Abstract

A comprehensive and detailed statistical database of any economic activity is a prerequisite for planning and policy making and this applies to economic activities that play a major role in most modern world economies.

The Palestinian Central Bureau of Statistics is pleased to issue the Economic Survey of Palestine, including statistical tables of findings. This edition presents the findings of the surveys conducted for 2019 as the reference year and covers most of the economic activities operating in Palestine since 1994. Economic surveys of various fields constitute the basic foundations for the compilation of National Accounts for Palestine

Geographic coverage

Palestine

Analysis unit

Enterprises

Universe

The economic survey series was conducted based on the Establishments Census of 2019 as a sampling frame. The economic surveys series covered activities in accordance with ISIC-4 (fifth digits).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample is One-Stage Stratified Systematic Random Sample (without replacement).

Sample Strata Three levels are used to divide the population into strata: 1. Region (North of the West Bank in addition to Jericho Governorate, Ramallah and Al-Bireh Governorate, Jerusalem Governorate, Bethlehem Governorate, Hebron Governorate, Gaza Strip) 2. Strata were created based on the fourth digit of ISIC-4, excluding services sector based on the second in which every activity presents an actual stratum. 3. Enterprise size (small, medium, large) by number of employees.

13,974 enterprises were reached of which 10,602 enterprises responded on financial questions (baseline of economic indicators)

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

All of the economic surveys series used the same questionnaire, with a few different characteristics for each survey. The design of the 2019 questionnaire takes into account the major economic variables pertaining to the sector examined and the needs to be met to compile the National Accounts for Palestine. The questionnaire included these variables: 1. The employed persons in enterprise and compensation of these employees. 2. Value of output from the main activity and secondary activity. 3. Production inputs of goods and services.

Cleaning operations

Data processing went through several phases since the beginning of the preparation of data collection on 21/06/2020 until the end of the fieldwork on 30/11/2020. This process included the following phases:

1. Programming Phase The data collection application was prepared by the IT staff at PCBS.

IT staff tested the application with the project director and all comments and updates were implemented, skips between questions, and some verification rules were also tested, a final version of the application was provided on time.

1. Training Phase All materials were prepared and included in the training manual on the requirements of data processing during fieldwork. The training halls were well prepared and contained microphones and a Wi-Fi. Training for Gaza Strip was carried out separately.

2. Verification Phase All verifications and consistency checks were applied to PC-Tablet applications. An error message pops up when entering a wrong value and some error messages show up in red for sensitive questions. The project coordinator tested the application by entering pilot questionnaires. In addition, there was a pretest by project director before collecting the data.

3. Other Data Processing Issues

· PC-Tablets: In general, PC-tablets were user friendly and familiar. During training, every interviewer was trained on a PC-tablet for their own use

· Data Collection Application (Survey Solution): The application was well designed and had a user friendly interface. Nevertheless, a programmer needed to be available when an error occurred by any of the supervisors and interviewers.

· Internet Connection (Wi-Fi): During the training, internet connection was available for trainers and trainees. During fieldwork, 81 SIM cards with internet connection were provided for each PC-tablet by Jawwal Company during data collection process.

· Administration Website: The website was friendly designed and easy to use, as it shows totals of completed questionnaire by interviewers.

Supervisors were supplied by PCBS with four PC-tablets operating on Windows operations system to review and follow up on the data and to fill the sections they were responsible for.

Response rate

Response rate:84.0%.

Sampling error estimates

Sampling Errors Data of this survey were affected by sampling errors due to use of the sample. Variance was calculated for the most important indicators, accordingly, it is possible to disseminate the results at regional level.

Non Sampling Error These types of errors could appear on one or on all of the survey stages that include data collection and data entry; they related to, respondents, fieldworkers, and data entry personnel. To avoid errors and mitigate their impact, a number of procedures were applied to enhance the accuracy of the data through a process of data collection from the field and data processing.

This data is about Economic Survey National Income for the period 1950-2020. Data from Ministry of Finance, India.Follow datasource.kapsarc.org for timely data to advance energy economics research.

This statistic shows the Economic Confidence Index, created by Gallup, on a monthly basis for the ongoing year. The survey is conducted doing weekly telephone interviews among approx. 2,499 adults in the U.S. The graph shows the results for the first update each month to depict an annual trend. The Index is computed by adding the percentage of Americans rating current economic conditions to the percentage saying the economy is (getting better minus getting worse), and then dividing that sum by 2. The Index has a value between null and +100. In December 2017, the U.S. Economic Confidence Index stood at 8.

