wdqi-sga-dfa-py-11-01.pdf

U.S. DEPARTMENT OF LABOR

Employment and Training Administration Notice of Availability of Funds and Solicitation for Grant Applications for the Workforce Data Quality Initiative

Announcement Type: Solicitation for Grant Applications (SGA) Funding Opportunity Number: SGA/DFA PY-11-01 Catalog of Federal Domestic Assistance (CFDA) Number: 17.261

Key Dates: The closing date for receipt of applications under this announcement is April 19, 2012. Applications must be received no later than 4:00 p.m. Eastern Time.

Addresses: Mailed applications must be addressed to the U.S. Department of Labor, Employment and Training Administration, Office of Grants Management, Attention: Latifa Jeter, Grant Officer, Reference SGA/DFA PY 11-01, 200 Constitution Avenue, N.W., Room N4716, Washington, DC 20210. For complete application and submission information, including online application instructions, please refer to section IV.

Summary:

The U.S. Department of Labor (the Department), Employment and Training Administration (ETA), announces the availability of approximately $12.1 million from funds made available through the Fiscal Year (FY) 2011 DOL appropriation for Training and Employment Services for grants to State Workforce Agencies (SWA) to develop the Workforce Data Quality Initiative (WDQI). For the remainder of this document, reference to the databases being created under the WDQI may be called “workforce longitudinal administrative databases” or “workforce longitudinal databases” interchangeably.

Grants awarded will provide SWAs the opportunity to develop and use State workforce longitudinal administrative data systems. These State longitudinal data systems will, at a minimum, include information on programs that provide training, employment services, and unemployment insurance, connect with education data contained in Statewide Longitudinal Data Systems (SLDS) databases and will be linked longitudinally at the individual level to allow for enhanced opportunity for evaluation of Federally and State supported education programs and lead to better information for customers and stakeholders of the workforce system. Where such longitudinal systems do not exist or are in early development, WDQI grant assistance may be used to design and develop workforce data systems that are longitudinal and which are designed to link with relevant education data or longitudinal education data systems. WDQI grant assistance may also be used to improve upon and more effectively use existing State longitudinal systems. Current WDQI grant recipients are ineligible for this competition.

This solicitation provides background information on workforce longitudinal database systems, describes the application submission requirements, outlines the process that eligible entities must use to apply for funds covered by this solicitation, and details how grantees will be selected.

I. Funding Opportunity Description

This initiative will support development of these longitudinal databases over a three-year grant period. Applicants will be expected to clearly demonstrate their plans to build or expand these databases, store and use the data in adherence to all applicable confidentiality laws and to identify what types of analysis they will conduct with their data while protecting Personally Identifiable Information (PII) for all data collected.

A. Preparing to Apply for this Solicitation: The following are important considerations for the development of State Workforce Longitudinal Administrative Data Systems (for more details, please see Section V. further in this SGA).

1. Determining Capacity In order for applicants for WDQI grants to submit plans to develop and fully implement workforce longitudinal data systems, they will have to identify their existing stage of development. Expectations for grantees will differ depending on their launch point, which will fall into one of three categories:

i. States without workforce longitudinal data systems are expected to: 1) develop and fully implement their systems, 2) enable their new workforce systems to be linked to existing education longitudinal data systems, and 3) begin conducting basic analysis and research, consistent with all applicable Federal and State privacy laws, with their completed systems within the three-year grant period.

ii. States with partial systems are expected to: 1) fully implement their systems, 2) enable linkages to existing education longitudinal data systems, consistent with the Family Educational Rights and Privacy Act (FERPA), 20 U.S.C. 1232g, which only permits the non-consensual disclosure of PII from these systems in limited circumstances, and 3) conduct significant analysis and research, consistent with all applicable Federal and State privacy laws, with their completed systems that will be accessible to policymakers and practitioners.

iii. States with comprehensive workforce longitudinal systems are expected to: 1) expand the breadth of workforce data collected, 2) improve linkages with educational systems consistent with FERPA, 3) complete and publicize extensive longitudinal analysis and research with their systems, consistent with all applicable Federal and State privacy laws, including developing prototype models of analysis that can be useful to other less advanced States, and 4) develop user-friendly platforms to show consumers performance data and analytical reports about education and workforce service providers, consistent with all applicable Federal and State privacy laws.

