CC0 1.0 Universal Public Domain Dedicationhttps://creativecommons.org/publicdomain/zero/1.0/
License information was derived automatically
Dataset Overview The dataset consists of 26,000 job listings, extracted from a Taiwanese job search platform, focusing on software-related careers. Each listing is detailed with various attributes, providing a comprehensive view of the job market in this sector. Here's a breakdown of the dataset columns:
職缺類別 (Job Category) 職位類別 (Position Category) 職位 (Position) 縣市 (City/County) 地區 (District/Area) 供需人數 (應徵人數) (Number of Applicants) 公司名稱 (Company Name) 職缺名稱 (Job Title) 工作內容 (Job Description) 職務類別 (Job Type) 工作待遇 (Salary) 工作性質 (Nature of Work) 上班地點 (Work Location) 管理責任 (Management Responsibility) 上班時段 (Working Hours) 需求人數 (Number of Positions) 工作經歷 (Work Experience) 學歷要求 (Educational Requirements) 科系要求 (Departmental Requirements) 擅長工具 (Tools Proficiency) 工作技能 (Job Skills) 其他條件 (Other Conditions) 資本額 (Capital Amount) 員工人數 (Number of Employees) 公司標籤 (Company Tags) Analytical Insights Exploratory Data Analysis Perform exploratory data analysis using libraries like Pandas and NumPy. Examine trends in job categories, salaries, and educational requirements. Analyze the distribution of jobs across different cities and districts. Visualization Create visual representations of the dataset using Python visualization libraries. Plot job distribution across various sectors or locations. Visualize salary ranges and compare them with educational and experience requirements. Practice with SQL or Pandas Queries Utilize the dataset to refine SQL query skills or Pandas data manipulation techniques. Execute queries to extract specific information, such as the most in-demand skills or the companies offering the highest salaries. NLP Analysis and Tasks for Software Jobs Dataset This dataset, encompassing 26,000 job listings from the Taiwanese software industry, is ripe for a variety of Natural Language Processing (NLP) analyses. Below are some recommended NLP tasks and analyses that can be conducted on this dataset.
Text Classification Job Category Prediction: Train a classification model to predict the job category (職缺類別) using job descriptions (工作內容). Salary Range Classification: Classify jobs into different salary brackets based on their descriptions and titles, helping to identify features associated with higher salaries. Sentiment Analysis Company Reputation Analysis: Analyze the sentiment of company tags (公司標籤) to assess the general sentiment or reputation of companies listed in the dataset. Topic Modeling Identifying Key Job Requirements: Apply LDA (Latent Dirichlet Allocation) to job descriptions for uncovering common themes or required skills in the software sector. Named Entity Recognition (NER) Information Extraction: Implement NER to extract specific entities like tools (擅長工具), skills (工作技能), and educational qualifications (學歷要求) from job descriptions. Text Summarization Summarizing Job Descriptions: Develop algorithms for generating concise summaries of job descriptions, enabling quick understanding of key points. Language Modeling Job Description Generation: Use language models to create realistic job descriptions based on input prompts, assisting in job listing creation or understanding industry language trends. Machine Translation (If Applicable) Dataset Translation for Global Accessibility: Translate the dataset content into English or other languages for international accessibility, using machine translation models. Predictive Analysis Predicting Applicant Volume: Use historical data to forecast the number of applicants (供需人數 (應徵人數)) a job listing might attract based on various factors. By leveraging these NLP techniques, insightful findings can be extracted from the dataset, beneficial for both job seekers and employers in the software field. This dataset offers a practical opportunity to apply NLP skills in a real-world setting.
CC0
Original Data Source: Taiwan 104.com jobs search JD
Apache License, v2.0https://www.apache.org/licenses/LICENSE-2.0
License information was derived automatically
Overview This dataset provides insights into salary distributions across various job classifications, enabling a deeper understanding of compensation trends across industries, experience levels, and geographical locations. It serves as a valuable resource for HR professionals, job seekers, researchers, and policymakers aiming to analyze pay scales, wage gaps, and salary progression trends.
Data Sources The data is aggregated from multiple employment and compensation reports, salary surveys, and publicly available job postings. It has been cleaned, standardized, and structured to ensure consistency and usability for analytical purposes.
Features Job Title: Specific title of the job (e.g., Data Analyst, Software Engineer, Marketing Manager).
Job Classification: Broad category of jobs (e.g., IT, Finance, Healthcare, Education).
Industry: The sector in which the job belongs (e.g., Technology, Banking, Retail).
Experience Level: Categorized as Entry-level, Mid-level, or Senior-level.
Education Requirement: Minimum qualification required for the job role.
Average Salary (INR/USD/Other Currency): The median or mean salary for a particular job classification.
Salary Range: The minimum and maximum salary offered for a role.
Location: Country or region where the job is based.
Employment Type: Full-time, Part-time, Contract, or Remote.
Company Size: Small, Medium, or Large enterprises.
Potential Use Cases Salary Benchmarking: Compare salary expectations across industries and job roles.
Career Planning: Identify lucrative career paths based on salary trends.
Wage Gap Analysis: Examine salary disparities by gender, location, or experience level.
Cost of Living Adjustments: Assess salaries relative to regional economic conditions.
HR and Recruitment Strategies: Optimize compensation packages to attract top talent.
Acknowledgments The dataset is compiled from various salary reports and job market research sources. Special thanks to contributors and organizations providing employment data for analysis.
License This dataset is shared for educational, research, and analytical purposes. Please ensure compliance with relevant data usage policies before any commercial applications.
Get Started The dataset can be explored using Python (Pandas), R, SQL, or visualization tools like Tableau and Power BI. Sample notebooks and analyses are available in the Kaggle notebook section.
VITAL SIGNS INDICATOR
Jobs by Industry (EC1)
FULL MEASURE NAME
Employment by place of work by industry sector
LAST UPDATED
December 2022
DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.
DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021
CONTACT INFORMATION
vitalsigns.info@bayareametro.gov
METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.
Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.
The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.
Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.
QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.
For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:
Farm:
(aggregation level: 74, NAICS code: 11)
- Contra Costa: 2008-2010
- Marin: 1990-2006, 2008-2010, 2014-2020
- Napa: 1990-2004, 2013-2021
- San Francisco: 2019-2020
- San Mateo: 2013
Information:
(aggregation level: 73, NAICS code: 51)
- Solano: 2001
Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53)
- Solano: 2001
Unclassified:
(aggregation level: 73, NAICS code: 99)
- All nine Bay Area counties: 1990-2000
- Marin, Napa, San Mateo, and Solano: 2020
- Napa: 2019
- Solano: 2001
Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126
Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.
Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:
Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.
Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.
Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126
Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.
Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:
Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.
Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.
VITAL SIGNS INDICATOR
Jobs by Industry (EC1)
FULL MEASURE NAME
Employment by place of work by industry sector
LAST UPDATED
December 2022
DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.
DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021
CONTACT INFORMATION
vitalsigns.info@bayareametro.gov
METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.
Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.
The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.
Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.
QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.
For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:
Farm:
(aggregation level: 74, NAICS code: 11)
- Contra Costa: 2008-2010
- Marin: 1990-2006, 2008-2010, 2014-2020
- Napa: 1990-2004, 2013-2021
- San Francisco: 2019-2020
- San Mateo: 2013
Information:
(aggregation level: 73, NAICS code: 51)
- Solano: 2001
Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53)
- Solano: 2001
Unclassified:
(aggregation level: 73, NAICS code: 99)
- All nine Bay Area counties: 1990-2000
- Marin, Napa, San Mateo, and Solano: 2020
- Napa: 2019
- Solano: 2001
One aim of the Soviet Union, and communist countries in general, was to achieve full employment. Official policy was designed to prevent unemployment, and the state stopped paying most unemployment benefits in the 1930s. Every citizen had the right (or requirement) to work, and jobs were allocated by the state, not competed for as they were in the west. People could apply for certain positions, based on their education, experience, or interests, but roles could often be distributed to meet employment demands, or preferential roles were distributed via nepotism. The socialist economic system removed job market competition, which provided increased job security but removed many of the incentives that boosted productivity (especially in later decades). In the 1970s and 1980s, average work weeks were under 35 hours long and people retired in their mid to late fifties. Compared to the U.S. in 1985, on average, work weeks were around four hours shorter in the USSR, and Soviet men retired five years earlier, while women retired nine years earlier than their American counterparts.
Wages In earlier years, wages had been tied to individual performance or output, however the de-Stalinization process of the 1960s introduced a more standardized system of payment; from this point onwards, base wages were more fixed, and bonuses had a larger impact on disposable income. Personal finances in the Soviet Union were very different from those in the west; wages were split into base salaries and bonuses, along with a social wage that was "paid" in the form of investments in housing, healthcare, education, and infrastructure, as well as subsidized vouchers for holidays and food. Many of these amenities were also provided by the state, which removed the individual costs that were required across the west and in post-Soviet states today. Overall, income and money in general had a much lower influence on daily life in the USSR than it did in the west, lessening factors such as financial stress and indebtedness, but restricting consumeristic freedom.
Gender differences A major difference between the East and West Blocs was the participation rate of women in the workforce. Throughout most of the USSR's history, women made up the majority of the workforce, with a 51.4 percent share in 1970, and 50.4 percent in 1989; in the U.S. figures for these years were 38 and 45 percent respectively. Although this was due to the fact that women also made up a larger share of the total population (around 53 percent in this period), Soviet women were possibly the most economically active in the world in these decades. When comparing activity rates of women aged between 40 and 44 across Europe in 1985, the USSR had a participation rate of 97 percent; this was the highest in the East Bloc (where rates ranged from 85 to 93 percent in other countries), and is much higher than rates in Northern Europe (71 percent), Western Europe (56 percent) and Southern Europe (37 percent).
https://creativecommons.org/publicdomain/zero/1.0/https://creativecommons.org/publicdomain/zero/1.0/
The dataset contains information about job positions from the Naukri job portal. The dataset has 18,000 rows, which means that there are 18,000 job positions included in the dataset. The dataset includes information about various aspects of the job positions, such as job title, company name, rating, experience required, salary, location, post date, number of applicants, role, industry type, department, employment type, role category, minimum educational qualification required, and key skills required.
With 18,000 rows, this dataset provides a substantial amount of information about job positions advertised on Naukri, and could be useful for various purposes, such as job market analysis, job seeker guidance, or company research. By analyzing this dataset, one could gain insights into the labor market trends, such as the most in-demand job positions, the companies that are hiring the most, and the key skills that are required for different job positions.
job_title: This column contains the title or name of the job position for which the data is being collected.
company: This column contains the name of the company that has advertised the job position.
rating: This column contains the rating of the company, which could be based on employee satisfaction, customer satisfaction, or other factors.
exp_req: This column contains information about the experience required for the job position, which could be measured in years or some other relevant metric.
salary: This column contains the salary range or specific salary amount offered for the job position.
location: This column contains the location of the job position, which could be a city, state, or country.
post_date: This column contains the date on which the job position was advertised or posted.
applicants: This column contains the number of applicants who have applied for the job position.
role: This column contains information about the role or responsibilities of the job position.
industry_type: This column contains information about the industry or sector to which the job position belongs.
department: This column contains information about the department within the company that the job position belongs to.
emp_type: This column contains information about the employment type, such as full-time, part-time, contract, or temporary.
role_category: This column contains information about the category of the job position, such as management, technical, sales, or administrative.
UG: This column contains information about the minimum educational qualification required for the job position, such as an undergraduate degree.
key_skills: This column contains information about the key skills or competencies required for the job position.
Explore wages and salaries data by establishment size and economic activity in Saudi Arabia. This dataset covers various industries such as manufacturing, health, financial intermediation, education, construction, and more.
