The statistic above represents the percentage of employers in the United States allowing flexibility in their employee's career management. In 2014, about ** percent of organizations allowed at least some employees to phase into retirement by working reduced hours over a period of time prior to full retirement.

In 2020, the market size of career management services in Japan amounted to **** billion euro. Career management service is one of the segments of the recruitment business and involves career transition and outplacement services.

Career Services Management Software Market Outlook

According to our latest research, the global Career Services Management Software market size stood at USD 1.87 billion in 2024, with a robust compound annual growth rate (CAGR) of 13.4% projected from 2025 to 2033. This growth trajectory will drive the market to reach approximately USD 5.84 billion by 2033. The surge in demand is primarily fueled by the increasing necessity for digital solutions in career development, the integration of artificial intelligence in talent management, and the growing focus on employability outcomes among educational institutions and corporate organizations.

The primary growth factor for the Career Services Management Software market is the accelerating adoption of digital transformation strategies across educational and corporate sectors. As institutions and employers strive to provide seamless career guidance and placement solutions, the reliance on robust software platforms has intensified. These platforms enable streamlined communication between students, alumni, employers, and career counselors, fostering a more efficient and data-driven approach to career development. The proliferation of remote learning and work-from-home trends has further catalyzed the demand for cloud-based career services management solutions, making them indispensable tools for managing virtual career fairs, internships, and job placements.

Another significant driver is the increasing emphasis on employability and skills development among higher education institutions and K-12 schools. As the global workforce evolves, educational entities are under mounting pressure to ensure their graduates are job-ready and equipped with relevant skills. Career services management software facilitates the tracking of student progress, skills acquisition, and employer engagement, enabling institutions to tailor their programs for better employment outcomes. Furthermore, the integration of analytics and reporting tools within these platforms allows for data-driven decision-making, enhancing the overall effectiveness of career services and improving institutional reputation.

The rapid advancements in artificial intelligence (AI), machine learning, and data analytics are also reshaping the landscape of career services management. Modern solutions leverage AI to match candidates with suitable job opportunities, predict career trajectories, and personalize guidance based on individual strengths and preferences. This technological evolution is not only enhancing user experience but also reducing administrative burdens on career centers and employers. Additionally, the growing demand for scalable solutions among corporates and government agencies to manage large talent pools and workforce transitions is significantly contributing to market expansion.

From a regional perspective, North America continues to dominate the Career Services Management Software market, accounting for the largest revenue share in 2024. This dominance is attributed to the high penetration of digital infrastructure, strong presence of leading software vendors, and a mature ecosystem of educational and corporate users. However, the Asia Pacific region is witnessing the fastest growth, driven by burgeoning investments in education technology, increasing student populations, and government initiatives to enhance employability. Europe and Latin America are also experiencing steady adoption rates, particularly as institutions seek to modernize their career services in response to changing labor market dynamics.

Component Analysis

The Career Services Management Software market by component is segmented into Software and Services. The software segment encompasses the core platforms and solutions that facilitate career guidance, job matching, event management, and analytics. These software solutions are increasingly being enhanced with features such as AI-driven recommendations, mobile access, and integration with third-party job boards and social media platforms. As institutions and employers prioritize digital efficiency, the demand for comprehensive, user-friendly, and customizable software platforms continues to rise. This trend is further amplified by the growing need to manage large volumes of student and employer data securely and efficiently.

The services segment covers a wide array of offerings,

This survey shows how important career development opportunities are to U.S. employees' job satisfaction as of *************. During the survey, ** percent of respondents stated that career development opportunities were important to their job satisfaction.

Career Services Management Software Market Outlook

According to our latest research, the global Career Services Management Software market size reached USD 1.23 billion in 2024, driven by a robust demand for digital transformation across educational institutions and corporate sectors. The market is projected to expand at a CAGR of 11.4% from 2025 to 2033, reaching an estimated USD 3.24 billion by 2033. This accelerated growth is largely attributed to the increasing need for scalable, efficient, and integrated solutions for career planning, placement management, and employer engagement within academic and professional environments. As per our latest analysis, factors such as rising adoption of cloud-based platforms, the proliferation of hybrid learning models, and the imperative for data-driven decision-making are fueling the expansion of the Career Services Management Software market globally.

