Notice of Availability of Funds and Solicitation for Grant Applications (SGA) to Fund Demonstration Projects, 27584-27599 [2010-11610]

Agencies

• Employment and Training Administration
• DEPARTMENT OF LABOR

[Federal Register Volume 75, Number 94 (Monday, May 17, 2010)]
[Notices]
[Pages 27584-27599]
From the Federal Register Online via the Government Printing Office [www.gpo.gov]
[FR Doc No: 2010-11610]


-----------------------------------------------------------------------

DEPARTMENT OF LABOR

Employment and Training Administration


Notice of Availability of Funds and Solicitation for Grant 
Applications (SGA) to Fund Demonstration Projects

Agency: Employment and Training Administration, U.S. Department of 
Labor.
    Announcement Type: New, Notice of Solicitation for Grant 
Applications (SGA).
    Funding Opportunity Number: SGA/DFA PY 09-10.
    Catalog of Federal Assistance Number: 17.261.

Summary: The U.S. Department of Labor (DOL), Employment and Training 
Administration (ETA), announces the availability of $12.2 million from 
funds made available through the FY 2010 DOL budget for Training and 
Employment Services for grants to State Workforce Agencies (SWA) to 
develop the Workforce Data Quality Initiative (WDQI). 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 and 
will be linked longitudinally at the individual level to allow for 
analysis which will lead to enhanced opportunity for program evaluation 
and lead to better information for customers and stakeholders of the 
workforce system. Where such longitudinal systems do not exist or are 
incipient, 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.
    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.

DATES: Key Dates: The closing date for receipt of applications under 
this announcement is August 16, 2010. Applications must be received at 
the address below no later than 4 p.m. (Eastern Time). Application and 
submission information is explained in detail in Section IV of this 
SGA. A pre-recorded Webinar for prospective applicants will be online 
at: https://www.workforce3one.org and available for viewing on June 21, 
2010, by 3 p.m. ET, and accessible any time after that date. Reviewing 
this Webinar is not mandatory but applicants are encouraged to take 
advantage of this resource to get questions answered.

ADDRESSES: Mailed applications must be addressed to the U.S. Department 
of Labor, Employment and Training Administration, Division of Federal 
Assistance, Attention: Willie E. Harris, Grant Officer, Reference SGA/
DFA PY 09-10, 200 Constitution Avenue, NW., Room N4716, Washington, DC 
20210. Applications sent via facsimile (fax), telegram or e-mail will 
not be accepted. Information about applying online also can be found in 
Section IV.C of this document. Applicants are advised that mail 
delivery in the Washington, DC area may be delayed due to mail 
decontamination procedures. Hand-delivered proposals will be received 
at the above address.

SUPPLEMENTARY INFORMATION:

A. 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 measure outcomes while 
protecting individual privacy. 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 Statewide 
Longitudinal Data Systems (SLDS) grant program administered by the 
Department of Education (ED).
    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 provide useful information about 
program operations and analyze the performance of education and 
training programs. Policymakers and practitioners can use this data 
analysis to make programmatic adjustments that improve the workforce 
system.
    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 https://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). Additional 
details on the ``launch point'' for States can be found in the section 
of this SGA in Section I.A.1.

B. Background

    President Obama's FY 2010 Budget requested $15 million and the 
Congress

[[Page 27585]]

appropriated over $12 million for the development of workforce 
longitudinal data systems. Single-state applicants can qualify for up 
to $1 million in funding. Multi-state consortium applicants are 
eligible for a grant amount of up to $3 million (see Section III.A for 
more information on funding eligibility).
    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. The American Recovery and Reinvestment Act (Recovery 
Act) appropriated $245 million to ED to support statewide (or in some 
cases, multi-State consortia) longitudinal education data systems with 
data on individuals participating in pre-K through grade 12 as well as 
post-secondary education and the workforce. The grant instructions for 
ED's SLDS program expressly include provisions to capture the data on 
workforce participation of students before and after they leave 
education systems. A request for applications was issued by ED on July 
24, 2009, and applications were due December 4, 2009. The grants are 
scheduled to be awarded in May 2010.
    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 
unemployment insurance (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.
    As with the above section and 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.

C. 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, 
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 Workforce Investment Act of 1998 (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, the Department of Defense and the U.S. Postal Service.
    Data for all of these programs can be linked for any worker because 
all of these programs collect the Social Security Number 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.

