Shows the "probability" of being without a job for those who would like to have one, broken-down by educational attainment level. Measures the difficulties that people with different levels of education have to face on the labour market. Gives an initial idea of the impact of education on reducing the chances of being unemployed. Educational attainment level is coded according to the International Standard Classification of Education (ISCED). Data until 2013 are classified according to ISCED 1997 and data as from 2014 according to ISCED 2011.

Educational status and labour market status of people aged 16 to 24 years, by sex, in and out of full-time education, UK, rolling three-monthly figures published monthly, seasonally adjusted. Labour Force Survey. These are official statistics in development.

Unemployment rate, participation rate, and employment rate by educational attainment, gender and age group, annual.

This table contains data on the percent of population age 25 and up with a four-year college degree or higher for California, its regions, counties, county subdivisions, cities, towns, and census tracts. Greater educational attainment has been associated with health-promoting behaviors including consumption of fruits and vegetables and other aspects of healthy eating, engaging in regular physical activity, and refraining from excessive consumption of alcohol and from smoking. Completion of formal education (e.g., high school) is a key pathway to employment and access to healthier and higher paying jobs that can provide food, housing, transportation, health insurance, and other basic necessities for a healthy life. Education is linked with social and psychological factors, including sense of control, social standing and social support. These factors can improve health through reducing stress, influencing health-related behaviors and providing practical and emotional support. More information on the data table and a data dictionary can be found in the Data and Resources section. The educational attainment table is part of a series of indicators in the Healthy Communities Data and Indicators Project (HCI) of the Office of Health Equity. The goal of HCI is to enhance public health by providing data, a standardized set of statistical measures, and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health. The creation of healthy social, economic, and physical environments that promote healthy behaviors and healthy outcomes requires coordination and collaboration across multiple sectors, including transportation, housing, education, agriculture and others. Statistical metrics, or indicators, are needed to help local, regional, and state public health and partner agencies assess community environments and plan for healthy communities that optimize public health. More information on HCI can be found here: https://www.cdph.ca.gov/Programs/OHE/CDPH%20Document%20Library/Accessible%202%20CDPH_Healthy_Community_Indicators1pager5-16-12.pdf The format of the educational attainment table is based on the standardized data format for all HCI indicators. As a result, this data table contains certain variables used in the HCI project (e.g., indicator ID, and indicator definition). Some of these variables may contain the same value for all observations.

📂 Dataset Title:

AI Impact on Job Market: Increasing vs Decreasing Jobs (2024–2030)

📝 Dataset Description:

This dataset explores how Artificial Intelligence (AI) is transforming the global job market. With a focus on identifying which jobs are increasing or decreasing due to AI adoption, this dataset provides insights into job trends, automation risks, education requirements, gender diversity, and other workforce-related factors across industries and countries.

The dataset contains 30,000 rows and 13 valuable columns, generated to reflect realistic labor market patterns based on ongoing research and public data insights. It can be used for data analysis, predictive modeling, AI policy planning, job recommendation systems, and economic forecasting.

📊 Columns Description:

Column Name Description

Job Title Name of the job/role (e.g., Data Analyst, Cashier, etc.) Industry Industry sector in which the job is categorized (e.g., IT, Healthcare, Manufacturing) Job Status Indicates whether the job is Increasing or Decreasing due to AI adoption AI Impact Level Estimated level of AI impact on the job: Low, Moderate, or High Median Salary (USD) Median annual salary for the job in USD Required Education Typical minimum education level required for the job Experience Required (Years) Average number of years of experience required Job Openings (2024) Number of current job openings in 2024 Projected Openings (2030) Projected job openings by the year 2030 Remote Work Ratio (%) Estimated percentage of jobs that can be done remotely Automation Risk (%) Probability of the job being automated or replaced by AI Location Country where the job data is based (e.g., USA, India, UK, etc.) Gender Diversity (%) Approximate percentage representation of non-male genders in the job

🔍 Potential Use Cases:

Predict which jobs are most at risk due to automation.

Compare AI impact across industries and countries.

Build dashboards on workforce diversity and trends.

Forecast job market shifts by 2030.

Train ML models to predict job growth or decline.

📚 Source:

This is a synthetic dataset generated using realistic modeling, public job data patterns (U.S. BLS, OECD, McKinsey, WEF reports), and AI simulation to reflect plausible scenarios from 2024 to 2030. Ideal for educational, research, and AI project purposes.

📌 License: MIT

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 mark et 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.

