Wage Record Information Systems

May 1994

OTA-BP-EHR-127

NTIS order #PB94-170404
Recommended Citation:

U.S. Congress, OffIce of Technology Assessment, Wage Record Information Systems,

OTA-BP-EHR-127 (Washington, DC, May 1994).
—

Project Staff

Clyde H. Behney PRINCIPAL STAFF

Assistant Director

Health, Education, and John Wirt

Environment Project Director

Nancy Carson

Program Director

Education and Human

Resources

R eviewers

Lorraine Amico

National Governors’ Association

Virginia Chupp

U.S. Department of Labor

Paul T. Decker

Mathematical Policy Research

Paula Duggan

Northeast- Midwest Institute

Karen Greene

U.S. Department of Labor

Juliette M. Lester

National Occupational Information

Coordinating Committee

Karen Levesque

MPR Associates

ADMINISTRATIVE STAFF

Cecile Parker

Office Administrator

Linda Rayford

P.C. Specialist

Tamara Kowalski

Research Secretary

Brian MacDonald

U.S. Department of Labor

Jay Pfeiffer

Florida Department of Education

Alan Phelps

University of Wisconsin

Thomas Plewes

U.S. Department of Labor

David Stevens

University of Baltimore

Mary Sue Vickers

National Occupational Information

Coordinating Committee

James Woods

National Occupational Information

Coordinating Committee

NOTE: OTA appreciates and IS grate fulfor the valuable ass~stance and thought@ crltlques provided by the rewewers. The rewewers do not,

however, necessarily approve, disapprove, or endorxe this report. OTA assumesfull responslbihtyfor the report and the accuracy oflts

contents,
Contents

Introduction

Purpose of this Paper

Background on Data Issues

Experience With Wage Record Information Systems

Quality of Studies Done With Wage Records

Possibilities for the Development of Wage Record Information Systems

Other References

1

1

2

7

11

15

19
WAGE RECORD INFORMATION

SYSTEMS

INTRODUCTION

This background paper responds to section

408 of the 1990 amendments to the Perkins Act,

which asks OTA to review activities to be

undertaken by the National Occupational

Information Coordinating Committee (NOICC) to

encourage the use of wage records from state

unemployment insurance systems for purposes of

conducting policy studies or monitoring the

outcomes of vocational education.

The legislation asks NOICC to demonstrate the use of wage record information

systems for these purposes, and develop

“procedures” for establishing and maintaining a nationally accessible database of

wage record information.

It also asks OTA to evaluate the quality

and usefulness of the demonstrations

supported by NOICC and the technical

problems involved in generating and using

earnings record information.

After consulting with a number of federal

agencies on these legislative requests, NOICC

has:

. Supported a study by the National Governors Association of the extent to which

state vocational education agencies are

using or experimenting with wage record

information systems.

The study shows that state vocational education agencies already have considerable

experience in using wage records from the

unemployment insurance system for different purposes.1

1 Am@ _ ‘Mate Capacity To Use Unemployment

Insurance Wage Records: The Vocational Education

● Sponsored the preparation of a comprehensive, “how to do it” guide for stales by

MPR Associates on setting up using it for

purposes of examining the outcomes of vocational

education and other training programs, conducting

followup studies, or for other purposes.

MPR Associates will submit the guide to

NOICC for publication in 1994.2

The guide will include an overview of wage

record information systems, a comprehensive

annotated bibliography of existing studies of wage

record information systems, and a review of data

quality issues.

PURPOSE OF THIS PAPER

The purposes of this background paper are:

●

●

To briefly summarize what has been

learned in vocational education and other

program areas from studies and demonstrations of the use of wage records for

purposes of program evaluation and policy

research;

To raise some issues about the quality of

data resulting from wage record information systems that need to be considered;

and

Experience,” Washington DC: National Governors’

Association and the Office of Adult and Vocational

Education, June 1993, pp. vii-viii.

2 Levesque, Kare~ and Karen Alt, “A Guide to Using

Unemployment Insurance Data for Program Follow

Up,” prepared for the National Occupational Information Coordinating Committee, Berkeley, CA:

MPR Associates and Institute for Family, Work, and

Community, forthcoming.
. To identify steps that could be taken by

the Congress to deal with these issues and

thus encourage the sound development of

this potentially important new source of

information.

