High Frequency Phone Survey, 2020
Solomon Islands, 2020
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Reference ID
SLB_2020_HFPS-W1_v01_EN_M_v01_A_OCS
Producer(s)
World Bank
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Metadata
Created on
Feb 02, 2021
Last modified
Nov 08, 2022
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Identification
Survey ID Number
SLB_2020_HFPS-W1_v01_EN_M_v01_A_OCS
Title
High Frequency Phone Survey, 2020
Country
| Name | Country code |
|---|---|
| Solomon Islands | SLB |
Study type
Socio-Economic/Monitoring Survey [hh/sems]
Series Information
This is the first round of the High Frequency Phone Survey on COVID-19 in Solomon Islands.
Abstract
A strong evidence base is needed to understand the socioeconomic implications of the coronavirus pandemic for the Solomon Islands. Round 1 (out of 5) interviewed 2,650 respondents across the country in late June 2020 on topics including awareness of COVID-19, employment and income, food security, coping strategies, and public trust and security. While these findings are not without their caveats due to the lack of baseline data, constraints of the mobile phone survey methodology, and data quality constraints, they represent the best estimates to date and supplement other data on macroeconomic conditions, exports, firm-level information, and etc. to develop an initial picture of the impacts of COVID-19 on the population.
Kind of Data
Sample survey data [ssd]
Unit of Analysis
Households
Scope
Keywords
| Keyword |
|---|
| COVID-19 |
| High Frequency Phone Survey |
| Education |
| Economic activity |
| Business |
| Income |
| Farming |
| Remittances |
| Food |
| Public services |
| Well-being |
Coverage
Geographic Coverage
National coverage
Universe
Respondents aged over 18.
Producers and sponsors
Primary investigators
| Name | Affiliation |
|---|---|
| World Bank | World Bank Group |
Producers
| Name | Affiliation | Role |
|---|---|---|
| Development Data Group | World Bank Group | Technical assistance |
| Poverty and Equity Global Practice | World Bank Group | Technical assistance |
| Research Triangle Institute | Technical assistance |
Funding Agency/Sponsor
| Name | Abbreviation | Role |
|---|---|---|
| World Bank Group | WBG | Funded the survey and analysis |
Sampling
Sampling Procedure
SAMPLING PROCEDURE
The implementation method was random digit dialling which was administered from call centers in Suva, Fiji and Honiara in the Solomon Islands. The target sample size was 2,650 respondents. This figure was determined based on budget constraints and the need to be able to disaggregate to subnational levels, as well as the expectation that some percentage of households would attain over the course of the subsequent rounds. Since limited auxiliary information was available for sample design, the high frequency phone survey targeted households in the same proportion as the 2015 Demographic and Health Survey (DHS).
The achieved sample heavily overrepresented the population in Honiara, with a total sample size of 921 for a target of 365, and slightly oversampled Rennell-Bellona, with a total sample of 18 compared to a target of 13. The oversampling in Honiara is most likely attributable to households in Honiara being more likely to have mobile phones that were switched on at the time of the call. The other provinces were under-sampled to varying degrees, with ratios of achieved-to-targeted samples varying from 40.9 percent in Makira-Ulawa to 87.7 percent in Malaita. Additionally, it was not possible to target between urban and rural areas as that information is not available in a Random Digit Dialling design. Due to the limited sample sizes outside of Honiara, most results are disaggregated into only three geographic regions: Honiara, other urban areas, and rural areas.
For more information on sampling, please refer to the report provided in the External Resources.
The implementation method was random digit dialling which was administered from call centers in Suva, Fiji and Honiara in the Solomon Islands. The target sample size was 2,650 respondents. This figure was determined based on budget constraints and the need to be able to disaggregate to subnational levels, as well as the expectation that some percentage of households would attain over the course of the subsequent rounds. Since limited auxiliary information was available for sample design, the high frequency phone survey targeted households in the same proportion as the 2015 Demographic and Health Survey (DHS).
The achieved sample heavily overrepresented the population in Honiara, with a total sample size of 921 for a target of 365, and slightly oversampled Rennell-Bellona, with a total sample of 18 compared to a target of 13. The oversampling in Honiara is most likely attributable to households in Honiara being more likely to have mobile phones that were switched on at the time of the call. The other provinces were under-sampled to varying degrees, with ratios of achieved-to-targeted samples varying from 40.9 percent in Makira-Ulawa to 87.7 percent in Malaita. Additionally, it was not possible to target between urban and rural areas as that information is not available in a Random Digit Dialling design. Due to the limited sample sizes outside of Honiara, most results are disaggregated into only three geographic regions: Honiara, other urban areas, and rural areas.