One of the primary growth drivers for the Career Services Management Software market is the increasing emphasis on employability and career readiness among students and professionals. As the global workforce landscape becomes more competitive, educational institutions and enterprises are prioritizing the integration of career services software to facilitate seamless connections between students, alumni, employers, and career advisors. These platforms streamline the process of job postings, resume reviews, interview scheduling, and career counseling, thereby enhancing placement rates and improving student outcomes. Furthermore, the growing awareness about the importance of career guidance in bridging the skills gap and aligning educational curricula with industry needs is prompting universities, colleges, and training institutes to invest in advanced career services management solutions.

Another significant factor contributing to market growth is the rapid digitalization of education and corporate training. The COVID-19 pandemic accelerated the adoption of online learning and remote work, compelling educational institutions and businesses to seek digital solutions for career services and talent management. Cloud-based Career Services Management Software has emerged as a preferred choice due to its scalability, flexibility, and ease of integration with existing learning management systems (LMS) and human resource management systems (HRMS). This shift towards digital platforms has enabled organizations to offer personalized career guidance, virtual job fairs, and real-time analytics, thus enhancing the overall user experience and operational efficiency. The ongoing trend of hybrid and remote work environments is expected to sustain the demand for these solutions in the coming years.

Additionally, the integration of artificial intelligence (AI), machine learning (ML), and data analytics into Career Services Management Software is reshaping the market landscape. These technologies facilitate intelligent matchmaking between job seekers and employers, predictive analytics for career pathing, and automated tracking of placement outcomes. AI-driven platforms can analyze vast datasets to recommend relevant career opportunities, identify skill gaps, and provide actionable insights for both students and career advisors. Such advanced functionalities are attracting investments from educational institutions and corporate organizations seeking to gain a competitive edge in talent acquisition and workforce development. The growing need for real-time reporting, compliance management, and data-driven decision-making is further propelling the adoption of next-generation career services management solutions.

From a regional perspective, North America continues to lead the Career Services Management Software market, accounting for the largest share in 2024. This dominance is attributed to the presence of top-tier universities, a mature corporate sector, and a high level of technological adoption. Europe and Asia Pacific are also witnessing substantial growth, driven by government initiatives to enhance employability and the increasing penetration of digital solutions in educational and corporate settings. Emerging economies in Latin America and the Middle East & Africa are gradually adopting career services management platforms, supported by investments in educational infrastructure and workforce development programs. Regional disparities in technology adoption, internet connectivity, and regulatory frameworks, however, present both opportunities and challenges for market expansion.

This dataset aims to investigate the relationship between supervisors' bottom-line mentality (BLM) and employees' career sustainability, focusing on the mediating roles of workplace dehumanization and emotional exhaustion and the moderating effect of employee spirituality. Building on the Conservation of Resources (COR) theory and the career sustainability literature, we propose that supervisors' BLM exerts both direct and indirect negative effects on employees' career sustainability. The indirect effects is mediated through a sequential process involving workplace dehumanization and emotional exhaustion. Furthermore, we propose that employees' spirituality moderates the relationship between emotional exhaustion and career sustainability.

Historical Dataset of Career Development Center is provided by PublicSchoolReview and contain statistics on metrics:Total Classroom Teachers Trends Over Years (2003-2023)

Dataset Overview: Total Records: 1000 university students Purpose: The dataset aims to assist in predictive modeling for career and entrepreneurial guidance, providing insights into how machine learning models can improve career planning and entrepreneurial success among students. Key Components: Demographic Information:

Student ID: A unique identifier for each student. Age: The age of the student. Gender: The student's gender (Male, Female, Other). Field of Study: The student's primary academic discipline (Engineering, Business, Arts, etc.). Year of Study: The student's current year in university (1st, 2nd, etc.). University Location: The geographical location or region of the university. Academic Performance:

GPA: The student's cumulative grade point average (GPA). Relevant Coursework: Indicates whether the student has completed courses relevant to career development or entrepreneurship (Yes/No). Employment & Entrepreneurial Experience:

Prior Employment: Whether the student has had any prior employment (Yes/No). Type of Employment: The nature of employment, such as part-time, full-time, or internships. Entrepreneurial Experience: Whether the student has prior entrepreneurial experience (Yes/No). Start-up Participation: Whether the student has been involved in any start-up ventures (Yes/No). Survey and Questionnaire Data:

Career Interests: The student's interest in specific career paths (e.g., Tech, Business, Design, etc.). Entrepreneurial Aspirations: The student's desire or aspiration to become an entrepreneur (Low, Medium, High). Career Guidance Satisfaction: A satisfaction rating (1-10 scale) based on the student's experience with career guidance services. Model Predictions:

Recommended Career Path: Career paths. Entrepreneurship Suitability Score: A numerical score predicting the student's suitability for entrepreneurship (0-100). Top Recommended Industries: Suggested industries for career focus (e.g., Tech, Finance, etc.). Predicted Job Success Probability: A probability score (0-100) predicting the student's likelihood of success in their recommended career. Outcome Variables:

User Satisfaction: A satisfaction rating (1-10) indicating how well the students feel the recommendations align with their career goals. Followed Recommendations: Whether the student followed the recommended career/entrepreneurial path (Yes/No). Employment Status Post-Graduation: The student’s employment status after completing their studies (Employed, Self-employed, Unemployed).

Description: These are research indicators of doctorate holders in Europe that were compiled from the criteria and factors of the Eurostat. This dataset consists of data in five categories (i.e. Career Development of Doctorate Holders; Labour Market - Job Vacancy Statistics; Skill-related Statistics; European and International Co-patenting in EPO Applications and Ownership of Inventors in EPO Applications). The Eurostat Research Indicators consist of (1) Doctorate holders who have studied, worked or carried out research in another EU country (%); (2) Doctorate holders by activity status (%); (3) Doctorate holders by sex and age group; (4) Employed doctorate holders working as researchers by length of stay with the same employer (%); (5) Employed doctorate holders working as researchers by job mobility and sectors of performance over the last 10 years (%); (6) Employed doctorate holders by length of stay with the same employer and sectors of performance (%); (7) Employed doctorate holders by occupation (ISCO_88, %); (8) Employed doctorate holders by occupation (ISCO_08, %); (9) Employed doctorate holders in non-managerial and non-professional occupations by fields of science (%); (10) Level of dissatisfaction of employed doctorate holders by reason and sex (%); (11) National doctorate holders having lived or stayed abroad in the past 10 years by previous region of stay (%); (12) National doctorate holders having lived or stayed abroad in the past 10 years by reason for returning into the country (%); (13) Non-EU doctorate holders in total doctorate holders (%); (14) Unemployment rate of doctorate holders by fields of science; (15) Employment in Foreign Affiliates of Domestic Enterprises; (16) Employment in Foreign Controlled Enterprises; (17) Employment rate of non-EU nationals, age group 20-64; (18) Intra-mural Business Enterprise R&D Expenditures in Foreign Controlled Enterprises; (19) Job vacancy rate by NACE Rev. 2 activity - annual data (from 2001 onwards); (20) Job vacancy statistics by NACE Rev. 2 activity, occupation and NUTS 2 regions - quarterly data; (21) Job vacancy statistics by NACE Rev. 2 activity - quarterly data (from 2001 onwards); (22) Value Added in Foreign Controlled Enterprises; (23) Graduates at doctoral level by sex and age groups - per 1000 of population aged 25-34; (24) Graduates at doctoral level, in science, math., computing, engineering, manufacturing, construction, by sex - per 1000 of population aged 25-34; (25) Level of the best-known foreign language (self-reported) by degree of urbanisation; (26) Level of the best-known foreign language (self-reported) by educational attainment level; (27) Level of the best-known foreign language (self-reported) by labour status; (28) Level of the best-known foreign language (self-reported) by occupation; (29) Number of foreign languages known (self-reported) by educational attainment level; (30) Number of foreign languages known (self-reported) by degree of urbanisation; (31) Number of foreign languages known (self-reported) by labour status; (32) Number of foreign languages known (self-reported) by occupation; (33) Population by educational attainment level, sex, age and country of birth (%); (34) Co-patenting at the EPO according to applicants’/inventors’ country of residence - % in the total of each EU Member State patents; (35) Co-patenting at the EPO: crossing inventors and applicants; (36) Co-patenting at the EPO according to applicants’/inventors’ country of residence - number; (37) EU co-patenting at the EPO according to applicants’/ inventors’ country of residence by international patent classification (IPC) sections - number; (38) EU co-patenting at the EPO according to applicants’/inventors’ country of residence by international patent classification (IPC) sections - % in the total of all EU patents; (39) Domestic ownership of foreign inventions in patent applications to the EPO by priority year; (40) Foreign ownership of domestic inventions in patent applications to the EPO by priority year; and (41) Patent applications to the EPO with foreign co-inventors, by priority year.

