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Motivation

This dataset was created to pilot techniques for creating synthetic data from datasets containing sensitive and protected information in the local government context. Synthetic data generation replaces actual data with representative data generated from statistical models; this preserves the key data properties that allow insights to be drawn from the data while protecting the privacy of the people included in the data. We invite you to read the Understanding Synthetic Data white paper for a concise introduction to synthetic data.

This effort was a collaboration of the Urban Institute, Allegheny County’s Department of Human Services (DHS) and CountyStat, and the University of Pittsburgh’s Western Pennsylvania Regional Data Center.

Collection

The source data for this project consisted of 1) month-by-month records of services included in Allegheny County's data warehouse and 2) demographic data about the individuals who received the services. As the County’s data warehouse combines this service and client data, this data is referred to as “Integrated Services data”. Read more about the data warehouse and the kinds of services it includes here.

Preprocessing

Synthetic data are typically generated from probability distributions or models identified as being representative of the confidential data. For this dataset, a model of the Integrated Services data was used to generate multiple versions of the synthetic dataset. These different candidate datasets were evaluated to select for publication the dataset version that best balances utility and privacy. For high-level information about this evaluation, see the Synthetic Data User Guide.

For more information about the creation of the synthetic version of this data, see the technical brief for this project, which discusses the technical decision making and modeling process in more detail.

Recommended Uses

This disaggregated synthetic data allows for many analyses that are not possible with aggregate data (summary statistics). Broadly, this synthetic version of this data could be analyzed to better understand the usage of human services by people in Allegheny County, including the interplay in the usage of multiple services and demographic information about clients.

Known Limitations/Biases

Some amount of deviation from the original data is inherent to the synthetic data generation process. Specific examples of limitations (including undercounts and overcounts for the usage of different services) are given in the Synthetic Data User Guide and the technical report describing this dataset's creation.

Feedback

Please reach out to this dataset's data steward (listed below) to let us know how you are using this data and if you found it to be helpful. Please also provide any feedback on how to make this dataset more applicable to your work, any suggestions of future synthetic datasets, or any additional information that would make this more useful. Also, please copy wprdc@pitt.edu on any such feedback (as the WPRDC always loves to hear about how people use the data that they publish and how the data could be improved).

Further Documentation and Resources

1) A high-level overview of synthetic data generation as a method for protecting privacy can be found in the Understanding Synthetic Data white paper.
2) The Synthetic Data User Guide provides high-level information to help users understand the motivation, evaluation process, and limitations of the synthetic version of Allegheny County DHS's Human Services data published here.
3) Generating a Fully Synthetic Human Services Dataset: A Technical Report on Synthesis and Evaluation Methodologies describes the full technical methodology used for generating the synthetic data, evaluating the various options, and selecting the final candidate for publication.
4) The WPRDC also hosts the Allegheny County Human Services Community Profiles dataset, which provides annual updates on human-services usage, aggregated by neighborhood/municipality. That data can be explored using the County's Human Services Community Profile web site.

Abstract

The survey was conducted in Kazakhstan between January and October of 2019. The survey was part of a joint project of the European Bank for Reconstruction and Development (EBRD), the European Investment Bank (EIB) and the World Bank Group (WBG). The objective of the Enterprise Survey is to gain an understanding of what firms experience in the private sector. As part of its strategic goal of building a climate for investment, job creation, and sustainable growth, the World Bank has promoted improving the business environment as a key strategy for development, which has led to a systematic effort in collecting enterprise data across countries. The Enterprise Surveys (ES) are an ongoing World Bank project in collecting both objective data based on firms’ experiences and enterprises’ perception of the environment in which they operate.

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

For the Kazakhstan ES. size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for 2019 Kazakhstan ES was selected using stratified random sampling, following the methodology explained in the Sampling Note.