D. Workforce Longitudinal Administrative Data Systems That Are in Place 
or in Progress

    From a recent survey conducted by the National Association of State 
Workforce Agencies (NASWA), DOL received information about the current 
extent of matching State education agency data with SWA data. The 
information was compiled from responses from the SWA research 
directors. Thirty-one responses were received from the 53 jurisdictions 
that have UI programs. These results are supported by recent data 
gathered through Carl D. Perkins Act accountability reporting which 
found that 31 States use UI wage records to determine employment after 
leaving post-secondary education.
    DOL also has supplementary information on the development of 
workforce longitudinal databases from a consortium of nine States that 
currently maintain longitudinal administrative data. ETA has had a 
longstanding contractual relationship with this consortium of States to 
conduct workforce research, analysis, and evaluations. This group is 
called the Administrative Data Research and Evaluation Project (ADARE) 
alliance,\1\ and the members are California, Florida, Georgia, 
Illinois, Maryland, Missouri,

[[Page 27586]]

Ohio, Texas, and Washington. DOL has funded the ADARE project since 
1998.\2\ 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 (except for the Florida ADARE partner which is the 
State education agency).
---------------------------------------------------------------------------

    \1\ Administrative Data Research and Evaluation (ADARE) Alliance 
Web site. 2009. https://www2.ubalt.edu/jfi/adare/.
    \2\ Stevens, David W. 2004. Responsible Use of Administrative 
Records for Performance Accountability: Features of Successful 
Partnerships. https://www.ubalt.edu/jfi/adare/reports/ADAREcookbook504.pdf.
---------------------------------------------------------------------------

    These two sources of information (the NASWA survey and the ADARE 
States) indicate that the extent of data matching and development of 
longitudinal data systems varies:
    i. About 20 States currently do not have their State workforce data 
arranged in longitudinal databases, nor do they match their workforce 
data with education data.
    ii. Almost 20 States do conduct some workforce data matching with 
State educational data, but they do not have State workforce data 
collected and arranged longitudinally.
    iii. About a dozen States have substantial State workforce 
longitudinal databases, and almost all of these databases have been 
linked to available State educational data (both longitudinal and non-
longitudinal data sets). Most of these States are part of the ADARE 
consortium.
    The goal of the WDQI is to substantially reduce this variation and 
build stronger longitudinal data systems through workforce data 
matching which can link to education data.

E. 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 2009, 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 memoranda of 
understanding and data-sharing agreements have been developed, to 
enable the State WIA, Wagner-Peyser Act, and unemployment insurance 
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 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.
    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 Maryland-Baltimore County 
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 primary 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, TANF High Performance 
Bonus Indicator 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.
    In addition to statewide data-sharing, the Jacob France Institute 
has been awarded grant funds to develop multi-state longitudinal data-
sharing systems among Delaware, the District of Columbia, New Jersey, 
Ohio, Pennsylvania, Virginia and West Virginia. This model of 
interstate data-sharing captures workforce and education data for 
individuals who are mobile in their pursuit of employment, training or 
education.
    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-1980s 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

[[Page 27587]]

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.
    The Upjohn Institute includes a number of labor economists doing 
applied research, frequently with large, longitudinal data sets. They 
have experience matching longitudinal data among States (Indiana, 
Georgia, Virginia, Washington and Ohio). By using a research 
organization, WTECB has been able to securely and effectively manage 
its commitment to accountability and performance monitoring. Through 
the Upjohn Institute, 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 data \3\, 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.
---------------------------------------------------------------------------

    \3\ Florida Case Study: Building a Student-Level Longitudinal 
Data System. The Data Quality Campaign, August 2006. https://www.dataqualitycampaign.org/files/State_Specific-Florida_2006_Site_Visit.pdf.
---------------------------------------------------------------------------

    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, the Temporary Assistance for Needy Families (TANF) 
program, and the State UI and Employment Service programs.
    Not only is Florida's longitudinal data system a unique example, 
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 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.

