LABOR MARKET ENGAGEMENT INDEXSummary

The labor market engagement index provides a summary description of the relative intensity of labor market engagement and human capital in a neighborhood. This is based upon the level of employment, labor force participation, and educational attainment in a census tract (i). Formally, the labor market index is a linear combination of three standardized vectors: unemployment rate (u), labor-force participation rate (l), and percent with a bachelor’s degree or higher (b), using the following formula:

Where means and standard errors are estimated over the national distribution. Also, the value for the standardized unemployment rate is multiplied by -1.

Interpretation

Values are percentile ranked nationally and range from 0 to 100. The higher the score, the higher the labor force participation and human capital in a neighborhood.

Data Source: American Community Survey, 2011-2015Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 9.

To learn more about the Labor Market Engagement Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

This RESTful API provides Australian Bureau of Statistics (ABS) labour force data such as employment statistics by region, sex, age groups, and labour utilisation using original, seasonally adjusted and trend markers since 1978.\r \r It connects to an existing ABS API and improves the usability of the information queried from ABS by transforming the SDMX formatted data into a JSON format. This allows developers to consume ABS data easily by using a standard format without requiring time-consuming reformatting and transformation of the data received.\r \r Version 1.0.0\r \r An API key will be issued if you wish to explore and understand the way this API operates.\r \r Access for this API is available via request through developer.vic.gov.au.

Success.ai’s Employee Data API empowers human resource teams, recruiters, and talent acquisition professionals to make data-driven decisions with instant access to over 700 million verified employee records worldwide. By providing accurate, continuously updated employee information, this API enables efficient recruitment, strategic workforce planning, and insightful HR analytics.

Whether you’re identifying top talent, benchmarking workforce skills, or optimizing organizational structures, Success.ai’s Employee Data API ensures you always have the most current and reliable data at your fingertips. Backed by our Best Price Guarantee, this solution is ideal for achieving sustainable growth, improving HR efficiencies, and maintaining a competitive edge in a constantly evolving global labor market.

Why Choose Success.ai’s Employee Data API?

1. Global Employee Insights at Scale

   • Access employee data from diverse regions, industries, and roles, enabling you to find the perfect candidates or assess new markets.
   • Effortlessly scale your HR strategies across borders, tapping into a wide talent pool no matter the region.

2. AI-Validated Accuracy

   • Benefit from 99% data accuracy through AI-driven validation, reducing the risk of erroneous outreach and missed opportunities.
   • Make confident decisions supported by current and reliable information, enhancing candidate quality and organizational outcomes.

3. Continuously Updated and Real-Time Data

   • Receive ongoing updates to maintain clean, relevant employee databases, ensuring you always work with the freshest data.
   • Quickly adapt to changing labor conditions, skill demands, and workforce shifts, seizing new opportunities as they arise.

4. Ethical and Compliant

   • Fully adheres to GDPR, CCPA, and other global data privacy regulations, ensuring responsible and lawful data usage for all HR activities.

Data Highlights:

• 700M+ Verified Global Employee Records: Engage with professionals across multiple industries, specialties, and experience levels.
• Role and Experience Data: Filter by job title, seniority, skills, and career trajectories to pinpoint ideal candidates.
• Real-Time Updates: Keep pace with evolving workforce trends, industry shifts, and organizational changes for proactive HR strategies.
• Best Price Guarantee: Access top-quality employee data at the most competitive prices, maximizing ROI on your HR initiatives.

Key Features of the Employee Data API:

1. On-Demand Data Enrichment

   • Instantly enhance your ATS, CRM, or HR systems with verified employee data, eliminating guesswork and manual updates.
   • Reduce administrative burden, allowing HR teams to focus on strategic, high-impact initiatives.

2. Advanced Filtering and Query Capabilities

   • Query the API to segment candidates or employees by geography, industry, skill set, or seniority.
   • Tailor talent acquisition campaigns, focusing on the most promising candidates aligned with your needs.

