African Cigarette Prices 2016-2021
Africa, 2016 - 2021
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Reference ID
afr-reep-acp-2016-2021-v1.5
Producer(s)
Research Unit on the Economics of Excisable Products
Created on
Nov 08, 2016
Last modified
Oct 05, 2022
Page views
33117
Downloads
2040
Identification
Survey ID Number
afr-reep-acp-2016-2021-v1.5
Title
African Cigarette Prices 2016-2021
Country
| Name | Country code |
|---|---|
| Africa | afr |
Study type
Price Survey
Abstract
The Data was collected by on the Data on Aliments, Tobacco and Alcohol in Africa Project which is based at the University of Cape Town. The DATA project collected prices of cigarettes sold at retail outlets and from street vendors in a number of African countries. The aim is to create a retail cigarette price database to enable researchers to make estimations about price differences in these countries, between brands, in urban and rural settlements, and over time. The 2021 Round 10 data is embargoed until 1 April 2023 at the request of the REEP researchers.
Kind of Data
Observation data
Unit of Analysis
Other
Version
Version Description
v1.5: Edited, anonymised data for public distribution
Version Date
2022-09-01
Version Notes
Version 1.2: All previous rounds and datafiles of the ACP were harmonized and combined into a single data series. Version 1.2 covered rounds 1-7. Prior to version 1.2 rounds were released separately.
Version 1.3: Round 8 was added. It was undertaken between June and August 2019.
Version 1.4: Round 9 was added. It was undertaken between November 2019 and Febuary 2020.
Version 1.5: Round 10 was added. It was undertaken in 2021
Version 1.3: Round 8 was added. It was undertaken between June and August 2019.
Version 1.4: Round 9 was added. It was undertaken between November 2019 and Febuary 2020.
Version 1.5: Round 10 was added. It was undertaken in 2021
Scope
Notes
The study collected data on prices of cigarettes from retail outlets and street vendors in several African countries.
Coverage
Geographic Coverage
The countries included in the study are: Botswana, Cameroon, Chad, Eswatini, Ethiopia, Ghana, Kenya, Lesotho, Madagascar, Malawi, Mauritius, Mozambique, Namibia, Nigeria, South Africa, Tanzania, Uganda, Zambia, and Zimbabwe. Not all countries are covered in all rounds.
Geographic Unit
The data was collected at the level of retailer (often incliuding GPS coordinates of the retail outlet). However, the lowest level of geographic detail available in the public use dataset is Suburb.
Universe
The study covered cigarette prices of retailers (including street vendors) in selected African countries.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Research Unit on the Economics of Excisable Products | University of Cape Town |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| International Development Research Centre | IDRC | Funder |
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2016 | 2021 | Rounds 1-10 |
Data Collection Mode
Other
Supervision
The Project received ethics approval for data collection from the Ethics Committee of the Faculty of Commerce at the University of Cape Town on the 8th of October 2015.
Data Collection Notes
For this project, the Data on Alcohol and Tobacco in Africa (DATA) Project organised for University of Cape Town students from different African countries to visit retail outlets and street vendors in their countries, where cigarettes were sold, and obtained permission to take photographs of cigarette prices, using their cellphones.
Fieldworkers working in the same areas were put in contact to avoid duplication of effort. As such, it is reasonable to assume that duplication was largely avoided and that a store being interviewed twice in one round occured quite rarely if at all. Students were somewhat self-governing in their choice of areas to visit and stores to gather data from. How the student groups covering an area divided up that area has not been well documented. As such, there may be georgraphic bias, for example against poorer areas that are more difficult to interview. This also means that a given interviewer might repeat whatever bias/mistakes they tend to make over all the rounds (if they remained on the project) making it hard to detect. In some cases the same store (retailer/spaza shop/street vendor) was interviewed in more than one round of data collection.
Fieldworkers working in the same areas were put in contact to avoid duplication of effort. As such, it is reasonable to assume that duplication was largely avoided and that a store being interviewed twice in one round occured quite rarely if at all. Students were somewhat self-governing in their choice of areas to visit and stores to gather data from. How the student groups covering an area divided up that area has not been well documented. As such, there may be georgraphic bias, for example against poorer areas that are more difficult to interview. This also means that a given interviewer might repeat whatever bias/mistakes they tend to make over all the rounds (if they remained on the project) making it hard to detect. In some cases the same store (retailer/spaza shop/street vendor) was interviewed in more than one round of data collection.