Job Growth Statistics: Statistics on job growth are essential in understanding the state and trajectory of an economy because they offer insight into the shifting dynamics of labor markets. By measuring net job addition or subtraction over a certain timeframe, employment growth statistics allow policymakers, companies, and individuals to make well-informed decisions regarding workforce planning, investment decisions, or career choices. Statistics on job growth provide a key measure of economic development as they show whether an economy is expanding, contracting, or remaining stable. Positive employment growth numbers often signal healthy economies with increased consumer spending and company confidence. Conversely, negative or stagnant job growth indicates a slowdown or recession. Furthermore, statistics on employment growth may also be used to highlight developing markets and professions for policymakers as well as job seekers in finding prospective development areas. As such, employment data provides an essential means of measuring an economy's current state and future direction, as well as helping shape policies and initiatives within it. Editor’s Choice From 2020-2030; job growth in the US is anticipated to be 5.3%. Nurse practitioners are predicted to experience the highest job growth; between 2021-2031 at 45.7%; 2019 alone saw sectors producing goods create 188,000 new jobs. Leisure and hospitality job creation decreased by 47% year-on-year between April 2020 and March 2021. President Clinton created 19 million new employment opportunities between June and July of 2022 and 528,000 nonfarm payroll employees were gained; yet by April 2020 20.5 million jobs had been lost from the economy as a whole. By 2031, it is projected that employment opportunities across the nation will reach 166.5 million; over that same timeframe childcare service workers have seen their ranks decline by 336,000. Since the COVID-19 outbreak, healthcare employment levels have suffered a dramatic decrease. By some accounts, over one and a half million employees may have left healthcare jobs since 2016. (Source: zippia.com)

Vinnytsia Career Management Co Limited Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

This dataset contains 10,000 synthetically generated records simulating the academic and career outcomes of students graduating from Indian universities, primarily focusing on Engineering and Management streams.

The primary goal of this synthetic dataset is to provide a rich, non-sensitive resource for:

Predictive Modeling: Training machine learning models to predict job placement success (Classification) and final CTC/Salary (Regression). Bias Analysis: Exploring the effects of categorical variables (like Gender or Tier of College) on placement and salary outcomes in a controlled, simulated environment. **Exploratory Data Analysis (EDA): **Investigating correlations between academic performance (CGPA, Projects, Internships) and career paths (Placed vs. Higher Studies).

******Crucial Note on Synthesis****** This data is synthetic and does not contain any real personally identifiable information (PII).

This dataset is structured to serve as a foundation for predicting suitable career paths based on students' skills and academic performance, making it ideal for career recommendation systems powered by machine learning algorithms.