2. Collection of Longitudinal Workforce Data Applicants will be expected to explain the scope of the longitudinal data system or data system enhancements which will be funded by this grant. Applicants will be asked to describe which programs will be included in the data system. At a minimum, the data systems should include disaggregated individual record data for the following programs: 1) Workforce Investment Act (WIA) Title I, 2) Wagner-Peyser Act, 3) Trade Adjustment Assistance program data, 4) Unemployment Insurance (UI) wage record data, 5) UI benefit data including demographic information associated with UI benefit payments, and 6) linkages to existing SLDSs housing K12 and post secondary data consistent with FERPA. Applicants are also encouraged to include data from other workforce and education programs such as Vocational Rehabilitation, Adult

Education, Career and Technical Education, or Registered Apprenticeship (RA) programs1.

States will need to describe any State legislative barriers that impede the linking of data sources and address how such impediments will be overcome. It will also be incumbent upon SWA applicants to determine the source of all planned workforce data used to build the workforce longitudinal databases. This is particularly relevant in the case of the RA program as DOL is the registration agency and collects and houses the data for many of the State’s RA programs.

Applicants must specify the planned data files – data records, elements, and fields - that will be contained in their workforce longitudinal data systems. Applicants should provide a detailed plan for designing, developing, storing and using the data as well as describe ongoing data-sharing and data storage procedures for both security and data quality purposes.

Applicants must also describe what procedures will be implemented to assure high standards of data quality as well as the protection of PII. Applicants should consult recommendations released from the National Institute of Standards and Technology on the protection of PII2.

WDQI grantees are expected to be a focal point for data quality assurance and must therefore indicate what steps they will take to assure that workforce data and data received from partner agencies meets rigorous data quality standards.

3. Partnerships Among Agencies Within the State Applicants must indicate which organizations will participate in the WDQI along with their authority and willingness to provide regular access to their data and to take an active role.

Workforce data may be supplied by organizations within the SWA as well as outside organizations. For example, UI wage records are kept by the State revenue agency in some States. The WIA program is also located outside the SWAs in some States. At a minimum, partnerships must be made with State education agencies, but cooperation is also encouraged with other State agencies, such as Vocational Rehabilitation or Apprenticeship agencies (in applicable States). Applicants should be prepared to describe potential legal or other barriers to data-sharing among partner agencies along with the strategies to overcome such barriers.

Applicants should provide information about the firmness of the commitment of the partners in their efforts to assemble data. Commitments should be demonstrated by submitting descriptions or evidence of planned or existing memoranda of understanding (MOU), letters of intent from partners, data-sharing agreements, or other supporting materials including legally binding agreements with partners.

4. Working with a Research Partner One common model that has been employed by many successful states (e.g., Kentucky, New Jersey and Wisconsin) has the State research university build and maintain the workforce longitudinal data system. However, alternative approaches that would maintain PII confidentiality and result in high-quality data systems will be considered for funding as well.

1 Reed, Debbie, et. Al, “An Effectiveness Assessment and Cost-Benefit Analysis of Registered Apprenticeship in 10 States” – Mathematica Policy Research, Inc. Employment and Training Administration (forthcoming).

2 McCallister, Erika, Grance, Tim, & Scarfone, Karen. “Guide to Protecting the Confidentiality of Personally Identifiable Information (PII). National Institute of Standards and Technology, U.S. Department of Commerce.

Special Publication 800-122. Retrieved from http://csrc.nist.gov/publications/nistpubs/800-122/sp800-122.pdf.

Legislation in many States does not support data-sharing between the State workforce and education agencies. As a result, for some SWAs, an alternative data storing and/or data analysis intermediary may be necessary. Private and non-profit organizations with the capacity to safely house and manipulate large data sets in accordance with State and Federal confidentiality provisions may be able to serve as partners, though under FERPA, these organizations generally could not re-disclose PII from education records, except back to the providing entity. Many State research universities have the capacity to carry out the building of longitudinal administrative databases and are situated advantageously throughout the country. Partnerships with a State research university are a proven model.

When working with a State research university or research organization, applicants should investigate the additional security measures that may be expected by the Institutional Review Board (IRB) of that university/research organization. The IRB will have to give approval for the State research institution’s involvement in this partnership that is based on its satisfaction that the plan for confidential transfer, storage and usage of data and protection of PII is sound.

Alternative models will be considered under the WDQI grant programs and it will be critical that the following considerations are incorporated into any partnership model.

i. The research partner chosen by the SWA must have demonstrable capacity to assist in the collection and storage of the longitudinal workforce data.

ii. This research partner entity must be able to ensure that the data collected will be stored in accordance with local, State and Federal confidentiality and PII protective provisions.

iii. This research partner will be responsible for processing data requests, conducting in-depth data analysis, preparing standard reports, responding to requests for additional papers and reporting on State and local workforce and education issues and trends as requested by external entities, consistent with all applicable Federal and State privacy laws.

