Disaster Poverty Household Survey 2017-2018, Dar es Salaam
Tanzania, 2017 - 2018
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Reference ID
TZA_2017-2018_DPHS_v01_M
Producer(s)
Alvina Erman, Silvia Malgioglio, Nobuo Yoshida, Stephane Hallegatte
Metadata
Created on
Jul 07, 2022
Last modified
Jul 07, 2022
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Identification
Survey ID Number
TZA_2017-2018_DPHS_v01_M
Title
Disaster Poverty Household Survey 2017-2018, Dar es Salaam
Country/Economy
| Name | Country code |
|---|---|
| Tanzania | TAZ |
Study type
Other Household Survey [hh/oth]
Series Information
The Disaster Poverty Household Survey (DPHS) is designed to collect information to assess the relationship between disaster risk (exposure, vulnerability, and capacity to recover) and poverty in the urban environment. The data can be used to explore policy-relevant research topics related to climate change adaptation, urbanization, urban poverty, and more.
DPHS data contains information on household characteristics, household expenditure, living conditions and household experience with disasters. Household characteristics include household size and member level information on religion, education, and labor. Household expenditure is collected using the Survey of Well-being via Instant and Frequent Tracking (SWIFT) methodology, which estimates household income (or consumption expenditure) based on non-monetary variables that are highly correlated with poverty. Information on living conditions covers housing quality, asset ownership, access to services and jobs, rent and housing costs and tenure arrangements. Information on experiences with disasters includes direct and indirect impacts of historic disasters on household assets, education, health and labor access, as well as impacts on public services. There is also information on coping behaviors and perception of risk of future exposure. The Disaster-Poverty survey can be customized to collect information on different disasters. So far, it has mainly focused on the impacts of urban flooding.
This initiative is led by Global Facility for Disaster Reduction and Recovery (GFDRR) and was developed and implemented in strong collaboration with the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL) at the World Bank, as well as counterparts (Ministries of Finance, local and city governments, national statistical agencies, disaster risk management agencies, etc.) and selected survey firms.
DPHS data contains information on household characteristics, household expenditure, living conditions and household experience with disasters. Household characteristics include household size and member level information on religion, education, and labor. Household expenditure is collected using the Survey of Well-being via Instant and Frequent Tracking (SWIFT) methodology, which estimates household income (or consumption expenditure) based on non-monetary variables that are highly correlated with poverty. Information on living conditions covers housing quality, asset ownership, access to services and jobs, rent and housing costs and tenure arrangements. Information on experiences with disasters includes direct and indirect impacts of historic disasters on household assets, education, health and labor access, as well as impacts on public services. There is also information on coping behaviors and perception of risk of future exposure. The Disaster-Poverty survey can be customized to collect information on different disasters. So far, it has mainly focused on the impacts of urban flooding.
This initiative is led by Global Facility for Disaster Reduction and Recovery (GFDRR) and was developed and implemented in strong collaboration with the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL) at the World Bank, as well as counterparts (Ministries of Finance, local and city governments, national statistical agencies, disaster risk management agencies, etc.) and selected survey firms.
Abstract
The DPHS in Dar es Salaam was conducted in two rounds in November-December 2017 and in September 2018, with the objective to assess the role of poverty and other social factors in urban flooding in the city. The survey data collected in 2017 focused on exposure to frequent flooding, while the follow up survey in 2018, targeting the same households, focused on the impact of a flood event that happened in April 2018. During the follow up survey in 2018, additional households were also added to the sample. The data collected is representative at the city level and overrepresented in areas that are flood prone.
This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR), the Tanzanian Urban Resilience Program (TURP), the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL). Data collection was carried out by UDA Consulting under World Bank supervision.
This project was a collaborative effort between Global Facility for Disaster Reduction and Recovery (GFDRR), the Tanzanian Urban Resilience Program (TURP), the Poverty Global Practice and Urban, Disaster Risk Management, Resilience and Land Global Practice (GPURL). Data collection was carried out by UDA Consulting under World Bank supervision.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
- Household
- Individual
- Individual
Version
Version Description
Version 01
Version Date
2022-06-23
Scope
Notes
The survey covered the following topics:
- Household information
- Household roster: demographic characteristic, education attainment, labor participation
- Asset ownership
- Housing and services: tenure arrangements, housing costs and rent, tenure security, housing quality, access to services, remittances
- Weather information
- Preventive measures flood
- General information on experience with flooding
- Information on specific floods
- Perception of risk and community engagement
- Household enterprises
- Investments in housing
- Food Insecurity (Reduced Coping Strategy Index CSI-R)
- Coping mechanism codes
- Household information
- Household roster: demographic characteristic, education attainment, labor participation
- Asset ownership
- Housing and services: tenure arrangements, housing costs and rent, tenure security, housing quality, access to services, remittances
- Weather information
- Preventive measures flood
- General information on experience with flooding
- Information on specific floods
- Perception of risk and community engagement
- Household enterprises
- Investments in housing
- Food Insecurity (Reduced Coping Strategy Index CSI-R)
- Coping mechanism codes
Coverage
Geographic Coverage
Dar es Salaam, Tanzania.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| Alvina Erman | The World Bank |
| Silvia Malgioglio | The World Bank |
| Nobuo Yoshida | The World Bank |
| Stephane Hallegatte | The World Bank |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| Global Facility for Disaster Reduction and Recovery | GFDRR | Funded the survey |
| Tanzanian Urban Resilience Program | TURP | Funded the survey |
Sampling
Sampling Procedure
The selection of households in the survey design had two objectives. First, to select a sample that represents the population of Dar es Salaam and second, to interview enough people who had experienced floods to be able to detect patterns in their socio-economic characteristics.