BACKGROUND ON DATA ISSUES

Purposes of Wage Record Information

Systems. Wage record information systems can

serve three main purposes of policy development

and consumer information at different levels of

government. These purposes are:

● To conduct policy-oriented research on

trends in the employment and earnings of

individuals in the labor market, and the

employment effects of alternative

strategies of income support, education

and training, and social services.

One example of the kinds of analyses that

might be done is to compare the

employment outcomes of strategies of

income support or other social services

with strategies of education and training.

Analyses might also be done to trace the

labor market experience of individuals

who are displaced from their jobs, and thus

improve our understanding of what forms

of support are most helpful in the long

run. Studies like these could be carried out

at the national, state, or local levels.

The longitudinal natuure of the earnings

data that can be assembled from wage

record data and the commonality of the

outcome information among individuals

and across states are what make such

studies possible. Most other federal

surveys of income and employment,

except for a few research data bases, are

cross-sectional. This means that income

data are available for only a single point in

time from a sample of individuals in the

whole population. This generally makes it

difficult to analyze the effects of programs

on earnings growth.

● To monitor and evaluate specific education and training or other social service

programs or service providers on the

basis of their effects on the economic

welfare of individuals in order to make

specific decisions about the improvement

or funding of programs or institutions

within a state, region, or at the national

level.

This purpose involves using wage record

information systems for managing

governmental programs on the basis of

their outcomes for individuals. These

techniques of performance management

may involve comparing specific providers

to each other or against certain criteria or

standards of performance. The comparisons could be made among all of the

providers within a certain program, such as

the Job Training Partnership Act (JTPA);

all training programs of a certain type

within a type of institution (such as all the

business programs in community colleges);

or particular categories of programs across

several types of institutions (all of the

community colleges, technical colleges,

and proprietary schools within a state).

With adequate means for describing participant populations, comparisons might

also be made across widely different but

nonetheless competing types of programs

such as JTPA, the JOBS part of the federal

welfare program, and the training components of many other federal programs.

Eventually, it might even be possible to

conduct such studies at the national level.

However, in some program areas, and especially vocational education, this would

require substantial capabilities for identifying program participants across the

country in common terms. These capabilities do not exist.

● To provide the public with information

about the outcomes of programs in their

local areas and trends of employment in

different industry, occupational and/or
geographic areas that is useful to them in

making career or consumer decisions.

These information services could provide

people with data on: (a) job growth by

industry and perhaps by occupation in a

local area and other areas, (b) the earnings

of graduates from different programs, and

(c) information about program enrollments

and trends.

The same kinds of information could be

used by local institutional leaders for

planning and evaluating their own

strategies of improvement, adjustment, or

expansion.

Clarity concerning these purposes is

important, because the design of a wage record

information system may need to be different

depending upon its purposes. Some purposes

may conflict with others in certain situations.

Some states and federal agencies may place much

higher priorities on some of the purposes than

others. This means that it may not be possible to

design one system meeting all purposes equally

well within a state or at any other level.

The Unemployment Insurance System and

Wage Records. The idea of wage record information systems is that the data for accomplishing

these purposes already exist in the earnings

reports that are collected from employers on a

quarterly basis by State Employment Security

Agencies SEASs). 3 This data is collected by

these state agencies as part of their normal process of administering the nationwide system of

unemployment compensation.

The employment and earnings data in these

record systems have been estimated to include

over 90 percent of the working population in the

United States, or well over 140 million people.4

3 The enct organization and administration of state

unemployment insurance systems differs among the

states. See Levesque, op. cit.

4 BajJohq Charles ET@ and David Stew~’’AFeasibility

Study of the Use of Unemployment Insurance WageRecord Data as an Evacuation Tool for JTP~” Report

Most state agencies accumulate this data over

time, so that longitudinal earnings histories

could be constructed. Not included are mainly

self-employed individuals, some agricultural

workers, people who are employed by the

federal government or are serving in the

military, independent contractors, and railroad

workers. 5

These earnings records, or wage record data,

as they are usually called, are collected by

SESAs in order to determine the tax liability of

employers for unemployment compensation and

verify the eligibility of applicants for

unemployment compensation. The key fact is

that three of the data elements collected are

common across the states. 6 These three data

elements are the Social Security numbers of all

employees in the state who are covered by

unemployment insurance, their quarterly

earnings, and the standard industrial code (SIC)

and/or the business name and address of the

employer, 7 Some states also collect other data

elements such as weeks worked.

