2018 Federal Standard of Excellence


Did the agency collect, analyze, share, and use high-quality administrative and survey data - consistent with strong privacy protections - to improve (or help other entities improve) federal, state, and local programs in FY18? (Examples: Model data-sharing agreements or data-licensing agreements; data tagging and documentation; data standardization; open data policies)

Administration for Children and Families (HHS)
  • In 2016, ACF established a new Division of Data and Improvement (DDI) providing federal leadership and resources to improve the quality, use, and sharing of data. DDI serves as ACF’s coordination point on all things related to administrative data and interoperability, with DDI staff providing support to ACF program offices and their stakeholders at all levels. DDI works to support the development of interoperable data systems, improve data quality and program integrity, and use data to build evidence and improve programs.
  • ACF’s Interoperability Initiative supports data sharing through developing standards and tools that are reusable across the country, and addressing common privacy and security requirements to mitigate risks. ACF has developed resources such as the National Human Services Interoperability Architecture, which proposes a framework to facilitate information sharing, improve service delivery, prevent fraud, and provide better outcomes for children and families; an Interoperability Toolkit to help state human services agencies connect with their health counterparts; and a Confidentiality Toolkit that supports state and local efforts by explaining rules governing confidentiality in ACF and certain related programs, by providing examples of how confidentiality requirements can be addressed, and by including sample Memoranda of Understandings and data sharing agreements. Several ACF divisions have also been instrumental in supporting cross-governmental efforts, such as the National Information Exchange Model (NIEM) that will enable human services agencies to collaborate with health, education, justice, and many other constituencies that play a role in the well-being of children and families. New pages published on the ACF website in 2018 highlight resources for Interoperability and Data Sharing and ACF Program Guidance on Sharing Administrative Data. ACF will soon announce an Interoperability Action Plan to continue this work.
  • An important element of the ACF Interoperability Action Plan is that all ACF programs will actively pursue actions that allow and encourage states and tribes to share data, including the removal of unnecessary restrictions that prevent legal, ethical, and authorized data sharing, for the benefit of clients served by these programs. ACF program offices will actively seek out opportunities to enhance and support integrated data initiatives such as coordinated case management and data-informed decision-making, and ACF will continue to expand efforts to make data available for research, evaluation, cross-program outcome measurement, and other statistical purposes to inform policymaking and program improvement. ACF and its program offices will develop and implement a Data Sharing First (DSF) strategy that starts with the assumption that data sharing is in the public interest. ACF will encourage and promote data sharing broadly, constrained only when required by law or when there are strong countervailing considerations.
  • ACF administers the Public Assistance Reporting Information System, a platform for exchange of data on benefits receipt across ACF, Department of Defense, and Veterans Affairs programs. This platform entails data sharing agreements between these three federal agencies and between ACF and state agencies.
  • In 2018 ACF produced a Compendium of ACF Administrative and Survey Data Resources. All major ACF person-level administrative data sets and surveys are included, including 11 administrative data sources and eight surveys. Each entry includes the following information: data ownership and staff experts, basic content, major publications and websites, available data sets (public, restricted use, in-house), restrictions on data sharing, capacity to link with other data sets along with history of such linking, data quality, and resources to collect, prepare, and analyze the data. The compendium is currently available for internal use at HHS; a public version is forthcoming.
  • ACF has numerous efforts underway to promote and support the use of data for research and improvement. Highlights of these efforts are listed below:
    • ACF has made numerous administrative and survey datasets publicly available for secondary use, such as data from the National Survey of Early Care and Education,Child Care and Development Fund, National Survey of Child and Adolescent Well-Being, and Adoption and Foster Care Analysis and Reporting System, among many other examples.
    • ACF’s National Directory of New Hires has entered into data sharing agreements with numerous agencies. For example, the Department of Labor’s Chief Evaluation Office and Employment & Training Administration have interagency agreements with ACF for sharing and matching earnings data on nine different formal net impact evaluations. The NDNH Guide for Data Submission describes an agreement with the Social Security Administration to use its network for data transmission.
    • ACF’s TANF Data Innovation Project supports innovation and improved effectiveness of state TANF programs by enhancing the use of data from TANF and related human services programs. This work includes encouraging and strengthening state integrated data systems, promoting proper payments and program integrity, and enabling data analytics for TANF program improvement. The project supports the use of data for understanding the broad impact that TANF has on families, and improving knowledge of how the federal government and state partners can use data to more efficiently and effectively serve TANF clients.