5. Confidentiality Applicants must describe the methods and procedures (e.g. through demonstrating existence of or plans to develop MOUs, letters of intent, and data-sharing agreements) for assuring the security and confidentiality of collection, storage and use of all data contained in the workforce longitudinal data system, including protection of PII. Methods must describe how confidentiality in research, evaluation and performance management will be maintained. The responsibilities of the SWA and its partners should be enumerated and explained. Procedures for ensuring compliance with the State and Federal PII privacy and confidentiality statutes and regulations should be discussed, especially for the actual collection of data, data transmission, and the maintenance of computerized data files. Applicants should describe confidentiality procedures that will be used to protect PII, including requirements for the reporting and publication of data.

Applicants should describe under what circumstances the PII data will be made available, to whom and to what level of specificity in accordance with confidentiality laws.

The applicant should also include within their description of personnel (see Section V.A.5 of this SGA) reference to the level of confidentiality or access to data to which those employees will be held based on their employment status. For example, generally employees of State research universities are State employees, are therefore agents of the State workforce or education agencies and are granted access to or restricted from sensitive data and PII based upon State laws. In addition, they are expected to observe rules set by the State university’s IRB. It can be assumed, for the purpose of this application, that all proposed employees will be subject to Federal laws governing data-sharing, transfer of data, and PII confidentiality, including but not limited to, the requirements of FERPA, 20 U.S.C. 1232g and 34 CFR Part 99.

6. Data-Sharing Agreements Grantees must have partnership agreements outlining the storage, use and ongoing maintenance of the longitudinal databases. These data-sharing agreements must address: how data will be exchanged between partners, the purposes for which the data will be used, how and when the data will be disseminated, which entity maintains control of the data, which entity actually owns the data, the intended methods of ensuring confidential collection, use and storage of the data, and which entities inside and outside of the data-sharing agreements will have access to the data.

Data-sharing agreements that involve the disclosure of PII from education records also must:

i. designate the entity collecting the data as the authorized representative of the State educational authority to evaluate a Federally or State supported education program;

ii. specify: (A) the PII to be disclosed; (B) the purpose for which the PII is disclosed to the authorized representative; and (C) a description of the activity with sufficient specificity to make clear that the work falls within the exception of 34 CFR §99.31(a)(3), including a description of how the PII will be used;

iii. require the authorized representative to destroy the PII when it is no longer needed for the purpose specified;

iv. specify the time period in which the PII must be destroyed; and

v. establish policies and procedures, consistent with FERPA and other Federal and State confidentiality and privacy provisions, to protect PII from further disclosure (except back to the disclosing entity) and unauthorized use, including limiting use of PII to only authorized representatives with legitimate interests in the audit or evaluation of a Federal- or State-supported education program. Data-sharing agreements also should contain specific plans for secure data transfer and storage.

It may also be advantageous for grantees to develop data-sharing agreements with DOL to obtain individual level data for various programs for which the DOL is the data administrator. DOL encourages the production of full or limited scope public use data files that will be hosted by the SWA or an agreed upon designated host.

7. Integration of Efforts with State Education Agencies SWAs must assemble and use longitudinal administrative data beyond workforce data. It is important to connect workforce and education data to analyze individuals’ receipt of both education and training services and to determine ways to maximize the outcomes of these services.

i. SWAs with longitudinal administrative databases are encouraged to develop new approaches to link these databases with education entities collecting comparable education data as well as with other State agencies.

ii. SWAs which will be proposing to have their State workforce longitudinal data systems operated by a State university should assure that the State university staff will work closely with the State education agency as well as the SWA. It is important to note that many of the statewide educational data systems are also in the process of development. If the State’s SLDS only contains K12 data, applicants would have to connect workforce data to the K12 SLDS as well as any relevant postsecondary databases. In States where that data is not stored longitudinally, applicants would have to plan to link to available non-longitudinal education data, for example, individual-level post-secondary education data, consistent with FERPA. If a State’s education agency has a partially or fully developed SLDS with both K12 and post secondary data, it will be the responsibility of the workforce grantee to work with that agency to link the education and the workforce data, consistent with FERPA. This is one example of the partnership that is expected between State workforce and education agencies in developing these linked longitudinal data systems.

Applicants must provide a description of the status of the development of the statewide longitudinal education data system in their State, including what data it houses and the institutions from which the State collects data; however, they will not be penalized for planning to incorporate, consistent with FERPA, education data which are not yet gathered longitudinally.