3. Real-Time Validation and Reliability

   • Rely on AI-driven validation processes to ensure data integrity and relevance.
   • Mitigate risks associated with outdated candidate information and strengthen trust in your hiring decisions.

4. Scalable and Flexible Integration

   • Seamlessly integrate the API into existing HR workflows, analytics platforms, or recruitment tools.
   • Adjust parameters as your workforce requirements evolve, ensuring continuous alignment with HR goals.

Strategic Use Cases:

1. Recruitment and Talent Acquisition

   • Source top-tier candidates rapidly by leveraging updated, verified employee data.
   • Enhance candidate pipelines and reduce time-to-fill for critical positions.

2. Workforce Planning and Succession Management

   • Identify skill gaps, map career pathways, and anticipate future labor demands with confidence.
   • Develop robust strategies that foster internal mobility, nurture high-potential employees, and support long-term growth.

3. Market Research and Competitive Analysis

   • Compare workforce compositions, skill distributions, and hiring trends across industries or regions.
   • Refine HR policies, compensation frameworks, and talent development programs based on market insights.

4. Diversity, Equity, and Inclusion (DEI) Initiatives

   • Evaluate workforce diversity profiles and track DEI goals over time.
   • Implement evidence-based interventions to create inclusive, high-performing teams.

Why Choose Success.ai?

1. Best Price Guarantee

   • Access premium-quality employee data at industry-leading prices, ensuring exceptional ROI on HR investments.

2. Seamless Integration

   • Easily incorporate the API into existing workflows, eliminating data silos and reducing manual data handling.

3. Data Accuracy with AI Validation

   • Rely on 99% accuracy to guide data-driven workforce decisions, re...

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 214M+ Job Postings Tracked – Data sourced from 92 company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Job Openings Docs https://docs.predictleads.com/v3/guide/job_openings_dataset

Els moviments registrats entre dos períodes pel que fa a la situació laboral dels treballadors aturats, ocupats o inactius.

This dataset provides comprehensive real-time data from Glassdoor. It includes detailed company information, employee reviews, salary data, interview data, and more for employers worldwide. The data covers company attributes like ratings, reviews, salaries, benefits, and workplace culture details. Users can leverage this dataset for employer research, job market analysis, and workplace intelligence. The API enables real-time access to Glassdoor's vast employer database and review data, helping businesses make data-driven decisions about recruitment, employer branding, and workplace culture. Whether you're conducting market analysis, tracking employer reputation, or building HR tools, this dataset provides current and reliable Glassdoor data. The dataset is delivered in a JSON format via REST API.

This dataset presents the number of individuals aged 15 years and above in the labor force, classified by major occupation groups. The values are reported in thousands and allow for comparison across time and occupational categories. This data supports labor market analysis, workforce planning, and policy evaluation.

This dataset provides the percentage distribution of the labor force aged 15 years and above in Qatar by employment status. Employment categories include wage-earning workers, employers, self-employed individuals, and unpaid family workers. The data offers insight into the composition of the workforce by employment type and highlights the dominance of wage employment in the labor market.

➡️ You can choose from multiple data formats, delivery frequency options, and delivery methods;

➡️ Extensive datasets with job postings data from 5 leading B2B data sources;

➡️ Jobs API designed for effortless search and enrichment (accessible using a user-friendly self-service tool);

➡️ Fresh data: daily updates, easy change tracking with dedicated data fields, and a constant flow of new data;

➡️ You get all necessary resources for evaluating our data: a free consultation, a data sample, or free credits for testing the API.

✅ For HR tech

Job posting data can provide insights into the demand for different types of jobs and skills, as well as trends in job postings over time. With access to historical data, companies can develop predictive models.

✅ For Investors

Explore expansion trends, analyze hiring practices, and predict company or industry growth rates, enabling the extraction of actionable strategic and operational insights. At a larger scale of analysis, Job Postings Data can be leveraged to forecast market trends and predict the growth of specific industries.