Other manufacturing, Remediation activities and other waste management services, Industry of paper and its products, Health and social work, Extraction of crude petroleum and natural gas, Social work activities without accommodation, Manufacture of food prod. and beverages, Manufacture of textiles, Financial intermediation, Motion picture, video & tv programme production, sound recording, Scientific research and development, Hotels and restaurants, Other personal service activities, Retail trade, except of motor vehicles and motorcycles, Information service activities, Manufacturing of apparel, preparing & tanning fur, Food and beverage service activities, Manufacture of food products, Manufacture of leather and related products, Repair and installation of machinery and equipment, Programming and broadcasting activities, Other mining and quarrying, Education, Manufacture of office, accounting and computing machinery, Creative, arts and entertainment activities, Insurance and pension funding, except compulsory social security, Construction, Sports activities and amusement and recreation activities, Printing and reproduction of recorded media, Travel agency, tour operator, reservation service & related activities, Computer programming, consultancy and related activities, Repair of computers and personal and household goods, Agriculture and hunting and related service activities, Manufacture of furniture, Activities auxiliary to financial intermediation, Fishing and aquaculture, Mining of coal and lignite, Manufacture of electrical machinery and apparatus, Advertising and market research, Printing & Publishing, Manufacture of radio, television and communication equipment and apparatus, Activities of head offices; management consultancy activities, Activities for mining and quarrying, Rental and leasing activities, Services to buildings and landscape activities, Office administrative, office support & other business support act's, Forestry and logging, Manufacture of other non-metallic mineral products, Air transport, Manufacture of furniture; manufacturing, Mining support service activities, Accommodation, Crop and animal production, hunting and related service activities, Post and telecommunications, Water collection, treatment and supply, Manufacture of machinery and equipment n.e.c., Land transport and transport via pipelines, Manufacture of medical, precision and optical instruments, watches and clocks, Manufacture of beverages, Activities of membership organizations n.e.c., Manufacture of non-metallic mineral products, Water transport, Wholesale trade, except of motor vehicles and motorcycles, Manufacture of products and preparations pharmaceutical, Wholesale & retail trade and repair of motor vehicles & motorcycles, Land transport; transport via pipelines, Manufacture of wood and of products of wood and cork, Real estate activities, Activities of membership organizations, Warehousing and support activities for transportation, Manufacture of wearing apparel, Legal and accounting activities, Manufacture of electrical equipment, Financial service activities, except insurance and pension funding, Architectural and engineering activities; technical testing & analysis, Manufacture of fabricated metal products, Manufacture of coke and refined petroleum products, Tanning and dressing of leather; manufacture of luggage and footwear, Retail trade and repair of personal and household goods, Supporting and auxiliary transport activities; activities of travel agencies, Sewerage, Activities, business services, Exploration of oil and natural gas, Publishing activities, Specialized construction activities, Insurance, reinsurance and pension funding, Employment activities, Manufacture of motor vehicles, trailers and semi-trailers, Construction of buildings, Libraries, archives, museums and other cultural activities, Mining of metal ores, Electricity, gas, steam and air conditioning supply, Wholesale trade and commission trade, service activities, Recycling, Manufacture of basic metals, Activities auxiliary to financial service and insurance activities, Recreational, cultural and sporting activities, Waste collection, treatment & disposal activities; materials recovery, Manufacture of computer, electronic and optical products, Veterinary activities, Fishing, Manufacture of tobacco products, Manufacture of machinery and equipment, Manufacture of paper and paper products, Security and investigation activities, Postal and courier activities, Residential care activities, Civil engineering, Computer and related activities, Human health activities, Total, Products of refined petroleum, Manufacture of chemicals , Articles and products, Sale, maintenance and repair of motor vehicles and motorcycles; retail sale of automotive fuel, Renting of machinery and equipment without operator and of personal and household goods, Manufacture of chemicals and chemical products, Telecommunications, Manufacture of other transport equipment, Collection, purification and distribution of water, Sewage and refuse disposal and sanitation, Electricity, gas and steam, Other professional, scientific and technical activities, Manufacture of rubber and plastics products, Research and development, Labor, Annual Economic Establishment Survey, Manufacturing
Saudi ArabiaFollow data.kapsarc.org for timely data to advance energy economics research..Data from the Annual Economic Establishment Survey.Do not include establishments operating in the governmental and external sectors. Including establishments operating in the private and public sector and not for profit.
Detailed Data Dictionary: https://docs.google.com/spreadsheets/d/1JKUYZYPNZfcg5Ol9LTk8fwe5hwiu7c5DSn-3Wia7mo8/edit?gid=1071313126gid=1071313126
Developed by a seasoned team of ML experts from Google, Meta, and Amazon and alumni of Stanford, Caltech, and Columbia, our AI-powered pipeline provides invaluable insights for HR tech, lead generation, market intelligence, and corporate development. With cutting-edge AI and LLMs, we transform raw job postings into actionable data, analyzing job titles, skills, predicted salaries, locations, and more.
Each posting undergoes multi-layered processing, with GPU-driven models delivering daily, weekly, and monthly data for a balanced real-time and historical view. Our processing pipeline integrates advanced AI models:
Delivered through S3, FTP, and Google Drive, Canaria’s dataset provides flexibility in integration, with APIs available on request. Combining real-time AI with human validation, Canaria’s data delivers business-ready insights to meet evolving HR and market demands.
Core Industry Applications - HR & Workforce Analytics: Access insights into salary trends, workforce demographics, and skill demands to drive strategic HR decisions. - Lead Generation: Identify target leads and hiring needs through granular job postings data. - Investment & Market Intelligence: Gain insights into competitor hiring strategies and industry shifts. - Education & Skill Development: Support curriculum development and training programs based on skill trends and emerging job requirements. - Corporate Development: Align growth strategies with real-time job market data. - Talent Sourcing: Streamline talent sourcing by identifying active job markets and regions with the highest demand for specific skills. - Job Market Forecasting: Analyze hiring trends and job postings data to forecast demand for specific roles and skills. - Economic Research: Provide labor market insights for economic studies, helping to assess job growth and employment shifts by industry.
The Labour Force Survey (LFS) is a household survey carried out monthly by Statistics Canada. Since its inception in 1945, the objectives of the LFS have been to divide the working-age population into three mutually exclusive classifications - employed, unemployed, and not in the labour force - and to provide descriptive and explanatory data on each of these categories. Data from the survey provide information on major labour market trends such as shifts in employment across industrial sectors, hours worked, labour force participation and unemployment rates, employment including the self-employed, full and part-time employment, and unemployment. It publishes monthly standard labour market indicators such as the unemployment rate, the employment rate and the participation rate. The LFS is a major source of information on the personal characteristics of the working-age population, including age, sex, marital status, educational attainment, and family characteristics. Employment estimates include detailed breakdowns by demographic characteristics, industry and occupation, job tenure, and usual and actual hours worked. This dataset is designed to provide the user with historical information from the Labour Force Survey. The tables included are monthly and annual, with some dating back to 1976. Most tables are available by province as well as nationally. Demographic, industry, occupation and other indicators are presented in tables derived from the LFS data. The information generated by the survey has expanded considerably over the years with a major redesign of the survey content in 1976 and again in 1997, and provides a rich and detailed picture of the Canadian labour market. Some changes to the Labour Force Survey (LFS) were introduced which affect data back to 1987. There are three reasons for this revision: The revision enables the use of improved population benchmarks in the LFS estimation process. These improved benchmarks provide better information on the number of non-permanent residents. There are changes to the data for the public and private sectors from 1987 to 1999. In the past, the data on the public and private sectors for this period were based on an old definition of the public sector. The revised data better reflects the current public sector definition, and therefore result in a longer time series for analysis. The geographic coding of several small Census Agglomerations (CA) has been updated historically from 1996 urban centre boundaries to 2001 CA boundaries. This affects data from January 1987 to December 2004. It is important to note that the changes to almost all estimates are very minor, with the exception of the public sector series and some associated industries from 1987 to 1999. Rates of unemployment, employment and participation are essentially unchanged, as are all key labour market trends. The article titled Improvements in 2006 to the LFS (also under the LFS Documentation button) provides an overview of the effect of these changes on the estimates. The seasonally-adjusted tables have been revised back three years (beginning with January 2004) based on the latest seasonal output.
https://dataintelo.com/privacy-and-policyhttps://dataintelo.com/privacy-and-policy
The global "Paying For Knowledge" market size was valued at approximately USD 150 billion in 2023 and is projected to reach around USD 350 billion by 2032, growing at a compound annual growth rate (CAGR) of 10%. One key growth factor driving this market is the increasing demand for continuous learning and professional development in an ever-evolving job market. This surge is amplified by the rapid digital transformation across various sectors, as well as the need for people to adapt to new technologies and evolving job roles.
