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A list of available data tools and ideas to address existing data gaps that limit understanding of current conditions, trajectory, and where things should be headed. Items will often provide ideas to improve data collection through existing collection processes as well as new ideas and partnerships to expand such efforts.
Governments should evaluate the extent to which psychometric testing—the use of questionnaires aimed at psychologically profiling job candidates—provides accurate insights about whether someone is qualified for a job. Decision-makers should consider regulating or limiting employers' use of these tools to make employment decisions based on their potential to create discriminatory outcomes.
Governments should rigorously evaluate, and consider regulating, employers' use of predictive algorithmic software to make decisions about job recruitment, hiring, compensation, and employee evaluation. In light of the growing prevalence of these tools, which create the potential for unfair profiling and discrimination, decision-makers should have accurate information about what inputs and functionalities produce these outcomes.
Governments should make available in cross-state wage data exchanges (such as the State Wage Interchange System) information that employers report quarterly to measure the outcomes of postsecondary education and training programs. States can also use this data to regularly evaluate whether schools should be included on their ETPLs.
Governments should collect information about credentials and their value through an assessment of credentialing options and their alignments with industry demands to ensure that programs are ready to evolve to meet future needs.
States should collect more data from employers to improve outcomes tracking for those participating in the UI system. Additional data points may include hours worked, occupational codes and position titles.
While some automated technologies raise productivity and deliver economic benefits that can help offset the impacts of displacement, others (labeled by some researchers as “so-so technology") may deliver limited benefit while adversely impacting workers. Governments and employers need more investigation and a more nuanced understanding of the merits of specific types of automation and technology, such as self-checkout. Defining “good” and “bad”—or, perhaps, “worse”—automation can be useful in regulating change, determining policy priorities, and deploying employer or worker incentives.
Policymakers and employers must address the question of what good jobs will be created to replace the lost jobs in administrative and clerical work, especially those that do not require an advanced degree to earn a family-sustaining wage. They should conduct further investigations to understand how clerical workers weathered transitions and job dislocation during the last several decades, when automation shrank the number of administrative jobs. The resulting lessons can be useful in connecting this workforce to new opportunities and in helping administrative workers keep pace with technological changes.
Governments should fully assess the impact of workforce changes occurring due to technological disruption, demographic changes and business innovation. Measures should take into account the impact of such changes on participation rate, new forms of work, skill gaps, sector obsolescence, and sector growth. Such measurements will allow policy makers to create timely, evidence-based policies.
Discussions on decent work should go beyond distinctions between standard and non-standard forms of work to consider the quality of jobs; governments should develop measures to assess this quality based on objective and measurable dimensions. This will focus solutions on improving the quality of jobs rather than simply the nature of the contract.
Governments should create regulations that require companies of a certain size to report on their contractual agreements with workers. Companies would be required to report the number of zero-hour contract workers who request and obtain fixed hour contracts, as well as the number of temporary employment agency workers who request and are granted permanent positions. This would make public company policies for outside observers that would otherwise be kept behind closed doors.
Businesses should measure and publish real time labor market data. Data covering skills demand and supply, future job forecasts, and other factors that influence employability could be used to create tools aimed at helping workers plan their careers and to design upskilling training program.. Policymakers and researchers could also use these data to better study local labor markets in order to plan for future changes.
Governments should use labor market data to develop new digital tools which make insights easier for job seekers to interpret. Such tools could help young people to make more informed training and skills development decisions, for instance, based on which jobs are likely to be at most risk of disappearing in the next two decades and the skills required for them. Such tools would help to match current skills development with future skills needs.
Governments should use state data systems to evaluate training programs. States could combine administrative data from Unemployment Insurance (UI) wage records with educational data via a state longitudinal data system (SLDS) to the measure the effectiveness of training programs. This increase in transparency would help workers and students to make more informed choices about training programs.
State policymakers should add new data fields to state Unemployment Insurance wage records, including occupational titles, work hours, credentials, and work sites. Most states currently do not include enough data in records to analyze training programs’ effectiveness, and some states do not link this employment data to educational data at all. A 2014 BLS survey found that states that collected enhanced wage records reported that the data were extremely helpful in estimating hourly earnings, understanding career progression from occupation to occupation, assessing the effectiveness of workforce training, and making occupational projections.
