Educators

Education and workforce training providers in Texas need to assess the employment outcomes of their graduates, but the necessary data is not collected.

In Texas, the state collects workforce data broadly, showing the broad industry in which workers are employed, but not their specific occupation or job function. This makes it difficult for educators to evaluate if their students obtain jobs in their fields of study. Additionally, the state of Texas does not collect hours worked, making it difficult to categorize jobs by full- or part-time.

The current lack of data about occupation and hours worked by Texans also makes it difficult for schools and training providers to assess the impact of their programs on their learners’ employment, potential career paths, and economic mobility.

Credentials and other skills-based programs outside of traditional university education are often launched to address specific needs, and usually do not provide academic credit. Because they are outside of the traditional academic system, there are no systematic processes to collect demographic, enrollment and completion data for learners. Even when skills-based programs are credit-based, the demographic information about learners is still limited because the established processes for collecting this information typically overlook non-traditional learners (like parents and veterans).

In lieu of better metrics, some schools survey graduates to better understand their career paths, occupation and salaries after they’ve completed their program. While these surveys help fill in gaps in employment data, they rely on self-reporting and do not provide a complete picture. Surveys vary by school, are optional, and their use within schools can vary by program and year. They also are subject to bias, including memory bias, and often have a low response rate.

A college administrator wants to know how many graduates from their program are employed in the field they studied. However, a graduate from their financial management program who works in the finance department of a hospital would be recorded as working in healthcare using industry-level data, and not specifically finance, because we do not collect job-level data.

Education and workforce training organizations want to understand if students and workers that completed their programs went on to earn a family-sustaining wage. While earnings data is collected for all non-contract workers in the state of Texas, that data is not linked to the individual students or education programs.

This prevents the institutions and organizations that provided those programs from knowing if their programming is resulting in successful earners.

Additionally, schools and training providers may know whether their graduates have found employment, but an ultimately more important question goes unanswered: are these jobs aligned with the specific skills that the graduates acquired through their programs?

Currently, data about employment, skills, and skills attainment are not connected at the state level. Without linking these data points together, it’s difficult to know whether programs are preparing workers for their chosen careers and how careers evolve over time.

A college administrator wants to evaluate the success of their program and whether they are on par with other schools in preparing students for the workforce. However, earnings data is not linked at scale to individuals or programs and when data is linked it is often out-of-date. The school has difficulty determining if they are under or outperforming other schools in preparing their students, and in using data to inform operational or program changes.

Understanding the learner or worker’s journey requires that educators and training providers piece together data from multiple sources, as there is no single comprehensive source available.

Since education and training providers do not all have equal capacity to access, combine, and analyze the workforce data, some institutions and programs are at a disadvantage in tracking the long-term success of their graduates. Ultimately, this lack of accessibility to comprehensive data hampers efforts to ensure that all learners are being adequately prepared for the workforce and advancing in their careers.

Third-party solutions where labor and wage linkages are inferred from internet sources can be used in lieu of linked data, but these solutions are often cost-prohibitive and are only a proxy for linking skills with employment data.

Occupation data, when it’s collected, is typically not disaggregated and schools do not have access to more specific data. Because the data is too broad, schools struggle to evaluate how programs, grants, and resources might affect students of different identities or experiences.

There are grants for training programs awarded to students above the age of 50, such as grants awarded to special populations like veterans. However, college administrators may not know the impact of the grants on the grantees in the long-run because employment data, such as employment status, type of employment, and industry of employment, is not always available disaggregated by age or veteran status.