering rural and urban areas
Producers and sponsors
Primary investigators
| Name |
|---|
| Utz J. Pape (World Bank) |
Producers
| Name | Role |
|---|---|
| University of California Berkeley | Collaborator |
| Kenya National Bureau of Statistics | Collaborator |
Funding Agency/Sponsor
| Name |
|---|
| IBRD |
Sampling
Sampling Procedure
The COVID-19 RRPS Household survey has two samples. The first is a randomly drawn subset of all households that were part of the 2015/16 Kenya Integrated Household Budget Survey (KIHBS) CAPI pilot and only includes individuals with a registered phone number. The 2015/16 KIHBS is representative at the national level stratified by county and place of residence (urban and rural areas). At least one valid phone number was obtained for 9,007 households with the target respondent as the primary male or female from the 2015/16 KIHBS. The target sample size was 4,000 households stratified by county and by place of residence (urban/rural). There were 3,631 completed interviews for this sample in the second wave of the RRPS on COVID-19. The second sample consists of households selected using the Random Digit Dialing method. A list of random mobile phone numbers was created using a random number generator from the 2020 Numbering Frame produced by the Kenya Communications Authority. The Initial sampling frame therefore consisted of 92,999,970 randomly ordered phone numbers assigned to three networks: Safaricom, Airtel and Telkom. An introductory text message was sent to 5000 randomly selected numbers to determine if numbers were in operation. Out of these, 4,075 were found to be active and formed the final sampling frame. There was no stratification and individuals that were called were asked about the households they live in. The target sample size was 750. There were 826 completed interviews for this sample in the second wave of the RRPS on COVID-19 RRPS.
Weighting
For the KNBS and RDD samples, to make the sample nationally representative of the current population of households with mobile phone access, we create weights in two steps.
Step 1: Construct raw weights combining the two national samples
The current population consists of
(I) households that existed in 2015/16, and did not change phone numbers,
(II) households that existed in 2015/16, but changed phone number,
(III) households that did not exist in 2015/16.
Abstracting from differential attrition, the weights from the 2015/16 KIHBS CAPI pilot make the KIHBS sample representative of type (I) households.
For RDD households, we ask whether they existed in 2015/16, when they had acquired their phone number, and where they lived in 2015/16, allowing us to classify them into type (I), (II) and (III) households and assign them to KIHBS strata. We adjust weights of each RDD household to be inversely proportional to the number of mobile phone numbers used by adult members of the household, and scale them relative to the average number of mobile phone numbers used in the KIHBS within each stratum. RDD therefore gives us a representative sample of type (II) and (III) households. We then combine RDD and KIHBS type (I) households by ex-post adding RDD households into the 2015/16 sampling frame and adjusting weights accordingly. Last, we combine our representative samples of type (I), type (II) and type (III), using the share of each type within each stratum from RDD (inversely weighted by number of mobile phone numbers). Variable: weight_raw
Step 2: Scale the weights to population proportions in each county and urban/rural stratum
We use post stratification to adjust for differential attrition and response rates across counties and rural/urban strata. We scale the raw weights from step 1 to reflect the population size in each county and rural/urban stratum as recorded in the 2019 Kenya Population and Housing Census conducted by the KNBS (2019 Kenya Population and Housing Census, Volume II: Distribution of Population by Administrative Units, December 2019, Kenya National Bureau of Statistics, https://www.knbs.or.ke/?wpdmpro=2019-kenyapopulation-and-housing-census-volume-ii-distribution-of-population-by-administrative-units).
Variable: weight
To construct panel weights, we follow the approach outlined in Himelein (2014): “Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking”. In each household we follow one target respondent. Wherever households split, only the current household of the target respondent was interviewed. The weights for the wave 1 and 2 balanced panel a constructed by applying the following steps separately for the national sample, (KNBS and RDD combined), and each of the UNHCR samples: 0. Wave 1 cross-sectional weights after post-stratification adjustment are used as a base. W_1 = W_wave1
1. Attrition adjustment through propensity score-based method: The predicted probability that a sample household was successfully re-interviewed in the second survey wave is estimated through a propensity score estimation. The propensity score (PS) is modeled with a linear logistic model at the level of the household. The dependent variable is a dummy indicating whether a household that has completed the survey in wave 1 has also done so in wave 2. The following covariates were used in the linear logistic model: Urban/rural dummy, County dummies, Household head gender, Household head age, Household size, Dependency ratio, Dummy: Is anyone in the household working, Asset ownership: Radio, Asset ownership: Mattress, Asset ownership: Charcoal Jiko, Asset ownership: Fridge, Wall material: 3 dummies, Floor materials: 3 dummies, Connection to electricity grid, Number of mobile phones numbers household uses, Number of phone numbers recorded for follow-up, Sample dummy for estimation with national samples
2. Rank households by PS and split into 10 equal groups
3. Calculate attrition adjustment factor: ac (attrition correction) = the reciprocal of the mean empirical response rate for the propensity score decile
4. Adjust base weights for attrition: W_2 = W_1 * ac
5. Trim top 1 percent of the weights distribution (), by replacing the weights among the top 1 percent of the distribution with the highest value of a weight below the cutoff. W_3 = trim(W_2)
