Evaluation of the Hunger Safety Net Programme Phase 3: COVID-19 Cash Transfer 2020-2021
Kenya, 2020 - 2021
Get Microdata
Reference ID
KEN_2020-2021_HSNP3IE_v01_M
Producer(s)
Oxford Policy Management Limited
Collection(s)
Metadata
Created on
Dec 08, 2021
Last modified
Dec 08, 2021
Page views
2782
Downloads
18
Identification
Survey ID Number
KEN_2020-2021_HSNP3IE_v01_M
Title
Evaluation of the Hunger Safety Net Programme Phase 3: COVID-19 Cash Transfer 2020-2021
Country/Economy
| Name | Country code |
|---|---|
| Kenya | KEN |
Series Information
The baseline survey sampled and interviewed 1,000 beneficiaries split between Nairobi (n=500) and Mombasa (n=500) and was carried out between 8 October and 10 November 2020, following the staggered implementation timelines. The survey comprised a concise instrument, given the remote nature of data collection, to collect data on the following key outcome areas of interest: Demographic characteristics of the beneficiaries and their households; Attitudes and practices regarding COVID-19; Employment status of the COVID-19 CT recipient and other household-level income sources; Food security; Coping strategies; Access to safety nets; and Exposure and uses of the COVID-19 CT.
The midline survey aimed to interview the same individuals as baseline. Data collection took place between 4 November and 11 December 2020, about 1 month after the baseline survey. A total of 975 people were interviewed of which 483 were in Nairobi and 492 in Mombasa. This represents a sample achievement of 97.5%.
The endline survey aimed to interview the same individuals as midline. Data collection took place between 6 January and 9 February 2021, about 2 months after the midline survey and once the final CT payment was received. A total of 941 people were interviewed of which 463 were in Nairobi and 478 in Mombasa. This represents a sample achievement of 94.1%.
The midline survey aimed to interview the same individuals as baseline. Data collection took place between 4 November and 11 December 2020, about 1 month after the baseline survey. A total of 975 people were interviewed of which 483 were in Nairobi and 492 in Mombasa. This represents a sample achievement of 97.5%.
The endline survey aimed to interview the same individuals as midline. Data collection took place between 6 January and 9 February 2021, about 2 months after the midline survey and once the final CT payment was received. A total of 941 people were interviewed of which 463 were in Nairobi and 478 in Mombasa. This represents a sample achievement of 94.1%.
Abstract
To support the urban poor during the COVID-19 crisis, the UK government provided a monthly Cash Transfer (CT) of 4,000 Kenyan Shillings (KSH) (or £27) to approximately 52,000 vulnerable people living in informal settlements in Nairobi and Mombasa over a period of three months. The COVID-19 CT was implemented by a consortium led by GiveDirectly, and the monthly stipend was paid using mobile money transfers, with the first transfers taking place from October 2020. The CT was designed to support beneficiaries to buy food or meet other high-priority needs-such as purchasing water, paying for medical care, or making rent payments as well as to reduce the use of negative coping strategies (e.g., selling assets, borrowing money).
OPM was contracted to conduct the monitoring and evaluation of the COVID-19 CT. The main objective of this evaluation was to determine whether, and to what extent, the emergency COVID-19 CT had a positive effect on its target population in informal urban settlements in Kenya. The evaluation also provided an assessment of the implementation parameters and mechanisms adopted as part of the design and delivery of the COVID-19 CT.
To fulfil these aims, the evaluation was structured around two separate components-an impact evaluation and a process review-and drew on multiple research methods through a mixed methods research framework. The objective of the quantitative impact evaluation was to assess whether the COVID-19 CT has had an impact on its beneficiaries, and to quantify the scale of any effect detected. This estimation of impact was based on a longitudinal non-experimental design, focusing on a panel of beneficiaries interviewed at three points in time (baseline - prior to the intervention, midline, and endline - post-intervention) over the course of the implementation period. All quantitative data collection took place remotely using Computer-Assisted Telephone Interview (CATI) software.