For those States where the education statewide longitudinal data system is emerging or undeveloped, DOL understands that it will take time to link education data into the State workforce longitudinal database, consistent with FERPA, in order to contribute to longitudinal analysis. However, these grantees must use these education data to evaluate Federally or State supported education programs as soon as they have sufficient periods of longitudinal education data matched to the workforce data.

iii. Coordinated data-sharing and analysis: There are many urban labor markets that span State lines, presenting opportunities for innovative models in this initiative. States may choose to work alone in developing a longitudinal database and still partner with agencies across State lines to share data for expanded analysis through the use of MOUs. In this case, applicants must demonstrate how they will ensure that the confidentiality provisions of each State and protection of PII under all applicable State and Federal laws will be adhered to.

B. Grant Purpose The WDQI will provide funding to selected SWAs to accomplish a combination of the following objectives:

i. Develop or improve State workforce longitudinal data systems. Workforce data are already reported by localities, States, and nationally so grantees will not be creating entirely new data collection systems. What will be new, however, is coordinating or expanding/strengthening the coordination of these workforce data sources so individual-level records can be matched to one another across programs and over time.

ii. Enable workforce data to be matched with education data, to ultimately create longitudinal data systems with individual-level information from pre-kindergarten (pre-K) through post-secondary and into the workforce system to build capacity to evaluate the outcomes of Federally or State supporting education programs while protecting PII. For many years DOL has supported efforts to create workforce longitudinal administrative databases linked to data from other programs, including education data. The WDQI will greatly extend and expand this effort and complement the SLDS grant program administered by the U.S. Department of Education (ED). ED’s National Center for Education Statistics administers the SLDS grant program and provides technical assistance to all States on best practices and effective strategies to support their projects on areas critical to SLDS development. The Department’s Privacy Technical Assistance Center offers technical assistance to State education agencies, local education agencies, and institutions of higher education related to the privacy, security, and confidentiality of student records, which is also crucial to the development of these systems.

iii. Improve the quality and breadth of the data in workforce longitudinal data systems. It is important that data in the longitudinal systems are complete and accurate and include an array of performance information in order to enhance knowledge about the workforce system and the impact of State workforce development programs. Data collection systems might also be improved to strengthen data validity and to minimize the reporting burden on State agencies and training providers.

iv. Use longitudinal data to evaluate the performance of Federally and State supported education and job training programs. Policymakers and practitioners can use this data analysis to make programmatic adjustments that improve these programs.

v. Provide user-friendly information to consumers to help them select the education and training programs that best suit their needs. For example, Washington State displays information about training program outcomes at www.careerbridge.wa.gov, allowing consumers to compare the performance of different training providers.

The relative prominence of each objective for a given State will primarily be determined by the State’s “launch-point” for developing a workforce longitudinal data system that will ultimately be linkable to education data and will reflect high data quality standards while protecting individual privacy (see the Section I.A.5).

C. Background President Obama’s FY 2011 Budget requested $13.75 million and the Congress appropriated $12.475 million for the development of workforce longitudinal data systems. Approximately $12.1 million will be awarded to state workforce agency applicants; the remaining 3 percent, or $374,250, is designated for technical assistance. Single-state applicants can qualify for up to $1 million in funding (see Section III.A for more information on funding eligibility). In order to expand this Initiative to new states, applications will be accepted only from SWAs in states which are not current WDQI grantees (as a result of the prior solicitation SGA/DFA PY 09-10).

These funds will be made available through competitive WDQI grants administered by DOL in support of a parallel and much larger effort, the SLDS grants. This year, ED is providing $38 million to support statewide longitudinal education data systems that link early childhood, K through grade 12, postsecondary, and workforce. An allowable priority for ED’s SLDS program is using funds to capture data on workforce participation and outcomes of students after they leave education systems.

Some innovative States already have shown the advantages of SWAs partnering with education and other entities to create comprehensive, longitudinal data systems. The State of Florida, for example, has developed a comprehensive system that links individuals’ demographic information, high school transcripts, college transcripts, quarterly UI wage data, and workforce services data. Such data systems can provide valuable information to consumers, practitioners, policymakers, and researchers about the performance of education and workforce development programs.

D. Classification of Workforce System Data

Workforce system administrative data are collected as part of the operations of a variety of programs administered at the State and local level. These programs provide employment and training services, pay UI benefits to unemployed workers, and collect employer-paid UI payroll taxes that pay for UI benefits. The employment and training data come from a number of large and small workforce programs that provide employment and/or training services to employed and unemployed workers. Information is available for each service that is provided to each worker by each program. Below are examples of the most common types of workforce data.

i. Wage Records: The UI administrative data come from State UI programs through regular employer reporting on contributions to the UI payroll tax system. An important source of data on the employment and earnings of American workers comes from these UI wage record reports that are derived from the tax forms on covered establishments’ wage and salary employment filed quarterly by employers. UI wage record reports include: the number of workers, worker names, Social Security numbers (SSN), earnings, and employers’ industry codes and locations. UI wage records are comprehensive, as over 90 percent of wage and salary employment is in covered establishments. Data are also available for civilian and military Federal employees, but not for the self-employed.

ii. Employment and Training Services: Each of the workforce system programs provides employment and/or training services to unemployed, underemployed or employed individuals.