✅ For Lead generation

Coresignal’s Job Postings Data is ideal for lead generation and determining purchasing intent. In B2B sales, job postings can help identify the best time to approach a prospective client.

➡️ Why 400+ data-powered businesses choose Coresignal:

1. Experienced data provider (in the market since 2016);
2. Exceptional client service;
3. Responsible and secure data collection.

The Labour Force Survey (LFS) is the only survey conducted by Statistics Canada designed to provide the official unemployment rate every month, with a monthly sample size of approximately 56,000 households. It is the earliest and most timely indicator of the pulse of the labour market in Canada. Statistics Canada provides a Guide to the Labour Force Survey.Note: This dataset primarily focuses on employees: those who do paid work for others. Therefore, totals do not align to totals in Labour Force Characteristics dataset, which focuses on everyone in the labour force.DefinitionsEmployee - A person who does paid work for others.Work - Includes any work for pay or profit, that is, paid work in the context of an employer-employee relationship or self-employment. It also includes work performed by those working in family business without pay (unpaid family workers).Permanent - A permanent job is one that is expected to last as long as the employee wants it, business conditions permitting. That is, there is no predetermined termination date.Temporary - A temporary job has a predetermined end date, or will end as soon as a specified project is completed. Information is collected to allow the sub-classification of temporary jobs into four groups: seasonal; temporary, term or contract, including work done through a temporary help agency; casual job; and other temporary work.Employment - Employed persons are those who, during the reference week, did any work for pay or profit or had a job and were absent from work. Self-employment - Working owners of an incorporated business, farm or professional practice, or working owners of an unincorporated business, farm or professional practice. The latter group also includes self-employed workers who do not own a business (such as babysitters and newspaper carriers). Self-employed workers are further subdivided by those with or without paid help. Also included among the self-employed are unpaid family workers. They are persons who work without pay on a farm or in a business or professional practice owned and operated by another family member living in the same dwelling. They represented approximately 1% of the self-employed in 2016.Unemployment - Unemployed persons are those who, during reference week, were without work, were available for work and were either on temporary layoff, had looked for work in the past four weeks or had a job to start within the next four weeks.

The data contain a sample of 947,253 vacancies from HeadHunter database covering 2015-2021 period. Each row represents one vacancy. The columns descriptions are the following:

firm - the firm identifier that published a vacancy; occupation - the first mentioned occupation code in a vacancy according to HeadHunter classification (https://api.hh.ru/specializations); year_mon - year and month of vacancy published date; columns from ai to data - binary variables (skill groups) indicating the inclusion of specified skills into a particular skill group (according to Deming, D., & Noray, K. (2020). Earnings dynamics, changing job skills, and STEM careers. The Quarterly Journal of Economics, 135(4),1965–2005); wage - monthly wage suggested in a vacancy (in Rubles); region - the integer indicator of a vacancy posting region (Russian community zone) according to HeadHuter platform; firm_size - firm size 5-level categorical variable indicating the number of workers: "micro", 1–15 workers; "small", 16–100 workers; "medium", 101–250 workers; "large", 251–1,000 workers; "huge", more than 1,000 workers; industry - firm industry code according to Russian Classification of Economic Activities (OKVED); firm_ai - the measure of the AI skills demand (calculated based on Alekseeva, L., Azar, J., Gine, M., Samila, S., & Taska, B. (2021). The demand for AI skills in the labor market. Labour Economics, 71, 102002).

This dataset provides quarterly statistics on the number of economically inactive individuals aged 15 years and above in Qatar, disaggregated by nationality, gender, and level of education. It supports labor market planning and education-related policy-making.

