Hungry Cities Partnership Survey 2016-2019, Nairobi
Kenya, 2016 - 2019
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Reference ID
ken-acc-hcp-nrb-2016-2019-v1
Producer(s)
Hungry Cities Partnership, African Centre for Cities
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Created on
Dec 18, 2020
Last modified
Feb 08, 2021
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Identification
Survey ID Number
ken-acc-hcp-nrb-2016-2019-v1
Title
Hungry Cities Partnership Survey 2016-2019, Nairobi
Country
| Name | Country code |
|---|---|
| Kenya | ken |
Study type
Household Survey [hh]
Abstract
This study covers Nairobi, one of four African cities surved between 2013 and 2019 by the African Center for Cities. The African Center for cities is based at the University of Cape Town and is a partner of the Hungry Cities Partnership (HCP).
The HCP studies include household data on food insecurity, household food purchasing dynamics, nutritional discounting taking place in households, foods consumed and multidimensional measures of poverty. The household data is complimented with household member data and food retailer (vendor) data, including infomation on vendor employees.
The Hungry Cities Partnership is an international network of cities and city-based partner organizations which focuses on the relationships between rapid urbanization, informality, inclusive growth and urban food systems in the Global South.
The HCP studies include household data on food insecurity, household food purchasing dynamics, nutritional discounting taking place in households, foods consumed and multidimensional measures of poverty. The household data is complimented with household member data and food retailer (vendor) data, including infomation on vendor employees.
The Hungry Cities Partnership is an international network of cities and city-based partner organizations which focuses on the relationships between rapid urbanization, informality, inclusive growth and urban food systems in the Global South.
Kind of Data
Sample survey data
Unit of Analysis
Households and individuals
Version
Version Description
V1: Cleaned and anonymised for public use
Version Date
2020-12-17
Scope
Notes
The houeshold surveys adopted the USAID aligned Food and Nutrition Technical Assistance modules, detailed by the measures of the Household Food Insecurity Access Scale, Household Food Insecurity Access Prevalence Scale, Household Dietary Diversity Score and the Months of Adequate Food Provisioning. The surveys provide data on food insecurity, household food purchasing dynamics, nutritional discounting taking place in households, and foods consumed. Over and above this, the surveys also provide insights into the levels of multidimension poverty, through the use of the Lived Poverty Index. This combination highlights the connections between food insecurity and lived poverty.
The informal vendor survey instrument sought information on issues including vendor demographic characteristics, entrepreneurial motivations, business financing, enterprise character, operations, challenges, strategies, and aspirations of the vendors. The informal vendor surveys covered over 3500 vendors across the same scale.
The informal vendor survey instrument sought information on issues including vendor demographic characteristics, entrepreneurial motivations, business financing, enterprise character, operations, challenges, strategies, and aspirations of the vendors. The informal vendor surveys covered over 3500 vendors across the same scale.
Coverage
Geographic Coverage
The household sample aims to be representative of the city of Nairobi.
Geographic Unit
In the public release the lowest geographic level is the city (Nairobi). In the secure version of the data, the lowest geographic/administrative unit at which dissagregated data is available is the community. GPS data is also available in the secure version.
Universe
Households and Vendors in Nairobi.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Hungry Cities Partnership, African Centre for Cities | University of Cape Town |
Producers
| Name | Role |
|---|---|
| University of Nairobi | Partner Organisation |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| International Development Research Centre | IDRC | Funder |
| Social Sciences and Humanities Research Council | SSHRC | Funder |
Sampling
Sampling Procedure
Household: The survey report explains a two-stage sampling process. Firstly, 23 of the 111 administrative "sub-locations" in Nairobi were selected. 3 were selected randomly from each of the 8 divisions, excluding Kasarani which only had 2 selected. Then for the second stage, households were selected randomly from these 23 administrative locations. The number of households selected was proportional to the size (measured in households) of the administrative location, a "portional-to-population" strategy that was also employed in the Maputo household survey from 2014.
Vendor: 1267 food vendors were interviewed across Nairobi. The documentation sounds like a two-stage process was also followed. It states that at least 3 neighbourhoods were randomly selected in each of the eight administrative divisoins. The documentation states that vendors were randomly selected in the second stage, but then adds the following "respondents were randomly selected depending on the form and density of the location of business enterprises in the residential neighbourhood, category of food vendor, types of food items sold by the vendor, as well as willingness to participate in the survey." It is as such not clear to what extent this process was random.
