The dataset provided by Tarta.ai, created in February 2023, contains information on the number of jobs by company and city in Massachusetts. The data provides a comprehensive view of the job market, highlighting the companies and cities that have the highest number of job opportunities.

The dataset includes a list of companies and the number of jobs they offer in different cities.

The dataset provides valuable insights for job seekers, employers, and policymakers. It can help job seekers to identify companies and cities with the highest job opportunities in their preferred industry and location. Employers can use the data to understand the competitive landscape and adjust their recruitment strategies accordingly. Policymakers can leverage the information to develop policies that promote job growth and economic development in different regions.

Overall, the Tarta.ai dataset is a valuable resource for anyone interested in the job market and provides a comprehensive view of the employment landscape across different industries and regions.

Dataset Columns:

1. Company name
2. City
3. State
4. Number of active jobs

LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

Use Cases: What This LinkedIn Job Postings Data Solves

Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

What Makes This LinkedIn Job Postings Data Unique

AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

Who Uses Canaria's LinkedIn Data

HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

Summary

Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligen...

Unlock the full potential of LinkedIn data with our extensive dataset that combines profiles, company information, and job listings into one powerful resource for business decision-making, strategic hiring, competitive analysis, and market trend insights. This all-encompassing dataset is ideal for professionals, recruiters, analysts, and marketers aiming to enhance their strategies and operations across various business functions. Dataset Features

Profiles: Dive into detailed public profiles featuring names, titles, positions, experience, education, skills, and more. Utilize this data for talent sourcing, lead generation, and investment signaling, with a refresh rate ensuring up to 30 million records per month. Companies: Access comprehensive company data including ID, country, industry, size, number of followers, website details, subsidiaries, and posts. Tailored subsets by industry or region provide invaluable insights for CRM enrichment, competitive intelligence, and understanding the startup ecosystem, updated monthly with up to 40 million records. Job Listings: Explore current job opportunities detailed with job titles, company names, locations, and employment specifics such as seniority levels and employment functions. This dataset includes direct application links and real-time application numbers, serving as a crucial tool for job seekers and analysts looking to understand industry trends and the job market dynamics.

Customizable Subsets for Specific Needs Our LinkedIn dataset offers the flexibility to tailor the dataset according to your specific business requirements. Whether you need comprehensive insights across all data points or are focused on specific segments like job listings, company profiles, or individual professional details, we can customize the dataset to match your needs. This modular approach ensures that you get only the data that is most relevant to your objectives, maximizing efficiency and relevance in your strategic applications. Popular Use Cases

Strategic Hiring and Recruiting: Track talent movement, identify growth opportunities, and enhance your recruiting efforts with targeted data. Market Analysis and Competitive Intelligence: Gain a competitive edge by analyzing company growth, industry trends, and strategic opportunities. Lead Generation and CRM Enrichment: Enrich your database with up-to-date company and professional data for targeted marketing and sales strategies. Job Market Insights and Trends: Leverage detailed job listings for a nuanced understanding of employment trends and opportunities, facilitating effective job matching and market analysis. AI-Driven Predictive Analytics: Utilize AI algorithms to analyze large datasets for predicting industry shifts, optimizing business operations, and enhancing decision-making processes based on actionable data insights.

Whether you are mapping out competitive landscapes, sourcing new talent, or analyzing job market trends, our LinkedIn dataset provides the tools you need to succeed. Customize your access to fit specific needs, ensuring that you have the most relevant and timely data at your fingertips.

Xverum’s Global Job Market & Job Postings Data offers one of the largest datasets available, featuring 280B+ records updated daily. Covering 13M+ daily job postings, employee insights, and recruiting trends, our dataset provides a comprehensive view of global labor market dynamics. Designed to empower workforce analytics, talent acquisition, economic forecasting, and AI & ML model training, it’s an essential resource for data-driven decision-making.

Key Features:

1️⃣ Extensive Job Postings Data: Access 13M+ job postings daily from multiple industries and geographies. Detailed attributes include job titles, descriptions, locations, industries, and application requirements.