New Zealand Economy Survey: Manufacturing: Wood & Paper Product: Purchases & Operating Expenditure data was reported at 1,729.532 NZD mn in Mar 2018. This records a decrease from the previous number of 1,762.406 NZD mn for Dec 2017. New Zealand Economy Survey: Manufacturing: Wood & Paper Product: Purchases & Operating Expenditure data is updated quarterly, averaging 1,473.635 NZD mn from Dec 1992 (Median) to Mar 2018, with 102 observations. The data reached an all-time high of 1,806.195 NZD mn in Sep 2017 and a record low of 907.670 NZD mn in Mar 1993. New Zealand Economy Survey: Manufacturing: Wood & Paper Product: Purchases & Operating Expenditure data remains active status in CEIC and is reported by Statistics New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.S003: Economy Survey: ANZSIC06.

Abstract

A comprehensive and detailed statistical database of any economic activity is a prerequisite for planning and policy making and this applies to economic activities that play a major role in most modern world economies.

The Palestinian Central Bureau of Statistics is pleased to issue the twenty-second volume of the Economic Survey of Palestine, including statistical tables of findings. This edition presents the findings of the surveys conducted for 2016 as the reference year and covers most of the economic activities operating in Palestine since 1994. Economic surveys of various fields constitute the basic foundations for the compilation of National Accounts for Palestine

Geographic coverage

Palestine

Analysis unit

Enterprises

Universe

The twenty second round of the economic survey series was conducted based on the Establishments Census of 2012 as a sampling frame. The economic surveys series covered activities in accordance with ISIC-4 (fifth digits).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the economic surveys series was One-Stage Stratified Systematic Random Sample in which enterprises were divided into two types: the first type covered overall enterprises taken comprehensively, the second type covered enterprises selected in a systematic random way in which the enterprise constituted the sampling unit. Three levels of strata were used to draw up an efficient representative sample: 1. The frame was divided into two geographical locations: the West Bank excluding that part of Jerusalem governorate which was forcefully annexed by Israel following its occupation of the West Bank in 1967, and the Gaza Strip. 2. Strata were created based on the fourth digit of ISIC-4, excluding services sector based on the second in which every activity presents an actual stratum. 3. Within each stratum, new strata were created according to employment size.

According to services sector profit and non-profit enterprises are taking into consideration as a forth level.

The sample size in Palestine (excludes that part of Jerusalem governorate which was forcefully annexed by Israel following its occupation of the West Bank in 1967) in 2016 was 9,491 enterprises out of 143,140 enterprises comprising the survey sampling frame.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

All of the economic surveys series used the same questionnaire, with a few different characteristics for each survey. The design of the 2016 questionnaire takes into account the major economic variables pertaining to the sector examined and the needs to be met to compile the National Accounts for Palestine. The questionnaire included these variables: 1. The employed persons in enterprise and compensation of these employees. 2. Value of output from the main activity and secondary activity. 3. Production inputs of goods and services. 4. Payments and transfers. 5. Taxes on production. 6. Assets and capital formation.

Cleaning operations

·A specialized field work team with a background in economics was selected and trained theoretically and practically on the surveys' questionnaire. ·The main field work team was selected based on skills acquired from the training course. ·Project management received a daily report on the progress and response rates. ·Programs were designed to check and extract data through the web by project management and field work supervisors. ·A refreshment training course was conducted during the stage of data collection to reinforce the main points made during the training, and to answer questions by field workers about issues they faced in the field. ·Field visits were conducted from the project management team to check and progress of work for all governorates in the West Bank and Gaza Strip. ·Editing: PC-Tablets were used in collecting data in the West Bank and Gaza Strip, the sample was loaded onto the tablets and automated rules applied to the program. ·Coding: After finishing editing process, the completed questionnaires are subject to coding process to be prepared to the data entry process. ·Creation of a data entry program prior to the collection of data to ensure this would be ready in advance. ·A set of validation rules were applied to the program to check the consistency of data. · The efficiency of the program was pre-tested by entering several questionnaires including incorrect information and checking its efficiency in capturing the incorrect information

Response rate

Response rate:93.3%..

