This dataset contains measures of the number and per capita density of all eating and drinking places plus select subtypes – fast food restaurants, coffee shops, and bars – per United States census tract from 2006 through 2015. Establishment data was taken from the National Establishment Time Series (NETS) database which classifies establishments by North American Industry Classification System (NAICS) code and provides detailed address history.

This is the publicly-accessible portion of the dataset used to conduct the analysis for this study. It contains the following variables: a scrambled establishment ID that uniquely identifies each establishment, the establishment’s city, industry, year, year of random inspection, treated (has been randomly inspected), sales, employment, PAYDEX score, Composite Credit Appraisal. This dataset does not contain the following variables used in the analysis because of the confidentiality conditions under which they were obtained: establishment name, street address, ZIP code, DUNS number; annual payroll, injury count, injury cost, and average occupational riskiness. Researchers seeking full access to data on establishment names, addresses, DUNS numbers, sales, employment, PAYDEX scores, Composite Credit Appraisals, and industry (NAICS and SIC Codes) from the National Establishment Time-Series (NETS) database can contact Donald Walls, President, Walls & Associates (tel. +1-510-763-0641, dwalls2@earthlink.net). Researchers interested in obtaining data on the number and costs of workers’ compensation claims, occupational riskiness, payroll, and establishment names and addresses from the Workers’ Compensation Insurance Rating Bureau of California (WCIRB) may contact WCIRB’s Chief Actuary Dave Bellusci (tel. +1-415-777-0777, dbellusci@wcirbonline.org).

This dataset contains measures of the number and per capita density of personal service establishments – such as hairdressers, barber shops, nail salons, laundromats, and dry cleaners – per United States census tract from 2006 through 2015. Establishment data was taken from the National Establishment Time Series (NETS) database which classifies establishments by North American Industry Classification System (NAICS) code and provides detailed address history.

This dataset contains measures of the number and per capita density of select types of arts, entertainment, and recreation organizations – such as museums, libraries, spectator sports organizations, amusement parks, fitness centers, bowling alleys, and casinos – per United States census tract from 2006 through 2015. Establishment data was taken from the National Establishment Time Series (NETS) database which classifies establishments by North American Industry Classification System (NAICS) code and provides detailed address history.

This dataset contains measures of the number and per capita density of select types of religious, civic, and social organizations – such as churches, mosques, synagogues, ethnic associations, and veterans’ associations – per United States census tract from 2006 through 2015. Establishment data was taken from the National Establishment Time Series (NETS) database which classifies establishments by North American Industry Classification System (NAICS) code and provides detailed address history.

We study the impact of the USDA’s Broadband Initiatives Program (BIP) on business outcomes in program recipient areas. The BIP was established by the American Recovery and Reinvestment Act (ARRA) of 2009 and implemented by the Rural Utilities Service (RUS) of the USDA Rural Development Mission Area. It was a $2.5 billion program (appropriations) that provided grants and loans to support broadband provision in unserved and underserved areas that were primarily rural. This research combines RUS program administrative data on BIP loans and grants and business outcomes and attributes data from the National Establishment Time Series (NETS) data. We use a quasi-experimental research design that combines matching with difference-in-differences (DiD) estimation to identify the causal effect of the BIP program on employment change at the establishment level and on business survival. Focusing on businesses that already existed in 2010, we find that the average employment decreased in both BIP and non-BIP area businesses during the post-program period, but the decline was slower for businesses in BIP areas. The statistical significance of the differences in employment change between the two groups indicates a positive impact of the program. A disaggregated view of the employment impacts show that the positive employment impact is mainly found to be statistically significant in metro counties, the service sector, and employer establishments. Results also show that businesses in BIP areas were less likely to fail compared to businesses in non-BIP areas and this effect is found to be different across metro/nonmetro counties, employer vs. nonemployer businesses, and broad industrial sectors.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Jul 2025 about earnings, average, hours, establishment survey, wages, private, employment, and USA.

Graph and download economic data for All Employees, Food Services and Drinking Places (CES7072200001) from Jan 1990 to Jul 2025 about leisure, hospitality, establishment survey, food, services, employment, and USA.

Abstract

The central statistical offices in most countries place heavy emphasis on constructing sound databases for all activities within the services sector. PCBS’ Services Statistics Program is part of the Economic Statistics Program, which is part of the larger program for establishing the System of Official Statistics for Palestine. PCBS initiated, in the reference year 1994, the economic surveys series. The series includes, in addition to the services survey, surveys on industry, internal trade construction-contractors, and transport and storage sectors for the purpose of establishing a time series database of economic activities in line with international recommendations specified in System of National Account (SNA) 93 and in the UN Manual for Services Statistics. The sampling frame for the different economic surveys was based on the findings of the 2007 Establishment Census conducted by PCBS. The services survey provide data needed for:

Objectives: The objective of the survey was to obtain data on:

1. Number of enterprises and persons engaged in services by activity and location.
2. Value of output, intermediate consumption and stocks.
3. Value added components.
4. Payments and transfers.
5. Capital formation.
6. Contribution of the surveyed activities to the GDP and other National Accounts variables.