2️⃣ Real-Time Updates: Data refreshed daily ensures relevance and accuracy for live applications.

3️⃣ Global Coverage: One of the most extensive datasets available, with hiring activity tracked in every country worldwide.

4️⃣ GDPR-Compliant and Secure: Fully compliant with GDPR and CCPA regulations, ensuring ethical and safe data usage.

Primary Use Cases:

✳️ Workforce Analytics: Monitor job demand and labor market trends for strategic workforce planning.

✳️ Talent Acquisition and Recruiting: Analyze hiring activity to identify recruiting trends and optimize talent strategies.

✳️ Economic Forecasting: Use job postings data as an economic indicator to track industry growth and market opportunities.

✳️ Market Research: Gain insights into hiring activity across industries and regions to understand market dynamics.

✳️ Competitive Intelligence: Track competitor hiring patterns and job postings to benchmark market positioning.

✳️ AI/ML Model Training: Train predictive models for job matching, labor trend forecasting, and workforce optimization.

Why Choose Xverum’s Job Market Data? ✅ Massive Scale: 13M+ daily job postings and 280B+ records ensure unparalleled depth and global reach. ✅ Real-Time Updates: Daily refreshes ensure the latest job data for actionable insights. ✅ Comprehensive Coverage: Spanning industries, and geographies worldwide. ✅ GDPR-Compliant: Secure and ethically sourced data for peace of mind.

Key Data Attributes: 📎 Job title, description, and location. 📎 Industry classification and hiring organization. 📎 Posting date, application deadline, and employment type (e.g., full-time, remote).

Request a sample dataset today or contact us to tailor your job market data solution. Empower your business with Xverum’s Job Market & Job Postings Data for smarter, data-driven decision-making.

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

Techsalerator’s Job Openings Data in Oceania provides a detailed and comprehensive dataset designed to offer businesses, recruiters, labor market analysts, and job seekers a clear view of employment opportunities across the Oceania region. This dataset aggregates job postings from a diverse range of sources on a daily basis, ensuring users have access to the most current and extensive collection of job openings available in Oceania.

Key Features of the Dataset: Broad Coverage:

The dataset consolidates job postings from a variety of sources including company career pages, job boards, recruitment agencies, and professional networking platforms. This extensive coverage ensures that users receive a wide array of job opportunities from multiple channels. Daily Updates:

Job posting data is updated daily, providing users with real-time insights into the job market. This frequent updating ensures that the information is current and reflects the latest job openings and market conditions. Sector-Specific Data:

Job postings are categorized by industry sectors such as technology, healthcare, finance, education, hospitality, and more. This segmentation allows users to analyze trends and opportunities within specific sectors. Regional Breakdown:

The dataset includes detailed information on job openings across various countries and territories within Oceania. This regional breakdown helps users understand job market dynamics and opportunities in different geographic locations. Role and Skill Insights:

The dataset provides information on job roles, required skills, qualifications, and experience levels. This feature helps job seekers find opportunities that match their expertise and aids recruiters in identifying candidates with the desired skill sets. Company Information:

Users can access details about the companies posting job openings, including company names, industries, and locations. This data is useful for understanding which companies are hiring and where the demand for talent is high. Historical Data:

The dataset may include historical job posting data, enabling users to analyze trends and changes in the job market over time. This feature supports trend analysis and longitudinal studies. Oceania Countries and Territories Covered: Countries: Australia Fiji Kiribati Marshall Islands Micronesia (Federated States of) Nauru New Zealand Palau Papua New Guinea Samoa Solomon Islands Tonga Tuvalu Vanuatu Territories: American Samoa (U.S. territory) French Polynesia (French overseas collectivity) Guam (U.S. territory) New Caledonia (French special collectivity) Northern Mariana Islands (U.S. territory) Wallis and Futuna (French overseas collectivity) Benefits of the Dataset: Effective Recruitment: Recruiters and HR professionals can use the dataset to identify hiring trends, understand competitive hiring practices, and refine recruitment strategies based on real-time market insights. Labor Market Analysis: Analysts and policymakers can leverage the dataset to study employment trends, identify skill gaps, and evaluate job market opportunities across different regions and sectors. Job Seeker Support: Job seekers can access a comprehensive and updated list of job openings tailored to their skills and preferred locations, improving the efficiency and effectiveness of their job search. Workforce Planning: Companies can gain valuable insights into the availability of talent in various countries and territories, assisting with strategic decisions related to market expansion and talent acquisition. Techsalerator’s Job Openings Data in Oceania is a crucial resource for understanding the diverse job markets across the region. By providing up-to-date and detailed information on job postings, it supports informed decision-making for businesses, job seekers, and labor market analysts.

Revolutionize Your Business Strategy with Cutting-Edge Job Listing Dataset

Gain a competitive edge and stay ahead of the curve with our comprehensive job listing datasets, providing real-time insights into the dynamic job market landscape and competitor hiring activity.

Leveraging information collected from over 13 million job ads daily, our job postings data offer a goldmine of business-critical data types, empowering you to make data-driven decisions and unlock lucrative opportunities for growth and optimization.

No-Integration needed!

Leverage our industry-standard, user-friendly CSV formats to seamlessly integrate our datasets with machine learning, artificial intelligence training, and similar applications. The swift and reliable historical job data retrieval process facilitated by our robust, easy-to-implement integration ensures that your access to critical insights is always just a few clicks away.

Market research with daily job postings

In today's fast-moving business world, it's really important to have access to the latest job information to make smart decisions. Our daily updates on over 13M job posts provide all the important details about job trends and company activities. This can help you find new business opportunities and figure out what skills are in high demand for different jobs. Our data is updated regularly and goes back in time, so you can see how things have changed.