Vendor: 1267 food vendors were interviewed across Nairobi. The documentation sounds like a two-stage process was also followed. It states that at least 3 neighbourhoods were randomly selected in each of the eight administrative divisoins. The documentation states that vendors were randomly selected in the second stage, but then adds the following "respondents were randomly selected depending on the form and density of the location of business enterprises in the residential neighbourhood, category of food vendor, types of food items sold by the vendor, as well as willingness to participate in the survey." It is as such not clear to what extent this process was random.
Deviations from the Sample Design
Due to security reasons, the survey could only take place during daylight hours and as such the study may have missed businesses operating solely at night.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2016 | 2019 | Both surveys |
| 2016-05 | 2016-05 | Household survey |
| 2019-10 | 2019-10 | Vendor survey |
Data Collection Mode
Face-to-face [f2f]
Supervision
This project received ethics approval by the Ethics in Research Commitee of the Faculty of Commerce at the University of Cape Town on the 13th of April 2015.
Data Collection Notes
The Nairobi survey team comprised the coordinator, a field supervisor, a data capturing manager and 29 enumerators. The survey took 14 days to complete.
Data Collectors
| Name | Abbreviation |
|---|---|
| University of Nairobi | UoN |
Questionnaires
Questionnaires
There are two questionnaires per city, a household questionnaire and a vendor questionnaire. The household questionnaire has a subsection for household members (persons), and the vendor quesitonnaire has a subsection for employees. Answers to these subsections are supplied in separete datafiles, which can be matched to (merged with) the questoinnaire as necessary.
DataFirst has not received documentation to confirm this, but it is likely that the protocols from the other cities were followed, in that 1) vendor surveys were administered to the person directly responsible for the running of the business using handheld tablets and 2) the household survey was administered to a senior adult member of the household, someone who could speak for the household.
DataFirst has not received documentation to confirm this, but it is likely that the protocols from the other cities were followed, in that 1) vendor surveys were administered to the person directly responsible for the running of the business using handheld tablets and 2) the household survey was administered to a senior adult member of the household, someone who could speak for the household.
Data Processing
Data Editing
Datafiles were received by DataFirst in SPSS (.sav) and Excel (.xlsx) format. Variables had to be named and variable labels were taken from question text. Variables were named accoriding to question number and subject matter, in a hierachical fasion.
An effort was made to keep question numbers and value labels consistent across cities where the same questions were asked for the 2013-2019 surveys. For the vendor data, Cape Town, Maputo and Nairobi had almost identical questionnaires and so the question numbers were naturally the same across these cities (harmonized). For the household data, Maputo, Nairobi and Windhoek were similar and could be harmonized. This means users could try stack these datafiles. This also means that list numbers/value codes might not match the questionnaire for a given city.
Missing values of 97, 98, and 99 were converted to -97, -98 and -99. There were some question numbers wrong in the vendor data questionnaires (typos) that were corrected.
In the household data, the confusingly numbered 10.c and 10.d were renamed to 10b1 and 10b2, to avoid confusion with 10c. and 10d., which were different questions.
An effort was made to keep question numbers and value labels consistent across cities where the same questions were asked for the 2013-2019 surveys. For the vendor data, Cape Town, Maputo and Nairobi had almost identical questionnaires and so the question numbers were naturally the same across these cities (harmonized). For the household data, Maputo, Nairobi and Windhoek were similar and could be harmonized. This means users could try stack these datafiles. This also means that list numbers/value codes might not match the questionnaire for a given city.
Missing values of 97, 98, and 99 were converted to -97, -98 and -99. There were some question numbers wrong in the vendor data questionnaires (typos) that were corrected.
In the household data, the confusingly numbered 10.c and 10.d were renamed to 10b1 and 10b2, to avoid confusion with 10c. and 10d., which were different questions.
Data Appraisal
Data Appraisal
In general the lists change subtly between cities, for example the lists of foods in question 8 of the household data. As such the user should take caution when comparing across cities, and refer to the questionnaires. When the lists differed in a potentially confusing way, list item letters (a-z) were left in the variable name as a second way for the user to check that the data match the questionnaire correctly.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst Support | University of Cape Town | support@data1st.org | www.support.data1st.org |
Citation requirements
Hungry Cities Partnership, African Centre for Cities, University of Cape Town. Hungry Cities Partnership Survey, Nairobi 2016-2019. [dataset]. Version 1. Cape Town: HCP [producer], 2020. Cape Town: DataFirst [distributor], 2020. DOI: https://doi.org/10.25828/dw66-w410
Access authority
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst | University of Cape Town | support@data1st.org | support.data1st.org |
Metadata production
Producers
| Name | Affiliation | Role |
|---|---|---|
| DataFirst | University of Cape Town | Metadata producer |
Date of Metadata Production
2020-12-17