For more information on sampling, please refer to the report provided in the External Resources.
Response Rate
A total of 2,665 household members were successfully interviewed. Below are the completion rates by Province + Honiara:
-Choiseul: 69.3%;
-Western: 66.1%;
-Isabel: 86%;
-Central: 72.8%;
-Rennell-Bellona: 138.5%;
-Guadalcanal: 83.2%;
-Malaita: 87.7%;
-Makira-Ulawa: 40.9%;
-Temotu: 68.5%;
-Honiara: 252.3%.
-Choiseul: 69.3%;
-Western: 66.1%;
-Isabel: 86%;
-Central: 72.8%;
-Rennell-Bellona: 138.5%;
-Guadalcanal: 83.2%;
-Malaita: 87.7%;
-Makira-Ulawa: 40.9%;
-Temotu: 68.5%;
-Honiara: 252.3%.
Weighting
The sampling weights were developed for round one of the Solomon Islands high frequency phone survey in a series of steps. As the main shortcoming of using random digit dialing is that the resulting data is representative of the population of mobile phone owners, and according to the most recent data available for mobile phone penetration estimates usage as 74 percent of the population, coverage is concentrated in population centers and better off households and individuals are more likely to have a mobile phone which is charged and turned on. Therefore, the pool of respondents is very different from a representative sample of the Solomon Islands population.
Auxiliary data to serve as inputs to the weights is severely limited as there are few recent nationally representative sources. The results from the recently completed census are not yet available and the last Household Income and Expenditure Survey (HIES) was from 2012/2013. The most recent nationally representative dataset including a measure of welfare was the 2015 Demographic and Health Survey (DHS) and therefore this survey is used as the base for the re-weighting.
Sampling was conducted using random digit dialing with a target sample size of 2,650 respondents. The mobile phone survey sample was designed to mimic the proportions of the 2015 DHS but for a smaller total overall sample. The achieved sample heavily overrepresented the population on Honiara, with a total sample size of 921 for a target of 365, and slightly oversampled Rennell-Bellona, with a total sample of 18 compared to a target of 13. The oversampling in Honiara is most likely attributable to households in Honiara being more likely to have mobile phones that were switched on at the time of the call.
The other provinces were under-sampled to varying degrees, with ratios of achieved-to-targeted samples varying from 40.9 percent in Makira-Ulawa to 87.7 percent in Malaita. Additionally, it was not possible to target between urban and rural areas as that information is not available in a Random Digit Dialing design. Due to the limited sample sizes outside of Honiara, most results are disaggregated into only three geographic regions: Honiara, other urban areas, and rural areas.
Weights are required for unbiased estimation. In addition to the geographic oversampling above, because the survey was administered by mobile phones, the respondents were a representative sample of mobile phone holders, not the population overall, and non-random non-response can exacerbate these differences. Previous literature has shown that mobile phone holders are more likely to be male, urban, wealthier, and more highly educated. To make inferences at the level of the population instead of mobile phone holders, it was necessary to reweight the survey data.
Though it is possible to reweight data to yield unbiased estimates, it is not possible to create additional observations for populations of interest using standard statistical approaches.
Definitionally, the DHS deciles each contain 10 percent of the sample. Using the maximum and minimum threshold values for the DHS deciles to map the mobile phone survey results, it is clear there is a strong bias toward the upper deciles (wealthier) households in the distribution. While weighting can adjust for the bias, there are only 2 and 9 observations in the bottom two deciles of the distribution, respectively. These sample sizes are too small to yield estimates of adequate precision to report results.
Therefore, direct analysis is limited to the bottom four deciles (bottom 40 percent), and then the middle two deciles (middle quintile) and top four deciles (top 40 percent). In addition, each statistic is reported with its confidence interval and all econometric findings are statistically significant, unless otherwise stated.
For more information on weighting, please refer to the "Weighting" section (p.42) of the report provided in the External Resources.
The "weight" variable in the dataset is called "weight".
Auxiliary data to serve as inputs to the weights is severely limited as there are few recent nationally representative sources. The results from the recently completed census are not yet available and the last Household Income and Expenditure Survey (HIES) was from 2012/2013. The most recent nationally representative dataset including a measure of welfare was the 2015 Demographic and Health Survey (DHS) and therefore this survey is used as the base for the re-weighting.
Sampling was conducted using random digit dialing with a target sample size of 2,650 respondents. The mobile phone survey sample was designed to mimic the proportions of the 2015 DHS but for a smaller total overall sample. The achieved sample heavily overrepresented the population on Honiara, with a total sample size of 921 for a target of 365, and slightly oversampled Rennell-Bellona, with a total sample of 18 compared to a target of 13. The oversampling in Honiara is most likely attributable to households in Honiara being more likely to have mobile phones that were switched on at the time of the call.