    • The Family Self-Sufficiency Data Center, a cooperative agreement with the University of Chicago, supports the development, implementation, and ongoing operations of the Data Center to support family self-sufficiency research and activities. To date, the project has: conducted a comprehensive needs assessment; developed a prototype for a web-based data archive and analysis tool; worked with states and localities, providing modeling, analytic, and technical support to providers and users of family self-sufficiency data; and generated publicly-available resources, including data models and code to help state-level data users produce analyses. The Center is currently analyzing TANF caseload data through a data sharing agreement with ACF’s Office of Family Assistance. One goal is to assess data quality and opportunities for matching with other administrative data sources. Another is to produce descriptive information about caseload dynamics over time.
    • ACF is producing a resource series on Supporting the Use of Administrative Data in Early Care and Education Research. This set of resources is intended to strengthen the ability of state/territory child care administrators and their research partners to use administrative data to address policy-relevant early care and education research questions.
    • OPRE actively promotes archiving of research and evaluation data for secondary use. In FY18, ACF OPRE research contracts included a standard clause requiring contractors to make data and analyses supported through federal funds available to other researchers and to establish procedures and parameters for all aspects of data and information collection necessary to support archiving information and data collected under the contract. Many datasets from past OPRE projects are stored at archives including the ACF-funded Child Care & Early Education Research Connections site and the ICPSR data archive.
    • New pages published on the ACF website in 2018 include resources for Administrative Data for Research and Improvement.
Administration for Community Living
  • ACL makes the majority of its administrative and survey datasets publicly available through the following systems:
    • The National Adult Maltreatment Reporting System provides national data on the exploitation and abuse of older adults and adults with disabilities. ACL has developed a best practices and policies for data submission and specifications.
    • The Aging Integrated Database (AGID) is an integrated, user-friendly online data system, including data on ACL programs and Census bureau data.
    • The Burn Model System (BMS) provides a comprehensive and longitudinal record of health and community outcomes of burn survivors with more severe injuries. It is the only project that collects long-term outcomes on both pediatric and adult patients to better understand the relation between the injury, acute care, rehabilitation, and long-term functioning of people with burn injury.
    • NIDILRR’s Traumatic Brain Injury Model Systems National Data and Statistical Center (TBINDSC) advances medical rehabilitation by increasing the rigor and efficiency of scientific efforts to longitudinally assess the experience of individuals with traumatic brain injury (TBI).
    • The National Spinal Cord Injury Statistical Center (NSCISC) supports and directs the collection, management and analysis of the world’s largest and longest spinal cord injury research database.
  • The Office of Information Resources Management manages ACL information and technology services, including providing IT governance and managing network security and privacy responsibilities. Prior to the collection of data, all programs must complete Privacy Impact Assessments specifying how performance and evaluation data will be secured and its privacy protected.
  • ACL has an internal policy requiring that all evaluation contracts include the following language: “The contractor shall develop an IT Security Plan and conduct related security assessments in accordance with the Federal Information Security Management Act (FISMA). The IT Security Plan must ensure the integrity, confidentiality, when appropriate, and availability of all data collected on behalf of the Federal government. All records that are the property of the Federal government must be maintained in accordance with HHS policies and procedures, and National Archives and Records Administration (NARA) disposition schedules. The Contractor shall provide a certification statement concerning the proper maintenance of all records to the COR at the beginning of the contract. The Contractor shall discuss the disposition of records with the COR and obtain COR approval before any records are disposed. The Contractor shall notify the COR within 24 hours concerning any loss of data integrity, any unauthorized disclosure of data, or any misuse of data.” Contractor staff are required to sign confidentiality agreements prior to accessing any ACL data.
  • The majority of ACL’s data is aggregated at the grantee (e.g., State) level, limiting the ability to link those data to other datasets. For individual evaluation projects, for which individual-level data are collected, ACL has had success using administrative data sets from other federal agencies.
    • Specifically, as part of the Older Americans Act Nutrition Services Program (NSP) evaluation, ACL’s contractor, Mathematica, used Medicare claims and enrollment data to construct outcome measures and define Medicare beneficiary characteristics such as hierarchical condition category (HCC) scores, the original reason for an individual’s Medicare eligibility, whether the individual had dual enrollment in Medicare and Medicaid, and whether the individual had chronic conditions.
    • To describe NSP participants’ geographic access to food, the contractor used residential address information for each respondent in the outcomes survey, data from the Census Bureau, and address data for food retailers from the U.S. Department of Agriculture (USDA). Using this information, the research team calculated measures of geographic access to food and determined whether a respondent lived in an urban or rural area.
    • The research team used data from the American Community Survey to obtain local-area population characteristics to better describe the communities in which meal participants lived.