Three levels of stratification were used in this country: industry, establishment size, and region. The original sample design with specific information of the industries and regions chosen is described in "The Kazakhstan 2019 Enterprise Surveys Data Set" report, Appendix C.

Industry stratification was designed in the way that follows: the universe was stratified into six manufacturing industries and two services industries: Food and Beverages (ISIC Rev. 4 codes 10 and 11), Garments (ISIC code 14), Non-Metallic Mineral Products (ISIC code 23), Fabricated Metal Products (ISIC code 25), Machinery and Equipment (ISIC code 28), Other Manufacturing (ISIC codes 12, 13, 15-22, 24, 26, 27, 29, 30-33), Retail (ISIC code 47), and Other Services (ISIC codes 41-43, 45, 46, 49-53, 55, 56, 58, 61, 62, 79, 95).

For the Kazakhstan ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees). Regional stratification for the Kazakhstan ES was done across eleven regions: Akmola Region; Aktobe Region; Almaty; Almaty Region; Nur-Sultan; Atyrau Region; Mangystau and West Kazakhstan; East Kazakhstan; Karaganda Region; Kostanay, North Kazakhstan, Pavlodar and Kyzylorda Region, South Kazakhstan, Jambyl.

Note: See Sections II and III of “The Kazakhstan 2019 Enterprise Surveys Data Set” report for additional details on the sampling procedure.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires - Manufacturing and Services were used to collect the survey data.

The Questionnaires have common questions (core module) and respectfully additional manufacturing- and services-specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module).

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond (-8) as a different option from don’t know (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary. However, there were clear cases of low response. Please, note that for this specific question, refusals were not separately identified from “Don’t know” responses.

The number of interviews per contacted establishments was 12.5%. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units.

The share of rejections per contact was 36.1%.

Comprehensive list containing 15 verified Debt collection agency businesses in Illinois, United States with lastest contact information, ratings, reviews, and location data.

This dataset includes data collected in support of the Minerals Management Service-supported program entitled "The Deepwater Program: Northern Gulf of Mexico Continental Slope Habitat and Benthic Ecology". This dataset includes physical, chemical, and biological oceanographic data as well as surveys to characterize the principal components of benthic communities.

Find key information on state library agencies.

These data include imputed values for state libraries that did not submit information in this data collection.

Imputation is a procedure for estimating a value for a specific data item where the response is missing.

Download SLAA data files to see imputation flag variables or learn more on the imputation methods at https://www.imls.gov/research-evaluation/data-collection/state-library-administrative-agency-survey

Sports Data Services Market Outlook

According to our latest research, the Sports Data Services market size reached USD 5.3 billion in 2024, reflecting the sector’s accelerating digitization and the growing reliance on advanced analytics across the global sports ecosystem. The market is experiencing robust expansion, with a recorded CAGR of 18.7% during the forecast period. By 2033, the global Sports Data Services market is projected to attain a value of USD 24.5 billion, driven by technological advancements, the proliferation of connected devices, and the increasing demand for real-time, actionable sports insights. This remarkable growth trajectory is underpinned by the integration of AI-powered analytics, the surge in fan engagement platforms, and the expanding footprint of legalized sports betting worldwide.

A primary growth factor for the Sports Data Services market is the rapid adoption of data-driven decision-making within professional sports organizations. Teams and leagues are leveraging sophisticated data collection and analysis tools to optimize performance, minimize injury risks, and refine game strategies. The integration of wearable technologies, IoT sensors, and machine learning algorithms enables the capture and interpretation of granular performance metrics, offering unprecedented insights into athlete health, fitness, and tactical execution. This data-centric approach is not only transforming on-field operations but also influencing coaching methodologies, talent scouting, and long-term player development, thereby fueling the demand for comprehensive sports data services.

