Over Half of Grantees Could Track Some Individuals from Early Education into the Workforce, but Data Are Generally Limited

		Over Half of Grantees Have the Ability to Match Some Individual Records from Early Education to Workforce

After analyzing data from DQC’s 2013 survey, we determined that over  half of grantees have the ability to match data—reliably connect the same  record in two or more databases—for some individuals from early   As shown in figure 1, individuals can  education and into the workforce.take different paths to move from early education into the workforce: (1)  via K-12 or (2) via K-12 and postsecondary. Regardless, as the match  rate—that is, the percent of unique student records reliably connected  between databases—increases, the number of grantees able to match  data between sectors decreases. Our analysis of the DQC survey data also shows that more grantees  match data among the education sectors than between the education and  workforce sectors, though—as was the case with matching data from  early education to workforce—the number of grantees that match data   For example, 43  decreases as the match rate increases (see table 4).grantees reported matching data between the K-12 and early education  sectors, and 31 grantees reported matching data between the K-12 and  workforce sectors at least to some degree; however, the number of  grantees that reported matching data between these same sectors drops  to 37 and 9, respectively, at a match rate of 95 percent or more. For example,  only 6 of 31 grantees reported that they were able to match data on all  seven programs between the K-12 and workforce sectors, which include  unemployment insurance wage records, unemployment benefit claims  data, Workforce Investment Act of 1998 (WIA) adult or dislocated worker  program, WIA youth program, adult basic and secondary education,  Wagner-Peyser Act employment services, and Temporary Assistance for  Needy Families (TANF). For example, officials in  Washington said they used their initial SLDS and WDQI grants to focus  on building their K-12 data system and workforce systems, respectively. Longitudinal Data Analysis Has Informed State and Local Policy Making and Helped States Shape Their Research Agendas

		Grantees Reported Using Some Longitudinal Data to Inform Decision and Policy Making

According to our analysis of the DQC survey data and our interviews with  selected states, SLDS and WDQI grantees use longitudinal data to  examine education outcomes and to inform policy decisions. They also help guide  school-, district-, and state-level improvement efforts. Also, 29 grantees responded to the DQC  survey that they produce early warning reports, which identify students  who are most likely to be at risk of academic failure or dropping out of  school. While many grantees reported on the DQC survey that they use  longitudinal data to analyze outcomes for students and workers and to  make policy decisions, officials from all five grantee states we interviewed  told us that these analyses are limited because they are still developing  their longitudinal data systems. Each provided technical comments, which we incorporated as  appropriate. Appendix I: Objectives, Scope, and Methodology

The objectives of this report were to examine: (1) the extent to which  Statewide Longitudinal Data Systems (SLDS) and Workforce Data Quality  Initiative (WDQI) grantees match individual student and worker records  and share data between the education and workforce sectors; and (2)  how grantees are using longitudinal data to help improve education and  workforce outcomes. To answer our objectives, we analyzed state-level data from a 2013  survey conducted by the Data Quality Campaign (DQC), a nonprofit  organization that works with state officials and others to support the  effective use of data to improve student achievement. In addition to our analysis of DQC survey data we conducted interviews  with a nongeneralizable sample of five grantees as well as relevant  federal agencies and nonprofit organizations. We selected these grantees based  on factors including the differing levels of progress they have made in  establishing data linkages and the federal funding they have received  from the SLDS and WDQI programs. based on  individual student data. College and career readiness reports

Reports designed to identify students who are on track for readiness or  success in college or careers.
Why GAO Did This Study

From fiscal years 2006 through 2013, the Departments of Education and Labor provided over $640 million in grants to states through the SLDS and WDQI grant programs. These grants support states' efforts to create longitudinal data systems that follow individuals through their education and into the workforce. Analyzing data in these systems may help states improve outcomes for students and workers.
GAO was asked to review the status of grantees' longitudinal data systems. This report examines (1) the extent to which SLDS and WDQI grantees match individual student and worker records and share data between the education and workforce sectors and (2) how grantees are using longitudinal data to help improve education and workforce outcomes. To answer these questions, GAO analyzed data from a 2013 survey conducted by the DQC. This survey collected information from states on data linkages among education and workforce programs and on how states use longitudinal data. In addition, GAO interviewed a nongeneralizable sample of five grantees, which were selected based on the progress they have made in matching data and on the funding they have received from the SLDS and WDQI programs. GAO also reviewed relevant federal laws and regulations.
GAO is not making recommendations in this report. GAO received technical comments on a draft of this report from the Department of Education and the Department of Labor, and incorporated them as appropriate.

What GAO Found

Over half of 48 grantee states that received a Statewide Longitudinal Data Systems (SLDS) or Workforce Data Quality Initiative (WDQI) grant have the ability to match data on individuals from early education into the workforce, based on GAO's analysis of 2013 Data Quality Campaign (DQC) survey data. The DQC is a nonprofit organization that supports the effective use of data to improve student achievement. In its survey, DQC collected self-reported information from states on their ability to match, or connect the same individual record, between the (1) K-12 and early education, postsecondary, and workforce sectors and between the (2) postsecondary and workforce sectors. However, as the match rate—that is, the percent of unique individual records reliably connected between databases—increases, the number of grantees able to match data decreases. GAO found that more grantees reported being able to match data among the education sectors than between the education and workforce sectors. Further, most grantees reported that they are not able to match data comprehensively. For example, only 6 of 31 grantees reported that they match K-12 data to all seven possible workforce programs covered by the DQC survey, which include adult basic and secondary education as well as unemployment insurance wage records. State officials cited several challenges to matching data, including state restrictions on the use of a Social Security number. Specifically, officials in three of five grantee states GAO spoke with said state law or agency policy prohibit collecting a Social Security number in K-12 data, which can make it more difficult to directly match individuals' K-12 and workforce records.
According to GAO analysis of the DQC survey data, grantees use some longitudinal data to inform policy decisions and to shape research agendas. All 48 grantees reported analyzing aggregate-level data to help guide school-, district-, and state-level improvement efforts. For example, 27 grantees said they analyze data on college and career readiness to help schools determine whether students are on track for success in college or in the workforce. Grantees also reported using longitudinal data to analyze outcomes for individual students. For example, 29 grantees reported that they produce early warning reports that identify students who are most likely to be at risk of academic failure or dropping out of school. Data from the DQC survey also show that 39 grantees reported developing a research agenda in conjunction with their longitudinal data systems.