  • ACL collects administrative data from grantees to improve its programs and the capacity of service providers:
    • In 2014, ACL developed a Dementia Capability Assessment Tool in support of their programs to expand dementia capability in communities. In 2017 the tool was translated into an onlineformat, making it possible to analyze each program’s progress toward dementia capability in its entirety or broken down by sector.
    • VD-HCBS data is used to understand the effects of the program and to provide targeted TA to network locations that are having issues with the VA program.
    • AIDD is building the capacity of state developmental disabilities agencies to gather vital information on service outcomes through the National Data Measurement Project and the adoption of the National Core Indicators (NCI) as the uniform dataset. The NCI framework comprises over 100 key outcome indicators that are designed to gather valid and reliable data across five broad domains: individual outcomes; family outcomes; health, welfare, and rights; staff stability; and system performance.
Corporation for National and Community Service
  • As the nation’s largest grant maker for service and volunteering, CNCS collects data about service program members, volunteers, and the organizations in which members and volunteers are placed. Member/volunteer demographic, service experience, and outcome data are collected in a variety of ways – both through administrative processes and through surveys.
    • CNCS cleared an internal Data Sharing Policy in January 2018 which further strengthens the agency’s data management capacities. The purpose of the policy is to provide the agency with a standard policy, practice, and approval process for identifying and releasing data assets.
    • In FY18, CNCS enhanced its National Service State Reports. Through the “National Service in Your State,” the public can now view comprehensive data about CNCS resources that were invested in each state over the past 12 months. In addition, CNCS staff now are able to run a variety of data reports at the state, city, county, and Congressional District levels at any time along with state-specific social media graphics. These reports are used for a range of operational and educational purposes, and users can choose the type of report, geographic level, and which components they want to run.
  • The Administrative Data Pilot competition ($4.05 million) will continue until September 2019 and is designed to support current Pay for Success projects’ access to high-quality, less expensive data for evaluation purposes so they can improve the outcomes of their interventions. The grantees will create a mechanism for service recipients to systematically take advantage of emerging best practices. Three projects and 11 service recipients were selected:The Urban Institute supports 4 projects and is helping each develop a work plan for accessing and sharing administrative data; Utah’s Sorenson Impact Center supports 4 projects and is helping them identify appropriate methods and metrics for measuring results as well as obtaining and analyzing data; and Third Sector Capital Partner (TSCP) and Stanford’s Center on Poverty and Inequality (CPI) have three projects to increase their capacity to link and analyze data.
  • CNCS has posted the Organizational Capacity Assessment Tool to encourage its use for data collection both internally and externally. The CNCS Chief Risk Officer has asked the Director of R&E to help use this tool for modifying the agency’s grant risk assessment and monitoring protocol.
  • In FY18, data collected from Americorps member exit survey allowed CNCS to generate more accurate reports on key experiences and anticipated college, career, and civic engagement outcomes, which were shared internally. Survey results were shared with program and agency leadership in FY18 for program improvement purposes. In FY18, R&E finalized a data request form and an MOU template so that program-level and state-level data sets and reports can be shared with partners. The agency is working on protocols to share these data through its open data platform.
  • Volunteering statistics were made available, through a data sharing agreement with the Census Bureau, on an interactive platform for the first time as well as service location data. The goal was to make these data more accessible to all interested end-users. Similarly, the dataset of alumni identified for the alumni outcome survey pilot was shared with the Census Bureau and matched with the Longitudinal Employer-Household Dynamics (LEHD) survey data, with findings expected in late FY18. This administrative data match between alumni records and the Census’ LEHD dataset to obtain employment and employment sector outcomes for AmeriCorps alumni will help the agency reduce its reliance on traditional survey methods so that key economic outcomes can be obtained from more objective sources and for less cost.
  • CNCS worked closely with the U.S. Census Bureau in FY17 to revise the Current Population Survey supplements to improve the data quality of these instruments. One supplement was created based on a thorough literature review, psychometric testing, cognitive interviews, and public comment. The instrument was cleared by the Office of Information and Regulatory Affairs, and data collection occurred in September 2017. CNCS will release the statistics in the Fall of 2018.
  • In April of 2018, the CNCS Chief of Staff asked the Director of R&E to help stand up a Data Analytics Unit over the following 6 months. A Data Analytics Working Group was identified and started convening in May 2018. The final scheduled meeting took place in September 2018. Recommendations for improving the quality and transparency of CNCS data management policies, structures, and processes was presented to the Executive Leadership Team in October 2018. This effort reflects the CEO and Chief of Staff’s commitment to ongoing efforts to improve the efficiency and effectiveness of data infrastructure and to demonstrate agency success through credible and compelling data.