Another significant driver is the escalating emphasis on fan engagement and personalized experiences. As sports audiences become more digitally savvy, there is a heightened expectation for interactive content, real-time statistics, and immersive viewing experiences. Sports Data Services providers are responding with advanced visualization tools, live data feeds, and augmented reality solutions that enhance fan interaction across digital platforms. This trend is especially pronounced in major leagues and tournaments, where broadcasters and media companies are investing heavily in data-driven storytelling to differentiate their offerings and capture viewer attention. The convergence of sports entertainment and technology is thus a pivotal force propelling market growth.

The global expansion of sports betting and gambling is also a critical catalyst for the Sports Data Services market. With the legalization of sports betting in several key jurisdictions, there is a surging demand for accurate, real-time data feeds that support odds calculation, risk management, and in-play betting. Betting companies are increasingly partnering with data service providers to access reliable, up-to-the-second information on player statistics, match events, and historical trends. This symbiotic relationship not only ensures the integrity and fairness of betting operations but also opens new revenue streams for sports organizations and data vendors alike, further accelerating market momentum.

From a regional perspective, North America continues to dominate the Sports Data Services market, supported by a technologically advanced sports infrastructure, high digital penetration, and the widespread legalization of sports betting in the United States. Europe follows closely, with a rich sporting heritage and a mature ecosystem for data analytics in football, rugby, and other popular sports. The Asia Pacific region is emerging as a high-growth market, fueled by rising investments in sports technology, expanding fan bases, and the increasing commercialization of sports leagues. Latin America and the Middle East & Africa are also witnessing steady growth, albeit from a smaller base, as local sports organizations embrace digital transformation and data-driven engagement strategies.

Service Type Analysis

Within the Sports Data Services market, the segmentation by service type plays a pivotal role in catering to the diverse needs of stakeholders across the sports value chain. Data Collection services form the foundational layer, encompassing the acquisition of raw data from live sporting events, training sessions, and athlete performance metrics. This segment is witnessing significant investment in advanced sensor technologies, high-speed cameras, and IoT-enabled devices, which facilitate the capture of granular, real-time information. The growing sophistication of data acquisition tools is enablin

Abstract

The survey was conducted in Guinea between July - December 2016 as part of Enterprise Surveys project, an initiative of the World Bank. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries. Only registered businesses are surveyed in the Enterprise Survey.

Data from 150 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses. The data was collected using face-to-face interviews.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance.

Geographic coverage

Conakry

Analysis unit

The primary sampling unit of the study is an establishment. The 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

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into manufacturing industries (ISIC Rev. 3.1 codes 15-37) and services industries (ISIC codes 45, 50, 51, 52, 55, 60-64, and 72).

For the Guinea ES, size stratification was defined as follows: small (5 to 19 employees), medium (20 to 99 employees), and large (100 or more employees).

Regional stratification for the Guinea ES was done in Conakry.

The sample frame consisted of listings of firms from Banque Centrale de la République de Guinée Conakry.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 8.9% (34 out of 383 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The following survey instruments are available: - Manufacturing Module Questionnaire - Services Module Questionnaire

Questionnaires have common questions (core module) and respectfully additional manufacturing and services specific questions.

The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the core module, plus manufacturing specific questions). Retail firms have been interviewed using the Services questionnaire (includes the core module plus retail specific questions) and the residual eligible services have been covered using the Services questionnaire (includes the core module). Each variation of the questionnaire is identified by the index variable, a0.

The survey is fielded via manufacturing or services questionnaires in order not to ask questions that are irrelevant to specific types of firms, e.g. a question that relates to production and nonproduction workers should not be asked of a retail firm. In addition to questions that are asked across countries, all surveys are customized and contain country-specific questions. An example of customization would be including tourism-related questions that are asked in certain countries when tourism is an existing or potential sector of economic growth.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of interviews per contacted establishments was 0.39. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.20.

