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 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 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 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.

Graph and download economic data for Average Hourly Earnings of All Employees, Total Private (CES0500000003) from Mar 2006 to Aug 2025 about average, earnings, 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.

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.

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

description: This dataset provides a U.S. national 60-m, 19-class mapping of anthropogenic land uses for five time periods: 1974-1982-1992-2002-2012. The 2012 dataset is based on a slightly modified version of the National Land Cover Database 2011 (NLCD 2011) that was recoded to a schema of land uses, and mapped back in time to develop datasets for the four earlier eras. The time periods coincide with years in which U.S. Department of Agriculture (USDA) Census of Agriculture data were collected. Changes are derived from (a) known changes in water bodies from reservoir construction or removal, (b) housing unit density changes, (c) regional mining/extraction trends, (d) for 1999-2012, timber and forestry activity based on US Geological Survey (USGS) Landfire data, (e) county-level USDA Census of Agriculture change in cultivated land, and (f) establishment dates of major conservation areas. The data are compared to several other published studies and datasets as validation. Caveats are provided about limitations of the data for some classes. The work was completed as part of the USGS National Water-Quality Assessment (NAWQA) Program and termed the NAWQA Wall-to-Wall Anthropogenic Land Use Trends (NWALT) dataset, with anticipation of five year updates for future versions. The associated datasets include five 60-m geospatial rasters showing anthropogenic land use from 1974-2012 and 14 rasters showing the extent of timber clear-cutting and harvest for 1999-2012. The full report for the product is provided as USGS Data Series 2015-948, at http://dx.doi.org/10.3133/ds948.; abstract: This dataset provides a U.S. national 60-m, 19-class mapping of anthropogenic land uses for five time periods: 1974-1982-1992-2002-2012. The 2012 dataset is based on a slightly modified version of the National Land Cover Database 2011 (NLCD 2011) that was recoded to a schema of land uses, and mapped back in time to develop datasets for the four earlier eras. The time periods coincide with years in which U.S. Department of Agriculture (USDA) Census of Agriculture data were collected. Changes are derived from (a) known changes in water bodies from reservoir construction or removal, (b) housing unit density changes, (c) regional mining/extraction trends, (d) for 1999-2012, timber and forestry activity based on US Geological Survey (USGS) Landfire data, (e) county-level USDA Census of Agriculture change in cultivated land, and (f) establishment dates of major conservation areas. The data are compared to several other published studies and datasets as validation. Caveats are provided about limitations of the data for some classes. The work was completed as part of the USGS National Water-Quality Assessment (NAWQA) Program and termed the NAWQA Wall-to-Wall Anthropogenic Land Use Trends (NWALT) dataset, with anticipation of five year updates for future versions. The associated datasets include five 60-m geospatial rasters showing anthropogenic land use from 1974-2012 and 14 rasters showing the extent of timber clear-cutting and harvest for 1999-2012. The full report for the product is provided as USGS Data Series 2015-948, at http://dx.doi.org/10.3133/ds948.

In many developing economies, high and increasing public debt profile constitutes an essential means of financial risk. An appropriate debt management is germane for survival of business and good international reputation though its effect on private sector credit mobilization had been seldomly investigated. This study seeks to know whether strategic debt management approach exacts a significant effect on the Nigerian private sector and Africa at large resulting to higher credit availability for sustainable enterprise establishment. The study used a time-series observation spanning from 1981–2021. The method of data analysis employed the unit root test for stationarity. Johansen cointegration and vector error correction approach. The result of the unit root test indicates the series were all stationary after first difference and thus were integrated of order1. The Johansen cointegration test support the existence of a cointegrating series between the private credit and its determinants. More empirical evidence from the study shows that proper debt management and increase revenue generation through net taxes on products accounted for 0.93 and 1.32% increase in private sector credit mobilization, while total external debt stock was responsible for a significant negative influence of 0.60% on private sector credit mobilization. The study recommends that the government should always be proactive in their strategic and innovative approach to debt management, revenue generation and sources of funds. This will help not only to avoid crowding out of the private sector but will enhance adequate credit mobilization for effective operations of the private sector.