Millennium Challenge Corporation
  • From its founding in 2004, MCC’s model has been based on a set of core principles that are essential to effective development assistance: good governance, country ownership, focus on results, and transparency. MCC promotes transparency in order to provide people with access to information that facilitates their understanding of MCC’s model, its decision-making processes, and the results of its investments. Transparency (and therefore open data) is a core principle for MCC because it is the basis for accountability, provides strong checks against corruption, builds public confidence, and supports informed participation of citizens. As a testament to MCC’s commitment to and implementation of transparency and open data, the agency was the highest-ranked U.S. government agency in the 2018 Publish What You Fund Aid Transparency Index for the fifth year. In addition, the U.S. government is part of the Open Government Partnership, also a signatory to the International Aid Transparency Initiative (IATI), and under the Foreign Aid Transparency and Accountability Act (FATAA), all which require foreign assistance agencies to make it easier to access, use, and understand data. All of these actions have created further impetus for MCC’s work in this area, as they establish specific goals and timelines for adoption of transparent business processes.
  • MCC is committed to using high-quality data and evidence to drive its strategic planning and program decisions. The M&E plans for all programs and tables of key performance indicators for all projects are available online by compact/threshold program and by sector, for use by both partner countries and the general public. MCC makes Program Data, including financials and results data, available through its Open Data Catalog. DPE leads the MCC Disclosure Review Board (DRB) process for publicly releasing the de-identified microdata that underlies the independent evaluations on the Evaluation Catalog, following MCC’s Microdata Management Guidelines to ensure appropriate balance in transparency efforts with protection of human subjects’ confidentiality.
  • The Microdata Evaluation Guidelines inform MCC staff and contractors, as well as other partners, on how to store, manage, and disseminate evaluation-related microdata. This microdata is distinct from other data MCC disseminates because it typically includes personally identifiable information and sensitive data as required for the independent evaluations. With this in mind, MCC’s Guidelines govern how to manage three competing objectives: share data for verification and replication of the independent evaluations, share data to maximize usability and learning, and protect the privacy and confidentiality of evaluation participants. These Guidelines were established in 2013 and updated in January 2017. Following these Guidelines, MCC has publicly released 71 de-identified, public use microdata files for its evaluations. MCC’s experience with developing and implementing this rigorous process for data management and dissemination while protecting human subjects throughout the evaluation life cycle is detailed in Opening Up Evaluation Microdata: Balancing Risks and Benefits of Research Transparency.
  • MCC’s Economic Analysis division produces and publishes interactive, downloadable Economic Rate of Returns (ERR) spreadsheets that include the description of the project, including its economic rationale; the expected project impacts, including detailed cost and benefit estimates; and a tool allowing users to modify key assumptions and study the effects of those modifications on the project’s returns. The Cost Benefit Analyses that generate the ERRs are reported at the lowest level of disaggregated activity that data and program logic permit, and all this information is reported online. The ERR spreadsheets also include Beneficiary Analysis, which indicates to which segments of the population benefits are expected to accrue.
  • MCC and other donors are increasing the amount of gender data released and helping to improve international data transparency standards.
  • MCC has a partnership with the President’s Emergency Plan for AIDS Relief (PEPFAR), referred to as the Data Collaboratives for Local Impact (DCLI). This partnership is improving the use of data analysis for decision-making within PEPFAR and MCC partner countries – working toward evidence-based programs to address challenges in HIV/AIDS and health, empowerment of women and youth, and sustainable economic growth. Data-driven priority setting and insights gathered by citizen-generated data and community mapping initiatives contribute to improved allocation of resources in target communities to address local priorities, such as job creation, access to services, and reduced gender-based violence. DCLI continues to inform and improve the capabilities of PEPFAR activities through projects such as the Tanzania Data Lab, which has trained nearly 700 individuals, nearly 50% of whom were women, and has hosted a one-of-a-kind “Data Festival.” Recently, the lab has announced a partnership with the University of Virginia Data Science Institute and catalyzed launching of the first Masters in Data Science in East Africa, in partnership with the University of Dar Es Salaam.
  • After a sustained learning agenda around its evaluations, this year the M&E division is focused on the use of its monitoring data for real-time learning within compacts. The division is seeking to better understand how and when monitoring data are used and how its results can feed back into compact decisions. A critical component of this work is identifying and utilizing higher frequency monitoring data to build a real-time evidence base to better impact program implementation. Specifically, MCC seeks to measure high frequency outputs using newer and cheaper technologies such as cell phones, geospatial data, satellite imagery, the internet of things, and machine learning. To facilitate this data collection and use, MCC has issued a call for partnerships in these areas.