6. Apply post-stratification in the same way as for cross-sectional weights (step 2) Variable: weight_panel_w1_2.
Step 1: Construct raw weights combining the two national samples
The current population consists of
(I) households that existed in 2015/16, and did not change phone numbers,
(II) households that existed in 2015/16, but changed phone number,
(III) households that did not exist in 2015/16.
Abstracting from differential attrition, the weights from the 2015/16 KIHBS CAPI pilot make the KIHBS sample representative of type (I) households.
For RDD households, we ask whether they existed in 2015/16, when they had acquired their phone number, and where they lived in 2015/16, allowing us to classify them into type (I), (II) and (III) households and assign them to KIHBS strata. We adjust weights of each RDD household to be inversely proportional to the number of mobile phone numbers used by adult members of the household, and scale them relative to the average number of mobile phone numbers used in the KIHBS within each stratum. RDD therefore gives us a representative sample of type (II) and (III) households. We then combine RDD and KIHBS type (I) households by ex-post adding RDD households into the 2015/16 sampling frame and adjusting weights accordingly. Last, we combine our representative samples of type (I), type (II) and type (III), using the share of each type within each stratum from RDD (inversely weighted by number of mobile phone numbers). Variable: weight_raw
Step 2: Scale the weights to population proportions in each county and urban/rural stratum
We use post stratification to adjust for differential attrition and response rates across counties and rural/urban strata. We scale the raw weights from step 1 to reflect the population size in each county and rural/urban stratum as recorded in the 2019 Kenya Population and Housing Census conducted by the KNBS (2019 Kenya Population and Housing Census, Volume II: Distribution of Population by Administrative Units, December 2019, Kenya National Bureau of Statistics, https://www.knbs.or.ke/?wpdmpro=2019-kenyapopulation-and-housing-census-volume-ii-distribution-of-population-by-administrative-units).
Variable: weight
To construct panel weights, we follow the approach outlined in Himelein (2014): “Weight Calculations for Panel Surveys with Subsampling and Split-off Tracking”. In each household we follow one target respondent. Wherever households split, only the current household of the target respondent was interviewed. The weights for the wave 1 and 2 balanced panel a constructed by applying the following steps separately for the national sample, (KNBS and RDD combined), and each of the UNHCR samples: 0. Wave 1 cross-sectional weights after post-stratification adjustment are used as a base. W_1 = W_wave1
1. Attrition adjustment through propensity score-based method: The predicted probability that a sample household was successfully re-interviewed in the second survey wave is estimated through a propensity score estimation. The propensity score (PS) is modeled with a linear logistic model at the level of the household. The dependent variable is a dummy indicating whether a household that has completed the survey in wave 1 has also done so in wave 2. The following covariates were used in the linear logistic model: Urban/rural dummy, County dummies, Household head gender, Household head age, Household size, Dependency ratio, Dummy: Is anyone in the household working, Asset ownership: Radio, Asset ownership: Mattress, Asset ownership: Charcoal Jiko, Asset ownership: Fridge, Wall material: 3 dummies, Floor materials: 3 dummies, Connection to electricity grid, Number of mobile phones numbers household uses, Number of phone numbers recorded for follow-up, Sample dummy for estimation with national samples
2. Rank households by PS and split into 10 equal groups
3. Calculate attrition adjustment factor: ac (attrition correction) = the reciprocal of the mean empirical response rate for the propensity score decile
4. Adjust base weights for attrition: W_2 = W_1 * ac
5. Trim top 1 percent of the weights distribution (), by replacing the weights among the top 1 percent of the distribution with the highest value of a weight below the cutoff. W_3 = trim(W_2)
6. Apply post-stratification in the same way as for cross-sectional weights (step 2) Variable: weight_panel_w1_2.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2020-05-14 | 2020-06-08 | Wave 1 |
| 2020-07-16 | 2020-09-18 | Wave 2 |
Data Collection Mode
Computer Assisted Telephone Interview [cati]
Data Collection Notes
PRE-LOADED INFORMATION: Basic household information was pre-loaded in the CATI assignments for each enumerator. The information, for example the household’s location, household head name, phone numbers et cetera, was used to help enumerators call and identify the target households. The list of individuals from the KIHBS CAPI pilot and their basic characteristics were uploaded which helped maintain the panel of individuals and ensured the status of each individual in the wave 1 and 2 surveys.