The business community should be more transparent with its workforce data so that governments, nonprofit organizations, and other service providers can promote the use of education and workforce data to inform students, educators, policymakers, and investors. Additionally, in order for policymakers to address employer hiring challenges and skills gaps reported by employers, better information and analysis improves decisions with regard to where to make investments.
Employers and governments should commit resources to developing new online analytical tools for a more effective matching process among jobs, workers, and training programs. These tools can employ data on required competencies, resumes, online job ads, and occupational demand to connect job seekers to jobs and postsecondary education and training programs that meet their needs. They can also assist mid- and late-career adults who need additional education, training, and career services to remain in the workforce.
Policymakers should use projections of education demand to inform workforce development planning. Proprietary analytical information and college administrative data can also be linked with state wage records in the process of retaining and attracting employers and industries to the state. Employer demand for talent has elevated the importance of workers with specific skills gained through postsecondary education and training.
Educational institutions should explore the use of occupational data and employer/industry expert feedback to develop competencies and learning outcomes for postsecondary education and training programs. Employers also can tailor their job ads to include academic competencies that employees need. Whether as part of a competency-based or traditional education program, curriculum alignment that starts with data analysis is necessary for colleges to keep student learning relevant to the competencies demanded by industry, as well as to establish stronger ties to employers.
Employers need to be more transparent about what skills and knowledge they need in their future employees. Governments can assist in this effort by providing open, consistent standards for hiring announcements that would make the information easy to access and query, and easy for third-party providers to share through targeted applications. Achieving this goal will require close cooperation among governments, employers, and companies that offer online job listings or aggregate labor market data. This transparency and consistency will allow job seekers—including those pursuing educational and training opportunities—make more informed decisions.
Governments can create job registries that provide information to students, employees, and educational institutions about credentials and skills that are in demand. Such initiatives encourage employers to collaborate in forecasting their future workforce needs and create common definitions to signal those needs. That information in turn can help educational institutions develop or expand programs that lead to higher-quality jobs.
Governments should launch open data initiatives to encourage the sharing of public and private labor market data. Unleashing the full potential of labor market data though the creation of open public-private data infrastructures can empower students and employees and reduce labor market frictions.
Governments should evaluate the extent to which psychometric testing—the use of questionnaires aimed at psychologically profiling job candidates—provides accurate insights about whether someone is qualified for a job. Decision-makers should consider regulating or limiting employers' use of these tools to make employment decisions based on their potential to create discriminatory outcomes.
Governments should rigorously evaluate, and consider regulating, employers' use of predictive algorithmic software to make decisions about job recruitment, hiring, compensation, and employee evaluation. In light of the growing prevalence of these tools, which create the potential for unfair profiling and discrimination, decision-makers should have accurate information about what inputs and functionalities produce these outcomes.
Governments should make available in cross-state wage data exchanges (such as the State Wage Interchange System) information that employers report quarterly to measure the outcomes of postsecondary education and training programs. States can also use this data to regularly evaluate whether schools should be included on their ETPLs.
Governments should collect information about credentials and their value through an assessment of credentialing options and their alignments with industry demands to ensure that programs are ready to evolve to meet future needs.
States should collect more data from employers to improve outcomes tracking for those participating in the UI system. Additional data points may include hours worked, occupational codes and position titles.
While some automated technologies raise productivity and deliver economic benefits that can help offset the impacts of displacement, others (labeled by some researchers as “so-so technology") may deliver limited benefit while adversely impacting workers. Governments and employers need more investigation and a more nuanced understanding of the merits of specific types of automation and technology, such as self-checkout. Defining “good” and “bad”—or, perhaps, “worse”—automation can be useful in regulating change, determining policy priorities, and deploying employer or worker incentives.
Policymakers and employers must address the question of what good jobs will be created to replace the lost jobs in administrative and clerical work, especially those that do not require an advanced degree to earn a family-sustaining wage. They should conduct further investigations to understand how clerical workers weathered transitions and job dislocation during the last several decades, when automation shrank the number of administrative jobs. The resulting lessons can be useful in connecting this workforce to new opportunities and in helping administrative workers keep pace with technological changes.