Collaboratory is a software product developed and maintained by HandsOn Connect Cloud Solutions. It is intended to help higher education institutions accurately and comprehensively track their relationships with the community through engagement and service activities. Institutions that use Collaboratory are given the option to opt-in to a data sharing initiative at the time of onboarding, which grants us permission to de-identify their data and make it publicly available for research purposes. HandsOn Connect is committed to making Collaboratory data accessible to scholars for research, toward the goal of advancing the field of community engagement and social impact.Collaboratory is not a survey, but is instead a dynamic software tool designed to facilitate comprehensive, longitudinal data collection on community engagement and public service activities conducted by faculty, staff, and students in higher education. We provide a standard questionnaire that was developed by Collaboratory’s co-founders (Janke, Medlin, and Holland) in the Institute for Community and Economic Engagement at UNC Greensboro, which continues to be closely monitored and adapted by staff at HandsOn Connect and academic colleagues. It includes descriptive characteristics (what, where, when, with whom, to what end) of activities and invites participants to periodically update their information in accordance with activity progress over time. Examples of individual questions include the focus areas addressed, populations served, on- and off-campus collaborators, connections to teaching and research, and location information, among others.The Collaboratory dataset contains data from 37 institutions beginning in March 2016and continues to grow as more institutions adopt Collaboratory and continue to expand its use. The data represent over 3,600 published activities (and additional associated content) across our user base.Please cite this data as:Medlin, Kristin and Seto, Matthew. Dataset on Higher Education Community Engagement and Public Service Activities, 2016-2021. Collaboratory [producer], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-11-01. https://doi.org/10.3886/E136322V1When you cite this data, please also include: Janke, E., Medlin, K., & Holland, B. (2021, November 9). To What End? Ten Years of Collaboratory. https://doi.org/10.31219/osf.io/a27nb

The National Health Interview Survey (NHIS) is the principal source of information on the health of the civilian noninstitutionalized population of the United States and is one of the major data collection programs of the National Center for Health Statistics (NCHS) which is part of the Centers for Disease Control and Prevention (CDC). The National Health Survey Act of 1956 provided for a continuing survey and special studies to secure accurate and current statistical information on the amount, distribution, and effects of illness and disability in the United States and the services rendered for or because of such conditions. The survey referred to in the Act, now called the National Health Interview Survey, was initiated in July 1957. Since 1960, the survey has been conducted by NCHS, which was formed when the National Health Survey and the National Vital Statistics Division were combined.
NHIS data are used widely throughout the Department of Health and Human Services (DHHS) to monitor trends in illness and disability and to track progress toward achieving national health objectives. The data are also used by the public health research community for epidemiologic and policy analysis of such timely issues as characterizing those with various health problems, determining barriers to accessing and using appropriate health care, and evaluating Federal health programs.
The NHIS also has a central role in the ongoing integration of household surveys in DHHS. The designs of two major DHHS national household surveys have been or are linked to the NHIS. The National Survey of Family Growth used the NHIS sampling frame in its first five cycles and the Medical Expenditure Panel Survey currently uses half of the NHIS sampling frame. Other linkage includes linking NHIS data to death certificates in the National Death Index (NDI).
While the NHIS has been conducted continuously since 1957, the content of the survey has been updated about every 10-15 years. In 1996, a substantially revised NHIS questionnaire began field testing. This revised questionnaire, described in detail below, was implemented in 1997 and has improved the ability of the NHIS to provide important health information.

Abstract

This survey was conducted in Georgia between December 2012 and August 2013 as part of the fifth round of the Business Environment and Enterprise Performance Survey (BEEPS V), a joint initiative of the World Bank Group and the European Bank for Reconstruction and Development. The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

In Georgia, data from 360 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The survey topics include firm characteristics, information about sales and suppliers, competition, infrastructure services, judiciary and law enforcement collaboration, security, government policies, laws and regulations, financing, overall business environment, bribery, capacity utilization, performance and investment activities, and workforce composition.