Some programs also provide services to new entrants to the labor market (with the exception of the UI program). Data on types of employment and training services received, such as self-service and informational activities, prevocational services, and specific training services, are available from a number of workforce programs including those authorized under the WIA, the Wagner-Peyser Act, from the Trade Adjustment Assistance program, the Registered Apprenticeship program and other workforce programs. Transaction information is available for each service (e.g., training receipt, job referral, job search assistance) that is provided to participants in each program, together with their personal characteristics and other demographic information. Not only is information provided on participation numbers for employment services and training programs, information includes employment status, pre-program earnings, occupation of employment, and education participation or completion levels of individuals.

iii. Unemployment Insurance Benefits: The UI program also collects data on applicants for and recipients of UI benefits, including the number of persons that apply for UI benefits, the number that collect benefits, and the amount of benefits paid. Administrative data collected in the UI benefit claims process include worker demographic information such as age, former occupation and industry, in addition to residency information (including the street, city, State, and zip code).

iv. The Federal Employment Data Exchange System (FEDES): This data system provides States access to Federal civilian and military employment and earnings records maintained by the Office of Personnel Management and the Department of Defense.

Data for all of these programs can be linked for any worker in the database because all of these programs collect the SSN of the participating individual. Workforce data can determine whether individuals have been employed, what their earnings and industry of employment are if they work, whether they become unemployed, whether they collect unemployment insurance upon unemployment, what employment services they receive from SWAs, and whether they use training services.

E. Workforce Longitudinal Administrative Data Systems That Are In Place or In Progress DOL awarded first round WDQI funding to the following thirteen states: Florida, Iowa, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Missouri, North Dakota, Ohio, South Carolina, Texas, and Virginia. Nine states are expanding, improving, and enhancing current systems. Louisiana, Massachusetts, and Minnesota are creating new longitudinal data systems.

DOL also has supplementary information on the development of workforce longitudinal databases from a consortium of nine States that currently maintain longitudinal administrative data on the Department’s Web site. ETA has had a longstanding contractual relationship with this consortium of States to conduct workforce research, analysis, and evaluations. The ADARE alliance3 members consist of California, Florida, Georgia, Illinois, Maryland, Missouri, Ohio, Texas, and Washington. DOL has funded the ADARE project since 1998.4 However, recently funds have not been available to support research and analysis to make full use of the linkage between longitudinal workforce and education data. Nonetheless, the ADARE partners have developed working relationships with State education or research entities.

The goal of the WDQI is to substantially reduce variation among currently existing state databases and build stronger longitudinal data systems through workforce data matching which can link to education data, consistent with FERPA.

F. Existing State Examples of Workforce Longitudinal Data Systems Altogether, about a dozen States (including the nine ADARE States) have developed substantial State workforce longitudinal data systems. Most of these States created these systems using State funds for a variety of applications, including tracking program performance, analyzing program activities and conducting research and analysis. A small number of these States have accumulated workforce and other longitudinal data for several decades.

As of 2011, nine States continued to participate in the ADARE alliance – California, Florida, Georgia, Illinois, Maryland, Missouri, Ohio, Texas and Washington. All but two – Florida and Washington – use a State research university to assemble, house, and analyze their data. In all cases, cooperative arrangements through MOUs and data-sharing agreements have been developed, to enable the State WIA, Wagner-Peyser Act, and UI programs to share their workforce data as input to the workforce longitudinal administrative database.

In all cases, State agencies receive analyses and reports derived from the databases that can be used to understand and improve workforce programs. However, each State has initiated and operated its workforce longitudinal data system in a different manner.

WDQI applicants may be able to learn from the various approaches of the ADARE States.

These ADARE models form a useful set of examples for any SWA considering applying for a WDQI grant. While innovation is encouraged, applicants should make full use of the existing

3 Administrative Data Research and Evaluation (ADARE) Alliance Web site. 2009.

http://www2.ubalt.edu/jfi/adare/ 4 Stevens, David W. 2004. Responsible Use of Administrative Records for Performance Accountability: Features of Successful Partnerships. http://www.ubalt.edu/jfi/adare/reports/ADAREcookbook504.pdf http://www.ubalt.edu/jfi/adare/reports/ADAREcookbook504.pdf knowledge and various models for building workforce longitudinal databases that have been developed in this field. Provided below is a brief description of four different State approaches that highlight successful workforce longitudinal databases models and applications of the information these databases provide. We note that we take no position on whether any of the data sharing agreements that were used under these different models complied with applicable Federal and State privacy laws, including FERPA.

