Socio-Economic Impact of COVID-19 on Refugees 2021
Round 5
Kenya, 2021
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Reference ID
KEN_2020_COVID-SEIR-W5_v01_M
Producer(s)
UN Refugee Agency (UNHCR)
Collection(s)
Metadata
Created on
Jun 01, 2022
Last modified
Jun 01, 2022
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Identification
Survey ID Number
KEN_2020_COVID-SEIR-W5_v01_M
Title
Socio-Economic Impact of COVID-19 on Refugees 2021
Subtitle
Round 5
Country/Economy
| Name | Country code |
|---|---|
| Kenya | KEN |
Study type
Socio-Economic/Monitoring Survey [hh/sems]
Series Information
This dataset contains information from the five 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 June 2021.
Abstract
The participants of this phone interview were identified using mixed methods. Stratified random sampling were adopted for Persons of Concern (POC) to UNHCR based in Kakuma, Kalobeyei, Dadaab and Urban areas. While a census was used for all PoCs who were 18+ years amongst the Shona community; this cohort forms 48.6% of the enumerated population of the Shona people. The survey was conducted at two levels: household and individual.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Individual and Household
Version
Version Description
Edited, anonymous dataset for licensed distribution.
Version Date
2021-07
Scope
Notes
Households: Demographics, Employment, Food security, Income loss, Transfers, Subjective welfare, Health and COVID Knowledge
Topics
| Topic |
|---|
| Protection |
| Livelihood & Social cohesion |
| Health |
| Food Distribution |
| Income Generation |
Keywords
| Keyword |
|---|
| COVID-19 |
| Refugees |
| Kenya |
| Economic monitoring |
Coverage
Geographic Coverage
National coverage
Universe
All persons of concern for UNHCR
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| UN Refugee Agency (UNHCR) | UN |
Producers
| Name |
|---|
| World Bank |
| Kenya National Bureau of Statistics |
| University of California |
Sampling
Sampling Procedure
Individuals (18 years and above) with active phone numbers were randomly selected from UNHCR database for each of the four camp sites - Kakuma, Kalobeyei, Dadaab and Urban. For Shona, we took the sample from the Socioeconomic Assessment survey. Due to the smaller sample size of the Shona population (782), we use everybody in the sample. Those selected individuals from each site were sent an SMS, stating that they have been randomly selected to participate in a socio-economic impact of COVID-19 survey.
Weighting
Already computed; see the database. 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
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 |
|---|---|---|
| 2021-03-29 | 2021-06-13 | 5 |
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 Vyxer Research Management and Information Technology Consultancy Limited |
Questionnaires
Questionnaires
The questionnaire included 12 sections Section 1: Introduction Section 2: Household background Section 3: Travel patterns and interactions Section 4: Employment Section 5: Food security Section 6: Income Loss Section 7: Transfers Section 8: Subjective welfare (50% of sample) Section 9: Health Section 10: COVID Knowledge Section 11: Household and Social Relations (50% of sample) Section 12: Conclusion
Access policy
Citation requirements
UNHCR (2021). Socio-economic impact of COVID-19 on refugees in Kenya - Panel - Anonymized for Licensed Use. Accessed from https://microdata.unhcr.org on [date].
Access authority
| Name | Affiliation | |
|---|---|---|
| Curation Team | UN Refugee Agency | microdata@unhcr.org |
Metadata production
DDI Document ID
DDI_KEN_2020_COVID-SEIR-W5_v01_M
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| UN Refugee Agency | UNHCR | UN | Documentation of the study |
| Development Economics Data Group | DECDG | The World Bank | Metadata adapted for Microdata Library |
Date of Metadata Production
2021-07-14
DDI Document version
Version 01: This metadata was downloaded from the UNHCR Microdata Library catalog (https://microdata.unhcr.org/index.php). The following two metadata fields were edited - Document and Survey ID.