  • MCC’s Data Analytics Program (DAP) enables enterprise data-driven decision-making through the capture, storage, analysis, publishing, and governance of MCC’s core programmatic data. The DAP streamlines the agency’s data lifecycle, facilitating increased efficiency. Additionally, the program promotes agency-wide coordination, learning, and transparency. For example, MCC has developed custom software applications to capture program data, established the infrastructure for consolidated storage and analysis, and connected robust data sources to end user tools that power up-to-date, dynamic reporting and will ultimately streamline content maintenance on MCC’s public website. As a part of this effort, M&E has developed an Evaluation Pipeline application. It provides up-to-date information on the status, risk, cost, and milestones of the full evaluation portfolio for better performance management.
  • The Transparent and Accountable Governance Project in the Kosovo Threshold program aims to facilitate data-driven decision-making by promoting the public availability and analytical use of data across civil society and the government. The Kosovo Open Data Challenge (“Dig Data”) activity will award grants through a competitive process to individuals or organizations who have innovative ideas about how to use, analyze, and present data to influence and support the Government’s analytical and public communication needs. To ensure the newly available data resulting from the Program and other sources is used to drive decision-making, Dig Data will engage, support, and connect local innovators, developers, and solution providers to use open data to help produce tools and analysis that respond to Government needs, thereby creating examples of constructive relationships between the Government, private sector, and civil society. Dig Data will support relevant Government entities to creatively share data and formulate their critical needs or questions. The activity will also support the Government to implement or plan for implementation of solutions identified as part of the activity. Through this process, Dig Data will, in particular, emphasize identification of potential inequalities related to gender, ethnicity, region, or other relevant disaggregations, and solution-oriented analysis of data, and adoption of those solutions. The first window of the challenge is on labor force data and employment solutions. The second window will be on environmental air quality data, and the third will be on judicial data.
Substance Abuse and Mental Health Services Administration
  • SAMHSA has five data collection initiatives: National Survey on Drug Use and Health (NSDUH): population data; Treatment Episode Data Set – Admissions: client level data; National Survey of Substance Abuse Treatment Services (N-SSATS): substance abuse facilities data; Drug Abuse Warning Network: emergency department data; and the National Mental Health Services Survey (N-MHSS) and has made numerous administrative and survey datasets publicly available for secondary use. Each data collection can be sorted by metadata parameters such as geography, methodology, spotlights, data reviews, and data tables. CBHSQ oversees these data collection initiatives and provides publicly available datasets so that some data can be shared with researchers and other stakeholders while preserving client confidentiality and privacy. Some restricted data cannot be shared beyond federal staff.
  • SAMHSA’s Data Integrity Statement articulates the administration’s Center for Behavioral Health Statistics and Quality (CBHSQ), a Federal Statistical Unit, adherence to the federal common set of professional and operational standards that ensure the “quality, integrity, and credibility” of statistical activities.
  • SAMHSA’s Performance and Accountability and Reporting System (SPARS) hosts the data entry, technical assistance request, and training system for grantees to report performance data to SAMHSA. SPARS serves as the repository for the Administration’s three centers, Center for Substance Abuse and Prevention (CSAP), Center for Mental health Services (CMHS), and Center for Substance Abuse Treatment (CSAT). Due to concerns about confidentiality and privacy, the current data transfer agreement limits the use of grantee data to internal reports so that data collected by SAMHSA grantees will not be available to share with researchers or stakeholders beyond SAMHSA and publications based on grantee data will not be permitted. Enhancements to the existing data collection system to improve data transparency and sharing of administrative and performance data are being planned. The foundational system went live in February 2017. Going forward, changes will allow for analytic reports to be shared with grantees so that performance successes and gaps can be better tracked, both by the project officers overseeing the grantees and by the grantees themselves. It is anticipated that this will improve communication and oversight as well as offer more real-time opportunities for program performance. Enhancements to the existing data collection system to improve data transparency and sharing of administrative and performance data are currently being implemented. Information on latest available data for program staff can be found on the portal announcement section on the home page.
  • SAMHSA’s Performance and Accountability and Reporting System (SPARS) hosts the data entry, technical assistance request, and training system for grantees to report performance data to SAMHSA. SPARS serves as the repository for the Administration’s three centers, Center for Substance Abuse and Prevention (CSAP), Center for Mental health Services (CMHS), and Center for Substance Abuse Treatment (CSAT). Due to concerns about confidentiality and privacy, the current data transfer agreement limits the use of grantee data to internal reports so that data collected by SAMHSA grantees will not be available to share with researchers or stakeholders beyond SAMHSA and publications based on grantee data will not be permitted. We expect to revisit the issue once the Commission on Evidence Base Policymaking releases their findings in September 2017.
