Needed: More and Better Data for a More Equitable Federal Workforce
The federal government needs quality data systems in place to measure how well education and training programs are preparing skilled workers.
Black, Latino, and Indigenous populations in the United States have faced a long history of discrimination and institutional racism, including in the workforce. To achieve equity in education, the workforce, and economic opportunity, the federal government will need more and better highly accessible data to identify and analyze racial inequalities, develop solutions, and track progress.
A growing skills gap threatens the success of participants in the economy and workforce. Right now, the majority of jobs require skills training beyond high school, but too few workers can access the training and education needed to fill in-demand jobs. State and federal government officials need to put quality data systems in place to measure how well education and training programs are preparing skilled workers, and to ensure a thriving and equitable 21st century economy.
The Center for Open Data Enterprise, a Washington, D.C.-based nonprofit focused on maximizing the value of open data for the public good, developed their Open Data for Racial Equity program to aid federal efforts in measuring and supporting the overall advancement of racial equity. As part of this program, CODE has partnered with the IBM Center for the Business in Government to offer solutions for how the federal government can leverage data to improve racial equity in healthcare and fair housing. Now the IBM Center has published “Leveraging Data to Improve Minority Workforce Opportunity and Equity,” which examines the state of federal legislation for equity in labor opportunities, racial discrimination in the federal workforce, the use of workforce and education data, and federal data sources and initiatives. It also outlines some of the primary drivers of racial inequity in workforce opportunity, and identifies data-driven opportunities and models to address them.
Equitable data within federal sources. Demographics data is primarily collected voluntarily, often resulting in missing or incomplete data. Respondents may hesitate to provide this information due to mistrust and fear that the data will be misused. However, this information is necessary to gain an accurate representation of a community, school district, or workforce population, and when analyzing racial disparities in employment or school district funding. The Equitable Data Working Group, created by an executive order from President Biden, was tasked with prioritizing data related to equity within federal data sources. The group’s recent report includes recommendations for generating and using disaggregated survey data to characterize the experiences of historically underserved groups; increasing non-Federal access to disaggregated data to support equity efforts; and conducting equity assessments of Federal programs to identify areas for improvement.
Federal data sharing. Inter- and intra-agency data sharing is essential for agencies to develop data-driven policies to improve equitable labor outcomes. However, a lack of interoperable data, legal barriers, and legacy data systems make it extremely difficult for this sharing to take place efficiently. In addition to developing and implementing data standards, the federal government could build a core collection of agency templates and standard clauses that can be used to draft data use agreements. Agencies such as the Centers for Disease Control and Prevention and U.S. Geological Survey have developed their own sample data sharing agreements. These documents can be used as templates by other federal entities and as a model to create their own DUAs.
Unconscious bias in recruitment process and discriminative job requirements. Attitudes and behaviors from implicit biases can lead to discrimination in the recruitment, hiring, promotion, and treatment of minorities in the workplace. New and emerging technologies, such as open source recruitment platforms and machine learning algorithms, should be incorporated into the job recruitment process to systematically decrease the discriminatory consequences of unconscious bias. Organizations should also consider partnering with leaders of minority communities and minority serving institutions to improve their hiring practices.
Access to education and training to qualify for good jobs. The high school curriculum necessary to receive a diploma is often not sufficient for college admission, particularly when high schools lack advanced coursework such as high-level math courses, laboratory science, and foreign languages. Schools with high proportions of students of color and students from low-income families are less likely to offer these advanced courses. There is also insufficient evidence that public workforce training programs are effective at improving wages and increasing access to good jobs. Consequently, collaboration and coordination is essential to better align the requirements for high school graduation with the admissions requirements for state public university systems (i.e. high school-to-college intervention programs). Substantial Federal investment into skills and workforce training must also be a priority to improve outcomes for disadvantaged people, non-degree holders, and the economy.
Ineffective communication of employee skills and records of learning. Skills and competencies are the most essential requirements for success in the labor market. There is now a need to standardize ways of describing skills and competencies, in order to make it easier and more efficient to match job applicants’ skills to the needs of potential employers. Transferable data, including labeled skills and competency data can be used to train supervised learning algorithms to pick out appropriate text from resumes and other documents that reflect the desired expertise. The federal government can play a vital role in co-designing and communicating the value of such data to federal and state employers, and eventually to all employers throughout the nation.
A long history of discrimination and systemic inequity in the U.S. has resulted in major disparities in workforce opportunities for racial and ethnic minorities. A number of government programs and organizations are investing in infrastructure to support longitudinal data collection on education, employment statistics, skills information, and other factors to help reduce these disparities. The federal government should continue this progress, launch further initiatives, and provide the oversight needed to coordinate efforts and make lasting change.
For more information, contact Senior Research Consultant Temilola Afolabi at firstname.lastname@example.org.