OPM was contracted to conduct the monitoring and evaluation of the COVID-19 CT. The main objective of this evaluation was to determine whether, and to what extent, the emergency COVID-19 CT had a positive effect on its target population in informal urban settlements in Kenya. The evaluation also provided an assessment of the implementation parameters and mechanisms adopted as part of the design and delivery of the COVID-19 CT.
To fulfil these aims, the evaluation was structured around two separate components-an impact evaluation and a process review-and drew on multiple research methods through a mixed methods research framework. The objective of the quantitative impact evaluation was to assess whether the COVID-19 CT has had an impact on its beneficiaries, and to quantify the scale of any effect detected. This estimation of impact was based on a longitudinal non-experimental design, focusing on a panel of beneficiaries interviewed at three points in time (baseline - prior to the intervention, midline, and endline - post-intervention) over the course of the implementation period. All quantitative data collection took place remotely using Computer-Assisted Telephone Interview (CATI) software.
Unit of Analysis
Individuals
Households
Households
Version
Version Description
Version 1 (November 2021). Edited, anonymous dataset for public distribution.
Scope
Topics
| Topic |
|---|
| Demographic characteristics of beneficiaries |
| Attitudes and Practices Regarding COVID-19 |
| Employment status of the COVID-19 CT recipient |
| Other household level Income Sources |
| Food security |
| Coping Strategies |
| Access to Safety Nets |
Keywords
| Keyword |
|---|
| Cash Transfer (CT) |
| Poverty |
| Food Security |
| Livelihoods |
| Safety Nets |
| COVID - 19 |
| Resillence |
| Kenya |
| Urban |
Coverage
Geographic Coverage
Nairobi and Mombasa in Kenya
Universe
The study population consists of individuals included in the lists of enrolled beneficiaries covered by Give Directly for the COVID-19 CT.
Producers and sponsors
Primary investigators
| Name |
|---|
| Oxford Policy Management Limited |
Producers
| Name | Role |
|---|---|
| Research Guide Africa | Survey Partner |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| UK Foreign, Commonwealth and Development Office | UK (FCDO) | Funder |
Sampling
Sampling Procedure
The evaluation team implemented a stratified one-stage probability sampling strategy for the selection of survey respondents from the individuals included in the lists covered by Give Directly for the COVID-19 CT. The goal was to select at baseline a sample of 1,000 eligible individuals who would receive the COVID-19 CT, which would then be interviewed by the evaluation team at baseline, midline, and endline.
The sampling strategy considered the following process:
1) The sample was drawn once the COVID-19 CT beneficiaries were considered as enrolled into the intervention. After discussions with Give Directly, it was decided that an individual was considered a future COVID-19 CT recipient when he/she had responded to the short SMS-based survey delivered by Give Directly.
2) The sample was drawn in two separate batches. The first batch of recipients comprised 6,838 vulnerable individuals from informal settlements in Nairobi, while the second batch contained 1,596 vulnerable individuals from Mombasa. We sampled the same number of beneficiaries from the first and second batches (500 individuals from each batch).
3) Explicit stratification was first applied based on the geographical location of the COVID-19 CT recipient. This entailed that we sample 500 individuals from Nairobi from the first batch, and 500 from Mombasa from the second batch. This allowed us to disaggregate our quantitative findings between Nairobi and Mombasa, and produce informative descriptive and regression analyses for each of the two cities included in the intervention.
4) Implicit stratification was then applied based on the following categorical variables: i) local partner from which the eligible beneficiary was selected, and ii) gender of the COVID-19 CT recipient. The goal of this stratification process was to enhance the representativeness of our sample in terms of these variables, so that our evaluation sample resembled as much as possible the distribution of these characteristics in the target population (i.e. the list of beneficiaries of the COVID-19 CT used as sampling frame for our sample).
5) We did not cluster our survey respondents. Apart from spill-over effect issues, which were not a concern due to the lack of a counterfactual in our methodological approach, this is normally a logistical necessity for in-person surveys. This was not an issue either, given the remote nature of the data collection process.