Created with a 500 meter side hexagon grid, we undertook a regression analysis creating a correlation matrix utilising a number of demographic indicators from the Local Insight OCSI platform. This dataset is showing the distribution of metrics that were found to have the strongest relationships, with the base comparison metric of At risk employees (as a result of COVID-19) by employee residence. This dataset contains the following metrics:At risk employees (as a result of COVID-19) by employee residence - Shows the proportion of employees that are at risk of losing their jobs following the outbreak of COVID-19 - calculated based on the latest furloughing data from the ONS and the employee profile for each local authority. The data is derived from Wave 2 of the ONS Business Impact of Coronavirus Survey (BICS) which contains data on the furloughing of workers across UK businesses between March 23 to April 5, 2020 see https://www.ons.gov.uk/generator?uri=/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/furloughingofworkersacrossukbusinesses/23march2020to5april2020/574ca854&format=csv for details. This data includes responses from businesses that were either still trading or had temporarily paused trading. This has been mapped against the industrial composition of employee jobs at OA, LSOA, MSOA and Local Authority level to estimate which are most exposed to labour market risks associated with the Covid-19. The industrial composition of employee jobs is based on the employee place of residence rather than where they work. The data on the industrial composition of local areas comes from the 2011 Census Industrial classification, which is publicly accessible via NOMIS. The methodology is adapted from the RSA at-risk Local Authorities publication - https://www.thersa.org/about-us/media/2020/one-in-three-jobs-in-parts-of-britain-at-risk-due-to-covid-19-local-data-reveals This approach calculates the total number of employees at risk in each local area by identifying the number of employees in each industry in that area (based on employee residence) multiplied by the estimated percentage of those that have been furloughed on the Government's Coronavirus Job Retention Scheme (CJRS). The CRJS was set up by the Government specifically to prevent growing unemployment and the National Institute for Economic and Social Research (NIESR) has described furloughed workers as technically unemployed. It therefore looks to be the best available data with which to calculate medium-term employment risk as a result of Covid-19. This is then divided by the total number of employees in each local area (by place of residence) to calculate the percentage of employees at risk of losing their jobs. Note, employees in industry sectors which were not recorded in the ONS Business Impact of Coronavirus Survey (BICS) due to inadequate sample size have not been included in the numerator or denominator for this dataset - these include Agriculture, forestry and fishing, Mining and quarrying, Electricity, gas, steam and air conditioning supply, Financial and insurance activities, Real estate activities. Public administration and defence; compulsory social security and activities of households as employers; undifferentiated goods - and services - producing activities of households for own use. Social grade (N-SEC): 2. Lower managerial, administrative and professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 2. Lower managerial, administrative and professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Lower managerial, administrative and professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.IoD 2019 Education, Skills and Training Rank - The Indices of Deprivation (IoD) 2019 Education Skills and Training Domain measures the lack of attainment and skills in the local population. The indicators fall into two sub-domains: one relating to children and young people and one relating to adult skills. These two sub-domains are designed to reflect the 'flow' and 'stock' of educational disadvantage within an area respectively. That is the 'children and young people' sub-domain measures the attainment of qualifications and associated measures ('flow') while the 'skills' sub-domain measures the lack of qualifications in the resident working age adult population ('stock'). Children and Young People sub-domain includes: Key stage 2 attainment: The average points score/scaled score of pupils taking reading writing and mathematics Key stage 2 exams; Key stage 4 attainment: The average capped points score of pupils taking Key stage 4; Secondary school absence: The proportion of authorised and unauthorised absences from secondary school; Staying on in education post 16: The proportion of young people not staying on in school or non-advanced education above age 16 and Entry to higher education: The proportion of young people aged under 21 not entering higher education. The Adult Skills sub-domain includes: Adult skills: The proportion of working age adults with no or low qualifications women aged 25 to 59 and men aged 25 to 64; English language proficiency: The proportion of working age adults who cannot speak English or cannot speak English well women aged 25 to 59 and men aged 25 to 64. Data shows Average LSOA Rank, a lower rank indicates that an area is experiencing high levels of deprivation.Social grade (N-SEC): 1 Higher managerial, administrative and professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 1 Higher managerial, administrative and professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Higher managerial, administrative and professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Total annual household income estimate - Shows the average total annual household income estimate (unequivalised). These figures are model-based estimates, taking the regional figures from the Family Resources Survey and modelling down to neighbourhood level based on characteristics of the neighbourhood obtained from census and administrative statistics.Household is not deprived in any dimension - Shows households which are not deprived on any of the four Census 2011 deprivation dimensions. The Census 2011 has four deprivation dimension characteristics: a) Employment: Any member of the household aged 16-74 who is not a full-time student is either unemployed or permanently sick; b) Education: No member of the household aged 16 to pensionable age has at least 5 GCSEs (grade A-C) or equivalent AND no member of the household aged 16-18 is in full-time education c) Health and disability: Any member of the household has general health 'not good' in the year before Census or has a limiting long term illness d) Housing: The household's accommodation is either overcrowded; OR is in a shared dwelling OR does not have sole use of bath/shower and toilet OR has no central heating. These figures are taken from responses to various questions in census 2011. Rate calculated as = (Household is not deprived in any dimension (census QS119))/(All households (census QS119))*100.Occupation group: Professional occupations - Shows the proportion of people in employment (aged 16-74) working in the Occupation group: Professional occupations. An individual's occupation group is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Professional occupations (census KS608))/(All usual residents aged 16 to 74 in employment the week before the census (census KS608))*100.Social grade (N-SEC): 1.2 Higher professional occupations - Shows the proportion of people in employment (aged 16-74) in the Approximated Social grade (N-SEC) category: 1.2 Higher professional occupations. An individual's approximated social grade is determined by their response to the occupation questions in the 2011 Census. Rate calculated as = (Higher professional occupations (census KS611))/(All usual residents aged 16 to 74 (census KS611))*100.Sport England Market Segmentation: Competitive Male Urbanites - proportion of people living in the area that are classified as Competitive Male Urbanites in the Sports Market Segmentation.Net annual household income estimate after housing costs - Shows the average annual household income estimate (equivalised to take into account variations in household size) after housing costs are taken into account. These figures are model-based estimates, taking the regional figures from the Family Resources Survey and modelling down to neighbourhood level based on characteristics of the neighbourhood obtained from census and administrative statistics.