The sample size was selected to confidently represent the population of Dar es Salaam given the income level and income distribution. Accordingly, a sample size of 105 EAs and 10 households per EA were selected using Probability Proportion to Size (PPS). In 2018, 28 EAs to the original sample as part of an additional round of data collection.
To capture enough households that had experienced floods, a flood risk stratum was designed using the Ramani Huria community flood map. EAs were categorized according to three flood risk strata, i.e., “no risk”, “low to medium risk” and “high risk”, depending on how much of the EA was covered by the flood layer in the map. This categorization of the city was used to oversample in high risk and low-to-medium risk areas by selecting more of those EAs compared to the population living there. Finally, all the selected households were randomly drawn within each EA using satellite imagery.
Sampling weights were calculated to compensate for the oversampling in high-risk areas. When applying the sample weights, the dataset is representative at the city level.
References:
ERMAN, A. E., TARIVERDI, M., OBOLENSKY, M. A. B., CHEN, X., VINCENT, R. C., MALGIOGLIO, S., & YOSHIDA, N. (2019). Wading out the storm: The role of poverty in exposure, vulnerability and resilience to floods in Dar Es Salaam. World Bank Policy Research Working Paper, (8976).
The sample size was selected to confidently represent the population of Dar es Salaam given the income level and income distribution. Accordingly, a sample size of 105 EAs and 10 households per EA were selected using Probability Proportion to Size (PPS). In 2018, 28 EAs to the original sample as part of an additional round of data collection.
To capture enough households that had experienced floods, a flood risk stratum was designed using the Ramani Huria community flood map. EAs were categorized according to three flood risk strata, i.e., “no risk”, “low to medium risk” and “high risk”, depending on how much of the EA was covered by the flood layer in the map. This categorization of the city was used to oversample in high risk and low-to-medium risk areas by selecting more of those EAs compared to the population living there. Finally, all the selected households were randomly drawn within each EA using satellite imagery.
Sampling weights were calculated to compensate for the oversampling in high-risk areas. When applying the sample weights, the dataset is representative at the city level.
References:
ERMAN, A. E., TARIVERDI, M., OBOLENSKY, M. A. B., CHEN, X., VINCENT, R. C., MALGIOGLIO, S., & YOSHIDA, N. (2019). Wading out the storm: The role of poverty in exposure, vulnerability and resilience to floods in Dar Es Salaam. World Bank Policy Research Working Paper, (8976).
Response Rate
In the 2018 follow up interview, 419 were reached and interviewed out of the 1058 households in the original sample.
Weighting
The weights are calculated to compensate for the oversampling in flood prone areas and provide city-wide representativeness.
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2017-11 | 2017-12 | 1 |
| 2018-09 | 2018-09 | 2 |
Data Collection Mode
Computer Assisted Personal Interview [capi]
Supervision
The World Bank
Data Collection Notes
The DPHS was conducted in two waves. The main survey was conducted in November and December 2017 using the original sample of 1058 selected households. Shortly after in April 2018, there was a significant flood event affecting Dar es Salaam. To evaluate the impacts of the flooding, a phone-based follow up survey was carried out in September 2018. In parallel, the original sample size was increased with 282 additional households that were sampled from high-risk and low-to-high risk areas to further increase precision of estimates regarding this population group. Interviews with additional households were carried out in-person.
Data Collectors
| Name | Abbreviation |
|---|---|
| UDA Consulting | UDA |
Questionnaires
Questionnaires
The survey questionnaire consists of 13 sections that were used to collect the survey data. See the attached questionnaire.
Data Processing
Data Editing
The following data editing was done for anonymization purpose:
• Precise location data, such as GPS coordinates, were dropped
• Personal information, such as name, citizenship and phone number were dropped
• Information on from which region or country the respondent moved from before settling in current dwelling and where respondent was born was categorized into “in Dar es Salaam” and “outside Dar es Salaam” to protect privacy while preserving valuable data. District level information on origin was dropped.
• Household size exceeding seven household members was categorized as “above 7 members”
• Household member information for 7th member and above was dropped to avoid reconstruction of the household size variable.
For more information on the anonymization process, see the Technical Document.
• Precise location data, such as GPS coordinates, were dropped
• Personal information, such as name, citizenship and phone number were dropped
• Information on from which region or country the respondent moved from before settling in current dwelling and where respondent was born was categorized into “in Dar es Salaam” and “outside Dar es Salaam” to protect privacy while preserving valuable data. District level information on origin was dropped.
• Household size exceeding seven household members was categorized as “above 7 members”
• Household member information for 7th member and above was dropped to avoid reconstruction of the household size variable.
For more information on the anonymization process, see the Technical Document.
Access policy
Contacts
| Name | Affiliation | |
|---|---|---|
| Alvina Erman | GFDRR | aerman@worldbank.org |
Confidentiality
Confidentiality has been ensured through a process of anonymization (see Technical Document for details).
Access conditions
Data is accessible for licensed users only and further dissemination of data is not allowed.
Citation requirements
The World Bank. Disaster-Poverty Household Survey (DPHS), Dar Es Salaam, Tanzania 2018. Ref: TZA_2017-2018_DPHS_v01_M. Dataset downloaded from microdata.worldbank.org on [date].
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.
Metadata production
DDI Document ID
DDI_TZA_2017-2018_DPHS_v01_M_WB
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Development Economics Data Group | DECDG | THe World Bank | Documentation of the survey |
Date of Metadata Production
2022-07-05
DDI Document version
Version 01 (July 2022)