Abstract copyright UK Data Service and data collection copyright owner.The Annual Population Survey (APS) is a major survey series, which aims to provide data that can produce reliable estimates at local authority level. Key topics covered in the survey include education, employment, health and ethnicity. The APS comprises key variables from the Labour Force Survey (LFS), all its associated LFS boosts and the APS boost.The APS allows for analysis to be carried out on detailed subgroups and below regional level. In recent years (particularly with the sample size of the LFS 5 quarter dataset reducing) there has been some interest in producing a two year APS longitudinal dataset to look at any trends that may occur over a year. The APS Two-Year Longitudinal Datasets, covering 2012/13 onwards, have been deposited as a result of this work. Person- and Household-level APS datasets are also available. For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation.Occupation data for 2021 and 2022The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

See notice below about this dataset

This dataset provides the number of graduates who enrolled in each type of postsecondary education per district.

Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

List of Outcomes

• Total Postsecondary Enrollment
• In-State Public 2-Year
• In-State Public 4-Year
• In-State Private
• Out-of-State
• Total Employed
• Total Missing

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.

Data on labour force status including employment, unemployment and labour force participation rates by major field of study, highest level of education, location of study compared with location of residence, age and gender.

This dataset presents a dual-version representation of employment-related data from India, crafted to highlight the importance of data cleaning and transformation in any real-world data science or analytics project.

🔹 Dataset Composition:

It includes two parallel datasets: 1. Messy Dataset (Raw) – Represents a typical unprocessed dataset often encountered in data collection from surveys, databases, or manual entries. 2. Cleaned Dataset – This version demonstrates how proper data preprocessing can significantly enhance the quality and usability of data for analytical and visualization purposes.

Each record captures multiple attributes related to individuals in the Indian job market, including: - Age Group
- Employment Status (Employed/Unemployed)
- Monthly Salary (INR)
- Education Level
- Industry Sector
- Years of Experience
- Location
- Perceived AI Risk
- Date of Data Recording

Transformations & Cleaning Applied:

The raw dataset underwent comprehensive transformations to convert it into its clean, analysis-ready form: - Missing Values: Identified and handled using either row elimination (where critical data was missing) or imputation techniques. - Duplicate Records: Identified using row comparison and removed to prevent analytical skew. - Inconsistent Formatting: Unified inconsistent naming in columns (like 'monthly_salary_(inr)' → 'Monthly Salary (INR)'), capitalization, and string spacing. - Incorrect Data Types: Converted columns like salary from string/object to float for numerical analysis. - Outliers: Detected and handled based on domain logic and distribution analysis. - Categorization: Converted numeric ages into grouped age categories for comparative analysis. - Standardization: Uniform labels for employment status, industry names, education, and AI risk levels were applied for visualization clarity.

Purpose & Utility:

This dataset is ideal for learners and professionals who want to understand: - The impact of messy data on visualization and insights - How transformation steps can dramatically improve data interpretation - Practical examples of preprocessing techniques before feeding into ML models or BI tools

It's also useful for: - Training ML models with clean inputs
- Data storytelling with visual clarity
- Demonstrating reproducibility in data cleaning pipelines

By examining both the messy and clean datasets, users gain a deeper appreciation for why “garbage in, garbage out” rings true in the world of data science.

The share of young people who are not in employment, education, or training (NEET), as a percentage of the total number of young people in the corresponding age group, by gender.

Analysis of ‘International Educational Attainment by Year & Age’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/international-comp-attainmente on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

The National Center for Education Statistics (NCES) is the primary federal entity for collecting and analyzing data related to education in the U.S. and other nations. NCES is located within the U.S. Department of Education and the Institute of Education Sciences. NCES fulfills a Congressional mandate to collect, collate, analyze, and report complete statistics on the condition of American education; conduct and publish reports; and review and report on education activities internationally.

• Table 603.10. Percentage of the population 25 to 64 years old who completed high school, by age group and country: Selected years, 2001 through 2012
• Table 603.20. Percentage of the population 25 to 64 years old who attained selected levels of postsecondary education, by age group and country: 2001 and 2012
• Table 603.30. Percentage of the population 25 to 64 years old who attained a bachelor's or higher degree, by age group and country: Selected years, 1999 through 2012
• Table 603.40 Percentage of the population 25 to 64 years old who attained a postsecondary vocational degree, by age group and country: Selected years, 1999 through 2012
• Table 603.50 Number of bachelor's degree recipients per 100 persons at the typical minimum age of graduation, by sex and country: Selected years, 2005 through 2012
• Table 603.60. Percentage of postsecondary degrees awarded to women, by field of study and country: 2013
• Table 603.70. Percentage of bachelor's or equivalent degrees awarded in mathematics, science, and engineering, by field of study and country: 2013
• Table 603.80. Percentage of master's or equivalent degrees and of doctoral or equivalent degrees awarded in mathematics, science, and engineering, by field of study and country: 2013
• Table 603.90. Employment to population ratios of -25 to 64-year-olds, by sex, highest level of educational attainment, and country: 2014

Source: https://nces.ed.gov/programs/digest/current_tables.asp

This dataset was created by National Center for Education Statistics and contains around 100 samples along with Unnamed: 20, Unnamed: 24, technical information and other features such as: - Unnamed: 11 - Unnamed: 16 - and more.