Key Columns: 1: Sr.No.: Sequential identifier for each record. 2: Course: The educational course the student is enrolled in (this column appears to have some missing values). 3: Job Profession: The recommended or suitable profession for the student based on their skill profile (e.g., Astronomer). 4: Student: Unique identifier for each student (e.g., S1, S2, etc.). 5: Linguistic: Score reflecting the student’s verbal and linguistic abilities. 6: Musical: Score representing the student’s musical intelligence. 7: Bodily: Score for the student’s bodily-kinesthetic abilities, indicating their proficiency in physical tasks. 8: Logical-Mathematical: Score representing the student’s ability in logical reasoning and mathematical thinking. 9: Spatial-Visualization: Score reflecting the student’s spatial awareness and ability to visualize objects in space. 10: Interpersonal: Score for the student’s interpersonal intelligence, which indicates how well they interact with others. 11: Intrapersonal: Score reflecting the student’s self-awareness and capacity for introspection. 12: Naturalist: Score representing the student’s understanding and affinity for the natural world. P1, P2, P3, ..., P8: Performance indicators (e.g., AVG, POOR, BEST) for various subjects or competencies, showing how well the student performed in different areas.

Potential Use Cases: Career Guidance: This dataset can be used to build a machine learning model that suggests suitable careers based on the student's unique combination of skills and performance in different subjects. Skill Development Recommendations: By analyzing gaps in skills, the model can recommend courses or training to help students improve in specific areas. Personalized Learning: Educational institutions can use this data to provide personalized learning paths for students.

Financial overview and grant giving statistics of Empowering Center for Employment and Career Development

Historical Dataset of Jm Rapport School For Career Development is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2009-2023),Total Classroom Teachers Trends Over Years (2009-2023),Distribution of Students By Grade Trends,Student-Teacher Ratio Comparison Over Years (2009-2023),American Indian Student Percentage Comparison Over Years (2011-2023),Asian Student Percentage Comparison Over Years (2008-2023),Hispanic Student Percentage Comparison Over Years (2009-2023),Black Student Percentage Comparison Over Years (2009-2023),White Student Percentage Comparison Over Years (2009-2023),Diversity Score Comparison Over Years (2009-2023),Free Lunch Eligibility Comparison Over Years (2012-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2011-2012)

Historical Dataset of Career Development School is provided by PublicSchoolReview and contain statistics on metrics:Total Students Trends Over Years (2013-2023),Total Classroom Teachers Trends Over Years (2011-2018),Student-Teacher Ratio Comparison Over Years (2011-2018),American Indian Student Percentage Comparison Over Years (2013-2014),Hispanic Student Percentage Comparison Over Years (2011-2023),Black Student Percentage Comparison Over Years (2011-2020),White Student Percentage Comparison Over Years (2013-2023),Two or More Races Student Percentage Comparison Over Years (2011-2020),Diversity Score Comparison Over Years (2013-2023),Free Lunch Eligibility Comparison Over Years (2013-2023),Reduced-Price Lunch Eligibility Comparison Over Years (2012-2023),Graduation Rate Comparison Over Years (2012-2023)

Career Pathways Platforms Market Outlook

According to our latest research, the global Career Pathways Platforms market size stood at USD 2.85 billion in 2024, reflecting robust momentum driven by digital transformation across the education and workforce sectors. The market is anticipated to expand at a compound annual growth rate (CAGR) of 13.4% from 2025 to 2033. By the end of 2033, the Career Pathways Platforms market is forecasted to reach USD 8.87 billion, propelled by the increasing emphasis on skills-based learning, workforce readiness, and the integration of advanced analytics and artificial intelligence into career guidance solutions. As per our latest research, this growth is primarily fueled by the urgent need for scalable, personalized career guidance solutions in both educational and corporate environments worldwide.

One of the primary growth factors for the Career Pathways Platforms market is the global shift toward skills-based education and employment. As traditional job roles evolve due to technological advancements and changing industry demands, both educational institutions and employers are seeking comprehensive platforms that can bridge the gap between academic learning and workforce requirements. These platforms leverage AI-driven analytics to provide personalized career recommendations, skills gap analysis, and tailored learning pathways, ensuring that learners are better equipped for the future job market. The increasing adoption of competency-based education frameworks and micro-credentialing further amplifies the demand for these solutions, as stakeholders recognize the importance of continuous upskilling and reskilling in a rapidly changing economic landscape.