Target Population

PCBS depends on the International and Industrial Classification of all economic activities, version 3, (ISIC - 3) by the United Nation to classify the economic activities. All enterprises and establishments are classified according to the Establishments Census 2007, which works in agreement with (ISIC - 3).

The services survey covers the following activities: 1. Hotels and Restaurants 2. Real Estate, Renting and Business Activities 3. Education 4. Health and Social Work 5. Other Community, Social and Personal Service Activities

Geographic coverage

West Bank and Gaza Strip

Analysis unit

Enterprise constitutes the primary sampling unit (PSU)

Universe

Sample from Services Enterprises (private sector).

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample and Frame

The sample of the Services Survey 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 arrive at an efficient representative sample (i.e. economic activity, size of employment and geographical levels). The sample size amounted to 2,370 enterprises out of the 24,277 enterprises that comprise the survey frame in the Palestinian Territory.

Mode of data collection

Face-to-face [f2f]

Research instrument

There are two forms of the services survey questionnaire 2008 of West Bank, the first one is related to household and branches, and the second is related to non-finance companies sector. The questionnaire contains the following main variables: 1. Number of employees in a company and their compensations. 2. The output of the main and second activities. 3. Goods production inputs. 4. Various payments and transfers. 5. Indirect taxes. 6. Enterprises assets.

Because of the political conditions in Gaza Strip and Jerusalem (J1), there was a very brief model of the questionnaire, in order to collect basic data required for national accounts and without any details.

Cleaning operations

Data processing: For ensuring quality and consistency of data, a set of measures were taken into account for strengthening accuracy of data as follows: - Preparing data entry program before data collection for checking readiness of the program for data entry. - A set of validation rules were applied on the program for checking consistency of data. - Efficiency of the program was checked through pre-testing in entering few questionnaires, including incorrect information for checking its efficiency, in capturing these information. - Well trained data keyers were selected and trained for the main data entry. - Weekly or bi-weekly data files were received by project management for checking accuracy and consistency, notes of correction are provided for data entry management for correction.

Response rate

The original sample of the Palestinian Territory is 2,388 enterprise with transfer establishments from other activities. And the number of completed questionnaires are 1,886.

The response rate is (83.5%). The over coverage rate is (5.4%). The non response rate is (16.5%).

Sampling error estimates

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. 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.

Estimated results of time trend analysis.

Abstract

The Central Statistical Agency (CSA) has been providing labour force and related data at different levels and with varying details in their content. These include the 1976 Addis Ababa Manpower and Housing Sample Survey, the 1978 Survey on Population and Housing Characteristics of Seventeen Major Towns, the 1980/81 and 1987/88 Rural Labour Force Surveys, the 1984 and 1994 Population and Housing Census, and 2003 and 2004 Urban Bi-annual Employment Unemployment Survey. The 1996 and 2002 Surveys of Informal Sector and most of the household surveys undertaken by the Agency also provide limited information on the area. Still pieces of information in relation to that of employment can also be derived from small, large and medium scale establishment surveys.

Till the 1999 Labour Force Survey (LFS) there hasn't been a comprehensive national labour force survey representing both urban and rural areas. This 2005 LFS is the second in the series. Like the National Labour Force Survey of 1999, it covered both the urban and rural areas of all regions.

The specific objectives of this survey are to: - generate data on the size of work force that is available to participate in production process; - determine the status and rate of economic participation of different sub-groups of the population; - identify those who are actually contributing to the economic development (i.e., employed) and those out of the sphere; - determine the size and rate of unemployed population; - provide data on the structure of the working population; - obtain information about earnings from paid employment; - identify the distribution of employed population working in the formal/informal enterprises; and - provide time series data and trace changes over time.

Geographic coverage

The survey covered all rural and urban parts of the country except all zones of Gambella region excluding Gambella town, and the non-sedentary population of three zones of Afar & six zones of Somali regions.

Analysis unit

Household Individual

Universe

The survey covered all households in selected sample areas except residents of collective quarters, homeless persons and foreigners.

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLING FRAME: The list of households obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration (EASE) is used to select EAs from the rural part of the country. For urban sample EAs on the other hand the list consisting of households by EA, which was obtained from the 2004 Ethiopian Urban Economic Establishment Census, (EUEEC) was used as a frame. A fresh list of households from each urban and rural EA was prepared at the beginning of the survey period. The list was then used as a frame for selecting sample households of each EAs.

SAMPLE DESIGN: For the purpose of the survey the country was divided into three broad categories. That is; rural, major urban center and other urban center categories.