1. University-led Partnership to Manage Statewide Data-Sharing– Maryland: In Maryland, the research component of longitudinal data-sharing was prioritized at the outset of the partnership between the Jacob France Institute of the University of Baltimore and the Maryland SWA, now the Maryland Department of Labor, Licensing and Regulation (DLLR). The Jacob France Institute has been authorized through data-sharing agreements with DLLR and various other State agencies to hold shared performance evaluation responsibilities for Maryland’s WIA Title I-B (Adult, Dislocated Worker and Youth employment and training services), Title II (Adult Education and Literacy) and Title IV (Vocational Rehabilitation) programs, Temporary Assistance for Needy Families (TANF) performance calculations, and core indicators of the Carl D. Perkins Act secondary and post-secondary adult vocational education and training services.

As the steward of this performance reporting system, the Jacob France Institute has formed partnerships with the Governor’s Workforce Investment Board, the Maryland Higher Education Commission, the Maryland State Department of Education, the Maryland Department of Business and Economic Development, the Maryland Department of Human Resources, the University System of Maryland, and locally with the Montgomery County Public Schools, the Baltimore City Public Schools, the Empower Baltimore Management Corporation, and individual community colleges.

2. University-led Partnership with Common Performance Management System – Illinois: The longitudinal data system developed in Illinois is an example of a productive evolution of data-sharing among State agencies and educational institutions. In the mid-1980’s the Center for Governmental Studies (Center) at Northern Illinois University connected with the Department of Commerce and Community Affairs (DCCA) and the Illinois Department of Employment Security (IDES) to link UI wage records to program participant records under the Job Training Partnership Act (JTPA). Less than a decade later, after having established themselves as an authority on linking administrative databases, the Center was awarded a grant to fund a project linking UI administrative data from multiple States.

Beginning in 1994, the Center undertook a project to develop and implement a common performance management framework which led to the Illinois Common Performance Management System (ICPMS) linking UI wage records with client data from JTPA workforce development programs, adult education, primary and secondary vocational education, and welfare-to-work. With the implementation of WIA, the Center began a project to expand its administrative database longitudinally to include historical archives of UI wage records which were easily accessible. The Center benefits from the partnership by gaining access to data which allows for in-depth research. Likewise, the Illinois workforce agencies benefit from being able to use the database and related research to improve system performance. The partnership is based on transparency and cooperation and has led to analysis of longitudinal data that has influenced frontline program management and public policy.

3. Vendor Contracted Analysis of Longitudinal Data – Washington: The Washington State longitudinal administrative database began as a DOL project in the late 1970s and early 1980s, but has been maintained and expanded by Washington State since that time. Today, Washington State provides an alternative model for developing statewide longitudinal administrative databases of workforce and education information. The State workforce investment board (the Washington State Workforce Training and Education Coordinating Board or WTECB) collects and maintains the longitudinal State workforce data, but has contracted with a private, non-profit research organization, the Upjohn Institute for Employment Research, to conduct analysis of the longitudinal administrative data.

By using a research organization, WTECB has been able to securely and effectively manage its commitment to accountability and performance monitoring. Furthermore, WTECB is able to track the outcomes of individuals in terms of achievement of workplace competencies, placement in employment, increases in levels of earned income, increased productivity, advancement out of services and overall satisfaction with program services and outcomes. In Washington State, there has been a focus on evaluating the returns on investment of the State workforce system in recent years.

Aside from using a research institution instead of a research university, Washington State is also unique because the SWA’s high level of commitment to program evaluation through longitudinal data analysis is mirrored in the governor’s office.

4. State-led Education and Workforce Longitudinal Data System – Florida: In 1971, State legislation designed to spur improved accountability in education resulted in creation of the Florida Statewide Assessment Program. This program was deliberately designed to collect a broad array of data on individuals moving through the educational system (kindergarten through post-secondary, undergraduate levels) for the express purpose of assessing student strengths and weaknesses to assist with education reform efforts. In the 1980s, the focus for data collection expanded to include career and technical education data5, particularly at the post-secondary level. Since 1991, Florida State law has required community colleges and State universities to contribute their data to this data collection system.

The breadth of this data system relies upon a collaborative data collection and retention commitment from both the Office of Educational Accountability and Information Services and the Florida Agency for Workforce Innovation (FAWI). In addition to tracking student progress through career or technical education, university or community college, FAWI compiles information from workforce and social service programs that complements the education data.

This information includes data from WIA programs, TANF, and State UI and Employment Service programs.