One of the principal growth factors of the Paying For Knowledge market is the increasing importance placed on lifelong learning. As industries continuously evolve due to technological advancements, workers are required to update their skills and acquire new knowledge to stay relevant. This has led to a growing number of individuals seeking courses, certifications, and training programs, fueling the demand for paid knowledge services. Furthermore, the rise of remote work and the gig economy has necessitated that individuals acquire multifaceted skills, thus propelling the market forward.
Another significant factor contributing to market growth is the widespread availability and accessibility of online learning platforms. The digitization of education and professional development has broken geographical barriers, allowing people from different parts of the world to access high-quality educational content. Online platforms offer a variety of learning modules, ranging from academic to professional and personal development courses, catering to a broad spectrum of learners. This convenience and flexibility make paying for knowledge an attractive option for many, thereby driving market growth.
Moreover, the corporate sector's increasing investment in employee training and development programs is a critical driver of the Paying For Knowledge market. Companies are recognizing the value of having a well-trained workforce to maintain a competitive edge. As a result, they are allocating substantial budgets toward professional development programs, employee reskilling, and upskilling initiatives. This trend is expected to continue, contributing significantly to the market's expansion over the forecast period.
Semantic Knowledge Graphing is emerging as a transformative tool in the Paying For Knowledge market, offering a structured way to organize and retrieve information. By connecting concepts and data points through semantic relationships, knowledge graphs enable more intuitive and efficient access to educational content. This technology can significantly enhance the learning experience by providing personalized recommendations and insights based on a learner's unique needs and preferences. As the demand for tailored learning experiences grows, the integration of semantic knowledge graphing into online learning platforms is expected to play a crucial role in driving market innovation and growth.
Regionally, North America currently dominates the Paying For Knowledge market, owing to its advanced technological infrastructure and a high emphasis on education and professional development. However, the Asia Pacific region is expected to exhibit the highest growth rate due to the increasing adoption of digital learning platforms and a rising middle-class population that values education. Additionally, government initiatives aimed at enhancing digital literacy and skill development in countries like India and China are expected to further boost the market in this region.
The Paying For Knowledge market is segmented by service type into Subscription-Based, Pay-Per-Use, and Freemium models. Each of these service types offers unique advantages and caters to different user needs. The Subscription-Based model is highly popular among users who seek continuous access to educational content. This model provides learners with unlimited access to a wide array of courses, learning materials, and resources for a fixed monthly or annual fee. The predictability of costs and the extensive range of available content make the subscription model appealing to both individuals and corporate clients investing in employee training programs.
On the other hand, the Pay-Per-Use model is gaining traction among users who prefer flexibility and only want to pay for specific courses or content. This model is particularly attractive to
The EESs are conducted by the National Bureau of Statistics (NBS), as mandated by Statistics Act 2015, which empowers NBS to collect, compile and disseminate official statistics in the country. The summary is presented for the six main topical areas namely:- Employment Profile; Wage Rates Profile; Cash Earnings Profile; Annual Wage Bill Profile; Newly Recruited Workers; and Job Vacancies.
Employment Profile
The findings on employment profile reveal an increase in total employment in the formal sector from 2,334,969 employees in 2015 to 2,599,311 employees in 2016; which is an increase of 308,951 employees. The majority of employees are employed in the private sector (1,748,695 private and 850,616 public). Proportion of regular employment has increased from 88.2 percent in 2015 to 92.9 percent in 2016, while casual employment has decreased from 11.8 percent in 2015 to 7.1 under the same span of time. Education industry had the largest share of total employment with 18.5 percent followed by manufacturing industry (18.1 percent); and public administration and defence, compulsory social security industry with 13.6 percent of total employment. It is also indicated that there are more adult employees under regular employment (63.2 percent) compared to youth employees who accounted for 36.8 percent of the total regular employees. With regard to disability status, the results indicate that, there were 3,935 employees (about 0.2 percent of total employment in the formal sector) with various types of disabilities. The results also show that, Dar es Salaam region had the largest proportion of employment, with 31.2 percent of all employees, followed by Morogoro region (10.9 percent) and Arusha region (6.8 percent).
Wage Rates Profile
Regarding the wage rates of employees in the formal sector, the findings show that, overall in 2016, majority of citizen employees (22.9 percent) earned monthly wages between TZS 500,001 and 900,000. In the private sector however, there were more citizen employees in lower wage rates with 17.8 percent earning monthly wages between TZS 150,001 and 300,000 and 14.6 percent earning between TZS 100,001 and 150,000. For the public sector, about 15.3 percent of citizen employees earned between TZS 500,001 and 900,000 and about 8.1 percent earned monthly wages between TZS 300,001 and TZS 500,000. The findings also reveal that, about 4.5 percent of all citizen employees earned TZS 1,500,000 or above, with a slightly larger proportion in the private sector (2.7 percent) than public sector (1.8 percent). Financial and insurance activities had the highest proportion of employees (33.3 percent) earning wages above TZS 1,500,000 followed by information and communication industry (22.1 percent). Conversely, construction, wholesale and retail trade and repair of motor vehicles and motorcycles activities had larger proportions of their employees in lower wage rates between TZS 150,001 and 300,000.
Cash Earnings Profile
The findings indicate that, overall monthly average cash earnings for employees in the formal sector surged up slightly from TZS 403,729 in 2015 to TZS 448,462 in 2016. Monthly cash earnings in the public sector increased from TZS 1,063,064 in 2015 to TZS 1,243,945 in 2016, whereas in the private sector it increased slightly from TZS 353,589 to 362,400. The results boldly note that, on average, cash earnings for employees in the public sector were three times as much as that of the private sector. In addition, analysis of monthly cash earnings by sector reveals that, parastatal organizations had the highest monthly average cash earnings of TZS 1,452,326, while profit making institutions had the lowest monthly average cash earnings of TZS 339,229. It is also found that in 2016, financial and insurance activities had the highest monthly average cash earnings of TZS 1,388,070 followed by public administration and defense; compulsory social security with TZS 1,292,652.