  • SAMHSA’s Substance Abuse and Mental Health Data Archive (SAMHDA) contains substance use disorder and mental illness research data available for restricted and public use. SAMHDA promotes the access and use of SAMHSA’s substance abuse and mental health data by providing public-use data files and documentation for download and online analysis tools to support a better understanding of this critical area of public health.
  • Per SAMHSA’s Evaluation Policy & Procedure (P&P), CBHSQ will work with CMHS, CSAT, and CSAP Center Directors and other program staff to develop a SAMHSA Completed Evaluation Inventory of evaluations completed between FY11 and FY17. This inventory and the evaluation final reports will then be made available on SAMHSA’s intranet and internet sites. In addition, data files from completed evaluations will be made available on the intranet, and via a restricted access mechanism such as SAMHDA.
U.S. Agency for International Development
  • USAID has an open data policy which, in addition to setting requirements for how USAID data is tagged, submitted, and updated, also established the Development Data Library (DDL) as the agency’s repository of USAID-funded, machine readable data created or collected by the agency and its implementing partners. The DDL, as a repository of structured and quantitative data, complements the Development Experience Clearinghouse which publishes qualitative reports and information.
  • To improve linkages and break down silos, USAID continues to develop the Development Information Solution (DIS) — an enterprise-wide management information system that will enable USAID to collect, manage, and visualize performance data across units, along with budget and procurement information, to more efficiently manage and execute programming. Releases have begun on DIS work streams as of Q3 FY18, with an accelerated timeline for full implementation of core functionality by the end of 2019, which will then be followed by enhancements.
  • The United States is a signatory to the International Aid Transparency Initiative (IATI), a voluntary, multi-stakeholder initiative that created a data standard for publishing foreign assistance spending data and allowing comparison across publishers. USAID continues to improve and add to its published IATI data. Published location data for USAID projects can be viewed and queried on D-Portal for Mali, Lebanon, Colombia, Mozambique, Ethiopia, Bangladesh, The Democratic Republic of Congo, West Bank/Gaza, Jordan, and Georgia.
  • The USAID GeoCenter uses data and analytics to improve the effectiveness of USAID’s development programs by geographically assessing where resources will maximize impact. The GeoCenter team works directly with field missions and Washington-based bureaus to integrate geographic analysis into the strategic planning, design, monitoring, and evaluation of USAID’s development programs. The GeoCenter also provides important data-centered trainings to USAID staff.
  • The USAID Data Services team is dedicated to improving the usage of USAID data and information, so that the agency continues to ensure its development outcomes are supported by evidence. Through USAID Data Services, the development community has direct access to more than 100 sources of international development data via the International Data and Economic Analysis (IDEA) website and Foreign Aid Explorer, a site that reports comprehensively on U.S. government foreign assistance, from 1946 to the present.
  • USAID uses data and evidence to inform policy formulation, strategic planning, project design, project management and adaptation, program monitoring and evaluation, and learning what works, through a framework called the Program Cycle, which underwent major revisions in September 2016.
  • USAID’s Monitoring Country Progress (MCP) system is an empirical analytical system which tracks and analyzes country progress to facilitate country strategic planning.
  • USAID also publishes spending data alongside program results on the Dollars to Results (D2R) page of the USAID website.D2R provides illustrative information on USAID’s impact around the world by linking annual spending to results. USAID updated D2R in FY17 to include data on all of the countries where USAID works.
  • USAID’s Privacy Program discusses policies and practices for protecting personally identifiable information (PII) and data.
U.S. Department of Education
  • ED has several resources to support the high-quality collection, analysis, and use of high-quality data in ways that protect privacy. IES’ National Center for Education Statistics (NCES) serves as the primary federal entity for collecting and analyzing data related to education. Almost all of ED’s K-12 statistical and programmatic data collections are now administered by NCES via EDFacts. NCES also collects data through national and international surveys and assessments. Administrative institutional data and statistical sample survey data for postsecondary education is collected through NCES in collaboration with the Office of Postsecondary Education (OPE) and the Office of Federal Student Aid (FSA). Some data are available through public access while others only through restricted data licenses. ED’s Office for Civil Rights conducts the Civil Rights Data Collection (CRDC) on key education and civil rights issues in our nation’s public schools. Additionally, the Data Strategy Team helps to coordinate data activities across the Department and the Disclosure Review Board, the Family Policy Compliance Office (FPCO), the EDFacts Governing Board, and the Privacy Technical Assistance Center all help to ensure the quality and privacy of education data.