This dataset presents the labor force participation rate (LFPR) for individuals aged 15 years and above in the State of Qatar, disaggregated by nationality (Qatari and Non-Qatari) and gender (males and females) from 2018 to 2023. The LFPR is expressed as a percentage and indicates the proportion of the working-age population that is economically active. The data is valuable for understanding employment patterns and informing labor market policy and planning.

Dataset Overview

The "AI and ML Job Listings USA" dataset provides a comprehensive collection of job postings in the field of Artificial Intelligence (AI) and Machine Learning (ML) across the United States. The dataset includes job listings from 2022 to 2024, capturing the evolving landscape of AI/ML job opportunities. This dataset is valuable for researchers, job seekers, and data scientists interested in understanding trends, demands, and opportunities in the AI/ML job market.

Data Science Applications

This dataset can be utilized for various data science applications, including: - Trend Analysis: Identifying trends in job titles, locations, and required skills over time. - Demand Forecasting: Predicting future demand for AI/ML roles based on historical data. - Skills Gap Analysis: Analyzing the skills and experience levels in demand versus the available workforce. - Geospatial Analysis: Mapping job opportunities across different regions in the USA. - Salary Prediction: Developing models to predict salaries based on job descriptions and other attributes. Some job descriptions include salary information, which can be identified by exploring the 'description' column for mentions of compensation, pay, or salary-related terms.

Column Descriptors

1. title: The job title (e.g., AI/ML Engineer).
2. location: The location of the job (e.g., New York, NY).
3. publishedAt: The date the job was published (e.g., 2024-05-29).
4. companyName: The name of the company offering the job (e.g., Wesper).
5. description: A detailed description of the job (e.g., responsibilities, qualifications, and sometimes salary information).
6. applicationsCount: The number of applications received (e.g., Over 200 applicants).
7. contractType: The type of contract (e.g., Full-time).
8. experienceLevel: The level of experience required (e.g., Mid-Senior level).
9. workType: The type of work (e.g., Engineering and Information Technology).
10. sector: The industry sector of the job (e.g., Internet Publishing).

Ethically Mined Data

This dataset has been ethically mined using an API, ensuring no private information has been revealed. Sensitive data, such as the recruiter name, has been removed to protect privacy and comply with ethical standards.

Acknowledgments

• LinkedIn: For providing the platform where these job listings were originally posted.
• DALL·E 3: For generating the thumbnail image used for this dataset.

This dataset provides a rich resource for analyzing and understanding the AI and ML job market in the USA, offering insights into job trends, requirements, and opportunities in this rapidly growing field.

This dataset presents the annual unemployment rate in Qatar for individuals aged 15 years and above, disaggregated by nationality (Qatari and non-Qatari) and gender (male and female). It includes specific breakdowns by group and overall totals by gender and combined population. The dataset helps monitor labor market conditions and supports workforce policy decisions.

This dataset presents the annual employment rate in Qatar disaggregated by nationality (Qatari and non-Qatari) and gender (male and female). The data includes separate breakdowns for each group as well as overall totals by gender and combined totals. It reflects the high employment rates in the labor force across different population segments and supports labor market and economic planning.

This dataset was obtained from the Google Jobs API through serpAPI and contains information about job offers for data scientists in companies based in the United States of America (USA). The data may include details such as job title, company name, location, job description, salary range, and other relevant information. The dataset is likely to be valuable for individuals seeking to understand the job market for data scientists in the USA and for companies looking to recruit data scientists. It may also be useful for researchers who are interested in exploring trends and patterns in the job market for data scientists. The data should be used with caution, as the API source may not cover all job offers in the USA and the information provided by the companies may not always be accurate or up-to-date.

License

CC0

Original Data Source: 2023 Data Scientists Jobs Descriptions

This layer shows children by age group by parents' labor force participation. This is shown by tract, county, and state boundaries. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the percent of children with no available (residential) parent in the labor force. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B23008 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

This layer shows children by age group by parents' labor force participation. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the count and percent of children with no available (residential) parent in the labor force. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B23008 Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.