Sampling error estimates

Sampling Errors Data of this survey affected by sampling errors due to use of the sample and. Therefore, certain differences were expected in comparison with the real values obtained through censuses. Variance were calculated for the most important indicators as shown in tables below. Dissemination of results at the national level did not pose a problem, but there was high variance in some variables.

Non Sampling Error These types of errors could appear on one or on all of the survey stages that include data collection and data entry; they related to, respondents, fieldworkers, and data entry personnel. To avoid errors and mitigate their impact, a number of procedures were applied to enhance the accuracy of the data through a process of data collection from the field and data processing.

The project will produce a valuation function that depends on factors related to Steller sea lion (SSL) protection measures, and may include some combination of the expected aggregate size of the population and improvements to the ESA listing status resulting from protection measures, cost of the protection measures, and effects of protection measures on local economies, fishery participants, and consumer fish prices. This function can be used to identify non-consumptive use values for SSLs and how these values are affected by protection measures, thereby providing valuable information to policy makers.

Abstract

The World Bank Enterprise Survey (WBES) is a firm-level survey of a representative sample of an economy's private sector. The surveys cover a broad range of topics related to the business environment including access to finance, corruption, infrastructure, competition, and performance.

Geographic coverage

National coverage

Analysis unit

The primary sampling unit of the study is the establishment. An establishment is a physical location where business is carried out and where industrial operations take place or services are provided. A firm may be composed of one or more establishments. For example, a brewery may have several bottling plants and several establishments for distribution. For the purposes of this survey an establishment must make its own financial decisions and have its own financial statements separate from those of the firm. An establishment must also have its own management and control over its payroll.

Universe

All formal (i.e., registered) private sector businesses (with at least 1% private ownership) and with at least five employees. In terms of sectoral criteria, all manufacturing businesses (ISIC Rev 4. codes 10-33) are eligible; for services businesses, those corresponding to the ISIC Rev 4 codes 41-43, 45-47, 49-53, 55-56, 58, 61-62, 69-75, 79, and 95 are included in the Enterprise Surveys. Cooperatives and collectives are excluded from the Enterprise Surveys. All eligible establishments must be registered with the registration agency. In the case of the Philippines, the listing from the PSA’s List of Establishments (LE), a registrar of businesses operating in the Philippines, was used. The registration agency is the Securities and Exchange Commission (SEC).

Kind of data

Sample survey data [ssd]

Sampling procedure

The WBES use stratified random sampling, where the population of establishments is first separated into non-overlapping groups, called strata, and then respondents are selected through simple random sampling from each stratum. The detailed methodology is provided in the Sampling Note (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Sampling_Note-Consolidated-2-16-22.pdf). Stratified random sampling has several advantages over simple random sampling. In particular, it:

• produces unbiased estimates of the whole population or universe of inference, as well as at the levels of stratification
• ensures representativeness by including observations in all of those categories
• produces more precise estimates for a given sample size or budget allocation, and
• may reduce implementation costs by splitting the population into convenient subdivisions.

The WBES typically use three levels of stratification: industry classification, establishment size, and subnational region (used in combination). Starting in 2022, the WBES bases the industry classification on ISIC Rev. 4 (with earlier surveys using ISIC Rev. 3.1). For regional coverage within a country, the WBES has national coverage.

Note: Refer to Sampling Structure section in "The Philippines 2024 World Bank Enterprise Survey Green Economy Implementation Report" for detailed methodology on sampling.

Mode of data collection

Face-to-face [f2f]

Research instrument

The standard WBES questionnaire covers several topics regarding the business environment and business performance. These topics include general firm characteristics, infrastructure, sales and supplies, management practices, competition, innovation, capacity, land and permits, finance, business-government relations, exposure to bribery, labor, and performance. Information about the general structure of the questionnaire is available in the Enterprise Surveys Manual and Guide (https://www.enterprisesurveys.org/content/dam/enterprisesurveys/documents/methodology/Enterprise-Surveys-Manual-and-Guide.pdf).

The questionnaire implemented in the Philippines 2024 WBES Green Economy included additional questions tailored for the Business Ready Report covering infrastructure, trade, government regulations, finance, labor, and other topics.

Response rate

Overall survey response rate was 76.4%.

Abstract

A comprehensive and detailed statistical database of any economic activity is a prerequisite for planning and policy making and this applies to economic activities that play a major role in most modern world economies.

The Palestinian Central Bureau of Statistics is pleased to issue the eighteenth volume of economic surveys for the Palestine, including statistical tables of findings. This edition presents the findings of the surveys conducted for 2013 as the reference year and covers most of the economic activities operating in the Palestine since 1994.