Governments should fully assess the impact of workforce changes occurring due to technological disruption, demographic changes and business innovation. Measures should take into account the impact of such changes on participation rate, new forms of work, skill gaps, sector obsolescence, and sector growth. Such measurements will allow policy makers to create timely, evidence-based policies.
Discussions on decent work should go beyond distinctions between standard and non-standard forms of work to consider the quality of jobs; governments should develop measures to assess this quality based on objective and measurable dimensions. This will focus solutions on improving the quality of jobs rather than simply the nature of the contract.
Governments should create regulations that require companies of a certain size to report on their contractual agreements with workers. Companies would be required to report the number of zero-hour contract workers who request and obtain fixed hour contracts, as well as the number of temporary employment agency workers who request and are granted permanent positions. This would make public company policies for outside observers that would otherwise be kept behind closed doors.
Governments should use labor market data to develop new digital tools which make insights easier for job seekers to interpret. Such tools could help young people to make more informed training and skills development decisions, for instance, based on which jobs are likely to be at most risk of disappearing in the next two decades and the skills required for them. Such tools would help to match current skills development with future skills needs.
Governments should use state data systems to evaluate training programs. States could combine administrative data from Unemployment Insurance (UI) wage records with educational data via a state longitudinal data system (SLDS) to the measure the effectiveness of training programs. This increase in transparency would help workers and students to make more informed choices about training programs.
State policymakers should add new data fields to state Unemployment Insurance wage records, including occupational titles, work hours, credentials, and work sites. Most states currently do not include enough data in records to analyze training programs’ effectiveness, and some states do not link this employment data to educational data at all. A 2014 BLS survey found that states that collected enhanced wage records reported that the data were extremely helpful in estimating hourly earnings, understanding career progression from occupation to occupation, assessing the effectiveness of workforce training, and making occupational projections.
Employers and governments should commit resources to developing new online analytical tools for a more effective matching process among jobs, workers, and training programs. These tools can employ data on required competencies, resumes, online job ads, and occupational demand to connect job seekers to jobs and postsecondary education and training programs that meet their needs. They can also assist mid- and late-career adults who need additional education, training, and career services to remain in the workforce.
Policymakers should use projections of education demand to inform workforce development planning. Proprietary analytical information and college administrative data can also be linked with state wage records in the process of retaining and attracting employers and industries to the state. Employer demand for talent has elevated the importance of workers with specific skills gained through postsecondary education and training.
Employers need to be more transparent about what skills and knowledge they need in their future employees. Governments can assist in this effort by providing open, consistent standards for hiring announcements that would make the information easy to access and query, and easy for third-party providers to share through targeted applications. Achieving this goal will require close cooperation among governments, employers, and companies that offer online job listings or aggregate labor market data. This transparency and consistency will allow job seekers—including those pursuing educational and training opportunities—make more informed decisions.
Governments can create job registries that provide information to students, employees, and educational institutions about credentials and skills that are in demand. Such initiatives encourage employers to collaborate in forecasting their future workforce needs and create common definitions to signal those needs. That information in turn can help educational institutions develop or expand programs that lead to higher-quality jobs.
Governments should launch open data initiatives to encourage the sharing of public and private labor market data. Unleashing the full potential of labor market data though the creation of open public-private data infrastructures can empower students and employees and reduce labor market frictions.
While some automated technologies raise productivity and deliver economic benefits that can help offset the impacts of displacement, others (labeled by some researchers as “so-so technology") may deliver limited benefit while adversely impacting workers. Governments and employers need more investigation and a more nuanced understanding of the merits of specific types of automation and technology, such as self-checkout. Defining “good” and “bad”—or, perhaps, “worse”—automation can be useful in regulating change, determining policy priorities, and deploying employer or worker incentives.
Policymakers and employers must address the question of what good jobs will be created to replace the lost jobs in administrative and clerical work, especially those that do not require an advanced degree to earn a family-sustaining wage. They should conduct further investigations to understand how clerical workers weathered transitions and job dislocation during the last several decades, when automation shrank the number of administrative jobs. The resulting lessons can be useful in connecting this workforce to new opportunities and in helping administrative workers keep pace with technological changes.