6) Extensive replacement lists were created to maximise efficiency during survey implementation without sacrificing representativeness of the sample. A detailed replacement protocol was elaborated, which took into account the stratification process described above.
Given the longitudinal nature of the evaluation, the same baseline respondents were tracked and re-interviewed at midline and endline so as to create a panel of survey respondents.The final baseline quantitative survey sample achievement is shown below, including the distribution by county
Sample achievement
Baseline Survey
Nairobi 500
Mombasa 500
Total 1,000
Midline Survey
Nairobi 483
Mombasa 489
Total 972
Endline Survey
Nairobi 463
Mombasa 478
Total 941
The sampling strategy considered the following process:
1) The sample was drawn once the COVID-19 CT beneficiaries were considered as enrolled into the intervention. After discussions with Give Directly, it was decided that an individual was considered a future COVID-19 CT recipient when he/she had responded to the short SMS-based survey delivered by Give Directly.
2) The sample was drawn in two separate batches. The first batch of recipients comprised 6,838 vulnerable individuals from informal settlements in Nairobi, while the second batch contained 1,596 vulnerable individuals from Mombasa. We sampled the same number of beneficiaries from the first and second batches (500 individuals from each batch).
3) Explicit stratification was first applied based on the geographical location of the COVID-19 CT recipient. This entailed that we sample 500 individuals from Nairobi from the first batch, and 500 from Mombasa from the second batch. This allowed us to disaggregate our quantitative findings between Nairobi and Mombasa, and produce informative descriptive and regression analyses for each of the two cities included in the intervention.
4) Implicit stratification was then applied based on the following categorical variables: i) local partner from which the eligible beneficiary was selected, and ii) gender of the COVID-19 CT recipient. The goal of this stratification process was to enhance the representativeness of our sample in terms of these variables, so that our evaluation sample resembled as much as possible the distribution of these characteristics in the target population (i.e. the list of beneficiaries of the COVID-19 CT used as sampling frame for our sample).
5) We did not cluster our survey respondents. Apart from spill-over effect issues, which were not a concern due to the lack of a counterfactual in our methodological approach, this is normally a logistical necessity for in-person surveys. This was not an issue either, given the remote nature of the data collection process.
6) Extensive replacement lists were created to maximise efficiency during survey implementation without sacrificing representativeness of the sample. A detailed replacement protocol was elaborated, which took into account the stratification process described above.
Given the longitudinal nature of the evaluation, the same baseline respondents were tracked and re-interviewed at midline and endline so as to create a panel of survey respondents.The final baseline quantitative survey sample achievement is shown below, including the distribution by county
Sample achievement
Baseline Survey
Nairobi 500
Mombasa 500
Total 1,000
Midline Survey
Nairobi 483
Mombasa 489
Total 972
Endline Survey
Nairobi 463
Mombasa 478
Total 941
Weighting
Post-stratification weights were used to adjust the distribution of our study sample between Nairobi and Mombasa to the frequency distribution between the two counties in the target population. Post-stratification weights are structural adjustment weights that modify the structure of the sample to resemble the structure of the target (reference) population. This weighting approach works by adjusting estimates that come from a sample frequency distribution to the frequency distribution of the target population.
Table 2 below shows how the weight was constructed to adjust the equal distribution in our sample to the unequal distribution in the target population (as defined by the sampling frame used to draw our sample) between the two counties of Nairobi and Mombasa.
Table 2 Weight construction
County Target Population Sample Ratio Weight
Nairobi 31,601 500 0.015822 63.2020
Mombasa 5,778 500 0.086535 11.5560
Total 37,379 1000
In more technical terms, post-stratification weighting is operationalised as follows:
Wjpstr = 1/Pj
where Pj is the ratio (proportion/probability) that the stratum j represents in the target population. These strata are commonly defined in terms of demographic and geographical categories and are defined in this study as the two counties of Nairobi and Mombasa.