F. 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 evaluations \4\ of WIA 
programs to determine the effectiveness of the program and its 
components.
---------------------------------------------------------------------------

    \4\ 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. https://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. https://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id=2367&mp=y.
---------------------------------------------------------------------------

     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 States.\5\
---------------------------------------------------------------------------

    \5\ King, Christopher T. and Peter R Mueser. 2005. Welfare and 
Work: Experience in Six Cities. Kalamazoo, MI: W. E. Upjohn 
Institute for Employment Research. https://www.upjohninstitute.org/publications/titles/waw.html.
---------------------------------------------------------------------------

     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 data.\6\
---------------------------------------------------------------------------

    \6\ Hollenbeck, Kevin M. 2003. Net Impact Estimates of the 
Workforce Development System in Washington State. Upjohn Institute 
Staff Working Paper No. 03-92. https://wdr.doleta.gov/research/keyword.cfm?fuseaction=dsp_resultDetails&pub_id=2367.
---------------------------------------------------------------------------

     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.
     In 2008, a multi-state study \7\ followed the flow of TANF 
leavers into the labor force, measuring their employment and earnings, 
determining whether and when they became unemployed and whether they 
collected unemployment insurance. Further research has extended the 
analysis to examine whether they received employment services and 
whether these

[[Page 27588]]

services assisted these individuals in returning to work.
---------------------------------------------------------------------------

    \7\ O'Leary, Christopher J., and Kenneth J. Kline. 2008. UI as a 
Safety Net for Former TANF Recipients. Washington, DC: Office of the 
Assistant Secretary for Planning and Evaluation, U.S. Department of 
Health and Human Services. https://aspe.hhs.gov/hsp/08/UI-TANF/index.htm.
---------------------------------------------------------------------------

     In Texas, the Student Futures Project \8\ has used their 
longitudinal administrative database to create a feedback system that 
has led to improvements in the direct-to-college enrollment rates from 
54 percent to 62 percent between 2004 and 2009 in 10 participating 
local education districts. The project makes use of a number of 
secondary school administrative procedures (e.g., encouraging 
completion of student aid applications in class, taking of SATs, 
increasing assistance with post-secondary school applications). It 
assesses progress using an administrative database consisting of local 
education and workforce data that are collected and analyzed by the Ray 
Marshall Center at the University of Texas.
---------------------------------------------------------------------------

    \8\ King, Christopher T., Deanna Schexnayder, Greg Cumpton and 
Chandler Stolp. 2009. Education and Work After High School: Recent 
Findings from the Central Texas Student Future Project. Bureau of 
Business Research, IC2 Institute, the University of Texas at Austin. 
https://centexstudentfutures.org/pubs/TBRAUG09.pdf.
---------------------------------------------------------------------------

    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 or multi-state workforce 
longitudinal databases and linking them to comparable education 
databases, DOL, the States, and localities could more effectively: (1) 
Determine the employment outcomes for students (for secondary and post-
secondary students alike), (2) identify education exit points that 
maximize employment and earnings of former students, (3) analyze the 
cost effectiveness of training programs in terms of increased earnings 
for individuals, (4) relate employment outcomes to training and 
education program funding, (5) 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, and (6) 
determine the impact of education achieved on the incidence of 
individuals participating in the UI program or the TANF program.
    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 
or multi-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 (LAUS)--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 (MLS)--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 
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 GIS 
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 which may 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 (SAA) 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, a 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

[[Page 27589]]

visit the DOL Office of Apprenticeship's Web site (https://www.doleta.gov/oa/stateoffices.cfm and https://www.doleta.gov/oa/stateagencies.cfm) to identify the Registration Agency appropriate for 
their State.

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 individual privacy 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 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, and (3) conduct significant analysis and 
research with their completed systems that will be accessible to 
policymakers and practitioners.
    iii. States with comprehensive workforce longitudinal systems are 
expected to: (1) Expand and extend their systems, (2) improve linkages 
with educational systems, (3) complete and publicize extensive 
longitudinal analysis and research with their systems, 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.