This broad coverage and the possibility of

assembling longitudinal histories of the labor

market experience of individuals are the two

main potential advantages of wage record information systems. The similarity of the earnings

information among individuals and states means

that a common set of outcomes might be

developed for purposes of managing the

performance of a wide range of educational,

employment, income support, and social service

programs, and conducting research on the labor

market.

on Project’s Phase I Activities, Washington DC:

National Commission for Employment Policy, January

1991, p. 12.

5 bid. p. 8.

6 Private conversation with Brian MacDonal~ Offke of

Employment and Unemployment Statistic+ Bureau of

Labor Statistics, Washingto~ DC.

7 National Cornmission for Employment Policy, “Using

Unemployment Insurance Wage-Record Data for

JTPA Performance Management,” Research Report

91-07, Washington, DC, June 1992, table 1.

3
Accomplishing these purposes requires

knowing the Social Security numbers of all the

individuals involved in the programs that are to

be compared or analyzed. Once the Social

Security numbers of the individuals involved are

known, the data on their earnings in the files of

the state’s unemployment insurance system can

be obtained.

The agencies who assemble databases for

linking program participants with records of

their earnings are user agencies in each of the

states. These user agencies take lists of program

participants to the SESA agency and receive in

return the wage records for each person for

whom there is earnings data in the files of the

unemployment insurance system. The user

agency then takes these matched records of

program participation and earnings data from the

SESA and assembles them into a wage record

information system. This is done on an ongoing

basis to track program participation and

outcomes over time. These user agencies could

be either special statewide offices created to

assemble wage record information systems for

several program areas and provide resulting

analyses and reports to those areas, or offices

dedicated to individual program areas. How

these user agencies would be organized within a

state would probably vary greatly for a variety of

political, historical, and other reasons.

Obtaining Data From Other Sources. In

most situations, achieving a sufficient level of

accuracy in a wage record database may require

obtaining employment and earnings data from

the SESA agencies of neighboring states, other

agencies of the state and in other states, and

agencies at the national level, where earnings

and other kinds of activity information for

people who are not included in the

unemployment insurance system of a state can be

obtained. Program participants may be working

for the federal government, or may have moved

to another state and be working there, or may

not be in the records of the unemployment

insurance system because they have enlisted in

the military or are engaged in some other kind of

productive activity, such as enrollment in a

community college. Without this additional

information, there will be misleading gaps in the

wage record information. Many people will

appear to be doing nothing, when in fact they are

doing something.

The danger is that without earnings and

enrollment data for these others kinds of

activities, analyses done to compare the outcomes of specific social service or education and

training programs could be distorted. Analyses

using only wage record information from one

state’s unemployment insurance system could,

for example, show that certain programs in the

state are much worse than other programs, when

in fact people who participate in the second set

of programs are much more likely to have moved

out of state or to be enrolled in community or

four-year college program than those who

participate in the former group. Since these are

all gainful activities, it would not be appropriate

to penalize the second program without knowing

about these other activities. Simply being located

near the border of a state could be sufficient for

the employment and earnings rates of programs

to be seriously downrated in a scheme of

performance management where no wage records

from neighboring states have been obtained.

Solving this general problem requires adding

data on the employment and other activities of

individuals from other agencies and states.

Data-Sharing Agreements. Obtaining this

data from these other agencies and states

generally requires reaching a data-sharing

agreement. These data-sharing agreements

prescribe the terms of data transfer. SESA

agencies generally stipulate that the receiving

agency may use wage record data for certain

purposes and not any others. In most cases,

ownership of the wage record data is strictly

retained by the SESA, so that none of the

information belongs to the recipient agency, and

they are not allowed to share it in any way or

release it in any form where the identity of an

employer or an individual’s earnings would be

revealed. Strict regulation of the terms of datasharing are required in order to maintain the

“convenant” of confidentiality that exists
between SESA agencies and employers, which is

a bedrock of the unemployment insurance

system. Reaching these data sharing agreements

generally involves considerable cost in time and

effort.

National Wage Record Database (NWRD).

Recognizing these costs, Congress asked the

Bureau of Labor Statistics (BLS) to submit a

plan for creating a NWRD in Section 405 of the

Job Training Reform Amendments of 1992.