Economic surveys of various fields constitute the basic foundations for the compilation of National Accounts for Palestine. It is hoped that they will also fulfill the various needs and expectations of users in both the public and private sectors.

Geographic coverage

Palestine

Analysis unit

Enterprises

Universe

The eighteenth round of the economic survey series was conducted based on the Establishments Census of 2012. The economic surveys series cover activities in accordance with ISIC-4.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample of the economic surveys series is a single-stage stratified random-systematic sample in which the enterprise constitutes the primary sampling unit (PSU). Three levels of strata were used to draw up an efficient representative sample (i.e. economic activity, size of workforce and geographical location). The sample size in 2012 was 9,425 enterprises out of the 126,309 enterprises comprising the survey frame.

Mode of data collection

Face-to-face [f2f]

Research instrument

All of the economic surveys series used the same questionnaire, with a few different characteristics for each survey. The design of the 2013 questionnaire takes into account the major economic variables pertaining to the sector examined and the needs to be met to compile the National Accounts for Palestine.

The questionnaire included these variables: 1. The persons engaged in enterprise and compensation of these employees. 2. Value of output from the main activity and secondary activity. 3. Production inputs of goods and services. 4. Payments and transfers. 5. Taxes on production. 6. Assets and capital formation.

Cleaning operations

To ensure the quality and consistency of data, a set of measures was introduced as follows: - Creation of a data entry program prior to the collection of data to ensure this would be ready. - A set of validation rules were applied to the program to check the consistency of data. - The efficiency of the program was pre-tested by entering a few questionnaires, including incorrect information, and checking its efficiency in capturing the incorrect information. - Well-trained data entry personnel were selected and trained for main data entry. - Weekly data files were received by project management to be checked for accuracy and consistency: correction notes were provided to data entry management for implementation.

Response rate

Response rate: 85.2%

Sampling error estimates

Statistical Errors: - Statistical Errors: The findings of the survey are affected by statistical errors due to using sampling in conducting the survey for the units of the target population, which increases the chances of having variances from the actual values we expect to obtain from the data had we conducted the survey using comprehensive enumeration. The variance of the key goods in the survey was computed and dissemination was carried out on the level of the Palestinian Territory for reasons related to sample design and computation of the variance of the different indicators.

Non-Statistical Errors These types of errors could appear on one or all the survey stages that include data collection and data entry. - Response errors: these types of errors are related to, responders, fieldworkers, and data entry personnel's. And to avoid mistakes and reduce the impact has been a series of actions that would enhance the accuracy of the data through a process of data collection from the field and the data processing.

This dataset provides values for ECONOMY WATCHERS SURVEY reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.

LIVES is an initiative designed by the International Livestock Research Institute (ILRI), the International Water Management Institute (IWMI) and their national partners to build upon the success of the Canadian International Development Agency-funded project, Improving Productivity and Market Success of Smallholders in Ethiopia (IPMS). This dataset contains the household baseline Socio-economic survey.

Abstract

The Cambodia Socio-Economic Survey (CSES) asks questions to a country wide sample of households and household members about housing conditions, education, economic activities, household production and income, household level and structure of consumption, health, victimization, etc. There are also questions related to people in the labour force, e.g. labour force participation.

Poverty reduction is a major commitment by the Royal Government of Cambodia. Accurate statistical information about the living standards of the population and the extent of poverty is an essential instrument to assist the Government in diagnosing the problems, in designing effective policies for reducing poverty and in monitoring and evaluating the progress of poverty reduction. The Millennium Development Goals (MDG) has been adopted by the Royal Government of Cambodia and a National Strategic Development Plan (NSDP) has been developed. The MDGs are also incorporated into the “Rectangular Strategy of Cambodia”.

Cambodia is still a predominantly rural and agricultural society. The vast majority of the population get their subsistence in households as self-employed in agriculture. The level of living is determined by the household's command over labour and resources for own-production in terms of land and livestock for agricultural activities, equipments and tools for fishing, forestry and construction activities and income-earning activities in the informal and formal sector. The CSES aims to estimate household income and consumption/expenditure as well as a number of other household and individual characteristics.

The main objective of the survey is to collect statistical information about living conditions of the Cambodian population and the extent of poverty. The survey can be used for identifying problems and making decisions based on statistical data.

