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.