Enhance your workforce insights with comprehensive Employee Dataset, designed to help businesses improve recruitment strategies, track employment trends, and optimize workforce planning. This dataset provides structured and reliable employee data for HR professionals, recruiters, and analysts.

Dataset Features

Employee Profiles: Access detailed public employee data, including names, job titles, industries, locations, experience, and skills. Ideal for talent acquisition, workforce analytics, and competitive hiring strategies. Company Employment Data: Gain insights into company workforce distribution, employee tenure, hiring trends, and organizational structures. Useful for market research, HR benchmarking, and business intelligence. Job Listings & Open Positions: Track job postings, employment trends, and hiring patterns across industries. This data includes job titles, company names, locations, salary ranges, and job descriptions.

Customizable Subsets for Specific Needs Our Employee Dataset is fully customizable, allowing you to filter data based on industry, location, job role, or company size. Whether you need a broad dataset for market analysis or a focused subset for recruitment purposes, we tailor the dataset to your specific needs.

Popular Use Cases

Recruitment & Talent Sourcing: Identify top talent, analyze hiring trends, and enhance recruitment strategies with up-to-date employee data. HR Analytics & Workforce Planning: Optimize workforce management by tracking employee movement, industry hiring patterns, and job market trends. Competitive Intelligence: Monitor hiring activity, employee retention rates, and workforce distribution to gain insights into competitors’ strategies. Market Research & Business Expansion: Analyze employment trends to identify growth opportunities, emerging job markets, and industry shifts. AI & Predictive Analytics: Leverage structured employee data for AI-driven workforce predictions, job market forecasting, and HR automation.

Whether you're looking to improve recruitment, analyze workforce trends, or gain competitive insights, our Employee Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

The evaluation of the Transport Project and Farm to Market Roads Activity aimed to answer whether or not improved conditions throughout the road network: • Lowered transport costs and travel time for businesses, including farm households; • Provided better access to a wider range of job opportunities for individuals (labor market effects); • Lowered the price of consumables and inputs by increasing competition and reducing barriers to entry posed by poor transport infrastructure; and • Improved access to health establishments and schools

The overall expected result of these changes was an increase in overall incomes and employment at the household level. To comprehensively evaluate the impact of the MCA Honduras Transportation project, the Independent Evaluator used two methods: (i) a model-based approach, in which the treatment effect is represented by change in travel time, and the program impact is represented as a function of change in travel time. The model relies heavily on Geographic Information Systems (GIS) data for several purposes, including the estimation of changes in travel time; and (ii) HDM-IV analysis.

This project will assess the equality impact of the recent recession on the labour utilisation and position of women and men in the labour market, and on the employment of lone parents. These impacts will be considered against the backcloth of longer term demographic and policy developments leading up to and during the recent economic downturn. An important question to be considered is whether as a result of surplus labour, increased labour market competition, and intensified business conditions, recession acts to heighten the employment penalties experienced by women. This could occur through increased sex discrimination, or fewer efforts by employers to apply equality and diversity policy as a means of recruiting and retaining staff. To explore this question we will use recent innovations in statistical matching techniques to form comparison groups of men matched to women to explore whether women and men who are comparable in terms of their individual characteristics differ in their labour market outcomes. Secondary analysis. Analysis of trends in unemployment, economic activity and time related underemployment by NUTS 2 geographical level, comparing trends in Northern England counties against National and regional trends. The data was used to produce data tables for part of an appraisal of current modelling strategies used by local governments for labour market projections, which require re-evaluation in the context of the recent economic crisis.

💾 The data

The data comes from Datacamp competition. Data collected by a survey hosted by an HR consultancy, available in 'salaries.csv'.

