Since the early 2000s, states have been building longitudinal data systems to better address data collection and reporting requirements in federal laws—such as the No Child Left Behind Act of 2001 and the America Creating Opportunities to Meaningfully Promote Excellence in Technology, Education, and Science Act (America COMPETES Act)— and to inform stakeholders about student achievement and school performance. Federal, state, and private entities have provided funding for these systems. For example, in addition to the SLDS and WDQI programs, other recent federal grant programs including Race to the Top and the Race to the Top-Early Learning Challenge may support states’ efforts. The purpose of the SLDS grant program—administered by Education’s Institute for Education Sciences, National Center for Education Statistics—is generally to enable state educational agencies to design, develop, implement, and expand statewide longitudinal data systems to manage, analyze, disaggregate, and use individual student data. From fiscal years 2006 to 2013, Education has awarded approximately $613 million in SLDS grants (see table 1). For each grant competition, Education establishes the award period and range of grant amounts to be awarded; SLDS grants have ranged anywhere from 3 to 5 years, with a maximum award amount of $20 million per grantee. See appendix III for a list of states that received SLDS grants and the amount of their awards. Though the SLDS grant requirements have varied over time, states generally could use SLDS funds to build K-12 longitudinal data systems or to expand these systems to include data from other sectors, such as early education, postsecondary education, or workforce (see table 2). The long-term goal of the program is for states to create comprehensive “P20- W”—early learning through workforce—longitudinal data systems that, among other things, will allow for states, districts, schools, educators, and other stakeholders to make informed decisions and conduct research to improve student academic achievement and close achievement gaps. Under the WDQI grant program—administered by DOL’s Employment and Training Administration—states are expected to fully develop their workforce longitudinal data systems and then be able to match these data with available education data to analyze education and workforce outcomes.has chosen to award WDQI grants to states that have received an SLDS grant or have a longitudinal data system in place. Among other requirements, all grantees are required to develop or improve workforce longitudinal data systems and enable workforce data to be matched with education data to ultimately follow individuals through school and into the workforce. DOL has provided funding for approximately $36 million in WDQI grants to 33 states since fiscal year 2010 (see table 3). The award period for each grant is 3 years. See appendix III for a list of states that received WDQI grants and the amount of their awards. 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. For example, 31 of 48 grantees have the ability to track individuals between all sectors from early education to workforce to at least some degree, but only 6 grantees could do so at the highest match rate. 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. Not all grantees are matching data between all sectors, which may partially be the result of receiving grants with different grant requirements. For example, all 20 grantees that received a fiscal year 2009 SLDS ARRA grant were required to have longitudinal data systems that include individual student-level data from preschool through postsecondary education and into the workforce (see table 2). However, fiscal year 2012 grantees could choose from among three different grant priorities, so some grantees may be focused on building a K-12 longitudinal data system while others may be using their grant funds to link existing K-12 data to other sectors. In addition, grantees may have been in different stages of developing their longitudinal data systems prior to receiving a grant, which may help explain why some grantees are able to match data between more sectors than others. Programs Included in the Data Quality Campaign Survey, 2013 Early education: early intervention, Head Start/Early Head Start, special education, state prekindergarten, subsidized child care K-12: elementary and secondary education Postsecondary institutions: less than 2-year public, less than 2-year private not-for-profit, less than 2-year private for-profit, 2-year public, 2-year private not-for-profit, 2-year private for-profit, 4-year and above public, 4- year and above private not-for-profit, 4-year and above private for-profit Workforce: unemployment insurance wage records, unemployment benefits claim 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, Temporary Assistance for Needy Families (TANF) Of those grantees that match data, we found that few generally do so for all of the possible programs between particular sectors (see sidebar), based on our analysis of DQC survey data (see table 5). 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). We also analyzed DQC’s data to determine which programs are most commonly matched by grantees between particular sectors (see fig. 2). See appendix IV for a list of the specific programs matched by each grantee. Most grantees that match data also share data between sectors; that is, they exchange at least one type of data (e.g., demographic, enrollment, program participation, etc.) between two databases in at least one direction, based on our analysis of DQC data. However, in general, few grantees share all possible types of data (see sidebar). For example, only 3 of 36 states that match data between the postsecondary and workforce sectors reported sharing all 10 types of data asked about by DQC, which include information on postsecondary degree completion, earnings and wages, and industry of employment, among others (see table 6). Officials in all five grantee states we spoke with said matching K-12 education and workforce data is challenging without using a Social Security number (SSN) that uniquely identifies an individual and, as a result, some states may have greater difficulty tracking particular groups of students over time. SLDS officials in three states—Ohio, Pennsylvania, and Virginia—said collecting a SSN in K-12 education data