Not only is Florida’s longitudinal data system a unique example of the potential uses of a longitudinal data system, but it also shows the diversity of partnerships formed in the creation of this data system. Through the Florida Education and Training Placement Information Program (FETPIP), agencies such as the Florida Department of Corrections, the Florida Department of

5 Florida Case Study: Building a Student-Level Longitudinal Data System. The Data Quality Campaign, August 2006. http://www.dataqualitycampaign.org/files/State_Specific-Florida_2006_Site_Visit.pdf http://www.dataqualitycampaign.org/files/State_Specific-Florida_2006_Site_Visit.pdf

Education, the U.S. Department of Defense, the U.S. Office of Personnel Management, the U.S.

Postal Service, the Florida Department of Management Services, the Florida Agency for Workforce Innovation, Workforce Florida and numerous others have benefitted from information sharing or analysis of available data. The analysis from the Florida workforce longitudinal database has resulted in a detailed performance measurement system that goes far beyond the measures required by DOL or ED and has allowed for in-depth evaluation of State labor and education programs.

For more information about longitudinal data systems in other ADARE States, visit the Weblinks available in the first and second footnotes.

G. Selected Benefits and Uses of State Longitudinal Data Systems State workforce longitudinal data systems can be used for a variety of purposes. DOL has primarily used the data to conduct evaluation and research. Most States have used these systems for measuring performance of workforce and educational programs, and generally to guide program operations and program development. Localities have been interested in how their school district or local One-Stop Career Centers are performing.

In recent years, DOL funded two evaluations6 of WIA programs to determine the effectiveness of the program and its components.

In conjunction with welfare reform in the United States, DOL began administering grants for welfare-to-work programs. The ADARE alliance members came together to evaluate the welfare-to-work programs in six urban areas located in six of the ADARE States7.

Washington State had a number of its State-and Federally-funded workforce programs evaluated by an outside research organization, by awarding this organization a contract and giving it access to their workforce longitudinal administrative data8.

Currently, Maryland makes use of its longitudinal data system for a wide variety of purposes. A recent study followed the employment history of graduates from high schools in a single county, for seven years. It used UI wage record data from Maryland and surrounding States, as well as data on Federal civilian and military employees to conduct analysis.

The examples above show some of what can be done with State workforce longitudinal data systems. Many other uses are possible. For example, by developing these statewide workforce longitudinal databases and linking them to comparable education databases, DOL, the States, and

6 Heinrich Carolyn J., Peter R. Mueser and Ken Troske. 2009. Workforce Investment Act Non-Experimental Evaluation: Final Report. Washington DC: U.S. Department of Labor.

http://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id=2419&mp=y

Hollenbeck, Kevin, Daniel Schroeder, Christopher T. King, and Wei-Jung Huang. 2008. Net Impact Estimates for Services Provided Through the Workforce Investment Act. Washington, DC: U.S. Department of Labor.

http://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id =2367&mp=y 7 King, Christopher T. and Peter R Mueser. 2005. Welfare and Work: Experience in Six Cities. Kalamazoo MI: W.

E. Upjohn Institute for Employment Research.

http://www.upjohninstitute.org/publications/titles/waw.html 8 Hollenbeck, Kevin M. 2003. Net Impact Estimates of the Workforce Development System in Washington State.

Upjohn Institute Staff Working Paper No. 03-92.

http://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id=2367 http://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id=2419&mp=y http://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id=2419&mp=y http://www.upjohninstitute.org/publications/titles/waw.html localities could more effectively: 1) determine the employment outcomes for students (for secondary and post-secondary students alike) to evaluate Federally or State supported education programs, 2) analyze the cost effectiveness of Federally or State supported training programs in terms of increased earnings for individuals, 3) relate employment outcomes to Federally or State supported training and education program funding, and 4) illustrate the cost effectiveness of providing employment services programs by demonstrating whether there is a corresponding reduction in payment of UI and TANF benefits among individuals exiting the WIA and Wagner- Peyser programs. In the future, DOL is likely to fund projects focusing on program evaluation made possible through the development of these longitudinal workforce databases similar to the work of the ADARE States. As these databases are built, therefore, grantees should be prepared to address national research queries.