Each row represents a single employee's salary record for a given year:

• work_year - The year the salary was paid.
• experience_level - Employee experience level:
  • EN: Entry-level / Junior
  • MI: Mid-level / Intermediate
  • SE: Senior / Expert
  • EX: Executive / Director
• employment_type - Employment type:
  • PT: Part-time
  • FT: Full-time
  • CT: Contract
  • FL: Freelance
• job_title - The job title during the year.
• salary - Gross salary paid (in local currency).
• salary_currency - Salary currency (ISO 4217 code).
• salary_in_usd - Salary converted to USD using average yearly FX rate.
• employee_residence - Employee's primary country of residence (ISO 3166 code).
• remote_ratio - Percentage of remote work:
  • 0: No remote work (<20%)
  • 50: Hybrid (50%)
  • 100: Fully remote (>80%)
• company_location - Employer's main office location (ISO 3166 code).
• company_size - Company size:
  • S: Small (<50 employees)
  • M: Medium (50–250 employees)
  • L: Large (>250 employees)

While the firm-level distributional consequences of market liberalization are well understood, previous studies have paid only limited attention to how variations in domestic institutions across countries affect the winners and losers from opening up to trade. We argue that the presence of coordinated wage bargaining institutions, which impose a ceiling on wage increases, and state-subsidized vocational training, which creates a large supply of highly skilled workers, generate labor market frictions. Upward wage rigidity, in particular, helps smaller firms weather the rising competition and increasing labor costs triggered by trade liberalization. We test this hypothesis using a firm-level dataset of European Union countries, which includes more than 800,000 manufacturing firms between 2003 and 2014. We find that, for productive firms, gains from trade are 20 percent larger in countries with liberal market economies than they are in coordinated market economies. Symmetrically, less productive firms in coordinated market economies experience significantly lower revenue losses compared to liberal market economies. We show that both the presence of an institutionalized wage ceiling and the availability of subsidized vocational training are key mechanisms for reducing the reallocation of revenue from unproductive to productive firms in coordinated market economies compared to liberal market economies. In line with our theory, we find that wages and employment in liberalized industries increase differentially across both types of labor markets. Finally, we provide suggestive evidence that trade liberalization triggers a differential demand for redistribution at the individual level across different labor markets, which is in line with our firm-level analysis.

Slovakia SK: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data was reported at 109.006 2015=100 in 2025. This records a decrease from the previous number of 110.181 2015=100 for 2024. Slovakia SK: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data is updated yearly, averaging 97.347 2015=100 from Dec 1993 (Median) to 2025, with 33 observations. The data reached an all-time high of 110.181 2015=100 in 2024 and a record low of 64.260 2015=100 in 1994. Slovakia SK: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Slovakia – Table SK.OECD.EO: Trade Statistics: Competitiveness Indicators In International Trade: Forecast: OECD Member: Annual. ULCDR - Indicator of competitiveness based on relative unit labour costs in total economyCompetitiveness-weighted relative unit labour costs for the overall economy in dollar terms. Competitiveness weights take into account the structure of competition in both export and import markets of the goods sector of 53 countries. An increase in the index indicates a real effective appreciation and a corresponding deterioration of the competitive position. Index, OECD reference year OECD calculation, see OECD Economic Outlook database documentation

Through questionnaire to employees in the automotive industry The measuring instrument includes 23 items and is structured in five sections. The first one, consisting of four items, deals with items on the employee’s general information. The second section includes a) freedom of association or right to bargain, b) access to social or job security, and c) development of intra- and inter-industry trade. Section 3 covers: a) employees’ personality and attitude, b) skilled manpower, c) unskilled manpower, and d) innovation in productive processes/technology introduction. Section 4 considers a) export support programs, b) subsidies and financial support, c) political and trade reforms (FDI law), and d) economic model (privatizations, industry deregulation, and FDI).Section 5 is composed by: a) annual average of sales growth in the last two years, b) sales percentage related to new products in the past two years, and c) market growth percentage regarding competition in the past two years.