2. Collection of Longitudinal Workforce Data

    Applicants will be expected to explain the scope of the 
longitudinal data system 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) WIA Title I, (2) 
Wagner-Peyser Act, (3) Trade Adjustment Assistance program data, (4) UI 
wage record data, (5) UI benefit data including demographic information 
associated with UI benefit payments, and (6) linkages to existing State 
education agency longitudinal data. Applicants are also encouraged to 
include data from other workforce programs such as Vocational 
Rehabilitation or RA programs. 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 should 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 
individual privacy. 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 will be expected to 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 from 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

    The success of most ADARE States and other States (e.g., Kentucky, 
New Jersey and Wisconsin) which have worked with a State research 
university for building, maintaining and using their workforce 
longitudinal data systems offers an approach that States may use. 
However, alternative approaches that would maintain confidentiality and 
result in high-quality data systems will be considered for funding as 
well.
    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 could serve as partners. Many 
State research universities have the capacity to carry out the building 
of longitudinal administrative databases and are situated 
advantageously throughout the country and partnerships with a State 
research university are a proven model (please refer to Section B. 
above for further information).
    When working with a State research university, applicants should 
investigate the additional security measures that may be expected by 
the Institutional Review Board (IRB) of that university. 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

[[Page 27590]]

confidential transfer, storage and usage of data 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 to local, State and Federal 
confidentiality 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. It is expected, therefore, that the institution 
partnering with the SWA will have the capacity to fulfill these 
responsibilities.

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. 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 privacy and confidentiality statutes and 
regulations should be discussed, especially regarding 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 personally identifiable 
information, including requirements for the reporting and publication 
of data. Applicants should describe under what circumstances the 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 key 
types of personnel (see Section V.A.5 further in 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 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 confidentiality.

6. Data-Sharing Agreements

    It is to be expected that grantees in this initiative will 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 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 are expected to assemble (or plan to assemble) and use 
longitudinal administrative data beyond only 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. DOL encourages all SWAs which apply for WDQI grants to take 
their workforce longitudinal administrative database in whatever stage 
it may be in, develop it fully, and then enable the data to be matched 
with similar longitudinal education databases.
    ii. 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.
    iii. 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 supported by the ED are also in a State of development. On the 
education side there may be no longitudinal data systems in place, in 
which case, qualifying grantees would have to plan to link to available 
non-longitudinal education data, for example, individual-level post-
secondary education data. If a State's education agency has a partially 
or fully developed statewide longitudinal education data system, it 
will be the responsibility of the workforce grantee to work with that 
agency to link the education and the workforce data. 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 (e.g., nothing in place, statewide longitudinal data system 
planned but not yet implemented, longitudinal data system partially 
developed or fully developed) but they will not be penalized for 
planning to incorporate education data which are not yet gathered 
longitudinally.
    For those States where the education statewide longitudinal data 
system is incipient or undeveloped, DOL understands that it will take 
time to link education data into the State workforce longitudinal 
database in order to contribute to longitudinal analysis. The 
expectation is that these grantees will use these education data for 
analysis as soon as they have sufficient periods of longitudinal 
education data matched to the workforce data.
    B. Multi-State Partnerships: Collaborative approaches will result 
in more complete data sets and efficient use of resources. DOL 
encourages States to partner in submitting applications and work 
together on developing workforce longitudinal databases. Applicants 
should explain their role relative to State partners in the ``Plan 
Outline'' and ``Description of Partnership Strategies'' sections of 
their application. Collaborations among or between States may take 
three forms:
    i. Multi-state data systems: This is the most collaborative 
approach and may be the most efficient, because it allows States to 
share technology and

[[Page 27591]]

administrative resources. More than one State would contribute raw data 
into a merged matching system, maintained by the lead administrative 
agent, creating a multi-state workforce longitudinal data system. DOL 
strongly encourages this approach. The following points may be helpful 
in considering how to structure multi-state workforce longitudinal 
administrative databases:
     Look to States in close proximity whose educational 
capacity is robust (particularly in the case of a major State research 
university) in order to make assessments on which entities will be 
approached for a proposed partnership.
     Show figures on the interstate flow of residents through 
education pathways and beyond to demonstrate the usefulness of 
developing multi-state longitudinal databases.
     Outline the contribution of data and resources of the 
grant-seeking State to the multi-state longitudinal database system as 
it is developing or its contribution to a system already in existence.
     Anticipate what the role of this SWA will be in joining a 
collaborative that is either already in progress or getting underway.
    ii. Individual State data systems operated by a single entity: 
States may not have the capacity to develop a workforce longitudinal 
data system on their own. They may lack appropriate staff at the SWA, 
State universities or other institutions to carry out this complex 
process. These States can still develop data systems through 
partnerships with a State education agency with sufficient capacity or 
with workforce agencies and/or research universities in other States. 
In such cases, two or more States would develop separate workforce 
longitudinal data systems at one SWA or research entity.
    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 yet still partner with 
agencies across State lines to share data for expanded analysis. In 
this case, applicants must demonstrate how they will ensure that the 
confidentiality provisions of each State will be adhered to.