All of the units (COVID-19 CT beneficiaries) belonging to the same stratum (either Nairobi or Mombasa county) receive the same weight. The post-stratification weight is then used to align the different sample structure to the target population in the quantitative analysis.
Table 2 below shows how the weight was constructed to adjust the equal distribution in our sample to the unequal distribution in the target population (as defined by the sampling frame used to draw our sample) between the two counties of Nairobi and Mombasa.
Table 2 Weight construction
County Target Population Sample Ratio Weight
Nairobi 31,601 500 0.015822 63.2020
Mombasa 5,778 500 0.086535 11.5560
Total 37,379 1000
In more technical terms, post-stratification weighting is operationalised as follows:
Wjpstr = 1/Pj
where Pj is the ratio (proportion/probability) that the stratum j represents in the target population. These strata are commonly defined in terms of demographic and geographical categories and are defined in this study as the two counties of Nairobi and Mombasa.
All of the units (COVID-19 CT beneficiaries) belonging to the same stratum (either Nairobi or Mombasa county) receive the same weight. The post-stratification weight is then used to align the different sample structure to the target population in the quantitative analysis.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2020-10-08 | 2020-11-10 | Baseline |
| 2020-11-04 | 2020-12-11 | Midline |
| 2021-01-06 | 2021-02-09 | Endline |
Data Collection Mode
Computer Assisted Telephone Interviewing (CATI)
Data Collectors
| Name | Abbreviation |
|---|---|
| Research Guide Africa | RGA |
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Michele Binci | Oxford Policy Management Ltd. | Michele.binci@opml.co.uk |
| Alexandra Doyle | Oxford Policy Management Ltd. | alexandra.doyle@opml.co.uk |
Access conditions
The datasets have been anonymised and are available as a Public Use Dataset. They are 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 organisations without the written agreement of Oxford Policy Management Ltd.
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 organisations.
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 Oxford Policy Management Ltd.
4. No attempt will be made to produce links among datasets provided by Oxford Policy Management Ltd, or among data from Oxford Policy Management Ltd and other datasets that could identify individuals or organisations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from Oxford Policy Management Ltd will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to Oxford Policy Management Ltd.
The original collector of the data and the funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
1. The data and other materials will not be redistributed or sold to other individuals, institutions, or organisations without the written agreement of Oxford Policy Management Ltd.
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 organisations.
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 Oxford Policy Management Ltd.
4. No attempt will be made to produce links among datasets provided by Oxford Policy Management Ltd, or among data from Oxford Policy Management Ltd and other datasets that could identify individuals or organisations.
5. Any books, articles, conference papers, theses, dissertations, reports, or other publications that employ data obtained from Oxford Policy Management Ltd will cite the source of data in accordance with the Citation Requirement provided with each dataset.
6. An electronic copy of all reports and publications based on the requested data will be sent to Oxford Policy Management Ltd.
The original collector of the data and the funding agencies bear no responsibility for use of the data or for interpretations or inferences based upon such uses.
Citation requirements
Oxford Policy Management Limited. Kenya - Evaluation of the Hunger Safety Net Programme Phase 3 (HNSP3): COVID-19 Cash Transfer 2020-2021, Version 2.1 of the public use dataset (November 2021). Ref:KEN_2020-2021_HSNP3IE_v01_M. Downloaded from [uri] on [date]
Access authority
| Name | Affiliation | |
|---|---|---|
| Michele Binci | Oxford Policy Management Ltd. | Michele.binci@opml.co.uk |
| Alexandra Doyle | Oxford Policy Management Ltd. | alexandra.doyle@opml.co.uk |
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.
Copyright
Copyright (c) 2021, Oxford Policy Management Limited
Metadata production
DDI Document ID
DDI_KEN_2020-2021_HSNP3IE_v01_M
Producers
| Name | Abbreviation |
|---|---|
| Oxford Policy Management Ltd. | OPML |
Date of Metadata Production
2021-12-06
DDI Document version
Version 01 (December 2021)