This layer shows median household income by race and by age of householder. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Median income and income source is based on income in past 12 months of survey. This layer is symbolized to show median household income. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B19013B, B19013C, B19013D, B19013E, B19013F, B19013G, B19013H, B19013I, B19049, B19053Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Abstract

A strong evidence base is needed to understand the socioeconomic implications of the COVID-19 pandemic for the Solomon Islands. High Frequency Phone Surveys (HFPS) are designed to collect data on the evolving implications of the COVID-19 pandemic over several years. This data is the second of at least five planned rounds of mobile surveys. The first round of survey was already completed in late June 2020. Round 2 interviewed 2,882 households across the country in December 2020 and early January 2021, on topics including awareness of COVID-19, employment, and income, coping strategies, and public trust and security.

Geographic coverage

Urban and rural areas of Solomon Islands.

Analysis unit

Household and Individual.

Universe

Respondents aged 18 and over.

Kind of data

Sample survey data [ssd]

Sampling procedure

As the objective of the survey was to measure changes as the pandemic progresses, Round Two data collection sought to re-contact all 2,665 households contacted in Round One. The protocols for re-contact were a maximum of 3 attempts per caller shift, spaced between 1.5 and 2.5 hours apart depending on whether the phone was busy or there was no answer, and 15 attempts in total. Of the Round One households, 1,048 were successfully re-contacted. In Round One, Honiara was over-represented in the World Bank HFPS (constituting 32.8 percent of the survey sample). All other provinces were deemed under-represented, with the largest differences being for Makira-Ulawa, which represented 3.9 percent of the survey sample compared to 7.2 percent of the population in the census, and Guadalcanal, which represented 14.3 percent of the survey sample compared to 21.4 percent of the population in the census. Urban areas constituted almost half (49.2 percent) of the survey sample, compared to a quarter (25.6 percent) of the census. To reach the target sample size of at least 2500 households, 1,833 replacement households were added to the World Bank survey. The target geographic distribution for the survey was based on the population distribution across provinces from the preliminary 2019 census results. According to the population census, Honiara constituted almost one quarter (18.0 percent) of the total population. Compensating factors for these differences were developed and included in the re-weighting calculations.

The majority of these were replaced through Random Digit Dialing, but the project did attempt to leverage contact information from ward-level focal points for the Rural Development Project (RDP) in provinces underrepresented in Round One. Of the 145 RDP contacts provided to the call center, 41 were reached, who in turn provided 379 numbers which were attempted as part of regular call schedule. Overall, the sample size achieved for the second round of the HFPS was 2,882 households.

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.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Research instrument

The questionnaire - that can be found in the External Resources of this documentation - was developed both in English and in Solomons Pijin. The survey instrument for the second round consisted of the following modules: -Basic information, -Awareness of COVID-19, -Employment and Income loss, -Coping strategies, -Public trust and security, -and Assets and wellbeing.

All respondents were aged 18 years and above.

Cleaning operations

At the end of data collection, the 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 STATA.
The data is presented in two data sets: household data set and individual data set. The total number of observations in the household data set is 2,882 and is 4,279 in the individual data set. The individual data set contains employment information for some household members. The household data set contains information about public services, income, coping strategies, and awareness of COVID-19.

Response rate

Re-contact was attempted with all households from the World Bank Round Two HFPS sample, by phone, for follow up interviews for the UNICEF SIAS. Up to 5 re-contact call attempts were made per house, resulting in 1530 households being interviewed successfully including households without children. Of these households, a total of 1197 had at least one child (aged 0 to 14 years of age). While the goal was to recontact at least 1500 households with at least one child in the household, this was not possible due to lower than hoped for response rate. Given the time elapsed between the Round Two HFPS and the UNICEF SIAS, the response rate may have suffered because of some households changing phone numbers.