In addition, SWAs (or their data analysis partner) will be expected to use outside data resources to improve the breadth and depth of State workforce analysis. The following are examples of potentially useful data sets that can either be directly incorporated into the workforce longitudinal data system or used in conjunction with findings generated through that data system:

i. Local Area Unemployment Statistics program – This is a Federal-State joint program providing monthly estimates of total employment and unemployment for areas including, census regions and divisions, States, some metropolitan areas, small labor market areas, counties and county equivalents and cities and towns of 25,000 people or more.

ii. Quarterly Census of Employment and Wages (QCEW) file – the Bureau of Labor Statistics (BLS) collects data on establishments reported by UI-covered employers, including information on industry, domain (public or private), geographic location etc., which could add information on the type of employment held by individuals in addition to wage levels and duration of employment. The establishment-level information in the QCEW database is protected by the Confidential Information Protection and Statistical Efficient Act (CIPSEA) and therefore may not be shared outside the cooperative statistical system. However, States have the capability of generating a version of this dataset that is not protected by CIPSEA. Further, BLS provides to the State cooperative statistical agency the linkages of establishments from quarter to quarter. Depending on State law and policy, the version of the establishment data not protected by CIPSEA and the quarter-to-quarter establishment linkages may be provided for use in the State longitudinal data system.

iii. Business Employment Dynamics (BED) – is a set of statistics generated from the Quarterly Census of Employment and Wages, or ES-202, program. This data set is focused on employer data at the establishment level enabling BLS to track which firms are changing hands, ceasing to exist or acquiring additional resources. The BED file is built on the UI tax reports of each establishment which shows employment changes when companies form and fold. By showing quarterly gross job losses and gains (from 1992 onward) these data can highlight the dynamic changes occurring in the job market at a very local level or aggregated up to the State level.

iv. Mass-Layoffs Statistics program – BLS uses the volume of unemployment claims reported by each establishment in the U.S. to determine monthly mass layoff numbers. These mass layoffs are charted by month and by quarter for regions and industries and can be an effective tool to show major job losses affecting the local or State workforce.

v. Longitudinal Employer-Household Dynamics (LEHD) – The U.S. Census Bureau uses modern statistical and computing techniques to combine Federal and State administrative data on employers and employees with core Census Bureau censuses and surveys while protecting the PII confidentiality of people and firms that provide the data. The LEHD research program is centered on the creation and empirical analysis of confidential, longitudinally linked employer-household micro-data for Federal and State administrative purposes as well as confidential Census Bureau surveys and censuses. The LEHD’s Local Employment Dynamics (LED) is a voluntary partnership between State labor market information agencies and the U.S. Census Bureau. LED uses State UI Quarterly Census of Employment and Wage data micro-data provided by States. In many of the 47 participating States, longitudinal data reaches back nearly 20 years. The LED Internet-based tools include geographic information system mapping and localized workforce and industry reports.

vi. Registered Apprenticeship (RA) Program Data – An additional source of data on individuals who may not be represented in other workforce programs or in the education system is through the RA program. Applicants that include data from RA programs should note that the DOL Office of Apprenticeship is the registration agency for RA programs in 25 States and the data for RA programs in these States are maintained in DOL’s Registered Apprenticeship Partners Information Data System (RAPIDS). In the other 25 States, the District of Columbia, and U.S. Territories, the registration agency is a State Apprenticeship Agency recognized by DOL that has responsibility for registering apprenticeship programs and maintaining apprenticeship data. In cases where successful applicants propose to include apprenticeship data that are maintained in DOL’s RAPIDS, an MOU, letter of intent, data-sharing agreement, or other supporting materials legally binding the use of RAPIDS, are not required. DOL will work with these grantees to provide access to apprenticeship data. Applicants should visit the DOL Office of Apprenticeship’s Web site (http://www.doleta.gov/oa/stateoffices.cfm and http://www.doleta.gov/oa/stateagencies.cfm) to identify the Registration Agency appropriate for their State.

II. Award Information A. Award Amount ETA has approximately $12.1 million available under this competition and expects to fund approximately twelve grants. Individual grant amounts will not exceed $1 million, for any single-state grant. Any grant application with a proposed value greater than $1 million from a single state grantee will be deemed non-responsive and will not be considered. In the event additional funds become available, ETA reserves the right to use such funds to select additional grantees from applications submitted in response to this solicitation.

B. Period of Performance The period of grant performance will be up to 36 months. This performance period includes all necessary implementation and start-up activities. Applicants must plan to fully expend grant funds during the period of performance while ensuring full transparency and accountability for all expenditures.

III. Eligibility Information A. Eligible Applicants Eligible applicants for this solicitation are those SWAs within the 50 states, the District of Columbia, Puerto Rico and the U.S. Virgin Islands that were not recipients of a round one WDQI grant (as a result of solicitation SGA/DFA PY 09-10). States ineligible for this round of funding consists of the following: Florida, Iowa, Louisiana, Maine, Maryland, Massachusetts, Minnesota, Missouri, North Dakota, Ohio, South Carolina, Texas, and Virginia.

B. Cost Sharing or Matching Cost sharing or matching funds are not required as a condition for application.