is prohibited either by state law or agency policy; in the other two states—South Dakota and Washington—officials said collecting a SSN is optional and whether to do so is determined at the district level.unique statewide student identifier is a technical requirement of the SLDS grant program, states can choose the format of the identifier used. Education suggested, in a November 2010 SLDS Technical Brief, that states use a unique identifier distinct from a student’s SSN for privacy reasons; however, Education also stated that states should maintain a While establishing a student’s SSN as a data element in order to link data between systems. According to a 2010 report from the Social Security Administration’s Office of the Inspector General, 28 states collect a SSN in K-12 education data. Unlike the SLDS program, in its evaluation criteria for WDQI grants, DOL specifies that states use SSNs as a personal identifier, as they are already in use throughout the workforce system. To match education and workforce data absent a SSN, state officials said they are developing algorithms to match individual records using other identifiers, which could include an individual’s first name, last name, and date of birth. However, a person’s last name can change, which Pennsylvania SLDS officials said can make it difficult to reliably track individuals over time. Further, Ohio WDQI officials explained that the absence of a SSN makes it particularly difficult to track students who drop out of high school or to track high school graduates who do not move on to the workforce. Similarly, Ohio SLDS officials said tracking students that do not go on to postsecondary education is a challenge because there is no readily available identifier to determine any workforce participation by those individuals. In four of five grantee states we spoke with, officials also cited data governance as a challenge. Data governance is the exercise of decision- making and authority for data-related matters using agreed-upon rules that describe who can take what actions with what information and when, under what circumstances, and using what methods. SLDS grantees are generally required to develop a governance structure involving both state and local stakeholders that includes a common understanding of data ownership, data management, as well as data confidentiality and access. All WDQI grantees are expected to establish partnerships with relevant workforce agencies and with state education agencies for the purposes of data sharing. Pennsylvania and Ohio officials said it has not been easy to get the various workforce agencies that maintain data on individual workforce programs to share their data as the agencies often operate independently from one another. As a result, Pennsylvania officials said agencies are territorial about their data, making it difficult to build consensus around developing a longitudinal data system. In Ohio, officials said that each agency has to be approached separately to obtain commitment to share data in a longitudinal system. Similarly, officials in Virginia said collecting data on early education programs has been a challenge as the data are scattered across different agencies. An official from the Early Childhood Data Collaborative explained that it can be easier to facilitate data matching between early education programs under the purview of one agency, such as state prekindergarten and special education, which are generally overseen by state educational agencies in addition to K-12 data. Based on our interviews with grantee states, state officials we spoke with said they are in different stages of developing a data governance structure. For example, Pennsylvania WDQI officials said they have not yet established a formal data governance structure. In contrast, Virginia officials have established a data governance structure; officials said they spent 18 months working through the different priorities, cultures, and agendas of the various agencies providing data to the longitudinal data system. State officials in all five grantee states we spoke with also said they have had to manage public concerns about the purpose of data collection or about data privacy. For example, in Ohio, SLDS officials told us there is a lack of understanding about the value of building a longitudinal data system; officials have had to counter misperceptions about what data are being collected in the state’s longitudinal data system, what the data will be used for, and why data need to be connected between the education and workforce sectors. South Dakota officials said they have had to respond to concerns from parents and other education stakeholders about the privacy of longitudinal data. Grantees have tried to provide information to the public about the purposes of the data system and steps taken to safeguard information. Forty-six grantees reported using outreach tools to communicate the availability of the data to non-educator stakeholders, according to our analysis of the DQC survey data. These grantees reported using traditional outreach measures, which could include public service announcements, press conferences and news releases, and posting information about the data on the state education agency’s website. For example, four of five grantee states we interviewed have web pages dedicated to their longitudinal data systems. These web pages can include overviews of the systems, answers to frequently asked questions, trainings on how to use or access the data, and examples of research studies that use the data. Further, 44 grantees reported on the DQC survey that they take advantage of in-person opportunities, which could include meetings, conferences, and presentations. Lastly, 35 grantees reported using electronic or social media to promote the data, which could include Facebook, Twitter, blogs, and webinars. In the context of discussing the challenge of managing public concerns about data collection or privacy, officials in three of the five grantee states we spoke with specifically said they have provided information about how they protect individual data. Pennsylvania SLDS officials said they took considerable time to convey to parents and taxpayers the steps they are taking to ensure data privacy. Similarly, Virginia officials from both grant programs said explaining all of the precautions the state is taking with respect to data privacy seems to help in reducing concerns. Ohio officials said the state’s Department of Education has convened a new workgroup to see if there are better ways to address misperceptions about data collection and use. Lastly, state officials cited the importance of federal funding to their efforts to build their longitudinal data systems and expressed concerns about sustaining their systems after their grants end. Officials we interviewed in all five grantee states said they would not be as far along in developing their longitudinal data systems without the federal funding provided through the SLDS and WDQI programs. 