Response rate for returning households: 39.32%

Abstract

The Zimbabwe National Statistics Agency, ZIMSTAT, is in the process of updating a Central Business Register, CBR. A CBR is a database of enterprises/establishments operating in the economy covering all industries and geographical regions engaged in the production of goods and/or services. The CBR is an important statistical tool that besides providing a master frame for conducting the sample survey for collection of data also provides basic statistics such as employment by sex, turnover by industry, sector, and region. A register of good quality will help to improve the efficiency of the National Statistical System, which in turn shall help to reduce response burden on the businesses.

The objectives of the survey would be: • To create a comprehensive list of establishments and enterprises • To establish a business directory based on the BR for public use • To provide a master frame for the economic and social censuses and surveys: - CIP (Manufacturing, Mining & Quarrying, Construction, Electricity and Water Supply and Distribution) - Quarterly Employment Inquiry (QEI) - Volume of Manufacturing Index (VMI) - Business Tendency Survey (BTS) - Census of Services (CoS) - Census of Transport (CoT) - Information and Communications Technologies (ICT) • To derive basic economic statistics such as number of employees, turnover, etc.

Geographic coverage

National coverage

Analysis unit

The CBR is an economic survey and the unit of analysis is the establishment.

Universe

All establishments operating in Zimbabwe.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

In conducting the CBR inquiry, the bottom-up approach was used whereby field staff administered the CBR questionnaire to all establishments that were on the master list. The master list was an amalgamation of registers from ZIMSTAT, local authorities NSSA and business associations. In addition, establishments that were not on the master list but found on the ground were also covered. The alternative could have been to use the less expensive top-down approach based on ZIMRA tax records. However, the top-down approach could not be used owing to the confidentiality clause in the ZIMRA Act that restricts the Revenue Authority from disclosing individual company tax records.

In the bottom-up approach, all local authorities were requested to provide information on establishments operating under their jurisdictions. ZIMSTAT provided the local authorities with a template showing how the information was to be provided. The template contained variables such as legal and trading names, physical addresses and economic activity among others.

Cleaning operations

The Project Team as secondary editors complemented the efforts of the data entry supervisors on internal consistency checks. Some of the checks done included: - Ensuring that the main and secondary economic activities described in the CBR questionnaire were assigned the correct ISIC codes at 4 digit level. - Ensuring establishments clearly described the economic activities which they are engaged in according to the products or service lines they offered, and this made it easy to determine the industrial class of any economic activity given. - When recovering CBR questionnaires from establishments, ZIMSTAT enumerators would ensure that the economic activities were described in not less than two words. - The CBR editing/coding team was issued with some editing and coding instructions which included some ISIC Rev. 4 coding manuals. The International Standard Industrial Classification of all economic activities (ISIC) Revision 4 coding manuals have some mutually exclusive categories at the highest level called sections which are alphabetically coded A to U: - Where the description of the economic activity was not clear, the CBR editor/coder would make some follow-ups by making phone calls where details were provided. - Checking for duplicate serial numbers, duplication of establishments, i.e. serial numbers versus unique identification codes. - Identify the corporate structure of enterprises and their respective establishments, i.e. enterprise profiling. All establishments belonging to an enterprise were determined using the names and physical addresses. Establishments that belong to the same enterprise have been linked using names and addresses, (the bottom up approach) and assigning numbers to them. What is required now is to do further profiling. It is after profiling that we can update the database and the quality of establishment level reporting improved. - Checking of typographical errors that might cause noise in the data. - Checking for missing serial numbers that may result in omissions.

Data appraisal

A series of data quality tables and graphs are available to review the quality of the data and include the following: • Number and Percent Distribution of Establishments by Province • Number and Percent Distribution of Establishments by Industry according to the International Standard Industrial Classification of All Economic Activities (ISIC) Rev 4. • Number and Percent Distribution of Establishments by Employment Size • Number of Establishments by Age • Number and Percent Distribution of Employees by Province • Value and Percent Distribution of Salaries, Wages and Allowances by Province • Number of Establishments by Employment size and Annual Turnover (US$) • Number and Percent Distribution of Establishments by Majority Share Ownership and Type of Ownership • Number of New Establishments by Industry