II. Award Information

A. Award Amount

    Approximately $12.2 million is available for awards under this 
solicitation. DOL reserves the right to award varying grant amounts 
depending on the quality of the applications received, the scope of the 
proposed activities, and the feasibility of the budget projections 
contained in the application; and also reserves the right to award 
additional grants depending on the availability of additional funds. 
Grant awards may be up to $1 million but must not exceed $1 million per 
grant for any single-state grantee. Grant awards may be up to $3 
million per grant under a multi-state consortium model but are not to 
exceed $3 million. Applications requesting funds exceeding the amounts 
specified above will be found non-responsive and will not be 
considered.

B. Period of Performance

    The period of grant performance will be up to 36 months from the 
date of execution of the grant documents. This performance period 
includes all necessary implementation and start-up activities. 
Applicants should plan to fully expend grant funds and submit all 
reports during the period of performance, while ensuring full 
transparency and accountability for all expenditures. Grants may be 
extended at no additional cost to the government with adequate 
justification and approval by the grant officer.

III. Eligibility Information

A. Eligible Applicants

    Eligible applicants are all SWAs. These SWAs include those within 
the 50 States, the District of Columbia, Puerto Rico and the U.S. 
Virgin Islands. SWAs can apply for their individual State or they can 
work cooperatively with one or more SWAs in other States in a multi-
state consortium or through a multi-state data-sharing agreement.

B. Cost Sharing

    Cost sharing or matching funds are not required as a condition for 
application, but applicants should note that their plan for WDQI 
sustainability will be taken into account in the scoring under Section 
V.A.2 further in this SGA.

IV. Application and Submission Information

A. How to Obtain an Application Package

    This SGA contains all of the information and links to forms needed 
to apply for grant funding.

B. Content and Form of Application Submission

    The proposal will consist of three separate and distinct parts--(I) 
a cost proposal, (II) a technical proposal, and (III) attachments to 
the technical proposal. Applications that fail to adhere to the 
instructions in this section will be considered non-responsive and will 
not be considered. Please note that it is the applicant's 
responsibility to ensure that the funding amount requested is 
consistent across all parts and sub-parts of the application.
    Part I. The Cost Proposal. The Cost Proposal must include the 
following four items:
     The Standard Form (SF)-424, ``Application for Federal 
Assistance'' (available at https://www07.grants.gov/agencies/forms_repository_information.jsp and https://www.doleta.gov/grants/find_grants.cfm). The SF-424 must clearly identify the applicant and be 
signed by an individual with authority to enter into a grant agreement. 
Upon confirmation of an award, the individual signing the SF-424 on 
behalf of the applicant shall be considered the authorized 
representative of the applicant.
     Applicants must supply their D-U-N-S[supreg] Number on the 
SF-424. All applicants for Federal grant and funding opportunities are 
required to have a Data Universal Numbering System (D-U-N-S[supreg] 
Number). See Office of Management and Budget (OMB) Notice of Final 
Policy Issuance, 68 FR 38402, Jun. 27, 2003. The D-U-N-S[supreg] Number 
is a non-indicative, nine-digit number assigned to each business 
location in the Duns & Bradstreet database having a unique, separate, 
and distinct operation, and is maintained solely by D-U-N-S[supreg] 
Number. The D-U-N-S[supreg] Number is used by industries and 
organizations around the world as a global standard for business 
identification and tracking. If you do not have a D-U-N-S[supreg] 
Number, you can get one for free through the Small Business Solutions 
site: https://smallbusiness.dnb.com/webapp/wcs/stores/servlet/Glossary?fLink=glossary&footerflag=y&storeId=10001&indicator=7.
     The SF-424A Budget Information Form (available at https://www07.grants.gov/agencies/forms_repository_information.jsp and https://www.doleta.gov/grants/find_grants.cfm). In preparing the Budget 
Information Form, the applicant must provide a concise narrative 
explanation to support the request, explained in detail below.
     Budget Narrative: The budget narrative must provide a 
description of costs associated with each line item on the SF-424A. It 
should also include leveraged resources provided to support grant 
activities. In addition, the applicant should address precisely how the 
administrative costs support the project goals. The entire Federal 
grant amount requested should be included