Japan JP: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data was reported at 81.352 2015=100 in 2025. This records an increase from the previous number of 81.258 2015=100 for 2024. Japan JP: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data is updated yearly, averaging 120.533 2015=100 from Dec 1970 (Median) to 2025, with 56 observations. The data reached an all-time high of 207.765 2015=100 in 1995 and a record low of 72.004 2015=100 in 1970. Japan JP: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Japan – Table JP.OECD.EO: Trade Statistics: Competitiveness Indicators In International Trade: Forecast: OECD Member: Annual. ULCDR - Indicator of competitiveness based on relative unit labour costs in total economyCompetitiveness-weighted relative unit labour costs for the overall economy in dollar terms. Competitiveness weights take into account the structure of competition in both export and import markets of the goods sector of 53 countries. An increase in the index indicates a real effective appreciation and a corresponding deterioration of the competitive position. Index, OECD reference year OECD calculation, see OECD Economic Outlook database documentation

Argentina AR: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data was reported at 5.307 2015=100 in 2025. This records an increase from the previous number of 4.650 2015=100 for 2024. Argentina AR: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data is updated yearly, averaging 182.705 2015=100 from Dec 1970 (Median) to 2025, with 56 observations. The data reached an all-time high of 1,588.545 2015=100 in 1974 and a record low of 4.650 2015=100 in 2024. Argentina AR: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Argentina – Table AR.OECD.EO: Trade Statistics: Competitiveness Indicators In International Trade: Forecast: Non OECD Member: Annual. ULCDR - Indicator of competitiveness based on relative unit labour costs in total economyCompetitiveness-weighted relative unit labour costs for the overall economy in dollar terms. Competitiveness weights take into account the structure of competition in both export and import markets of the goods sector of 53 countries. An increase in the index indicates a real effective appreciation and a corresponding deterioration of the competitive position. Index, OECD reference year OECD calculation, see OECD Economic Outlook database documentation

Between 1985 and 2014, the number of US doctoral graduates in Anthropology increased from about 350 to 530 graduates per year. This rise in doctorates entering the work force along with an overall decrease in the numbers of tenure-track academic positions has resulted in highly competitive academic job market. We estimate that approximately79% of US anthropology doctorates do not obtain tenure-track positions at BA/BS, MA/MS, and PhD institutions in the US. Here, we examine where US anthropology faculty obtained their degrees and where they ultimately end up teaching as tenure-track faculty. Using data derived from the 2014–2015 AnthroGuide and anthropology departmental web pages, we identify and rank PhD programs in terms of numbers of graduates who have obtained tenure-track academic jobs; examine long-term and ongoing trends in the programs producing doctorates for the discipline as a whole, as well as for the subfields of archaeology, bioanthropology, and sociocultural anthropology; and discuss gender inequity in academic anthropology within the US.