The other provinces were under-sampled to varying degrees, with ratios of achieved-to-targeted samples varying from 40.9 percent in Makira-Ulawa to 87.7 percent in Malaita. Additionally, it was not possible to target between urban and rural areas as that information is not available in a Random Digit Dialing design. Due to the limited sample sizes outside of Honiara, most results are disaggregated into only three geographic regions: Honiara, other urban areas, and rural areas.
Weights are required for unbiased estimation. In addition to the geographic oversampling above, because the survey was administered by mobile phones, the respondents were a representative sample of mobile phone holders, not the population overall, and non-random non-response can exacerbate these differences. Previous literature has shown that mobile phone holders are more likely to be male, urban, wealthier, and more highly educated. To make inferences at the level of the population instead of mobile phone holders, it was necessary to reweight the survey data.
Though it is possible to reweight data to yield unbiased estimates, it is not possible to create additional observations for populations of interest using standard statistical approaches.
Definitionally, the DHS deciles each contain 10 percent of the sample. Using the maximum and minimum threshold values for the DHS deciles to map the mobile phone survey results, it is clear there is a strong bias toward the upper deciles (wealthier) households in the distribution. While weighting can adjust for the bias, there are only 2 and 9 observations in the bottom two deciles of the distribution, respectively. These sample sizes are too small to yield estimates of adequate precision to report results.
Therefore, direct analysis is limited to the bottom four deciles (bottom 40 percent), and then the middle two deciles (middle quintile) and top four deciles (top 40 percent). In addition, each statistic is reported with its confidence interval and all econometric findings are statistically significant, unless otherwise stated.
For more information on weighting, please refer to the "Weighting" section (p.42) of the report provided in the External Resources.
The "weight" variable in the dataset is called "weight".
Data Collection
Dates of Data Collection
| Start | End | Cycle |
|---|---|---|
| 2020-06-20 | 2020-07-04 | Data collection |
Data Collection Mode
Computer Assisted Telephone Interview [cati]
Questionnaires
Questionnaires
The questionnaire - that can be found in the External Resources of this documentation - was developped both in English and in Solomons Pijin. The survey instrument for the first round consisted of the following modules:
-Basic information,
-Awareness of COVID-19,
-Employment and Income loss,
-Food access and Food security,
-Coping strategies,
-Public trust and security,
-and Assets and wellbeing.
-Basic information,
-Awareness of COVID-19,
-Employment and Income loss,
-Food access and Food security,
-Coping strategies,
-Public trust and security,
-and Assets and wellbeing.
Data Processing
Data Editing
CLEANING OPERATIONS
At the end of data collection, the raw dataset was cleaned by the World Bank team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.
Data was edited using the software Stata.
At the end of data collection, the raw dataset was cleaned by the World Bank team. This included formatting, and correcting results based on monitoring issues, enumerator feedback and survey changes.
Data was edited using the software Stata.
Data Appraisal
Data Appraisal
Data was collected and managed using the Survey Solutions software package.
Access policy
Contacts
| Name | Affiliation | URL |
|---|---|---|
| World Bank | World Bank Group | Link |
Confidentiality
See https://microdata.pacificdata.org/index.php/terms-of-use
Access conditions
Licensed dataset, accessible under conditions.
Before being granted access to the dataset, all users have to formally agree:
1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Before being granted access to the dataset, all users have to formally agree:
1. To make no copies of any files or portions of files to which s/he is granted access except those authorized by the data depositor.
2. Not to use any technique in an attempt to learn the identity of any person, establishment, or sampling unit not identified on public use data files.
3. To hold in strictest confidence the identification of any establishment or individual that may be inadvertently revealed in any documents or discussion, or analysis. Such inadvertent identification revealed in her/his analysis will be immediately brought to the attention of the data depositor.
Citation requirements
"Solomon Islands, High Frequency Phone Survey on COVID-19 2020 (HFPS 2020), Version 01 of the licensed dataset (November 2020), provided by the Pacific Data Hub - Microdata Library. https://microdata.pacificdata.org/index.php/home"
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_SLB_2020_HFPS_W1_v01_EN_M_v01_A_OCS_FAO
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Office of Chief Statistician | OCS | Food and Agriculture Organization | Adoption of metadata for FAM |
| Statistics for Development Division | SDD | Pacific Community | Documentation of the study |
| World Bank Group | WBG | Documentation of the study |
DDI Document version
SLB_2020_HFPS-W1_v01_EN_M_v01_A_OCS_v01