  • Department data are made publicly available online and can be located in the ED Data Inventory. In FY17, ED continued to maintain and grow the Data Inventory, ensuring the information for ED contacts are up to date and expanding the library to include additional years of existing data sets as well as adding new data sets. Additionally, ED is exploring ways to leverage revisions to a technical system to use the data generated through information collection approval process to populate new entries within the Data Inventory.
  • ED made concerted efforts to improve the availability and use of its data in FY17. With the release of the revised College Scorecard, the Department now provides newly combined data in a tool that helps students choose a school that is well-suited to meet their needs, priced affordably, and consistent with their educational and career goals. Additionally, the College Scorecard promotes the use of open data by providing the underlying data in formats that researchers and developers can use through downloadable data files and Application Program Interface (API). In fall 2017, ED updated the Scorecard as part of its annual data refresh and launched a new comparison tool to further promote informed educational choices. The 2018 updates are currently underway.
  • InformED, the ED’s primary open data initiative, works to improve the Department’s capacity to make public education data accessible and usable in innovative and effective ways for families, policy makers, researchers, developers, advocates and other stakeholders. Through InformED, ED has:
    • Continued to leverage its interactive data story template and used it to deliver rich and accessible data narratives around pressing education topics. This has included launching a data story focused on the educational experiences of English learners, accessible here. There are additional data stories under development or under consideration
    • Developed an Open Data IT plan to create an enterprise-wide solution to improve data dissemination capabilities making public data more discoverable, accessible, and usable for the public, while still protecting student privacy. The plan identified enterprise solutions to enhance open data projects at ED.
    • Supported data-informed decision-making internally by piloting data dashboards that provide data on key metrics while leveraging best practices in data visualization.
    • Continued to maintain and support ED’s data landing page to make it easier to identify and navigate to data sources and data tools from across the agency.
  • ED also continued to participate in The Opportunity Project initiative, now coordinated by the U.S. Department of Commerce. In 2017, ED participated in the initiative’s federal agency cohort of projects and worked with external developers to support the development of multiple tools. The tools centered on one of two use cases identified by ED around (1) promoting access to and interest in STEM fields, and (2) supporting States in developing data report cards.
  • ED partnered with the U.S. Department of Housing and Urban Development to deliver a webinar entitled, “Connecting Housing and Education: How a Data-Sharing Partnership Can Improve Outcomes for Children in your Community.” This webinar, which had over 1,000 registrants, largely pulled from the tool: Data Sharing Road Map: Improving Student Outcomes through Partnerships between Public Housing Agencies and School Districts, which was jointly developed by ED and HUD.
  • Additionally, ED administers the Statewide Longitudinal Data System (SLDS) program ($32.3 million in FY18), which provides grants to states to develop their education-related data infrastructure and use these data for education improvement.
U.S. Dept. of Housing & Urban Development
  • HUD has an ambitious open data program. The HUDUSER.gov web portal provides researchers, practitioners, and the public with PD&R datasets including the American Housing Survey, HUD median family income limits and Fair Market Rents, and Picture of Subsidized Households tabulations of administrative tenant records that cross program silos and provide summary statistics at multiple geographic levels. HUD’s eGIS portal provides geo-identified versions of these datasets, administrative data, and other datasets to support public analysis of housing and community development issues related to multiple programs and policy domains using GIS tools. HUD sponsors custom tabulations of American Community Survey data that make standard adjustments of household incomes and units for household size to enable researchers and practitioners to analyze state and local housing needs. HUD provides researchers with microdata from experimental program demonstrations and research initiatives on topics such as housing discrimination, the HUD-insured multifamily housing stock, and the public housing population. To help users identify which data are useful to them, reference guides identify datasets and characterize their relevance and usefulness for research in designated categories. HUD continues its partnership with the Census Bureau to enhance public access to the American Housing Survey with a custom table creator, administrative data linkages to break down data silos, infographics to summarize results, and stronger data privacy controls.
  • PD&R has authority to enter into cooperative agreements with research organizations, including both funded Research Partnerships and unfunded Data License Agreements, to support innovative research projects that leverage HUD’s data assets and inform HUD’s policies and programs. A dedicated subject-matter expert is available to answer questions for those seeking a data license. Data licensing protocols ensure that confidential information is protected.
  • PD&R partnered with the National Center for Health Statistics (NCHS) at the U.S. Centers for Disease Control to link HUD administrative data for assisted renters with respondents to two national health surveys and made the linked data available to researchers to begin building a picture of tenant health issues. In FY18, the data linkage is being extended to include survey and administrative data for 1999 through 2016. Data access is provided through the NCHS research data centers to ensure that confidential information is protected.