Annual Wage Bill Profile
Analysis on annual wage bill indicates that, overall, employers in public and private sectors had collective annual wage bill of TZS 23,637 billion in 2016, with employers in private sector having higher annual wage bill than in the public sector. The largest annual wage bills were incurred by employers in private profit- making institutions amounting to TZS 9,536 billion followed by employers in private nonprofit -making institutions with TZS 5,295 billion. Employers in parastatal institutions, including both non - profit and profit - making institutions had relatively smaller annual wage bills of TZS 662 billion and TZS 236 billion respectively. Results on annual wage bill by industry indicates that, the largest proportion of wage bill were in the education industry with 20.4 percent. Public administration and defence; compulsory social security had the second largest annual wage bill of 16.6 percent.
Newly Recruited Workers
The findings on the newly recruited workers reveal that, total number of newly recruited workers in 2016 was 69,639 of which 34,594 employees filled newly created posts and 35,045 employees filled existing vacancies. The findings also indicate that, among the newly recruited employees, there were more females (19,433) than males (15,161). On the other hand, private sector had more new recruits with 51,251 employees compared to public sector with 18,388. It is further indicated that, occupations of service workers and shop sales workers; and technicians and associate professionals had larger number of new recruits with 15,515 employees and 15,346 employees, respectively. With regard to education qualification, the findings indicate that, number of males with tertiary education are more in the new recruits (8,279 employees) equivalent to 23.7 percent compared to female comprised of 4,559 employees (about 13.2 percent). Moreover, it is established that, out of the total number of newly recruited employees, the largest proportion, 66.4 percent (46,262 employees) were employed on permanent basis followed by 25.3 percent (17,615 employees) who were engaged on contractual basis. Job Vacancies Analysis for job openings in the formal sector shows that, the largest proportion of job vacancies in 2015/16 (63.6 percent) were for technicians and associate professionals, followed by professionals (21.6 percent). The remaining occupations each had less than 5 percent of the total job vacancies. The majority of these jobs, (85.3 percent) did not require any working experience. Vacancies that required prior working experiences of 1-2 and 3-4 years constituted about 10.6 percent and 2.8 percent respectively. The findings also reveal that, the largest proportion of the reported vacancies (93.3 percent) did not attach any sex preference for the potential candidates. However, 4.4 percent of vacancies preferred male employees compared to 2.3 percent which preferred female employees.
Tanzania Mainland Regions
Formal Establishment.
The survey covers formal establishments with employees in both private and public sectors. The establishments are divided into three main categories which are all public -sector establishments, all registered private establishments employing at least 50 persons and a sample of all registered private establishments whose number of employees are from 5 to 49 persons.
Sample survey data [ssd]
The Employment and Earnings Survey 2016 is an establishment- based survey covering a total of 9,628 establishments from a frame of 68,119 establishments. This frame consists of all public establishments and formal private establishments employing 5 persons and above.
As in previous surveys, the sampling unit of this survey is an establishment which is defined as a legal economic entity engaging itself in one main kind of economic activity at a fixed location. The EES 2016 covered formal establishments in both private and public sectors in Tanzania Mainland in such a way that they formed a representative sample, reflecting the level and magnitude of the economic activities within their respective industrial groups. The EES sample was based on a sampling frame obtained from the Central Register of Establishments (CRE) developed and maintained by NBS. The existing sampling frame was developed on the basis of International Standard Industrial Classification Revision 4 (ISIC Rev.4).
The survey covered all public -sector establishments and private sector establishments with at least 50 employees. Furthermore, the survey covered a sample of private establishments employing 5 to 49 persons. The sampling for this group involved stratifying establishments into those with 5 to 9 employees and those with 10 to 49 persons. Establishments in these strata were further stratified on the basis of their economic activities and ultimately a single stage sampling technique was used to derive representative establishments from each activity using the probability proportion to size (PPS).
Face-to-face [f2f]
The establishment based questionnaires were developed in English, and were translated into Kiswahili Language.
Data editing took place at a number of stages throughout the processing, including: a) Office editing and coding b) During data entry. c) Structural checking of SPSS data files.
90.8
Estimates from a sample survey are affected by two types of errors: 1) non-sampling errors and 2) sampling errors. Non-sampling
VITAL SIGNS INDICATOR
Jobs by Industry (EC1)
FULL MEASURE NAME
Employment by place of work by industry sector
LAST UPDATED
December 2022
DESCRIPTION
Jobs by industry refers to both the change in employment levels by industry and the proportional mix of jobs by economic sector. This measure reflects the changing industry trends that affect our region’s workers.
DATA SOURCE
Bureau of Labor Statistics, Quarterly Census of Employment and Wages (QCEW) - https://www.bls.gov/cew/downloadable-data-files.htm
1990-2021
CONTACT INFORMATION
vitalsigns.info@bayareametro.gov
METHODOLOGY NOTES (across all datasets for this indicator)
Quarterly Census of Employment and Wages (QCEW) employment data is reported by the place of work and represent the number of covered workers who worked during, or received pay for, the pay period that included the 12th day of the month. Covered employees in the private-sector and in the state and local government include most corporate officials, all executives, all supervisory personnel, all professionals, all clerical workers, many farmworkers, all wage earners, all piece workers and all part-time workers. Workers on paid sick leave, paid holiday, paid vacation and the like are also covered.
Besides excluding the aforementioned national security agencies, QCEW excludes proprietors, the unincorporated self-employed, unpaid family members, certain farm and domestic workers exempted from having to report employment data and railroad workers covered by the railroad unemployment insurance system. Excluded as well are workers who earned no wages during the entire applicable pay period because of work stoppages, temporary layoffs, illness or unpaid vacations.
The location quotient (LQ) is used to evaluate level of concentration or clustering of an industry within the Bay Area and within each county of the region. A location quotient greater than 1 means there is a strong concentration for of jobs in an industry sector. For the Bay Area, the LQ is calculated as the share of the region’s employment in a particular sector divided by the share of California's employment in that same sector. For each county, the LQ is calculated as the share of the county’s employment in a particular sector divided by the share of the region’s employment in that same sector.
Data is mainly pulled from aggregation level 73, which is county-level summarized at the North American Industry Classification System (NAICS) supersector level (12 sectors). This aggregation level exhibits the least loss due to data suppression, in the magnitude of 1-2 percent for regional employment, and is therefore preferred. However, the supersectors group together NAICS 11 Agriculture, Forestry, Fishing and Hunting; NAICS 21 Mining and NAICS 23 Construction. To provide a separate tally of Agriculture, Forestry, Fishing and Hunting, the aggregation level 74 data was used for NAICS codes 11, 21 and 23.
