COVID-19 Rapid Response Phone Survey Households 2020-2021, Panel
Kenya, 2020 - 2021
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_v03_M
Title
COVID-19 Rapid Response Phone Survey Households 2020-2021, Panel
Country
| Name | Country code |
|---|---|
| Kenya | KEN |
Study type
1-2-3 Survey, phase 3 [hh/123-3]
Series Information
The survey was conducted in 4 waves as follows:
Wave 1: May 14 to July 7, 2020
Wave 2: July 16 to September 18, 2020
Wave 3: September 18 to November 28, 2020
Wave 4: January 15 to March 25, 2021
Wave 1: May 14 to July 7, 2020
Wave 2: July 16 to September 18, 2020
Wave 3: September 18 to November 28, 2020
Wave 4: January 15 to March 25, 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 four waves of the COVID-19 RRPS, which is part of a bi-monthly panel survey that targets Kenyan nationals and started in May 2020. The same households are interviewed every two months, with interviews 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” variable represents in which wave the households were interviewed in.
The data set 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 and provided a phone number. 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. All waves of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge.
The data set contains three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_ID’. The third file is child level file, which contain information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.
The duration of data collection and sample size for each completed wave was:
Wave 1: May 14 to July 7, 2020; 4,063 Kenyan households
Wave 2: July 16 to September 18, 2020; 4,504 Kenyan households
Wave 3: September 18 to November 28, 2020; 4,993 Kenyan households
Wave 4: January 15 to March 25, 2021; 4,860 Kenyan households
The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/
The data set 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 and provided a phone number. 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. All waves of this survey include information on household background, service access, employment, food security, income loss, transfers, health, and COVID-19 knowledge.
The data set contains three files. The first is the hh file, which contains household level information. The ‘hhid’, uniquely identifies all household. The second is the adult level file, which contains data at the level of adult household members. Each adult in a household is uniquely identified by the ‘adult_ID’. The third file is child level file, which contain information for every child in the household. Each child in a household is uniquely identified by the ‘child_id’.
The duration of data collection and sample size for each completed wave was:
Wave 1: May 14 to July 7, 2020; 4,063 Kenyan households
Wave 2: July 16 to September 18, 2020; 4,504 Kenyan households
Wave 3: September 18 to November 28, 2020; 4,993 Kenyan households
Wave 4: January 15 to March 25, 2021; 4,860 Kenyan households
The same questionnaire is also administered to refugees in Kenya, with the data available in the UNHCR microdata library: https://microdata.unhcr.org/index.php/catalog/296/
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Household, Individual
Scope
Notes
The Kenya COVID-19 RRPS survey covers 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).
Coverage
Geographic Coverage
National coverage covering rural and urban areas
Producers and sponsors
Primary investigators
| Name |
|---|
| Utz J. Pape (World Bank) |
Producers
| Name |
|---|
| University of California Berkeley |
| Kenya National Bureau of Statistics |
Funding Agency/Sponsor
| Name | Abbreviation |
|---|---|
| International Bank of Reconstruction and Development | IBRD |
Sampling
Sampling Procedure
The COVID-19 RRPS with households with Kenyan nationals has two samples. The first is a randomly drawn subset of all households that were part of the 2015/16 KIHBS CAPI pilot and provided a phone number. The 2015/16 KIHBS CAPI pilot 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 and all of them were included in the COVID-19 RRPS sample. The target respondent was the primary male or female household member from the 2015/16 KIHBS CAPI pilot. 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.
Weighting
Weighting: Cross-Sectional weights
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 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
Panel Weights
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 to the full sample of Kenyan nationals:
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. 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 balanced panel weights including waves 3 and 4 were constructed using the same procedure. Variables: weight_panel_w1_2_3 and weight_panel_w1_2_3_4
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 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
Panel Weights
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 to the full sample of Kenyan nationals:
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. 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 balanced panel weights including waves 3 and 4 were constructed using the same procedure. Variables: weight_panel_w1_2_3 and weight_panel_w1_2_3_4
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2020/05/14 | 2020/07/07 | Wave 1 |
| 2020/07/16 | 2020/09/18 | Wave 2 |
| 2020/09/18 | 2020/11/28 | Wave 3 |
| 2021/01/15 | 2021/03/25 | Wave 4 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
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 etc, 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 as well as basic information from previous survey waves where available from wave 2 onward.