How to use this dataset

• Analyze Unnamed: 15 in relation to Unnamed: 6
• Study the influence of Unnamed: 1 on Unnamed: 10
• More datasets

Acknowledgements

If you use this dataset in your research, please credit National Center for Education Statistics

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

Further eduction learners not in employment, their reasons for doing the course, their satisfaction with the course and what impact it had on economic and social outcomes.

Employment income (in 2019 and 2020) by detailed major field of study and highest certificate, diploma or degree, including work activity (full time full year, part time full year, or part year).

Number of immigrants in the labour force (employment and unemployment) and not in the labour force, unemployment rate, participation rate, and employment rate, by educational attainment, immigrant status, gender, and age group.

The age groups available in the dataset are: 15+, 25+, 25-34, 25-54 and 25-64. Type of work includes full-time and part-time. The educational levels include: 0-8 yrs., some high school, high school graduate, some post-secondary, post-secondary certificate diploma and university degree. Wages include average weekly wage rate. The immigration statuses include: total landed immigrants (very recent immigrants, recent immigrants, established immigrants), non-landed immigrants and born in Canada.

Overview: Each quarter, the Temporary Foreign Worker Program (TFWP) publishes Labour Market Impact Assessment (LMIA) statistics on Open Government Data Portal, including quarterly and annual LMIA data related to, but not limited to, requested and approved TFW positions, employment location, employment occupations, sectors, TFWP stream and temporary foreign workers by country of origin. The TFWP does not collect data on the number of TFWs who are hired by an employer and have arrived in Canada. The decision to issue a work permit rests with Immigration, Refugees and Citizenship Canada (IRCC) and not all positions on a positive LMIA result in a work permit. For these reasons, data provided in the LMIA statistics cannot be used to calculate the number of TFWs that have entered or will enter Canada. IRCC publishes annual statistics on the number of foreign workers who are issued a work permit: https://open.canada.ca/data/en/dataset/360024f2-17e9-4558-bfc1-3616485d65b9. Please note that all quarterly tables have been updated to NOC 2021 (5 digit and training, education, experience and responsibilities (TEER) based). As such, Table 5, 8, 17, and 24 will no longer be updated but will remain as archived tables. Frequency of Publication: Quarterly LMIA statistics cover data for the four quarters of the previous calendar year and the quarter(s) of the current calendar year. Quarterly data is released within two to three months of the most recent quarter. The release dates for quarterly data are as follows: Q1 (January to March) will be published by early June of the current year; Q2 (April to June) will be published by early September of the current year; Q3 (July to September) will be published by early December of the current year; and Q4 (October to December) will be published by early March of the next year. Annual statistics cover eight consecutive years of LMIA data and are scheduled to be released in March of the next year. Published Data: As part of the quarterly release, the TFWP updates LMIA data for 28 tables broken down by: TFW positions: Tables 1 to 10, 12, 13, and 22 to 24; LMIA applications: Tables 14 to 18; Employers: Tables 11, and 19 to 21; and Seasonal Agricultural Worker Program (SAWP): Tables 25 to 28. In addition, the TFWP publishes 2 lists of employers who were issued a positive or negative LMIA: Employers who were issued a positive LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/90fed587-1364-4f33-a9ee-208181dc0b97/resource/b369ae20-0c7e-4d10-93ca-07c86c91e6fe); and Employers who were issued a negative LMIA by Program Stream, NOC, and Business Location (https://open.canada.ca/data/en/dataset/f82f66f2-a22b-4511-bccf-e1d74db39ae5/resource/94a0dbee-e9d9-4492-ab52-07f0f0fb255b). Things to Remember: 1. When data are presented on positive or negative LMIAs, the decision date is used to allocate which quarter the data falls into. However, when data are presented on when LMIAs are requested, it is based on the date when the LMIA is received by ESDC. 2. As of the publication of 2022Q1- 2023Q4 data (published in April 2024) and going forward, all LMIAs in support of 'Permanent Residence (PR) Only' are included in TFWP statistics, unless indicated otherwise. All quarterly data in this report includes PR Only LMIAs. Dual-intent LMIAs and corresponding positions are included under their respective TFWP stream (e.g., low-wage, high-wage, etc.) This may impact program reporting over time. 3. Attention should be given for data that are presented by ‘Unique Employers’ when it comes to manipulating the data within that specific table. One employer could be counted towards multiple groups if they have multiple positive LMIAs across categories such as program stream, province or territory, or economic region. For example, an employer could request TFWs for two different business locations, and this employer would be counted in the statistics of both economic regions. As such, the sum of the rows within these ‘Unique Employer’ tables will not add up to the aggregate total.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

APS Well-Being Datasets
From 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.

