Environment for Development Dar es Salaam Energy Survey - Energy Enterprises
SND-ID: 2022-91-2. Version: 1. DOI: https://doi.org/10.5878/qxs8-ej19
Is part of collection at SND: Environment for Development
Citation
Creator/Principal investigator(s)
Gunnar Köhlin - University of Gothenburg, Environment for Development, School of Business, Economics and Law
Marc Jeuland - Duke University, Global Health Institute
Martin Chegere - University of Dar es Salaam, Department of Economics
Leonard le Roux - Sciences Po, Department of Economics
Ipsita Das - Duke University, Sanford School of Public Policy
... Show more..Gunnar Köhlin - University of Gothenburg, Environment for Development, School of Business, Economics and Law
Marc Jeuland - Duke University, Global Health Institute
Martin Chegere - University of Dar es Salaam, Department of Economics
Leonard le Roux - Sciences Po, Department of Economics
Ipsita Das - Duke University, Sanford School of Public Policy
Remidius Ruhinduka - University of Dar es Salaam, Department of Economics
Byela Tibesigwa - University of Cape Town
Samwel Lwiza - University of Dar es Salaam
Show less..Research principal
University of Gothenburg
- Environment for Development, School of Business, Economics and Law, University of Gothenburg.
Description
The Environment for Development Dar es Salaam Energy Survey (EFD-DES) consists of a household survey and an enterprise survey that took place from the 29th of January to the 13th of February 2020. The survey work was funded by the Swedish International Development Cooperation Agency (Sida), through the Swedish embassy in Tanzania.
The primary goal of the household survey was to collect current and detailed information on the economic and socio-demographic profile of households in Dar es Salaam, with a specific focus on household energy use. In addition, the energy enterprise survey was intended to provide information on the nature of small-scale energy enterprises that sell and deliver fuels and cookstoves directly to households.
The survey was conducted primarily to inform a World Bank Policy Note on the transition towards clean, affordable and sustainable household energy in Dar es Salaam. In addition, the household survey forms the baseline for a longer-term study on the impacts of a UNIDO bioethanol cookstove program in Dar es Salaam, as well as an important and current source of informat
The primary goal of the household survey was to collect current and detailed information on the economic and socio-demographic profile of households in Dar es Salaam, with a specific focus on household energy use. In addition, the energy enterprise survey was intended to provide information on the nature of small-scale energy enterprises that sell and deliver fuels and cookstoves directly to households.
The survey was conducted primarily to inform a World Bank Policy Note on the transition towards clean, affordable and sustainable household energy in Dar es Salaam. In addition, the household survey forms the baseline for a longer-term study on the impacts of a UNIDO bioethanol cookstove program in Dar es Salaam, as well as an important and current source of information to study fuel use in Dar es Salaam.
The survey was designed and implemented by a collaborative group of researchers within the Environment for Development (EfD) network, including researchers at the University of Dar es Salaam, Duke University, the University of Gothenburg and the University of Cape Town. The household survey was based on an instrument developed by researchers at Duke University Sanford School of Public Policy, that has been used in Kenya and Nepal, allowing for some cross-country comparability. In total 1100 households, containing 4,396 individuals were interviewed. In addition, 225 energy enterprises were interviewed.
The energy enterprise survey was implemented at the same time and followed the sampling strategy of the Household survey. The intention of this survey was to gain an understanding of the energy supply landscape that households face, including obtaining information on the nature of energy enterprises in Dar es Salaam as well as price data to be used in further analysis.
As such, the enterprise survey took place in the same streets and wards as the household survey. Enumerators were instructed to interview small and medium-scale energy retailers and delivery firms in the same areas that households participating in the household survey were located. Table 2 in the attached document "Basic Information Document" (page 2) provides an overview of the enterprise survey sections. Show less..
Data contains personal data
No
Language
Unit of analysis
Population
Representative sample of urban Dar es Salaam Households
Time Method
Sampling procedure
Probability: Multistage
The sampling strategy was designed in such a way that the resulting data would be able to meet the following goals:
a) The sample should be as representative as possible of household energy use in Dar es Salaam.
b) The sample should be structured in such a way that enables an impact evaluation of the UNIDO bioethanol cookstove program in a future follow-up survey.
c) The household and enterprise surveys should take place in similar areas in order to inform an understanding of the energy enterprise landscape from a household perspective.
