COVID-19 Rapid Response Phone Survey With Households 2020, Panel
Kenya, 2020
Utz J. Pape (World Bank)
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December 01, 2020
Last modified
December 01, 2020
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Identification
Survey ID Number
KEN_2020_COVIDRS_v02_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
This dataset contains information from the first three waves of the COVID-19 RRPS Household Survey which is part of a five-wave bi-monthly panel survey that targets Kenyan nationals. The same households are interviewed every two months, between May 2020 and May 2021.
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 three waves of the COVID-19 RRPS Household Survey which is part of a five-wave bi-monthly panel survey that targets Kenyan nationals. The same households are interviewed every two months, between May 2020 and May 2021 and it is conducted using Computer Assisted Telephone Interviewing (CATI) techniques. Sampled households that were not reached in earlier waves were also contacted along with households that were interviewed before. The wave 1 status of these households can be checked from the variable "surveystatus_rd1" and for wave 2 from the variable "surveystatus_rd2". It 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. The second was obtained through the Random Digit Dialing method, by which active phone numbers created from the 2020 Numbering Frame produced by the Kenya Communications Authority are randomly selected. The samples cover urban and rural areas and are designed to be representative of the population of Kenya using cell phones. The third wave of this survey includes information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge for 4,979 households.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Household, Individual
Version
Version Description
Version 02
Version Date
2021-02-03
Version Notes
List of edits made for wave 1 and wave 2 micro-data
- The hhm file was renamed hh_adult file.
- Variables that were previously included in both the hh file and the hh_adult (previously hhm) file were removed from the hh file for both the waves.
- Variable and value labels for a small number of variables were corrected and labels for some missing values were added in the hh_adult file for both the waves
- Enterprise section 4b was included in the micro-data library for wave 2.
- Study updated with wave 3 data.
- The hhm file was renamed hh_adult file.
- Variables that were previously included in both the hh file and the hh_adult (previously hhm) file were removed from the hh file for both the waves.
- Variable and value labels for a small number of variables were corrected and labels for some missing values were added in the hh_adult file for both the waves
- Enterprise section 4b was included in the micro-data library for wave 2.
- Study updated with wave 3 data.
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 provided a 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 3,975 completed interviews for this sample in the third wave of the Kenya 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 1,004 completed interviews for this sample in the third 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-kenya-population-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 are constructed by applying the following steps separately for the national samples, KNBS and RDD combined:
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 The panel weights including wave 3 were constructed using the same procedure.
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-kenya-population-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 are constructed by applying the following steps separately for the national samples, KNBS and RDD combined:
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 The panel weights including wave 3 were constructed using the same procedure.
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 |
| 2020-09-28 | 2020-11-30 | Wave 3 |
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 2 survey
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. For the sample based on Random Digit Dialing, the target respondent was the owner the phone number that was randomly selected.
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. For the sample based on Random Digit Dialing, the target respondent was the owner the phone number that was randomly selected.
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_v02_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_v02_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_v02_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 02