Creation of the NWRD could provide an

efficient way of pooling at least all of the wage

records in the existing state unemployment

insurance systems into a national database and in

turn making all of this data available to all of the

states. The language of the legislation states that:

. . the Commissioner of Labor Statistics, in

cooperation with the states, shall determine

the appropriate procedures for establishing a nationwide database.. in a

longitudinal manner and for making such

information available for policy research

or program evaluation . . .

Many alternatives are open to BLS in

submitting the plan requested by Congress,

which will be submitted in June 1994. Core data

elements of the NWRD could reside in the states

or be sent to a central data bank operated by the

federal government. The database could consist

of a national sample of individuals, samples

within states, or the entire working population of

the United States.

“One stop shopping” for wage record data is

likely to be one of the main features of the

database design proposed by BLS. State user

agencies wanting wage record data would submit

lists of social security numbers to the state office

maintaining access to the central database, where

they would be matched to wage records from all

50 states. [BLS may recommend that the state

offices of labor market information should be the

access point for the NWRD or they m a y

recommend something else.]

With such a national database, the need for

coordinating data sharing agreements for wage

record data among states should decrease. If so,

this could substantially lower the cost to state

user agencies of developing wage record

information systems. But the cost to BLS and

SESA agencies of building and maintaining the

NWRD could substantially offset these savings. A

responsive data system containing the social

security numbers and several years of earnings

records for the complete working population of

the United States (or well over 140 million

people) would not be cheap.

Confidentiality Issues. The possibility of

developing a national database of wage record

information underscores serious issues of

confidentiality and data privacy (which clearly

also exist with existing arrangements for datasharing). BLS is currently planning to propose to

Congress statutory measures for dealing with

these confidentiality and privacy issues. One

possibility is that federal statutes might be

written to define any data transferred to

designated user databases as being statistical

information rather than administrative records,

and therefore not accessible with identifiers to

anyone or susceptible to a supoena. 8

Any wage record information systems developed will also need to be managed so as not to

undermine public perceptions of the primary

mission of state unemployment insurance systems and who the system is primarily intended to

serve. Preservation of these perceptions is likely

to require protecting the confidentiality of information in the systems. At the moment, most

people are probably not aware that fairly complete histories of their earnings reside in the

records of their state unemployment insurance

systems and are updated on a regular basis with

information from their employers.

A fourth general purpose of wage record

information systems could be to employ them in

enforcing compliance with policies of eligibility

8 Private conversation with David Stevens, University of Baltimore, Baltimore, Maryland.

5
and payment in various program areas at the

state and federal levels. Many state agencies are

already obtaining wage record data for this

purpose by going directly to SESA agencies. The

areas of activity include the enforcement of

income criteria for participation in a wide range

of state and federal programs, past due child

support payments that have been ordered by the

courts, past due student loans, law enforcement,

and many other possibilities. Several federal

agencies, especially in the Department of Health

and Human Services, are interested in wage

record information systems precisely for such

reasons.

The pressures from enforcement agencies to

gain access to the wage record information

systems of state user agencies are likely to grow

as the systems grow. The pools of data available

in the systems could become large and typically

there would be many more data elements than

are available from SESA agencies. Furthermore,

many of these wage record information systems

could turn out to be in the same departments or

program areas where the agencies wanting the

information for enforcement are located.

Arguments for obtaining the data for

enforcement purposes directly from the wage

record information systems rather than going to

the SESA agencies may be made in terms of the

efficiencies to be gained from using the programoriented systems for multiple purposes.

At the national level, it seems very unlikely

that any such uses of a national database of wage

records could be allowed without seriously

undermining its use for purposes of performance

management and research, because of the

problems of confidentiality and perceptions of

the purposes of the unemployment insurance

system. BLS is clearly correct to carefully

consider confidentiality issues as part of its

planning effort.

Privacy laws are also a factor that needs to be

considered in developing policy on wage record

information systems. Requirements associated

with the Buckley Amendments to the federal

Privacy Act, the Social Security Act, and state

versions of these laws often preclude being able

to obtain participant records linked to the wage

records. At the state level, fears regarding

invasion of privacy have resulted in some

specific curbs on programs for linking wage

records to records of program participation.