The main user is the Royal Government of Cambodia (RGC) as the survey supports monitoring the National Strategic Development Plan (NSDP) by different socio-economic indicators. Other users are university researchers, analysts, international organizations e.g. the World Bank and NGO’s. The World Bank has published a report on poverty profile and social indicators using CSES 2007 data . In this regard, the CSES continues to serve all stakeholders involved as essential instruments in order to assist in diagnosing the problems and designing their most effective policies. The CSES micro data at NIS is available for research and analysis by external researchers after approval by Senior Minister of Planning. The interesting research questions that could be put to the data are many; NIS welcomes new research based on CSES data.

General Objectives: CSES 2012 will continue the work started through CSES 2004 and the annual CSES 2007 and 2008 and would primarily aim at producing information needed for planning and policy making for reduction of poverty in Cambodia. Reduction of poverty has been given high priority in Cambodia's National Strategic Development Plan (NSDP 2009-2013). In addition to this, the survey data help in various other ways in developmental planning and policy making in the country. They would also prove useful for the production of National Accounts in Cambodia.

A long-term objective of the entire project is to build national capability in NIS for conducting socio-economic surveys and for utilizing survey data for planning for national development and social welfare.

Specific Objectives:

Among specific objectives, the following deserve special mention: 1) Obtain data on infrastructural facilities in villages, especially facilities for schooling and health care and associated problems. 2) Obtain data on retail prices of selected food, non-food and medicine items prevailing in the villages. 3) Collect data on utilization of education, housing and land ownership 4) Collect data on household assets and outstanding loans. 5) Collect data on household's construction activities. 6) Collect information on maternal health, child health/care. 7) Collect information on health care seeking and expenditure of the household members related to illness, injury and disability. 8) Collect information on economic activities including the economic activities for children aged between 5 and 17 years. 9) Collect information on victimization by the household 10) Collect information on the presence of the household members.

Geographic coverage

National Phnom Penh / Other Urban / Other Rural

Analysis unit

• Households
• Individuals

Universe

All resident households in Cambodia

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling design in the CSES 2012 survey is a three-stage design. In stage one a sample of villages is selected, in stage two an Enumeration Area (EA) is selected from each village selected in stage one, and in stage three a sample of households is selected from each EA selected in stage two.

Stage 1: A random sample of PSUs was selected from each stratum. The sampling method was systematic PPS (PPS=sampling with probability proportional to size). The size measure used was the number of households in the PSU according to the sampling frame.

Stage 2: One EA was selected by Simple Random Sampling (SRS), in each village selected in stage 1.

Stage 3: In each selected EA a sample of 10 households was selected. The selection of households was done in the field by the supervisors/interviewers. All households in selected EAs were listed by the enumerator. The sample of households was then selected from the list by systematic sampling with a random start (the start value controlled by NIS).

For the details of sample selection please refer to the document "Process Description: Design and Select the Sample for CSES 2012"

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different questionnaires or forms were used in the survey:

Form 1: Household listing sheets to be used in the sampling procedure in the enumeration areas.

Form 2: Village questionnaire answered by the village leader about economy and infrastructure, crop production, health, education, retail prices and sales prices of agriculture, employment and wages, and recruitment of children for work outside the village.

Form 3: Household questionnaire with questions for each household member, including modules on migration, education and literacy, housing conditions, crop production, household liabilities, durable goods, construction activities, nutrition, fertility and child care, child feeding and vaccination, health of children, mortality, current economic activity, health and illness, smoking, HIV/AIDS awareness, and victimization.

The interviewer is responsible for filling up Form 1 and Form 3 to respondents. For Form 2, the supervisors will be asked to canvass this form. In case that the supervisors are absent for any reason, the interviewers may be also asked to help fill up this form (Form 2).

Cleaning operations

The NIS team commenced their work of checking and coding and coding in begining of February after the first month of fieldwork was completed. Supervisors from the field delivered questionaires to NIS. Sida project expert and NIS Survey Manager helped in solving relevant matters that become apparent when reviewing questionires on delivery.

Response rate

The CSES 2012 enjoyed almost a 100 percent response rate. The high response rate together with close and systematic fieldwork supervision by the core group members were a major contribution for achieving high quality survey results.

Sampling error estimates

In order to provide a basis for assessing the reliability or precision of CSES estimates, the estimation of the magnitude of sampling error in the survey data were computed. Since most of the estimates from the survey are in the form of weighted ratios, thus variances for ratio estimates are computed.