QCEW reports on employment in Public Administration as NAICS 92. However, many government activities are reported with an industry specific code - such as transportation or utilities even if those may be public governmental entities. In 2021 for the Bay Area, the largest industry groupings under public ownership are Education and health services (58%); Public administration (29%) and Trade, transportation, and utilities (29%). With the exception of Education and health services, all other public activities were coded as government/public administration, regardless of industry group.
For the county data there were some industries that reported 0 jobs or did not report jobs at the desired aggregation/NAICS level for the following counties/years:
Farm:
(aggregation level: 74, NAICS code: 11)
- Contra Costa: 2008-2010
- Marin: 1990-2006, 2008-2010, 2014-2020
- Napa: 1990-2004, 2013-2021
- San Francisco: 2019-2020
- San Mateo: 2013
Information:
(aggregation level: 73, NAICS code: 51)
- Solano: 2001
Financial Activities:
(aggregation level: 73, NAICS codes: 52, 53)
- Solano: 2001
Unclassified:
(aggregation level: 73, NAICS code: 99)
- All nine Bay Area counties: 1990-2000
- Marin, Napa, San Mateo, and Solano: 2020
- Napa: 2019
- Solano: 2001
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The global online self-paced learning market size was valued at $15.5 billion in 2023 and is projected to reach $30.8 billion by 2032, growing at a compound annual growth rate (CAGR) of 7.9% during the forecast period. This growth is primarily driven by the increasing demand for flexible learning options, advancements in technology, and the rising number of internet users worldwide.
One of the main growth factors of the online self-paced learning market is the increasing need for flexible learning solutions. Traditional classroom-based education often comes with rigid schedules that do not accommodate the diverse needs of learners, particularly working professionals and adult learners. Online self-paced learning offers the flexibility to learn at one's own pace and convenience, making it an attractive option for those with demanding lifestyles or those looking to balance education with other responsibilities.
Technological advancements are also playing a significant role in driving the market. Innovations in artificial intelligence, virtual reality, and augmented reality are enhancing the learning experience by making it more interactive, engaging, and personalized. These technologies enable adaptive learning, where the course content adjusts in real-time to match the learner's pace and understanding, thereby improving the overall effectiveness of the education process.
Another critical factor contributing to the market growth is the increasing internet penetration and the proliferation of smartphones. As internet access becomes more widespread and affordable, more individuals can access online learning platforms from anywhere in the world. This is particularly significant in developing regions where traditional educational infrastructure may be lacking but mobile penetration is high, offering a viable alternative to conventional education systems.
From a regional perspective, North America currently holds the largest market share, followed by Europe and Asia Pacific. North America's dominance can be attributed to the high adoption rate of advanced technologies and the presence of numerous online learning providers. However, the Asia Pacific region is expected to witness the highest CAGR during the forecast period, driven by the rapidly growing population, increasing internet penetration, and a strong emphasis on education and skill development in countries like China and India.
E Learning Courses have become an integral part of the online self-paced learning market, offering a wide array of subjects that cater to diverse learning needs. These courses provide learners with the flexibility to study at their own pace, making it easier for individuals to fit education into their busy schedules. The convenience and accessibility of e-learning courses have made them a popular choice for both students and professionals looking to enhance their skills and knowledge. With advancements in technology, e-learning courses are becoming more interactive and engaging, providing a rich learning experience that rivals traditional classroom settings.
The course type segment in the online self-paced learning market is categorized into professional courses, academic courses, skill development courses, and others. Professional courses are designed to enhance the skills and knowledge required for specific professions, making them particularly popular among working professionals looking to advance their careers. These courses often cover areas such as business management, IT, healthcare, and engineering, providing specialized education that can lead to better job opportunities and higher salaries.
Academic courses, on the other hand, are focused on providing education that aligns with traditional school or university curricula. These courses are popular among K-12 and higher education learners who are looking to supplement their regular studies or prepare for examinations. The availability of a wide range of subjects and the ability to access high-quality educational content from reputed institutions make academic courses a significant segment in the online self-paced learning market.
Skill development courses aim to equip learners with specific skills that are in high demand in the job market. These can range from technical skills like coding and data analysis to soft skills like communication and leadership. G
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Labour cost index shows the short-term development of the total cost, on an hourly basis, for employers of employing the labour force. The index covers all market economic activities except agriculture, forestry, fisheries, education, health, community, social and personal service activities. Labour costs include gross wages and salaries, employers social contributions and taxes net of subsidies connected to employment. The labour cost index is compiled as a "chain-linked Laspeyres cost-index" using a common index reference period (2016 = 100). The index is presented in calendar and seasonally adjusted form. Growth rates with respect to the previous quarter (Q/Q-1) are calculated from seasonally and calendar adjusted figures while growth rates with respect to the same quarter of the previous year (Q/Q-4) are calculated from calendar adjusted figures. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright
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The global OCP Training Education Service market size was valued at approximately $1.5 billion in 2023 and is projected to reach around $3.2 billion by 2032, growing at a CAGR of 8.3% during the forecast period. This growth is driven by the increasing demand for Oracle Certified Professional (OCP) certifications, which are highly valued in the IT industry for enhancing career prospects and skillsets. As organizations continuously seek to implement Oracle solutions to streamline operations and drive efficiencies, the need for trained and certified professionals has become critical, contributing significantly to the market's expansion.
The primary growth factor for the OCP Training Education Service market is the rising adoption of Oracle solutions across various industries. Oracle systems are renowned for their robustness, scalability, and comprehensive functionalities, making them a preferred choice for enterprises looking to optimize their IT infrastructure. Consequently, there is a surge in demand for OCP-certified professionals who possess the expertise to effectively deploy, manage, and troubleshoot these systems. This increasing reliance on Oracle's technology stack is expected to drive the demand for OCP training services further.
Another significant factor fueling market growth is the evolving nature of the workforce and the increasing importance of continuous learning. With rapid technological advancements and the ever-changing IT landscape, professionals must constantly update their skills to stay relevant. OCP training programs offer this opportunity, enabling individuals to gain knowledge in the latest Oracle technologies and applications. This need for upskilling and reskilling is particularly pronounced in the IT sector, where certifications often translate to better job opportunities and higher salaries.