Category I: Rural: - This category consists of the rural areas of 8 regions and two city administrations found in the country. Regarding the survey domains, each region or city administration was considered to be a domain (Reporting Level) for which major findings of the survey are reported. This category totally comprises 10 reporting levels. A stratified two-stage cluster sample design was used to select samples in which the primary sampling units (PSUs) were EAs. Households per sample EA were selected as a second Stage Sampling Unit (SSU) and the survey questionnaire finally administered to all members of sample households.

Category II:- Major urban centers:- In this category all regional capitals and 15 other major urban centers that had a population size of 40,000 or more in 2004 were included. Each urban center in this category was considered as a reporting level. The category has totally 26 reporting levels. In this category too, in order to select the samples, a stratified two-stage cluster sample design was implemented. The primary sampling units were EAs. Households from each sample EA were then selected as a Second Stage Unit.

Category III: - Other urban centers: Urban centers in the country other than those under category II were grouped into this category. Excluding Gambella a domain of other urban centers is formed for each region. Consequently seven reporting levels were formed in this category. Harari, Addis Ababa and Dire Dawa do not have urban centers other than that grouped in category II. Hence, no domain was formed for these regions under this category. Unlike the above two categories a stratified three stage cluster sample design was adopted to select samples from this category. The primary sampling units were urban centers and the second stage sampling units were EAs. Households from each EA were finely selected at the third stage and the survey questionnaires administered for all of them.

SAMPLE SIZE AND SELECTION SCHEME: Category I: - Totally 830 EAs and 24,900 households were selected from this category. Sample EAs of each reporting level were selected using Probability Proportional to Size (PPS) systematic sampling technique; size being number of household obtained from the 2001/2 Ethiopian Agricultural Sample Enumeration. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and surveyed.

Category II: - In this category 720 EAs and 21,600 households were selected. Sample EAs from each reporting level in this category were also selected using probability proportional to size systematic sampling; size being number of households obtained from the 2004 EUEEC. From the fresh list of households prepared at the beginning of the survey 30 households per EA were systematically selected and covered by the study.

Category III:-127 urban centers, 275 EAs and 8,250 households were selected in this category. Urban centers from each domain and EAs from each urban center were selected using probability proportional to size systematic selection method; size being number of households obtained from the 2004 EUEEC. From the fresh listing of each EA 30 households were systematically selected and the study carried out on the 30 households ultimately selected.

Note: Distribution of number of samples planned and covered from each domain are given in the Summary Table 2.1, Table 2.2 and Table 2.3 of the 2005 National Labour Force Survey report which is provided as external resource.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey has used a structured questionnaire to produce the required data. Before taking its final shape, the draft questionnaire was tested by undertaking a pre-test. The pre-test was conducted in Addis Ababa, Sendoffs, Teji and their vicinity. Based on the findings of the pre-test, the content, layout and presentation of the questionnaire was amended comments and inputs on the draft contents of the survey questionnaire obtained from user-producer forum were also incorporated in the final questionnaire.

The contents of the questionnaire and methods used in this survey were further improved based on comment of international consultant. The consultancy was obtained as part of a joint World Bank/IMF project to improve statistics of countries in Anglo-phone Africa participating in the General Data Dissemination System (GDDS).

The questionnaire was organized in to five sections; Section 1 - Area identification of the selected household: this section dealt with area identification of respondents such as region, zone, wereda, etc.,

Section 2 - Socio- demographic characteristics of households: it consisted of the general sociodemographic characteristics of the population such as age, sex, education, status and type of disability, status and types of training, marital status and fertility questions.

Section 3 - Productive activities during the last seven days: this section dealt with a range of questions which helps to see the status and characteristics of employed persons in a current status approach such as hours of work in productive activities, occupation, industry, employment status, and earnings from employment. Also questions included are hours spent on fetching water, collection of firewood, and domestic chores and place of work.

Section 4 - Unemployment and characteristics of unemployed persons: this section focused on the size and characteristics of the unemployed population.

Section 5 - Economic activities during the last twelve months: this section covered the usual economic activity status (refereeing to the long reference period), number of weeks of employment /unemployment/inactive, reasons for inactivity, employment status, whether working in the agricultural sector or not and the proportion of income gained from non-agricultural sector. The questionnaire used in the field for data collection was prepared in Amharic language. Most questions have pre-coded answers. A copy of the questionnaire translated to English is provided as external resource.

Cleaning operations

Data Editing, Coding and Verification: The filled-in questionnaires that were retrieved from the field were first subjected to manual editing and coding. During the fieldwork the enumerator, the field supervisors, Statisticians and the heads of branch statistical offices have done some editing. However, the major editing operation was carried out at the head office. All the edited questionnaires were again fully verified and checked for consistency before they were submitted to the data entry.

Response rate

Ultimately 100.00 % EAs and 99.84% household were covered