RESPONDENTS: The COVID-19 RRPS had ONE RESPONDENT per household. The target respondent was defined as the primary male or female from 2015/16 KIHBS CAPI Pilot. They were randomly chosen where both existed to maintain gender balance. If the target respondent was not available for a call, the field team spoke to any adult currently living in the household of the target respondent. If the target respondent was deceased, the field team spoke to any adults that lived with the target respondent in 2015/16. Finally, if the household from 2015/16 split up, we targeted anyone in the household of the target respondent but did not survey a household member that no longer lives with the target respondent.
RESPONDENTS: The COVID-19 RRPS had ONE RESPONDENT per household. The target respondent was defined as the primary male or female from 2015/16 KIHBS CAPI Pilot. They were randomly chosen where both existed to maintain gender balance. If the target respondent was not available for a call, the field team spoke to any adult currently living in the household of the target respondent. If the target respondent was deceased, the field team spoke to any adults that lived with the target respondent in 2015/16. Finally, if the household from 2015/16 split up, we targeted anyone in the household of the target respondent but did not survey a household member that no longer lives with the target respondent.
Data Collectors
| Name |
|---|
| Vyxer Research Management and Information Technology Consultancy Limited |
Questionnaires
Questionnaires
English versions of the Wave 1 and 2 questionnaires are provided under the Documentation tab.
Data Processing
Data Editing
The following universal codes were applied for the survey options:
98 Refused to Answer
99 Don't know
77 Other/Any child member
98 Refused to Answer
99 Don't know
77 Other/Any child member
Other Processing
Section 4b, which contains information on enterprises, has been excluded from this data set and will be made available after the completion of wave 3 interviews.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Utz J. Pape | World Bank | upape@worldbank.org |
Confidentiality
Before being granted access to the dataset, all users have to formally agree: 1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor. 2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files. 3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Access conditions
The dataset has been anonymized and is available as a Public Use Dataset. It is accessible to all for statistical and research purposes only, under the following terms and conditions:
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the World Bank Microdata Library.
2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to the World Bank Microdata Library.
4. No attempt will be made to produce links among datasets provided by the World Bank Microdata Library, or among data from the World Bank Microdata Library and other datasets that could identify individuals or organizations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from the World Bank Microdata Library will cite the source of data in accordance with the Citation Requirement provided with each dataset.
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organizations without the written agreement of the World Bank Microdata Library.
2. The data will be used for statistical and scientific research purposes only. They will be used solely for reporting of aggregated information, and not for investigation of specific individuals or organizations.
3. No attempt will be made to re-identify respondents, and no use will be made of the identity of any person or establishment discovered inadvertently. Any such discovery would immediately be reported to the World Bank Microdata Library.
4. No attempt will be made to produce links among datasets provided by the World Bank Microdata Library, or among data from the World Bank Microdata Library and other datasets that could identify individuals or organizations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from the World Bank Microdata Library will cite the source of data in accordance with the Citation Requirement provided with each dataset.
Citation requirements
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
Example:
Utz.J.Pape (World Bank). Kenya COVID-19 Rapid Response Phone Survey With Households, Panel (COVIDRS) 2020. Ref: KEN_2020_COVIDRS_v01_M. World Bank. Downloaded from [url] on [date]
- 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
Example:
Utz.J.Pape (World Bank). Kenya COVID-19 Rapid Response Phone Survey With Households, Panel (COVIDRS) 2020. Ref: KEN_2020_COVIDRS_v01_M. World Bank. Downloaded from [url] on [date]
Disclaimer and copyrights
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.
Metadata production
DDI Document ID
DDI_KEN_2020_COVIDRS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | World Bank | Documentation of the DDI |
Date of Metadata Production
2020-11-23
DDI Document version
Version 01