Another significant driver is the growing partnership ecosystem between educational institutions, technology providers, and employers. Career Pathways Platforms are increasingly being integrated into K-12 schools, universities, and corporate training programs to provide holistic career development experiences. These platforms enable students and employees to explore diverse career options, access real-time labor market data, and connect with mentors or industry professionals. Governments and non-profit organizations are also investing in such platforms to promote workforce inclusivity and address unemployment challenges, especially among youth and underrepresented groups. The proliferation of cloud-based solutions has further democratized access, allowing even resource-constrained institutions to deploy advanced career guidance tools without heavy upfront investments.

The surge in remote learning and hybrid work models post-pandemic has also contributed substantially to the Career Pathways Platforms market expansion. As learners and professionals increasingly seek flexible, digital-first solutions for career planning and skills development, platform providers are responding with mobile-friendly interfaces, AI-powered chatbots, and integration with popular learning management systems (LMS). This digital evolution not only enhances user engagement but also allows for real-time tracking of progress, robust data analytics, and scalable deployment across geographies. The growing emphasis on lifelong learning and the gig economy further reinforces the need for dynamic, adaptive career pathways solutions that cater to diverse user profiles, from students to mid-career professionals.

The emergence of the Student Success Platform is revolutionizing the way educational institutions approach career guidance and student development. These platforms are designed to enhance student outcomes by integrating academic advising, career counseling, and skills development into a cohesive framework. By leveraging data analytics and AI, Student Success Platforms provide personalized insights into student performance, helping educators identify at-risk students and tailor interventions to improve retention and graduation rates. This holistic approach not only supports students in achieving their academic goals but also prepares them for successful transitions into the workforce, aligning educational pathways with career aspirations.

Regionally, North America holds the largest share of the Career Pathways Platforms market, driven by a mature digital infrastructure, high adoption rates among educational institutions and corporates, and strong government support f

This statistic shows the resources for career development used by workers worldwide as of September 2014, by generation. During the survey, ** percent of Generation Y workers stated employer-provided training as a resource they had used for career development within the past year.

Comparison of understanding of career planning and employment strategies.

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Second Career Program of ETD.The Second Career program provides training to unemployed or laid-off individuals to help them find employment in high demand occupations in Ontario. The intention of the SC program is to return individuals to employment by the most cost effective path. Second Career provides up to $28,000 to assist laid-off workers with training-related costs such as tuition, books, transportation, and basic living expenses, based on individual need. Additional allowances may be available for people with disabilities, and for clients needing help with the costs of dependent care, living away from home and literacy and basic skills upgrading, also based on individual need. People with disabilities may also be given extensions on training and upgrading durations, to meet their specific needs. Clients may be required to contribute to their skills training, based on the client’s total annual gross household income and the number of household members.About This DatasetThis dataset contains data on SC clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). These clients have been distributed across Local Board areas based on the client’s home address, not the address of their training institution(s).Different variables in this dataset cover different groups of Second Career clients, as follows:Demographic and skills training variables are composed of all SC clients that started in 2015/16.At exit outcome variables are composed of all SC clients that completed their program in 2015/16.12-month outcome variables are composed of all SC clients that completed a 12-month survey in 2015/16.The specific variables that fall into each of the above categories are detailed in the Technical Dictionary. As a result of these differences, not all variables in this dataset are comparable to the other variables in this dataset; for example, the outcomes at exit data is not the outcomes for the clients described by the demographic variables.About Local BoardsLocal Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario. The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest;creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; andorganizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce AuthorityPeel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning BoardElgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-EssexMLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016: Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) ApprenticeshipThis dataset contains the 2015/16 SC data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.Terms and Definitions

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups (three-digit codes), and 500 unit groups (four-digit codes). This dataset uses four-digit NOC codes from the 2011 edition to identify the training programs of Second Career clients.Notes

Data reporting on 5 individuals or less has been suppressed to protect the privacy of those individuals.Data published: Feb 1, 2017Publisher: Ministry of Labour, Training and Skills Development (MLTSD)Update frequency: Yearly Geographical coverage: Ontario