In 2011, the innovation module was added to the standard set of Enterprise Surveys questionnaires to examine in detail how introduction of new products and practices influence firms' performance and management.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an establishment. The 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

The whole population, or universe of the study, is the non-agricultural economy. It comprises: all manufacturing sectors according to the group classification of ISIC Revision 3.1: (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities-sectors.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Georgia ES was selected using stratified random sampling. Three levels of stratification were used in this country: industry, establishment size, and region.

Industry stratification was designed in the way that follows: the universe was stratified into one manufacturing industry, and two service industries (retail, and other services).

Size stratification was defined following the standardized definition for the rollout: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not common practice, apart from the construction and agriculture sectors which are not included in the survey.

Regional stratification was defined in 6 regions (city and the surrounding business area) throughout Georgia.

Database from the National Statistical Office of Georgia was used as the frame for the selection of a sample with the aim of obtaining interviews at 360 establishments with five or more employees.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 26.4% (341 out of 1,290 establishments).

The variables a2 (sampling region), a6a (sampling establishment's size), and a4a (sampling sector) contain the establishment's classification into the strata chosen for each country using information from the sample frame. The strata were defined according to the guidelines described above. Variable a4a is coded using ISIC Rev 3.1 codes for the chosen industries for stratification. These codes include most manufacturing industries (15 to 37), retail (52), and (45, 50, 51, 55, 60-64, 72) for other services.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three different versions of the questionnaire were used. The basic questionnaire, the Core Module, includes all common questions asked to all establishments from all sectors. The second expanded variation, the Manufacturing Questionnaire, is built upon the Core Module and adds some specific questions relevant to manufacturing sectors. The third expanded variation, the Retail Questionnaire, is also built upon the Core Module and adds to the core specific questions.

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether, while the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect the refusal to respond as a different option from don’t know. b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.28. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.19.

This datasets contains information about NYC Resident Economic Empowerment and Sustainability (REES) service, a service offered by the New York City Housing Authority (NYCHA) that connects residents to services and opportunity through a formal place-based network. Each row in the dataset represents the number of public housing residents on a Borough-level who receive or utilize this service.

For datasets related to other services provided to NYCHA residents, view the data collection “Services available to NYCHA Residents - Local Law 163”.

Abstract

The survey was conducted in Uganda between January 2013 and August 2013 as part of the Africa Enterprise Survey 2013 roll-out, an initiative of the World Bank. Data from 640 establishments was analyzed. Stratified random sampling was used to select the surveyed businesses.

The objective of the survey is to obtain feedback from enterprises on the state of the private sector as well as to help in building a panel of enterprise data that will make it possible to track changes in the business environment over time, thus allowing, for example, impact assessments of reforms. Through interviews with firms in the manufacturing and services sectors, the survey assesses the constraints to private sector growth and creates statistically significant business environment indicators that are comparable across countries.

The standard Enterprise Survey topics include firm characteristics, gender participation, access to finance, annual sales, costs of inputs and labor, workforce composition, bribery, licensing, infrastructure, trade, crime, competition, capacity utilization, land and permits, taxation, informality, business-government relations, innovation and technology, and performance measures. Over 90 percent of the questions objectively ascertain characteristics of a country’s business environment. The remaining questions assess the survey respondents’ opinions on what are the obstacles to firm growth and performance. The mode of data collection is face-to-face interviews.

Geographic coverage

National

Analysis unit

The primary sampling unit of the study is an 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

The whole population, or the universe, covered in the Enterprise Surveys is the non-agricultural private economy. It comprises: all manufacturing sectors according to the ISIC Revision 3.1 group classification (group D), construction sector (group F), services sector (groups G and H), and transport, storage, and communications sector (group I). Note that this population definition excludes the following sectors: financial intermediation (group J), real estate and renting activities (group K, except sub-sector 72, IT, which was added to the population under study), and all public or utilities sectors. Companies with 100% government ownership are not eligible to participate in the Enterprise Surveys.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for Uganda was selected using stratified random sampling. Three levels of stratification were used in this country: firm sector, firm size, and geographic region.