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. They said the second SLDS grant they received was instrumental in building a P-20W system to connect data between all sectors. Ohio officials said the SLDS funds have provided, among other uses, critical funding for further development of the longitudinal data system, technological updates, and access to technical assistance. However, officials in all five grantee states also expressed concerns about sustaining the systems moving forward. For example, officials in Virginia said they have created a legislative committee to focus on sustainability efforts and will need to request additional funding to keep the system sustainable. Officials in Pennsylvania said they are trying to leverage the existing technical infrastructure and use other available resources, but it is difficult to find funding for their workforce data efforts. 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. All 48 grantees responded that their state educational agency uses the data to analyze aggregate education outcomes (see fig. 3). For example, the three most common types of analyses are related to high school feedback, cohort graduation or completion, and growth (i.e., changes in the achievement of the same students over time). These aggregate data are used to analyze a particular cohort of students and develop information on students’ outcomes over time. They also help guide school-, district-, and state-level improvement efforts. For example, officials from three of the five grantee states we interviewed told us they have used the data to assess kindergarten readiness for children who attended state early education programs. Also, 27 grantees responded to the DQC survey that they use the data to analyze college and career readiness. More specifically, to better understand the courses and achievement levels that high school graduates need to be successful in college, Virginia followed students who graduated from high school from 2006 to 2008 and analyzed enrollment and academic achievement patterns for different groups of students. According to agency officials in Virginia, this analysis resulted in changes to the course requirements for graduation. In addition to examining education outcomes, states also use longitudinal data to assess how cohorts of students fare once they are in the workforce. Washington’s Education Research and Data Center, a state center dedicated to analyzing education and workforce issues across the P-20W spectrum, has published several studies examining workforce outcomes for high school and college graduates. For example, one study compared earnings for workers with bachelor’s degrees from Washington state colleges and universities to earnings of workers with only diplomas from public high schools. In addition to analyzing aggregate student outcomes, grantees also indicated that they analyze individual-level student outcomes. Our analysis of DQC survey data shows that 45 of 48 grantees examine outcomes for individual students (see fig. 4). Student-level data provide teachers and parents with information they can use to improve student achievement. For example, 32 grantees reported that the data are used in diagnostic analysis, which help teachers identify individual students’ strengths and academic needs. 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. For example, Virginia’s early warning report shows demographic and enrollment information about an individual student; flags for warning indicators such as attendance, GPA, and suspensions; and a record of interventions the school has taken to help the student (see fig. 5). Further, officials in three of the grantee states we interviewed told us that educators have access to student-level analyses. In Pennsylvania, teachers can use an educator dashboard, which includes longitudinal data, to determine the educational needs of their students and adjust their teaching plans. Forty-one of 48 grantees reported to the DQC that they use longitudinal data to inform policy and continuous improvement efforts. Specifically, grantees reported that they use the data to inform school turnaround efforts (34 grantees), evaluate intervention strategies or programs (14 grantees), or identify and reward schools that demonstrate high growth (27 grantees), among other things. Officials in three of five grantee states we spoke with provided more specific examples of how they use or plan to use longitudinal data to inform their efforts. Ohio officials told us they used longitudinal data to study students in remediation to help develop a remediation policy. They also said they have been working on a workforce success measures dashboard to compare outcomes across state programs. For example, the dashboard will allow policy makers to assess how successful the state’s adult basic education program is compared to the state’s vocational education program. Pennsylvania officials told us they will develop a similar dashboard. Washington state officials told us that longitudinal data helped address a concern in the state legislature about whether math and science teachers were leaving to work in the private sector. Researchers identified common teacher and school district characteristics associated with teachers who left for employment in other fields and found that math and science teachers did not leave the field at a higher rate than other teachers. Officials told us that this analysis prompted the state legislature to focus its attention on improving the recruitment of math and science teachers rather than improving retention. 