Managers’ attitude towards the changes of the general economic conditions. Topics: Area of responsibility of the interviewees; company size: branch; number of business partner countries within the European Union: percentage of the turnover generated in those EU countries; influence of measures taken by the EU to create a uniform European domestic market on one’s own company in terms of product standards, packaging standards, order placement procedure, elimination of customs documents as well as custom controls, value-added tax, liberalisation of capital flows, regulations for the settlement of a company in the European Union; assessment of the influence of the European domestic market of one’s own company in terms of company strategies regarding: pricing, purchases and sales as well as deliveries from all as well as to all EU countries, services in EU countries, investments from other EU countries in one’s own company, cooperation with companies from EU countries, marketing strategies; increase in competition through domestic and foreign companies; employment of employees from member states; most important reason for not employing somebody; assessment of the influence of the EU expansion in 2004 on: the price of raw goods, pay-rate, access to new markets, retail prices, productivity, increase in employment; business contact with old EU members; importance of the future domestic market activities for one’s own company: removal of formal obstacles in trading goods and services, patent applications, stronger protection of intellectual property, further opening of the supply market, application of an integrated European financial market, safeguarding of fair competition in supply services such as telecommunications, transport and postal as well as energy supply, standardisation of regulations for business activities within the domestic market, simplification of work mobility; main reasons for the company not exporting to other EU countries. Einstellung von Managern zu den Veränderungen der wirtschaftlichen Rahmenbedingungen. Themen: Verantwortungsbereich des Befragten; Unternehmensgröße; Branche; Anzahl der Handelspartnerländer innerhalb der Länder der Europäischen Union; Prozentanteil des Umsatzes, der in diesen EU-Ländern erwirtschaftet wurde; Einfluss von Maßnahmen der EU zur Schaffung eines einheitlichen europäischen Binnenmarkts auf die eigene Firma hinsichtlich Produktstandards, Auszeichnungs- und Verpackungsstandards, Auftragsvergabeverfahren, Abschaffung von Zollpapieren sowie Grenzkontrollen, Umsatzsteuer, Liberalisierung von Kapitalströmen, Regelungen für die Ansiedlung eines Unternehmens in EU-Ländern; Einschätzung des Einflusses des europäischen Binnenmarkts auf die Firmenstrategien hinsichtlich: Preisgestaltung, An- und Verkäufen sowie Lieferung aus anderen EU-Ländern bzw. an andere EU-Länder, Dienstleistungen in EU-Ländern, Investitionen in andere EU-Ländern, Investitionen aus EU-Ländern in die eigene Firma, Kooperation mit Unternehmen aus EU-Ländern, Marketing-Strategien; Anstieg der Konkurrenz durch inländische bzw. ausländische Unternehmen; Anstellung von Mitarbeitern aus Mitgliedsstaaten; wichtigste Gründe für eine Nichtanstellung; Einschätzung des Einflusses der EU-Erweiterung 2004 auf: den Preis von Rohstoffen, die Höhe der Löhne, den Zugang zu neuen Märkten, Verkaufspreise, Produktivität, Wirtschaftlichkeit, Beschäftigungswachstum; Handelskontakte mit alten EU-Mitgliedern; Wichtigkeit von zukünftigen Binnenmarkt-Aktivitäten für die eigene Firma: Entfernen formaler Hindernisse beim Handel von Gütern und Dienstleistungen, Patentanmeldungen, stärkerer Schutz geistigen Eigentums, weitere Öffnung des Beschaffungsmarktes, Umsetzung eines integrierten europäischen Finanzmarktes, Sicherstellung von fairem Wettbewerb innerhalb des europäischen Binnenmarktes, verstärkter Wettbewerb bei Versorgungsdienstleistungen wie Telekommunikations-, Transport- und Postdienstleistungen sowie Energieversorgung, Vereinheitlichung von Vorschriften bei Geschäftstätigkeiten innerhalb des Binnenmarktes, Vereinfachung von Arbeitsmobilität; Hauptgründe der Firma nicht in andere EU-Länder zu exportieren.

Lithuania LT: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data was reported at 150.794 2015=100 in Dec 2025. This records an increase from the previous number of 150.479 2015=100 for Sep 2025. Lithuania LT: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data is updated quarterly, averaging 100.238 2015=100 from Mar 1997 (Median) to Dec 2025, with 116 observations. The data reached an all-time high of 150.794 2015=100 in Dec 2025 and a record low of 63.589 2015=100 in Mar 1997. Lithuania LT: Competitiveness Indicator: Relative Unit Labour Costs: Overall Economy data remains active status in CEIC and is reported by Organisation for Economic Co-operation and Development. The data is categorized under Global Database’s Lithuania – Table LT.OECD.EO: Trade Statistics: Competitiveness Indicators In International Trade: Forecast: OECD Member: Quarterly. ULCDR - Indicator of competitiveness based on relative unit labour costs in total economyCompetitiveness-weighted relative unit labour costs for the overall economy in dollar terms. Competitiveness weights take into account the structure of competition in both export and import markets of the goods sector of 53 countries. An increase in the index indicates a real effective appreciation and a corresponding deterioration of the competitive position. Index, OECD reference year OECD calculation, see OECD Economic Outlook database documentation