Data Collectors
| Name | Abbreviation | Affiliation |
|---|---|---|
| Research Unit on the Economics of Excisable Products (formerly Economics of Tobacco Control Project) | REEP | University of Cape Town |
Data Processing
Data Editing
Data collected by REEP was in excel format and not serialised. Datafirst has serialised and cleaned the data to create a research-ready series. Appending the rounds required harnomising variable names. The brand names (eg Marborough) and sub-brands (eg gold) required significant cleaning due to typos and errors in data collection. Some observations with bad values (around 20) were dropped from the final dataset.
Data Appraisal
Data Appraisal
Store IDs. Store IDs were created based on what is known about data collection design. Specifically, that each fieldworker took one photo of each store they enumerated per round. Fieldworkers in the same area were put in contact; making the assumption that they managed to avoid any overlap, stores can be uniquely identified by their fieldworker-photoID-round. This is how the StoreIDs in version 1.3. were created. There are two consequences of this. Firstly, if the same store was visited twice in a round, it will be recorded as two separete stores. Secondly, this design unfortunately means that the same phyicsal store will get different IDs in each round of data collection. One can only indentify stores across rounds in the disclosive version of the dataset using GPS information.
StoreIDs in Round 1. It has been noted that some of the PhotoIDs in round 1 did not map 1-to-1 with the phyiscal stores. By example, two different enumerators used the same photoID to record photos of two different stores. As such, the storeIDs for round 1 are not relaible. An analyist would need to look at store name and suburb to get a better idea of how to idenitfy distinct phyiscal stores in round 1.
There are other types of tobacco products still in the data. The analyst can use the variable "Product" to remove such non-cigratte products (eg cigars). For the same reason, the analyst may also want to check the fieldworker's comments. Brand and sub-brands including "King Size" might affect analysis as this is a bigger size of cigarette and thus should cost more.
It is slightly unclear how to treat the sub-brand "filter" or "filters". In general a filter cigarette is a standard (filterless are very rare these days), and many data collectors may have opted not to include the word "filter" as it is implied. If the sub-brand is captured as "filter 20" this generally just means a standard pack of 20. Despite this, the data cleaning policy was not to remove the word "filter" but rather leave it up to the analyst. The recommendation would be for the analyst to consider these as being equivalent to no sub-brand. For example "Camel Filters" is really the same thing as "Camel". However, in the case of specfic flavours, such as "Camel - Blue Ice", this is not the standard Camel cigarrette but a particular flavour. If filter/filters appeared here, the policy was to remove the word.
StoreIDs in Round 1. It has been noted that some of the PhotoIDs in round 1 did not map 1-to-1 with the phyiscal stores. By example, two different enumerators used the same photoID to record photos of two different stores. As such, the storeIDs for round 1 are not relaible. An analyist would need to look at store name and suburb to get a better idea of how to idenitfy distinct phyiscal stores in round 1.
There are other types of tobacco products still in the data. The analyst can use the variable "Product" to remove such non-cigratte products (eg cigars). For the same reason, the analyst may also want to check the fieldworker's comments. Brand and sub-brands including "King Size" might affect analysis as this is a bigger size of cigarette and thus should cost more.
It is slightly unclear how to treat the sub-brand "filter" or "filters". In general a filter cigarette is a standard (filterless are very rare these days), and many data collectors may have opted not to include the word "filter" as it is implied. If the sub-brand is captured as "filter 20" this generally just means a standard pack of 20. Despite this, the data cleaning policy was not to remove the word "filter" but rather leave it up to the analyst. The recommendation would be for the analyst to consider these as being equivalent to no sub-brand. For example "Camel Filters" is really the same thing as "Camel". However, in the case of specfic flavours, such as "Camel - Blue Ice", this is not the standard Camel cigarrette but a particular flavour. If filter/filters appeared here, the policy was to remove the word.
Access policy
Contacts
| Name | Affiliation | URL | |
|---|---|---|---|
| DataFirst Support | University of Cape Town | support@data1st.org | www.support.data1st.org |
Access conditions
Public access data for use under a Creative Commons CC-BY (Attribution-only) License
Citation requirements
Research Unit on the Economics of Excisable Products. African Cigarette Prices 2016-2021 [dataset]. Version 1.5. Cape Town: REEP [producer], 2022. Cape Town: DataFirst [distributor], 2022. DOI: https://doi.org/10.25828/nvz2-ah77
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
2022-09-19
DDI Document version
Version 1