Furthermore, the rise of digital transformation initiatives across industries is also propelling the market. As more companies embark on digital transformation journeys, the demand for skilled IT professionals who can manage sophisticated Oracle systems grows. This trend is evident in sectors such as finance, healthcare, retail, and manufacturing, where Oracle solutions are extensively utilized for data management, enterprise resource planning (ERP), and customer relationship management (CRM). The increasing investment in digital transformation projects is anticipated to drive the need for OCP training services significantly.
From a regional perspective, North America continues to be a dominant player in the OCP Training Education Service market due to the early adoption of advanced technologies and the presence of a large number of Oracle-based enterprises. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period, driven by the rapid economic development and burgeoning IT industry in countries like India and China. The increasing emphasis on education and professional development in these regions is creating substantial opportunities for market growth.
Corporate Training Services have become increasingly vital in the current business landscape, as organizations recognize the importance of equipping their workforce with the latest skills and knowledge. These services are tailored to meet the specific needs of companies, focusing on relevant technologies and methodologies that align with their operational goals. By investing in corporate training, enterprises can ensure that their employees are well-versed in the latest industry trends and practices, ultimately leading to enhanced productivity and competitive advantage. The demand for Corporate Training Services is expected to rise as businesses continue to prioritize employee development and organizational growth.
The OCP Training Education Service market is segmented by service type into Online Training, Classroom Training, Corporate Training, and Certification Programs. Online Training has emerged as a significant segment due to the flexibility and convenience it offers to learners. With internet penetration and digital literacy on the rise globally, more individuals are opting for online courses that allow them to learn at their own pace from the comfort of their homes. This is particularly advantageous for working professionals who need to balance their job responsibilities with their education.
Classroom
Labour cost index shows the short-term development of the total cost, on an hourly basis, for employers of employing the labour force. The index covers all market economic activities except agriculture, forestry, fisheries, education, health, community, social and personal service activities. Labour costs include gross wages and salaries, employers social contributions and taxes net of subsidies connected to employment. The labour cost index is compiled as a "chain-linked Laspeyres cost-index" using a common index reference period (2016 = 100). The index is presented in calendar and seasonally adjusted form. Growth rates with respect to the previous quarter (Q/Q-1) are calculated from seasonally and calendar adjusted figures while growth rates with respect to the same quarter of the previous year (Q/Q-4) are calculated from calendar adjusted figures.
This survey intends to: - · Measure the labour force or economically active population size in relation to the general population in the country. · Identify and analyse the factors leading to the emergence and growth of Labour Force in the country. · Monitor the labour force participation. · Identify and measure the informal sector from within the labour force. · Monitor other Key Indicators of the Labour Market such as employment rates,unemployment rates, hours of work, average income and/or wages etc.
Furthermore, the survey seeks to examine the relationships of socio-economic factors such as education, health, social security, employment within the labour force, and more importantly to measure the causes and effects of children’s involvements in economic activities with special focus on the conditions and environment under which affected children operate.
The main objective of the 2012 LFS was to collect data on the social and economic activities of the population, including detailed information on employment, unemployment, underemployment, wages, informal sector, general characteristics of the labour force and economically inactive population. The survey was designed to specifically measure and monitor Key Indicators of the Labour Market (KILM) such as employment levels, unemployment, income and child labour in Zambia. The measurement of the KILM was with a view to informing users and policy-makers for decision-making. The methodology used in carrying out the survey and the design of questionnaire conform to internationally acceptable standards.
The 2012 Labour Force Survey (LFS) was a nation-wide survey covering household population in all the ten provinces and, in both rural and urban areas.
The survey covered a representative sample of 11, 520 households, which were selected at two stages. In the first stage, 576 Standard Enumeration Areas (SEAs) were selected from a sampling frame developed from the 2010 Census of Population and Housing. In the second stage, households in each of the selected SEA were first listed/updated and then 20 households for enumeration were selected. The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results.
The unit of analysis was Households and Individuals (men and women of 5 years and older).
The survey covered all de jure household members (usual residents) in non-institutionalised housing units, all women and men aged 5 years and older.
The survey excluded institutional populations such as those in hospitals, barracks, prisons or refugee camps. This is because the survey was intended only for usual members of the households, i.e. members who lived together as a household for at least six months or who intended to live together as a household for more than six months - who constituted a household.
Sample survey data [ssd]
The sample was designed to allow separate estimates at national level for rural and urban areas. Further, it also allowed for provincial estimates. A cluster, which is equivalent to a Standard Enumeration Area (SEA), was the primary sampling unit in the first stage. In the second stage, a household was a sampling unit for enumeration purposes.
Zambia is administratively divided into ten provinces. Each province is in turn subdivided into districts. For statistical purposes each district is subdivided into Census Supervisory Areas (CSAs) and these are in turn demarcated into Standard Enumeration Areas (SEAs). The Census mapping exercise of 2006-2010 in preparation for the 2010 Census of Population and Housing, demarcated the CSAs within wards, wards within constituencies and constituencies within districts. As at the time of the survey, Zambia had 74 districts, 150 constituencies, 1,430 wards and about 25,000 SEAs. Information borne on the list of SEAs from the sampling frame also includes number of households and the population size as at the last update of the SEA. The number of households determined the selection of primary sampling units (PSU). The SEAs are stratified as urban and rural.
The total sample of 11,520 households was first allocated between rural, urban and the provincial domains in proportion to the population of each domain according to the 2010 Census results. The proportional allocation does not however allow for reliable estimates for lower domains like district, ward or constituency. Adjustments to the proportional allocation of the sample were made to allow for reasonable comparison to be achieved between strata or domains. Therefore, disproportionate allocation was adopted, for the purpose of maximizing the precision of survey estimates. The disproportionate allocation is based on the optimal square root allocation method designed by Leslie Kish. The sample was then selected using a stratified two-stage cluster design.
There was no deviation from sample design.
Face-to-face [f2f]
Two types of questionnaires (Form A and Forma B) were used to collect data from the household members. Form A was used in the first stage for listing purposes while Form B was used in the second stage for collecting detailed data from the selected households. It was a requirement for each household member to provide responses during the face-to-face interview to the questions that were asked.
The main questionnaire has ten sections namely: a. Demographic Characteristics b. Education, Literacy and Skills Training c. Economic Activity d. Employment e. Hours of Work and Underemployment f. Income g. Unemployment/Job Search h. Previous Work Experience i. Household Chores j. Working Conditions (i.e. Forced labour)
Data editing took place at a number of stages throughout the processing. These included: 1. Field editing 2. Office editing and coding 3. During data entry 4. Structure checking and completeness 5. Secondary editing 6. Strucural checking of SAS data files
At the end of the field work and editing in the provinces, a total of at least 11,000 of completed questionnaires, representing a 99.8 percent response rate were sent to Head Office for data processing.