The Coefficients of Variation (CV) on national level estimates are generally below 4 percent. The exception is the CV for total value of assets where there are rather high CVs especially in the urban areas, which should be expected.

The CVs are somewhat higher in the urban and rural domains but still generally below 7 percent. For the five zones, the average CVs are in the range 5 to 13 percent with a few exceptions where the CVs are above 20 percent. For provinces the CVs for food consumption are 9 percent on average.

The sample take within Primary Sampling Units (PSU) was set to 10 households per PSU in the CSES 1999. When data on variances became available, it was possible to make crude calculations of the optimal sample take within PSU. Calculations on some of the central estimates in the CSES 1999 show that the design effects in most cases are in the range 1 to 5.

Intra-cluster correlation coefficients have been calculated based on the design effects. These correlation coefficients are somewhat high. The reason is that the characteristics that are measured tend to be concentrated (clustered) within the PSUs. The optimal sample size within PSUs under different assumptions on cost ratios and intra-cluster correlation coefficients was then calculated. The cost ratio is the average cost for adding a village to the sample divided by the average cost of including an extra household in the sample. In the CSES, it was chosen to adopt a fairly low cost ratio due to the fact that the interview time per household is long. Under this assumption the optimal sample size is probably around 10 households per village for many of the CSES indicators.

New Zealand Economy Survey: Manufacturing: Sales: sa: Meat & Dairy Product data was reported at 7,975.021 NZD mn in Mar 2018. This records a decrease from the previous number of 8,370.430 NZD mn for Dec 2017. New Zealand Economy Survey: Manufacturing: Sales: sa: Meat & Dairy Product data is updated quarterly, averaging 4,820.441 NZD mn from Mar 1995 (Median) to Mar 2018, with 93 observations. The data reached an all-time high of 8,492.095 NZD mn in Dec 2013 and a record low of 2,524.197 NZD mn in Mar 1995. New Zealand Economy Survey: Manufacturing: Sales: sa: Meat & Dairy Product data remains active status in CEIC and is reported by Statistics New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.S005: Economy Survey: ANZSIC06: Seasonally Adjusted.

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The Vehicle Inventory and Use Survey (VIUS) is conducted in partnership with the Bureau of Transportation Statistics, Federal Highway Administration, and the U.S. Department of Energy to better understand the characteristics and use of trucks on our nation's roads. The survey universe for the VIUS includes all private and commercial trucks registered (or licensed) in the United States. This includes: pickups; minivans, other light vans, and sport utility vehicles; other light single-unit trucks (GVW = 26,000 lbs.); and truck tractors. The VIUS sample excludes vehicles owned by federal, state, and local governments; ambulances; buses; motor homes; farm tractors; unpowered trailer units; and trucks reported to have been disposed of prior to January 1 of the survey year. VIUS provides data on the physical and operational characteristics of the nation's truck population. Its primary goal is to produce estimates of the total number of trucks and truck miles. This dataset provides national and state-level summary statistics for in-scope vehicles, excluding pickups, SUVs, minivans, and other light vans, that were used at least partially for commercial purposes.

Provides data for employer firms by sector, gender, ethnicity, race, veteran status, and years in business for the U.S., states, and fifty most populous MSAs, including detailed business characteristics.

County Business Patterns (CBP) is an annual series that provides sub-national economic data by industry. This series includes the number of establishments, employment during the week of March 12, first quarter payroll, and annual payroll. This data is useful for studying the economic activity of small areas, analyzing economic changes over time, and as a benchmark for other statistical series, surveys, and databases between economic censuses. Businesses use the data for analyzing market potential, measuring the effectiveness of sales and advertising programs, setting sales quotas, and developing budgets. Government agencies use the data for administration and planning.

New Zealand Economy Survey: Manufacturing: Furniture & Other: Purchases & Operating Expenditure data was reported at 295.911 NZD mn in Mar 2018. This records a decrease from the previous number of 342.618 NZD mn for Dec 2017. New Zealand Economy Survey: Manufacturing: Furniture & Other: Purchases & Operating Expenditure data is updated quarterly, averaging 302.715 NZD mn from Dec 1992 (Median) to Mar 2018, with 102 observations. The data reached an all-time high of 375.240 NZD mn in Dec 2004 and a record low of 189.450 NZD mn in Mar 1993. New Zealand Economy Survey: Manufacturing: Furniture & Other: Purchases & Operating Expenditure data remains active status in CEIC and is reported by Statistics New Zealand. The data is categorized under Global Database’s New Zealand – Table NZ.S003: Economy Survey: ANZSIC06.