  • HUD is involved in a wide array of data-sharing agreements described under Data Infrastructure in the Roadmap Update (pp. 52–56). Notably, HUD and the Census Bureau have entered into an interagency agreement for the Bureau’s Center for Administrative Records Research and Applications (CARRA) to link data from HUD’s tenant databases and randomized control trials with the Bureau’s survey data collection and other administrative data collected under the privacy protections of its Title 13 authority. These RCT datasets are the first intervention data added to Federal Statistical RDCs by any federal agency, and strict protocols ensure that confidential information is protected.
U.S. Department of Labor
  • DOL makes the majority of its administrative and survey datasets publicly available for secondary use. For more information, see CEO’s Public Use Datasets and ETA’s repository of public use datasets.
  • DOL’s worker protection agencies have open-data provisions on enforcement activity for firms online and accessible through the Data Enforcement site (Mine Safety and Health Administration, Wage and Hour Division, Occupational Safety and Health Administration, and the Employee Benefits Security Administration).
  • DOL’s Bureau of Labor Statistics (BLS) (approximately $612 million in FY18) serves as the principal federal agency responsible for measuring labor market activity, working conditions, and price changes in the economy. BLS has 111 cooperative agreements with 50 States and four Territories for labor market and economic data sharing. For calendar year 2017, there were 525 “letters of agreement” on data usage with academics to conduct statistical research, and 95 data sharing agreements with federal/state agencies including the Bureau of Economic Analysis and the Census Bureau, for a total of 620 agreements (see here for a link to the FY 2017 agreements).
  • DOL’s ETA has agreements with 50 states, the District of Columbia, and Puerto Rico for data sharing and exchange of wage data for performance accountability purposes. In FY15, DOL’s ETA began work with the U.S. Department of Education’s (ED) Office of Career Technical and Adult Education, Office of Special Education and Rehabilitation Services’, Rehabilitative Services Administration, and Office of the General Counsel to revise and renegotiate the agreements that ETA shares with 50 states and territories to facilitate better access to quarterly wage data by states for purposes of performance accountability and research and evaluation requirements under WIOA. This work aims to expand access to interstate wage data for Education’s Adult and Family Literacy Act programs (AEFLA) and Vocational Rehabilitation programs among others. This work has continued through FY18 and is being conducted in collaboration with state agencies that are subject to the performance accountability and research and evaluation requirements of WIOA and of the State Unemployment Insurance Agencies regarding access to wage records.
  • DOL’s CEO, ETA, and VETS have worked with the U.S. Department of Health and Human Services (HHS) to develop a secure mechanism for obtaining and analyzing earnings data from the Directory of New Hires. In this past year DOL has entered into interagency data sharing agreements with HHS and obtained data to support 10 job training and employment program evaluations.
  • The privacy provisions for BLS and ETA are publicly available online.
  • In FY18, DOL continued efforts to improve the quality of and access to data for evaluation and performance analysis through the Data Analytics Unit in DOL’s CEO office, and through new pilots beginning in BLS to access and exchange state labor market and earnings data for statistical and evaluation purposes. The Data Analytics unit has also updated its Data Exchange and Analysis Platform (DEAP) with high processing capacity and privacy provisions to share, link, and analyze program and survey data across DOL programs and agencies and with other agencies. Internal use of DEAP is available now, and public access will be available in the future.
  • The DOL Data Board, a DOL interagency working group, was formed in 2017 and expanded in 2018 to promote data capacity among DOL agencies and establish a new data governance model advancing DOL’s management of data as a strategic asset and service.
  • WIOA calls for aligned indicators of performance for WIOA core programs. ETA has worked within DOL and with ED to implement this alignment, including indicators definitions, data elements, and specifications to improve the quality and analytic value of the data. DOL chose to include several additional DOL programs in this process, which will result in unprecedented alignment of data and definitions for 13 federal programs (11 at DOL and two at ED).
  • DOL and ED issued five WIOA Final Rules, which all became effective October 18, 2016. Tie regulations cover WIOA programs under Title I, II, III, and IV, in addition to other miscellaneous changes. The aligned indicators of performance are included in the DOL-ED Joint Rule for WIOA, part 677.
  • ETA continues funding and providing technical assistance to states under the Workforce Data Quality Initiative to link earnings and workforce data and education data longitudinally to support state program administration and evaluation. ETA and VETS also have modified state workforce program reporting system requirements to include data items for a larger set of grant programs, which will improve access to administrative data for evaluation and performance management purposes. An example of the expanded data reporting requirements is the Homeless Veterans Reintegration Program FY16 grants.
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