Governments should create regulations that require companies of a certain size to report on their contractual agreements with workers. Companies would be required to report the number of zero-hour contract workers who request and obtain fixed hour contracts, as well as the number of temporary employment agency workers who request and are granted permanent positions. This would make public company policies for outside observers that would otherwise be kept behind closed doors.
Businesses should measure and publish real time labor market data. Data covering skills demand and supply, future job forecasts, and other factors that influence employability could be used to create tools aimed at helping workers plan their careers and to design upskilling training program.. Policymakers and researchers could also use these data to better study local labor markets in order to plan for future changes.
The business community should be more transparent with its workforce data so that governments, nonprofit organizations, and other service providers can promote the use of education and workforce data to inform students, educators, policymakers, and investors. Additionally, in order for policymakers to address employer hiring challenges and skills gaps reported by employers, better information and analysis improves decisions with regard to where to make investments.
Employers and governments should commit resources to developing new online analytical tools for a more effective matching process among jobs, workers, and training programs. These tools can employ data on required competencies, resumes, online job ads, and occupational demand to connect job seekers to jobs and postsecondary education and training programs that meet their needs. They can also assist mid- and late-career adults who need additional education, training, and career services to remain in the workforce.
Policymakers should use projections of education demand to inform workforce development planning. Proprietary analytical information and college administrative data can also be linked with state wage records in the process of retaining and attracting employers and industries to the state. Employer demand for talent has elevated the importance of workers with specific skills gained through postsecondary education and training.
Educational institutions should explore the use of occupational data and employer/industry expert feedback to develop competencies and learning outcomes for postsecondary education and training programs. Employers also can tailor their job ads to include academic competencies that employees need. Whether as part of a competency-based or traditional education program, curriculum alignment that starts with data analysis is necessary for colleges to keep student learning relevant to the competencies demanded by industry, as well as to establish stronger ties to employers.
Employers need to be more transparent about what skills and knowledge they need in their future employees. Governments can assist in this effort by providing open, consistent standards for hiring announcements that would make the information easy to access and query, and easy for third-party providers to share through targeted applications. Achieving this goal will require close cooperation among governments, employers, and companies that offer online job listings or aggregate labor market data. This transparency and consistency will allow job seekers—including those pursuing educational and training opportunities—make more informed decisions.
Governments should make available in cross-state wage data exchanges (such as the State Wage Interchange System) information that employers report quarterly to measure the outcomes of postsecondary education and training programs. States can also use this data to regularly evaluate whether schools should be included on their ETPLs.
While some automated technologies raise productivity and deliver economic benefits that can help offset the impacts of displacement, others (labeled by some researchers as “so-so technology") may deliver limited benefit while adversely impacting workers. Governments and employers need more investigation and a more nuanced understanding of the merits of specific types of automation and technology, such as self-checkout. Defining “good” and “bad”—or, perhaps, “worse”—automation can be useful in regulating change, determining policy priorities, and deploying employer or worker incentives.
Policymakers and employers must address the question of what good jobs will be created to replace the lost jobs in administrative and clerical work, especially those that do not require an advanced degree to earn a family-sustaining wage. They should conduct further investigations to understand how clerical workers weathered transitions and job dislocation during the last several decades, when automation shrank the number of administrative jobs. The resulting lessons can be useful in connecting this workforce to new opportunities and in helping administrative workers keep pace with technological changes.
Policymakers should use projections of education demand to inform workforce development planning. Proprietary analytical information and college administrative data can also be linked with state wage records in the process of retaining and attracting employers and industries to the state. Employer demand for talent has elevated the importance of workers with specific skills gained through postsecondary education and training.
Educational institutions should explore the use of occupational data and employer/industry expert feedback to develop competencies and learning outcomes for postsecondary education and training programs. Employers also can tailor their job ads to include academic competencies that employees need. Whether as part of a competency-based or traditional education program, curriculum alignment that starts with data analysis is necessary for colleges to keep student learning relevant to the competencies demanded by industry, as well as to establish stronger ties to employers.