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. In addition, only three of these states— Ohio, Virginia, and Washington—are conducting education to workforce analyses. Officials in Pennsylvania and South Dakota said they plan to do this type of analysis, but only after they finish putting all the education and workforce data into their systems and matching these data. Data from the 2013 DQC survey show that 39 SLDS or WDQI grantees have developed research agendas articulating and prioritizing research or policy questions that can be answered with longitudinal data. These research agendas were developed in partnership with higher education institutions, independent researchers, or others. Of the five grantee states we interviewed, only Virginia and Ohio have fully developed their research agendas. Pennsylvania, South Dakota and Washington officials told us they are in the process of doing so. State officials shared two approaches for creating these agendas. Under the first approach, stakeholders from various state agencies comprise a committee that identifies research questions. Virginia took this approach and drafted a list of “burning questions” to answer using longitudinal data. Officials in Virginia explained that they purposefully kept the agenda broad so that the questions will remain relevant over the long term. Washington’s Education Research and Data Center has similarly developed a list of critical questions it would like to answer using longitudinal data. Under the second approach, state agencies use information requests and stakeholder feedback on sample reports to shape the research agenda. For example, officials from the South Dakota Department of Education told us they have solicited feedback after training districts on the data and reviewed requests from the governor’s office and state legislators. They also told us that they are following the number of hits for individual reports on the state’s Department of Education’s electronic portal. Forty-three of 48 grantees reported that they have a process by which researchers who are not employees of the state can propose their own studies for approval, according to the 2013 DQC survey data. Four of the grantee states we interviewed have established a formal request process for researchers who would like to access longitudinal data and the fifth state is reviewing its protocols and expects to develop a formal application process. Officials in two grantee states told us that the request process is intended to streamline access to the data and make it easier for researchers to seek approval for data requests. In addition, officials in Ohio told us that when researchers apply for access to Ohio’s data, they must include information in their application about how the study will meet the state’s research priorities. Since fiscal year 2006, the federal government has made a significant investment—over $640 million in SLDS and WDQI grant funds—to help states build P20-W longitudinal data systems that track individuals from early education and into the workforce. The different grant requirements for linking data between sectors may have contributed to states being in different stages of developing their longitudinal data systems. That is, some grantees are just building their K-12 longitudinal data systems while others are matching data between education and workforce sectors. It remains to be seen whether all grantees will ultimately achieve the long- term goal of developing complete P20-W longitudinal data systems or how long that will take, particularly in light of unresolved concerns about limitations to matching data using a Social Security number and sustainability. Further, even among those grantees that can match data between sectors, most can only do so for a limited number of programs or data types. As grantees continue to refine their systems, maximizing the potential of these systems will rest, in part, with the ability to more fully match information on specific programs and characteristics of individuals that could help in further analyzing education and workforce outcomes. We provided a draft of this report to Education and DOL for their review. Each provided technical comments, which we incorporated as appropriate. As agreed with your office, unless you publicly announce its contents earlier, we plan no further distribution until 30 days after the date of this letter. At that time, we will send copies of this report to the appropriate congressional committees and the Secretaries of Education and Labor. In addition, the report is available at no charge on GAO’s website at http://www.gao.gov. If you or your staff have any questions about this report, please contact me at (617) 788-0580 or nowickij@gao.gov. Contact points for our Offices of Congressional Relations and Public Affairs may be found on the last page of this report. GAO staff who made major contributions to this report are listed in appendix VII. 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. DQC’s survey focused on 10 “State Actions” the DQC has developed to ensure effective data use (see table 7). DQC has conducted this annual survey since 2005. The survey data include self-reported information on how data are matched and shared between the early education, K-12, postsecondary education, and workforce sectors, as well as information on specific programs within these sectors, how states analyze and use the data, and who has access to the data. To conduct the survey, DQC used an online tool to collect information and invited the governor’s office in all 50 states and the District of Columbia to participate. According to DQC, the governor’s office is in the best position to bring stakeholders together to respond to the survey. As part of their survey response, states were asked to provide documents or website links as evidence of having specific policies or reports. After survey responses were received, DQC worked with each state to ensure the information reported was as accurate as possible. We analyzed data from eight survey questions (see table 8 in appendix II) to determine the extent to which SLDS and WDQI grantees match individual records and share data among the education sectors and between the education and workforce sectors. For the purposes of our report, a grantee is one of the 48 states that received a SLDS grant, a WDQI grant, or both and responded to the 2013 DQC survey. We considered the District of Columbia to be a state. We excluded Alabama, New Mexico and California from our review because neither Alabama