The U.S. Census Bureau.s economic indicator surveys provide monthly and quarterly data that are timely, reliable, and offer comprehensive measures of the U.S. economy. These surveys produce a variety of statistics covering construction, housing, international trade, retail trade, wholesale trade, services and manufacturing. The survey data provide measures of economic activity that allow analysis of economic performance and inform business investment and policy decisions. Other data included, which are not considered principal economic indicators, are the Quarterly Summary of State & Local Taxes, Quarterly Survey of Public Pensions, and the Manufactured Homes Survey. For information on the reliability and use of the data, including important notes on estimation and sampling variance, seasonal adjustment, measures of sampling variability, and other information pertinent to the economic indicators, visit the individual programs' webpages - http://www.census.gov/cgi-bin/briefroom/BriefRm.

Release Date: 2021-12-09.Release Schedule:.The data in this file come from the 2020 Annual Survey of Manufactures data files released in December 2021. For more information about the Annual Survey of Manufactures data, see About: Annual Survey of Manufactures...Key Table Information:.Includes only establishments of firms with payroll..Data may be subject to employment- and/or sales-size minimums that vary by industry...Data Items and Other Identifying Records: .Sales, value of shipments, or revenue ($1,000) .Relative standard error for estimate of sales, value of shipments, or revenue (%) .Value of donated products ($1,000) .Relative standard error for estimate of value of donated products (%) ...Geography Coverage:.The data are shown for employer establishments and firms for the U.S. and State levels that vary by industry..For information about 2020 Annual Survey of Manufactures, see About: Annual Survey of Manufactures...Industry Coverage:.The data are shown at the 2-through 6-digit 2017 NAICS code levels for the U.S. and at the 2-digit 2017 NAICS code level for States. For information about NAICS, see Annual Survey of Manufactures (ASM): Technical Documentation: ASM Product Class Codes and Descriptions...Footnotes:.Not applicable...FTP Download:.Download the entire table at: https://www2.census.gov/programs-surveys/asm/data/2020/AM1831BASIC05.zip..API Information:.Annual Survey of Manufactures API data are housed in the Census Bureau API. For more information, see ASM API..Methodology:.To maintain confidentiality, the U.S. Census Bureau suppresses data to protect the identity of any business or individual. The census results in this file contain sampling and/or nonsampling error. Data users who create their own estimates using data from this file should cite the U.S. Census Bureau as the source of the original data only..To comply with disclosure avoidance guidelines, data rows with fewer than three contributing establishments are not presented. Additionally, establishment counts are suppressed when other select statistics in the same row are suppressed. For detailed information about the methods used to collect and produce statistics, including sampling, eligibility, questions, data collection and processing, data quality, review, weighting, estimation, coding operations, confidentiality protection, sampling error, nonsampling error, and more, see Annual Survey of Manufactures (ASM): Technical Documentation: Annual Survey of Manufactures Methodology...Symbols:.D - Withheld to avoid disclosing data of individual companies; data are included in higher level totals.N - Not available or not comparable.S - Estimate does not meet publication standards because of high sampling variability, poor response quality, or other concerns about the estimate quality. Unpublished estimates derived from this table by subtraction are subject to these same limitations and should not be attributed to the U.S. Census Bureau. For a description of publication standards and the total quantity response rate, see link to program methodology page..X - Not applicable.A - Relative standard error of 100% or more.r - Revised (represented as a superscript).s - Relative standard error is 40 percent or more and less than 100 percent (data variable displayed as a superscript).For a complete list of all economic programs symbols, see the Economic Census: Technical Documentation: Data Dictionary...Source:.U.S. Census Bureau, 2020 Annual Survey of Manufactures (ASM).For information about the Annual Survey of Manufactures (ASM), see Business and Economy: Annual Survey of Manufactures (ASM)..Contact Information:.U.S. Census Bureau.For general inquiries:.(800) 242-2184/ (301) 763-5154.ewd.surveys@census.gov.For specific data questions:.(844) 303-7713.For additional contacts, see Annual Survey of Manufactures (ASM): About: Contact Us

Data contains information on variable and fixed costs about small scale fishermen