[[Page 27592]]

on both the SF-424 and SF-424A (not just one year). No leveraged 
resources should be shown on the SF-424 and SF-424A. Please note that 
applicants that fail to provide a SF-424, SF-424A, a D-U-N-S[supreg] 
Number, and a budget narrative will be removed from consideration prior 
to the technical review process.
     Applicants are also encouraged, but not required, to 
submit OMB Survey N. 1890-0014: Survey on Ensuring Equal Opportunity 
for Applicants, which can be found under the Grants.gov, Tips and 
Resources From Grantors, Department of Labor section at https://www07.grants.gov/applicants/tips_resources_from_grantors.jsp#13 
(also referred to as Faith Based EEO Survey PDF Form).
    Part II. The Technical Proposal. The applicant will present the 
State's overall strategy for building workforce longitudinal databases 
with the capacity to link to longitudinal education databases and 
consists of six parts: (1) Statement of Current Longitudinal Database 
Capacity, (2) Plan Outline, (3) Description of Partnership Strategies, 
(4) Description of Database Design, Data Quality Assurance and Proposed 
Uses, (5) Staffing Capacity, and (6) Bonus Points--Other Data Linkages. 
Applicants will be evaluated on the completeness and quality of their 
submissions. A description of the criteria that will be used to 
evaluate each submission and points awarded are outlined in Section V 
of this SGA.
    The Technical Proposal is limited to 30 double-spaced single-sided 
pages with 12 point text font and 1 inch margins. Any materials beyond 
these page limits will not be read. Applicants should number the 
Technical Proposal beginning with page number 1. Applicants that do not 
provide Part II, the Technical Proposal of the application will be 
removed from consideration prior to the technical review process.
    Part III. Attachments to the Technical Proposal. In addition to the 
30-page Technical Proposal, the applicant must submit an Abstract, not 
to exceed one page, summarizing the proposed project including 
applicant name, project title, a description of the area to be served, 
and the funding level requested. Consortium applications must also 
clearly specify the lead State, which is the State serving as the 
fiscal agent and as the administrative lead and identify each State 
that is participating in the project.
    The applicant may supply evidence or descriptions of planned or 
existing MOUs, Letters of Intent or other statements attesting to the 
formation of data-sharing partnerships as attachments to the Technical 
Proposal. Detailed descriptions/qualifications for proposed staff 
positions to be included in the development of these workforce 
longitudinal databases may also be included as an attachment. 
Attachments may not exceed 35 pages Any materials beyond this page 
limit will not be read.

C. Submission Process, Date, Times, and Addresses

    The closing date for receipt of applications under this 
announcement is August 16, 2010. Applications must be received at the 
address below no later than 4 p.m. (Eastern Time). Applications sent by 
e-mail, telegram, or facsimile (FAX) will not be accepted. Applications 
that do not meet the conditions set forth in this notice will not be 
honored. No exceptions to the mailing and delivery requirements set 
forth in this notice will be granted. Mailed applications must be 
addressed to the U.S. Department of Labor, Employment and Training 
Administration, Division of Federal Assistance, Attention: Willie E. 
Harris, Grant Officer, Reference SGA/DFA, PY 09-10, 200 Constitution 
Avenue, NW., Room N4716, Washington, DC 20210. Applicants are advised 
that mail delivery in the Washington area may be delayed due to mail 
decontamination procedures. Hand-delivered proposals will be received 
at the above address. All professional overnight delivery service will 
be considered to be hand-delivered and must be received at the 
designated place by the specified closing date and time.
    Applicants may apply online through Grants.gov (https://www.grants.gov); however, due to the expected increase in system 
activity applicants are encouraged to use an alternate method to submit 
grant applications during this heightened period of demand. While not 
mandatory, DOL encourages the submission of applications through 
professional overnight delivery service.
    Applications that are submitted through Grants.gov must be 
successfully submitted at 
                    

                        This site is protected by reCAPTCHA and the Google
                        Privacy Policy and
                        Terms of Service apply.