A series of data quality tables and graphs are available to review the quality of the data and in addition to this, external resources such as the 2012 Labour Force Survey report has been attached.
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The global home moving services market size was estimated at USD 19.8 billion in 2023 and is expected to reach USD 29.4 billion by 2032, growing at a CAGR of 4.2% during the forecast period. This growth is primarily driven by increasing urbanization and high mobility rates across various regions. Additionally, the rise in disposable incomes coupled with advancements in moving technologies is contributing to the market's rapid expansion.
One of the primary growth factors for the home moving services market is the increasing rate of urbanization. With more people moving to urban areas for better job opportunities, educational facilities, and lifestyle, the demand for professional moving services has surged. Urbanization not only leads to higher numbers of individual relocations but also creates the need for more commercial moves as businesses expand or relocate to urban centers. This trend is particularly noticeable in emerging economies where rapid urban development is taking place.
Another significant growth driver is the rise in disposable incomes, which has led to a higher propensity for people to opt for professional moving services rather than doing it themselves. Higher disposable incomes mean that individuals are more willing to pay for the convenience and security that professional moving services provide. This trend is noticeable across both developed and developing nations. Moreover, with the growth of e-commerce and the gig economy, businesses, particularly small and medium enterprises (SMEs), are increasingly relying on moving services for logistics and delivery purposes.
Technological advancements in the moving industry also play a crucial role in market growth. The integration of technology in the form of mobile apps, GPS tracking, and automated booking systems has streamlined the moving process, making it more efficient and customer-friendly. These technological improvements have enhanced the overall customer experience, resulting in higher customer satisfaction rates and repeat business. Companies that leverage these technologies are likely to gain a competitive edge, thereby fostering market expansion.
The increasing demand for Employee Relocation Service is also contributing to the growth of the home moving services market. As companies expand and seek to attract top talent, they are increasingly offering relocation packages to potential employees. This trend is particularly prevalent in industries such as technology, finance, and healthcare, where skilled professionals are in high demand. Employee relocation services typically include assistance with finding housing, moving household goods, and settling into a new community, making them an attractive option for both employers and employees. This service not only facilitates a smooth transition for the employee but also helps companies retain valuable talent by reducing the stress and logistical challenges associated with moving.
From a regional perspective, North America holds a significant share of the home moving services market, driven by high mobility rates and a well-established moving services infrastructure. However, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. This can be attributed to rapid urbanization, economic growth, and increasing disposable incomes in countries like China and India. Europe, with its steady economy and high standard of living, also contributes substantially to the market, while Latin America and the Middle East & Africa are emerging as potential growth areas due to improving economic conditions and urban development projects.
In the home moving services market, service type is a crucial segment that includes local moving, long-distance moving, international moving, and others. Local moving services dominate the market due to the high frequency of relocations within cities and towns. These services are particularly popular among students, working professionals, and small families who frequently move within urban areas. The cost-effectiveness and convenience of local moving services make them a preferred choice for many customers.
Long-distance moving services are also experiencing significant growth. This segment caters to individuals and families moving from one city or state to another. The rise in job relocations, better employment opportunities in different states, and the avail
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Dataset Overview The dataset consists of 26,000 job listings, extracted from a Taiwanese job search platform, focusing on software-related careers. Each listing is detailed with various attributes, providing a comprehensive view of the job market in this sector. Here's a breakdown of the dataset columns:
職缺類別 (Job Category) 職位類別 (Position Category) 職位 (Position) 縣市 (City/County) 地區 (District/Area) 供需人數 (應徵人數) (Number of Applicants) 公司名稱 (Company Name) 職缺名稱 (Job Title) 工作內容 (Job Description) 職務類別 (Job Type) 工作待遇 (Salary) 工作性質 (Nature of Work) 上班地點 (Work Location) 管理責任 (Management Responsibility) 上班時段 (Working Hours) 需求人數 (Number of Positions) 工作經歷 (Work Experience) 學歷要求 (Educational Requirements) 科系要求 (Departmental Requirements) 擅長工具 (Tools Proficiency) 工作技能 (Job Skills) 其他條件 (Other Conditions) 資本額 (Capital Amount) 員工人數 (Number of Employees) 公司標籤 (Company Tags) Analytical Insights Exploratory Data Analysis Perform exploratory data analysis using libraries like Pandas and NumPy. Examine trends in job categories, salaries, and educational requirements. Analyze the distribution of jobs across different cities and districts. Visualization Create visual representations of the dataset using Python visualization libraries. Plot job distribution across various sectors or locations. Visualize salary ranges and compare them with educational and experience requirements. Practice with SQL or Pandas Queries Utilize the dataset to refine SQL query skills or Pandas data manipulation techniques. Execute queries to extract specific information, such as the most in-demand skills or the companies offering the highest salaries. NLP Analysis and Tasks for Software Jobs Dataset This dataset, encompassing 26,000 job listings from the Taiwanese software industry, is ripe for a variety of Natural Language Processing (NLP) analyses. Below are some recommended NLP tasks and analyses that can be conducted on this dataset.
Text Classification Job Category Prediction: Train a classification model to predict the job category (職缺類別) using job descriptions (工作內容). Salary Range Classification: Classify jobs into different salary brackets based on their descriptions and titles, helping to identify features associated with higher salaries. Sentiment Analysis Company Reputation Analysis: Analyze the sentiment of company tags (公司標籤) to assess the general sentiment or reputation of companies listed in the dataset. Topic Modeling Identifying Key Job Requirements: Apply LDA (Latent Dirichlet Allocation) to job descriptions for uncovering common themes or required skills in the software sector. Named Entity Recognition (NER) Information Extraction: Implement NER to extract specific entities like tools (擅長工具), skills (工作技能), and educational qualifications (學歷要求) from job descriptions. Text Summarization Summarizing Job Descriptions: Develop algorithms for generating concise summaries of job descriptions, enabling quick understanding of key points. Language Modeling Job Description Generation: Use language models to create realistic job descriptions based on input prompts, assisting in job listing creation or understanding industry language trends. Machine Translation (If Applicable) Dataset Translation for Global Accessibility: Translate the dataset content into English or other languages for international accessibility, using machine translation models. Predictive Analysis Predicting Applicant Volume: Use historical data to forecast the number of applicants (供需人數 (應徵人數)) a job listing might attract based on various factors. By leveraging these NLP techniques, insightful findings can be extracted from the dataset, beneficial for both job seekers and employers in the software field. This dataset offers a practical opportunity to apply NLP skills in a real-world setting.
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Original Data Source: Taiwan 104.com jobs search JD