Industry stratification was designed in the way that follows: the universe was stratified into three manufacturing industry (food, textiles and garments, other manufacturing) and two service sectors (retail and other services).

Size stratification was defined following the standardized definition for the Enterprise Surveys: small (5 to 19 employees), medium (20 to 99 employees), and large (more than 99 employees). For stratification purposes, the number of employees was defined on the basis of reported permanent full-time workers. This seems to be an appropriate definition of the labor force since seasonal/casual/part-time employment is not a common practice, except in the sectors of construction and agriculture.

Regional stratification for the Uganda ES was defined in six regions (city and the surrounding business area): Jinja, Kampala, Lira, Mbale, Mbarara, and Wakiso.

Uganda Bureau of Statistics database was used as a sampling frame with the aim of obtaining interviews with 600 establishments.

Given the impact that non-eligible units included in the sample universe may have on the results, adjustments may be needed when computing the appropriate weights for individual observations. The percentage of confirmed non-eligible units as a proportion of the total number of sampled establishments contacted for the survey was 2% (36 out of 1,567 establishments).

Mode of data collection

Face-to-face [f2f]

Research instrument

The structure of the data base reflects the fact that 3 different versions of the survey instrument were used for all registered establishments. Questionnaires have common questions (Core module) and respectfully additional manufacturing and retail specific questions. The eligible manufacturing industries have been surveyed using the Manufacturing questionnaire (includes the Core module, plus manufacturing specific questions). Retail firms have been interviewed using the Retail questionnaire (includes the Core module plus retail specific questions) and the residual eligible services have been covered using the Core module only. Each variation of the questionnaire is identified by the index variable, a0.

All variables are named using, first, the letter of each section and, second, the number of the variable within the section, i.e. a1 denotes section A, question 1 (some exceptions apply due to comparability reasons). Variable names proceeded by a prefix "KEN" and "A2F" indicate questions specific to some countries in Africa, therefore, they may not be found in the implementation of the rollout in other countries. All other suffixed variables are global and are present in all country surveys over the world. All variables are numeric with the exception of those variables with an "x" at the end of their names. The suffix "x" denotes that the variable is alpha-numeric. In the implementation of the Africa roll out 2011 an experiment was carried in some of the countries to better estimate the effects of the use of show cards in data collection. In some of the sections (i.e. innovation) the enumerators were trained to alternatively implement the section using either show cards or asking only the questions without showing any cards, please see the variable "cards".

Cleaning operations

Data entry and quality controls are implemented by the contractor and data is delivered to the World Bank in batches (typically 10%, 50% and 100%). These data deliveries are checked for logical consistency, out of range values, skip patterns, and duplicate entries. Problems are flagged by the World Bank and corrected by the implementing contractor through data checks, callbacks, and revisiting establishments.

Response rate

Survey non-response must be differentiated from item non-response. The former refers to refusals to participate in the survey altogether whereas the latter refers to the refusals to answer some specific questions. Enterprise Surveys suffer from both problems and different strategies were used to address these issues.

Item non-response was addressed by two strategies: a- For sensitive questions that may generate negative reactions from the respondent, such as corruption or tax evasion, enumerators were instructed to collect "Refusal to respond" (-8) as a different option from "Don't know" (-9). b- Establishments with incomplete information were re-contacted in order to complete this information, whenever necessary.

Survey non-response was addressed by maximizing efforts to contact establishments that were initially selected for interview. Attempts were made to contact the establishment for interview at different times/days of the week before a replacement establishment (with similar strata characteristics) was suggested for interview. Survey non-response did occur but substitutions were made in order to potentially achieve strata-specific goals.

The number of contacted establishments per realized interview was 0.41. This number is the result of two factors: explicit refusals to participate in the survey, as reflected by the rate of rejection (which includes rejections of the screener and the main survey) and the quality of the sample frame, as represented by the presence of ineligible units. The number of rejections per contact was 0.20.