A multi-stage stratified random sampling design was followed in the selection of final wards, streets and households to include in the survey.
In the first stage, the intended sample size of 1000 was allocated between the three main districts of Dar es Salaam (Temeke, Ilala and Kinondoni). This was done in proportion to the population of each district, yielding the number of households to be interviewed in each district. The population numbers used were based on the 2012 Census data – the most recent census of households in Dar es Salaam
In the second stage,
a) The sample should be as representative as possible of household energy use in Dar es Salaam.
b) The sample should be structured in such a way that enables an impact evaluation of the UNIDO bioethanol cookstove program in a future follow-up survey.
c) The household and enterprise surveys should take place in similar areas in order to inform an understanding of the energy enterprise landscape from a household perspective.
A multi-stage stratified random sampling design was followed in the selection of final wards, streets and households to include in the survey.
In the first stage, the intended sample size of 1000 was allocated between the three main districts of Dar es Salaam (Temeke, Ilala and Kinondoni). This was done in proportion to the population of each district, yielding the number of households to be interviewed in each district. The population numbers used were based on the 2012 Census data – the most recent census of households in Dar es Salaam
In the second stage, the survey team visited the offices of the District councillors of each district and asked them to rank all the wards within their district by socio-economic status (from richest to poorest). District councillors were asked to assign a number to each ward in their District, where 1 is richest and n is the poorest (n depends on the number of wards per-district).
This ranking was used to divide wards into three socio-economic status groups. These groups were "Relatively Poor" "Middle" and "Relatively Rich", corresponding to the bottom, middle and top thirds of the socio-economic status rankings assigned by District councillors . The primary reason for this exercise was to ensure the inclusion of households across the income distribution in the survey.
Following this, six wards were selected from each District. Two “Relatively Poor” wards, two “Middle” wards and two “Relatively rich” wards were selected in each district, yielding a total number of 18 wards in this survey.
The following process was used to select these 6 wards from each district: In order to ensure the sampling design would be compatible with a later impact evaluation of the UNIDO ethanol stove program, in each district, 3 of the 6 wards (1 poor, 1 middle, 1 rich) were randomly selected from the set of wards targeted by the Ethanol stove rollout. The other 3 wards (1 poor, 1 middle, 1 rich) were randomly selected from the set of wards not targeted by Ethanol stove rollout .
Within each selected ward, two streets were randomly selected . Within each street, the number of households to be skipped by enumerators was determined by the dividing the estimated number of households per street (derived from the estimated number of main streets per ward) by the number of interviews to be completed on that street. Enumerators were then instructed to skip this number of households before interviewing another household. For details, see the attached document "Basic Information Document", section "Sample Design". Show less..
Time period(s) investigated
2020-01-29 – 2020-02-13
Number of individuals/objects
225
Geographic spread
Geographic location: Tanzania
Geographic description: In the first stage, the intended sample size of 1000 was allocated between the three main districts of Dar es Salaam (Temeke, Ilala and Kinondoni). This was done in proportion to the population of each district, yielding the number of households to be interviewed in each district. See the list of districts included on Table four, page five, in the attached document "Basic Information Document".
Lowest geographic unit
Parish
Highest geographic unit
Region
Responsible department/unit
Environment for Development, School of Business, Economics and Law, University of Gothenburg.
Commissioning organisation
World Bank
Research area
Consumption and consumer behaviour (CESSDA Topic Classification)
Energy and natural resources (CESSDA Topic Classification)
Environmental sciences (Standard för svensk indelning av forskningsämnen 2011)
Energy systems (Standard för svensk indelning av forskningsämnen 2011)
Social sciences (Standard för svensk indelning av forskningsämnen 2011)
Economics (Standard för svensk indelning av forskningsämnen 2011)
Economic systems and development (CESSDA Topic Classification)
General health and well-being (CESSDA Topic Classification)
Economic conditions and indicators (CESSDA Topic Classification)
Keywords