nor New Mexico received a SLDS or a WDQI grant and because California chose not to participate in DQC’s 2013 survey. We excluded the U.S. Virgin Islands and Puerto Rico because, while these territories received SLDS grants, DQC did not include them in its survey. We analyzed data on SLDS and WDQI grantee states because the SLDS and WDQI grant programs provide federal funds for developing longitudinal data systems and are complementary. We considered a grantee as matching data between sectors if a grantee matched data from at least one program between sectors (for a list of programs included in the DQC survey, see questions 1, 4, 7, and 10 in table 8 in appendix II). We considered a grantee as sharing data if a grantee matched data according to our definition and also reported exchanging at least one data element between sectors, in either direction (for a list of data elements, see questions 2, 5, 8, and 11 in table 8 in appendix II). We also analyzed data from another twelve survey questions to identify how grantees are using longitudinal data to help improve education and workforce outcomes (see table 9 in appendix II). We conducted a data reliability assessment by reviewing the survey instrument and related documentation, interviewing officials responsible for administering the survey, and testing the data for obvious inaccuracies. We determined that these data are sufficiently reliable for the purposes of this report. 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. During our interviews with the five grantee states—Ohio, Pennsylvania, South Dakota, Virginia, and Washington—we asked grantees to identify challenges they faced in building and implementing longitudinal data systems and discussed how grantees have used longitudinal data to inform decision-making in education and workforce programs. 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. Within each state, we spoke with relevant K-12, workforce, postsecondary education, and early education officials. We also interviewed officials at Education, DOL, and the Department of Health and Human Services to obtain information about their roles in helping states build longitudinal data systems. In addition, we spoke with officials from nonprofit organizations to obtain their views on states’ implementation of longitudinal data systems. These stakeholder organizations included the Early Childhood Education Collaborative, the State Higher Education Executive Officers Association, and the Workforce Data Quality Campaign. Finally, we reviewed relevant federal laws, regulations, requests for applications, and solicitations for grant applications to understand the requirements of these grants. As explained in appendix I, we analyzed data from DQC’s 2013 survey to answer our research objectives. Table 8 and table 9 show the specific questions we analyzed from DQC’s survey instrument. For some questions, DQC allowed states to select “other” as a response; we excluded these “other” responses from our analysis. In addition to the contact named above, Janet Mascia, Assistant Director, Jennifer Gregory, and Nisha R. Hazra made key contributions to this report. Also contributing to this report were Deborah Bland, David Chrisinger, Alex Galuten, Amanda Miller, Jeffrey G. Miller, Mimi Nguyen, Yunsian Tai, and Walter Vance. This glossary is provided for reader convenience. It is not intended as a definitive, comprehensive glossary of related terms. Group statistics (numbers, percentages, averages, etc.) based on individual student data. Reports designed to identify students who are on track for readiness or success in college or careers. The exercise of decision-making and authority for data-related matters using agreed-upon rules that describe who can take what actions with what information and when, under what circumstances, and using what methods. Information on individuals designed to identify each student’s strengths and academic needs. Programs that serve children prior to kindergarten. Programs include: early intervention, Head Start/Early Head Start, state prekindergarten, special education, and subsidized child care. A report designed to identify students who are most likely to be at risk of academic failure or dropping out of school. Information on outcomes for students after they graduate from a school or district. A report that shows changes in the achievement of the same students over time. Elementary and secondary education. Institutions of higher education. Types of institutions include: less than 2- year public, 2-year public, 4-year and above public, less than 2-year private not-for-profit, 2-year private not-for-profit, 4-year and above private not-for-profit, less than 2-year private for-profit, 2-year private for- profit, and 4-year and above private for-profit. A report that shows how students’ success later in the education/workforce pipeline is related to the status of the same students earlier in the pipeline. Reliably connecting the same individual record in two or more databases. The percent of unique individual records reliably connected across databases. Exchanging data between two databases, in either direction. Data elements that could be shared between early education and K-12 include: demographic, family characteristics, program participation, child-level development data; between K-12 and postsecondary: demographic, college readiness assessment scores, college placement assessment scores, high school transcript data, postsecondary enrollment, postsecondary remediation status, postsecondary progress, postsecondary credits earned, postsecondary enrollment intensity, postsecondary outcomes; between K-12 and workforce: demographic, enrollment, transcript data, earnings and wages, employment status, occupation, industry of employment; between post-secondary and workforce: demographic, enrollment, transcript data, financial aid, postsecondary degree completion, earnings and wages, employment status, occupation, industry of employment. Programs that serve individuals in the workforce. Programs include: adult basic and secondary education, TANF, unemployment benefits claims data, unemployment insurance wage records, Wagner-Peyser Act employment, WIA adult or dislocated workers program, and WIA youth program.

Summary: 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. 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.