Local businesses form the foundation of many economies. From independent service providers to growing regional companies, small and local businesses represent a large share of potential customers, partners, and opportunities.

The Local Business Database from Lead For Business provides structured information on local companies along with professional contact details connected to those businesses. The dataset helps organizations identify local companies, understand what they do, and connect with the professionals responsible for running them.

Rather than offering a simple list of businesses, the dataset combines business contact data with company information and location details. This allows teams to identify companies operating within specific areas and focus outreach on businesses that match their target market.

Organizations commonly use this dataset as a local business email list, a small business database, or a local business leads dataset when researching markets or building prospect lists.

Geographic Coverage

The dataset includes businesses operating across multiple cities, regions, and local markets.

Companies are associated with location information that allows users to identify businesses within specific geographic areas.

Typical geographic attributes included in the dataset may cover:

cities states or provinces regions postal areas

This location-based structure allows organizations to focus on companies operating within specific cities or local markets.

For example, a company expanding its services to a new city may use the dataset to identify businesses operating within that region and begin outreach to potential customers or partners.

Industry Representation

Local businesses operate across a wide range of industries, and the dataset reflects this diversity.

Common sectors represented include:

Professional services Retail businesses Healthcare and wellness Construction and home services Hospitality and tourism Education and training Marketing and advertising agencies Technology and IT services Automotive services Local service providers

Because business contacts are linked with company attributes, users can easily focus on specific industries or analyze businesses across sectors.

For example, a marketing agency may focus on retail and ecommerce businesses, while a technology provider may target professional services firms.

Data Fields Included

Each record combines business contact information with company and location details.

Business Contact Information

First Name Last Name Job Title Business Email Address LinkedIn Profile URL Company Name Company Domain Country City

Company Information

Company Name Website Domain Industry Employee Count Revenue Range Headquarters Location Company Description Founded Year

Location and Business Attributes

Company Size Business Category Geographic Region

These attributes help organizations understand both the company and the professionals working within it.

How Organizations Use This Dataset

Local business datasets are used by many types of organizations.

Sales Prospecting

Sales teams often target local businesses when introducing products or services. Access to business contacts allows them to reach companies within specific regions.

Local Marketing Campaigns

Marketing teams running regional campaigns may use the dataset to identify businesses operating within specific cities or markets.

Partnership Development

Companies seeking partnerships with local service providers or regional businesses can use the dataset to identify potential partners.

Market Research

Researchers may analyze business listings to understand how companies are distributed across local markets and industries.

Recruitment Research

Recruitment teams sometimes use company databases to identify small businesses operating in specific regions.

Because of these uses, the dataset is often searched as a business listing database, a local company database, or a small business contact list.

Data Sources and Organization

The dataset is compiled using information gathered from publicly available professional and business sources including:

company websites business directories professional profiles public business records

Collected information is structured and standardized so it can be easily used in CRM systems, analytics platforms, or internal prospecting tools.

This structured format helps ensure that businesses and contacts are represented consistently across the dataset.

Dataset Updates

Local business environments evolve as companies open, close, or change their operations.

To maintain relevance, the dataset is reviewed and refreshed periodically through update cycles that may include:

monthly updates quarterly dataset refreshes periodic record revisions

These updates help ensure that business listings and contact information remain useful over time.

Compliance

The dataset is maintained with attention to widely recognized data protection and marke...

Proportion of providers returning data to the further education vacancy data collection. Data provided for LA Providers with an Education remit, School based providers, Independent Training Providers (ITP) and Special Post-16 Institutions. These are sub groups of Private Sector Public Funded Providers and Other Public Funded Providers.

Forecast: Wages and Salaries of Collection Agencies and Credit Bureaus Services in France 2024 - 2028 Discover more data with ReportLinker!