RESPONDENTS: The COVID-19 RRPS had one respondent per household. For the sample from the 2015/16 KIHBS CAPI pilot, the target respondent was defined as the primary male or female adult household member. 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. Where the target respondent was not available for the interview, we spoke to any other adult household member of the target respondent.
RESPONDENTS: The COVID-19 RRPS had one respondent per household. For the sample from the 2015/16 KIHBS CAPI pilot, the target respondent was defined as the primary male or female adult household member. 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. Where the target respondent was not available for the interview, we spoke to any other adult household member of the target respondent.
Data Collectors
| Name |
|---|
| Vyxer Research Management and Information Technology Consultancy Limited |
Questionnaires
Questionnaires
Questionnaires for each wave are provided as external resources in pdf as well as Excel format, coded for SCTO.
Data Processing
Other Processing
Variable names were kept constant across survey waves. For questions that remained exactly the same across survey waves, data points for all waves can be found under one variable name. For questions where the phrasing changed (even in a minimal way) across waves, variable names were also changed to reflect the change in phrasing. To address potential inconsistencies in the employment data, some data points had to be dropped for waves 2, 3, and 4.
Despite the random allocation of households to enumerators, high variability was observed in reported employment across enumerators. To reduce inconsistencies, data on employment collected by some enumerators were set to missing. For each enumerator, the mean proportion of households without any employment was calculated. For waves 2 and 3, the 95 percent confidence interval of this mean proportion was established across all enumerators. Enumerators who displayed a proportion of households with no employment above the upper bound of the confidence interval were dropped. For wave 4, those enumerators with a mean proportion of households without any employment 1 standard deviation above the mean proportion across all enumerators were dropped. This resulted in dropping the data on employment for 596 households in wave 2, 1,109 households in wave 3, and 380 households in wave 4. To account for the dropped observations in the survey weights, the variable ‘weight_labor’ should be used for weighing when analyzing data on employment. It is constructed in the same way as the cross-sectional ‘weight’ variable, but only considering observations for which the data on employment is kept.
More detailed data on children was collected in waves 3 and 4, compared to waves 1 and 2. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the ‘hh’ data for waves 1 and 2. From wave 3 onwards, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the ‘child’ data set. The household level weights can be used for analysis of the children’s data.
Despite the random allocation of households to enumerators, high variability was observed in reported employment across enumerators. To reduce inconsistencies, data on employment collected by some enumerators were set to missing. For each enumerator, the mean proportion of households without any employment was calculated. For waves 2 and 3, the 95 percent confidence interval of this mean proportion was established across all enumerators. Enumerators who displayed a proportion of households with no employment above the upper bound of the confidence interval were dropped. For wave 4, those enumerators with a mean proportion of households without any employment 1 standard deviation above the mean proportion across all enumerators were dropped. This resulted in dropping the data on employment for 596 households in wave 2, 1,109 households in wave 3, and 380 households in wave 4. To account for the dropped observations in the survey weights, the variable ‘weight_labor’ should be used for weighing when analyzing data on employment. It is constructed in the same way as the cross-sectional ‘weight’ variable, but only considering observations for which the data on employment is kept.
More detailed data on children was collected in waves 3 and 4, compared to waves 1 and 2. In waves 1 and 2, data on children, e.g. on their learning activities, was collected for all children in a household with one question. Therefore, variables related to children are part of the ‘hh’ data for waves 1 and 2. From wave 3 onwards, questions on children in the household were asked for specific children. Some questions covered all children, while others were only administered to one randomly selected child in the household. This approach allows to disaggregate data at the level of the child household members, and the data can be found in the ‘child’ data set. The household level weights can be used for analysis of the children’s data.
Access policy
Access authority
| Name | Affiliation | URL |
|---|---|---|
| Microdata Library | World Bank | microdata.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 Households 2020-2021, Panel. Ref: KEN_2020_COVIDRS_v03_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 Households 2020-2021, Panel. Ref: KEN_2020_COVIDRS_v03_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_v03_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Data Group | DECDG | World Bank | Documentation of the study |
Date of Metadata Production
2021-04-22
DDI Document version
Version 03
Updated panel with all waves and observations in a single dataset
Updated panel with all waves and observations in a single dataset