APS disability variables
Over time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage.

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

Latest edition information

For the sixth edition (July 2023), the SOC variables NSECM20, NSECMJ20, SC20LMJ, SC20LMN, SC20MMJ, SC20MMN, SC20SMJ, SC20SMN, SOC20M, SC2010M and the person income weight PIWTA22 were replaced with revised versions. Further information can be found in the ONS article published on 11 July 2023: https://www.ons.gov.uk/employmentandlabourmarket/peopleinwork/employmentandemployeetypes/articles/revisionofmiscodedoccupationaldataintheonslabourforcesurveyuk/january2021toseptember2022">Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022.

For further detailed information about methodology, users should consult the Labour Force Survey User Guide, included with the APS documentation. For variable and value labelling and coding frames that are not included either in the data or in the current APS documentation, users are advised to consult the latest versions of the LFS User Guides, which are available from the ONS Labour Force Survey - User Guidance webpages.

Occupation data for 2021 and 2022
The ONS has identified an issue with the collection of some occupational data in 2021 and 2022 data files in a number of their surveys. While they estimate any impacts will be small overall, this will affect the accuracy of the breakdowns of some detailed (four-digit Standard Occupational Classification (SOC)) occupations, and data derived from them. None of ONS' headline statistics, other than those directly sourced from occupational data, are affected and you can continue to rely on their accuracy. The affected datasets have now been updated. Further information can be found in the ONS article published on 11 July 2023: Revision of miscoded occupational data in the ONS Labour Force Survey, UK: January 2021 to September 2022

APS Well-Being Datasets
From 2012-2015, the ONS published separate APS datasets aimed at providing initial estimates of subjective well-being, based on the Integrated Household Survey. In 2015 these were discontinued. A separate set of well-being variables and a corresponding weighting variable have been added to the April-March APS person datasets from A11M12 onwards. Further information on the transition can be found in the Personal well-being in the UK: 2015 to 2016 article on the ONS website.

APS disability variables
Over time, there have been some updates to disability variables in the APS. An article explaining the quality assurance investigations on these variables that have been conducted so far is available on the ONS Methodology webpage.

• age: single year of age, year and month of birth, age completed full-time education and age obtained highest qualification, age of oldest dependent child and age of youngest dependent child
• family unit and household: including a number of variables concerning the number of dependent children in the family according to their ages, relationship to head of household and relationship to head of family
• nationality and country of origin
• geography: including county, unitary/local authority, place of work, Nomenclature of Territorial Units for Statistics 2 (NUTS2) and NUTS3 regions, and whether lives and works in same local authority district
• health: including main health problem, and current and past health problems
• education and apprenticeship: including numbers and subjects of various qualifications and variables concerning apprenticeships
• industry: including industry, industry class and industry group for main, second and last job, and industry made redundant from
• occupation: including 4-digit Standard Occupational Classification (SOC) for main, second and last job and job made redundant from
• system variables: including week number when interview took place and number of households at address

The Secure Access data have more restrictive access conditions than those made available under the standard EUL. Prospective users will need to gain ONS Accredited Researcher status, complete an extra application form and demonstrate to the data owners exactly why they need access to the additional variables. Users are strongly advised to first obtain the standard EUL version of the data to see if they are sufficient for their research requirements.

See notice below about this dataset

This dataset provides the average earnings by student group per district.  Wage records are obtained from the Massachusetts Department of Unemployment Assistance (DUA) using a secure, anonymized matching process with limitations. For details on the process and suppression rules, please visit the Employment and Earnings of High School Graduates dashboard.

This dataset is one of three containing the same data that is also published in the Employment and Earnings of High School Graduates dashboard: Average Earnings by Student Group Average Earnings by Industry College and Career Outcomes

2025 Update on DESE Data on Employment and Earnings 

The data link between high school graduates and future earnings makes it possible to follow students beyond high school and college into the workforce, enabling long-term evaluation of educational programs using workforce outcomes.

While DESE has published these data in the past, as of June 2025 we are temporarily pausing updates due to an issue conducting the link that was brought to our attention in 2023 by a team of researchers. The issue impacts the earnings information for students who never attended a postsecondary institution or who only attended private or out-of-state colleges or universities, beginning with the 2017 high school graduation cohort, with growing impact in each successive high school graduation cohort.

The issue does not impact the earnings information for students who attended a Massachusetts public institution of higher education, and earnings data for those students will continue to be updated.

Once a solution is found, the past cohorts of data with low match rates will be updated. DESE and partner agencies are exploring linking strategies to maximize the utility of the information.

More detailed information can be found in the attached memo provided by the research team from the Annenberg Institute. We thank them for calling this issue to our attention.