High Frequency Phone Survey on COVID-19 2020, Round 1
Solomon Islands, 2020
World Bank
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May 09, 2021
Last modified
May 09, 2021
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Identification
Survey ID Number
SPC_SLB_2020_HFPS-W1_v01_M_v01_A_PUF
Title
High Frequency Phone Survey on COVID-19 2020, Round 1
Subtitle
Round 1
Country
| Name | Country code |
|---|---|
| Solomon Islands | SLB |
Study type
Other Household Survey [hh/oth]
Series Information
The World Bank is providing support to countries to help mitigate the spread and impact of the new corona-virus disease (COVID-19). One area of support is for data collection to inform evidence-based policies that may help mitigate the effects of this disease.
To monitor the socio-economic impacts of COVID-19 in Solomon Islands, five rounds of High Frequency Phone Survey on COVID-19 (HFPS) are planned. The documented dataset refers to the very first round of the HFPS of Solomon Islands.
To monitor the socio-economic impacts of COVID-19 in Solomon Islands, five rounds of High Frequency Phone Survey on COVID-19 (HFPS) are planned. The documented dataset refers to the very first round of the HFPS of 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
Household and Individual.
Version
Version Description
Version 01: Clean, labelled and anonymized version of the Master file.
Version Date
2020-11-01
Version Notes
Dataset distributed by the World Bank Group (WBG).
Scope
Notes
-HOUSEHOLD: Interview information; Basic information; Awareness of COVID-19; Employment and income loss; Access food and food security; Coping strategies; Public trust and security; Assets and well-being; Interview results.
-INDIVIDUAL: Interview information; Basic information; Employment and income loss.
-INDIVIDUAL: Interview information; Basic information; Employment and income loss.
Topics
| Topic |
|---|
| pacific-skills, education, training |
| pacific-skills, employment |
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: all 9 provinces covered.
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
The implementation method was random digit dialing 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 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.
For more information on sampling, please refer to the report provided in the External Resources.
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 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.
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%.
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%.
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 |
Time periods
| Start date | End date | Cycle |
|---|---|---|
| 2020-06-20 | 2020-07-04 | Round 1 |
Data Collection Mode
Computer Assisted Telephone Interview [cati]
Data Collection Notes
Field work was conducted through call centers set up by Tebbutt Research in Fiji and the Solomon Islands, with a staff of 33 interviewers and 6 supervisors between the two locations. Tebbutt Research is a full-service market research and social research agency with expertise in Pacific Islands research. The dates of implementation were June 20 through July 4, 2020, and the implementation method was Random Digit Dialing using mobile phone numbers. Since phone numbers in the Solomon Islands do not contain any location information, it was not possible to do any geographical targeting, and therefore the sample was developed based on targets for completed interviews by location. Further details on the implementation are available in the external resouce section.
Contact was attempted for a total of 23,632 unique numbers through 30,394 calls over the field period to generate 2,650 complete interviews. Of the total number of calls, 19,588 were to non-working numbers. The remaining non-contacts were due to a busy signal or no answer. Of those answering, 385 refused, 5 were commercial numbers, 69 were screened out for being below age 18, and 22 were unable to continue with the interview due to language constraints. Using the American Association for Public Opinion Research (AAPOR) response rate definitions, this survey had 46.4 percent response rate (using definition RR3), a 46.7 percent cooperation rate, a 6.4 percent refusal rate, and a 99.8 percent contact rate.
Interviewers were instructed to make every effort to reach the same respondent in subsequent rounds of the survey, in order to maintain the consistency of the information collected. However, in cases where the previous respondent was not available, interviewers would identify another knowledgeable adult household member to interview.
The median length of an interview was 24 minutes and 12 seconds.
For restrospective questions on employment and availability, the baseline is defined as "the start of this year 2020". This is the first of five planned rounds.
Contact was attempted for a total of 23,632 unique numbers through 30,394 calls over the field period to generate 2,650 complete interviews. Of the total number of calls, 19,588 were to non-working numbers. The remaining non-contacts were due to a busy signal or no answer. Of those answering, 385 refused, 5 were commercial numbers, 69 were screened out for being below age 18, and 22 were unable to continue with the interview due to language constraints. Using the American Association for Public Opinion Research (AAPOR) response rate definitions, this survey had 46.4 percent response rate (using definition RR3), a 46.7 percent cooperation rate, a 6.4 percent refusal rate, and a 99.8 percent contact rate.
Interviewers were instructed to make every effort to reach the same respondent in subsequent rounds of the survey, in order to maintain the consistency of the information collected. However, in cases where the previous respondent was not available, interviewers would identify another knowledgeable adult household member to interview.
The median length of an interview was 24 minutes and 12 seconds.
For restrospective questions on employment and availability, the baseline is defined as "the start of this year 2020". This is the first of five planned rounds.
Data Collectors
| Name |
|---|
| Tebbutt Research |
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.
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.
Data Processing
Data Editing
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 was edited using the software Stata.
Other Processing
Data was collected and managed using the Survey Solutions software package.
Access policy
Access authority
| Name | Affiliation | URL |
|---|---|---|
| World Bank | World Bank Group | https://www.worldbank.org/ |
Contacts
| Name | Affiliation | URL |
|---|---|---|
| World Bank | World Bank Group | https://www.worldbank.org/ |
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_SPC_SLB_2020_HFPS-W1_v01_M_v01_A_PUF
Producers
| Name | Abbreviation | Affiliation | Role |
|---|---|---|---|
| Statistics for Development Division | SDD | Pacific Community | Documentation of the study |
| World Bank Group | WBG | Documentation of the study |
Date of Metadata Production
2020-11-23
DDI Document version
Version 01 (November 2020): This is the first attempt at documenting the 2020 High Frequency Phone Survey on COVID-19 of Solomon Islands. Done by Statistics for Development Division at Noumea, New Caledonia.