Concerns about the privacy issue could lead not

only to the defeat of legislation to create wage

record information systems, but to the enactment

of laws making it illegal to collect social security

numbers for purposes other than administering

the Social Security Act. 9

Cost Burdens on Unemployment Insurance

Systems. In addition to these confidentially

issues, SESA agencies should not be forced to

bear any major new cost burdens in providing

wage record data to other agencies that might

hamper their ability to accomplish their primary

mission of helping the unemployed. Large

growth in the number of requests for wage

record data from enforcement agencies, state

user agencies, and the creation of a NWRD,

could impose significant new cost burdens on

SESAs. At a minimum, policies may need to

firmly establish that SESA agencies are fully

compensated for the costs of any services they

provide to state user agencies or any national

system of wage records.

Need for Congressional Review. The

submission of BLS’s plan for a NWRD will

bring many of these issues to a head, and create

a need for a careful review by Congress before

embarking on any substantial expansion. The

BLS plan is likely to focus mainly on issues

concerning the organization, development, and

costs of the NWRD. At least as much emphasis

in any congressional review should be placed on

issues of supporting the state use of data from

wage record information systems and the quality

of the studies that are being done. Issues of the

quality of information and how this quality is

related to the consequences of use have not

received the systematic attention that they

deserve. The potential advantages of wage record

9 Private communication with Jay Pfeiffer, State

Department of Education, Tallahassee, Florida.
information systems have been made clear

enough, but the potential pitfalls have not.

Sufficient experience with wage record

information systems exists in the states and the

research community to determine some of the

critical issues of quality and their consequences

for use, and how they might be dealt with.

EXPERIENCE WITH WAGE RECORD

INFORMATION SYSTEMS

States and the federal government have

acquired considerable experience with wage

record information systems in the past five to ten

years. One indication of this is the 50 percent

growth that occurred in the total number of data

sharing agreements at the state level between

1986 and 1991, as reported by the National

Commission on Employment Policy. 10 By 1991,

data sharing agreements had been reached in 43

states in the area of Child Support Enforcement

programs, in 28 states for the JOBS portion of

AFDC, and in 26 states in Food Stamps. The

average number of data sharing agreements per

state in 1991 was 17, and the range was between

one and 117 per state.

One of the important reasons for this growth

is undoubtedly the passage of the 1988

Amendments to the Deficit Reduction Act.

Under these amendments, states were required

for the first time to collect quarterly wage

reports from all employers as part of a new

Income Verification and Eligibility System.11

Prior to this legislation, many states monitored

eligibility for unemployment compensation using

a “wage request” approach, whereby employers

10 NcEp, op. Cit., p. 58”

11 JaroSik, Daniel, and Alan Phelps, “Empowering

Accountability for Vocational-Technical Education:

The Analysis and Use of Wage Records,” MDS-244,

Berkeley, CA: National Center for Research in

Vocational Education, November 1992, p. 1. Employers’ reports are required to include gross earnings by Social Security number, industry of employment by Standard Industrial Classification code, and

county of employment.

were only required to submit wage information

when it was necessary to process a claim. 12

Several persons interviewed in preparing this

paper stated that state interest in using wage

record data for purposes of performance management and policy research has continued

to grow, and seems to have accelerated since

1991.

Use of Wage Records in JTPA. The program

area where the most experience with wage

records lies is the JTPA. In the NCEP study

referred to above, 29 of the 47 states responding

to a survey reported having at least one datasharing agreement in JTPA. 13 In 25 of these

states the data were being used for “program

accountability purposes. ” In the four other states,

the data were being used for purposes of

verifying eligibility for JTPA or the results of

followup surveys. Sixteen state JTPA agencies

are now conducting demonstration projects with

support from the Office of Strategic Planning

and Policy Development (OSPPD) in the

Employment and Training Administration of the

U.S. Department of Labor, to assess the likely

effects on provider agencies and clients of

switching from questionnaire surveys to wage

records under JTPA performance standards. A

report will be released in fall 1994.

A Leading State User Agency. The most

advanced state-level user agency in the country is

generally considered to be Florida’s Education

and Training Placement Information Program

(FETPIP). This program started out as a project

of the state legislature. It was originally located

in the State Job Training Coordinating Council

(SJTCC), but was funded from educational

resources. Initially reports were issued on the

employment and educational outcomes of

vocational education programs. After about 4

years of development the project was moved to

the Department of Education. In the program,

earnings records obtained from the state’s

unemployment insurance system are combined

12 Baj, op. Ck., P. ‘“

13 NCEP, Op. Cit., p. 59s

7
with data from the state’s community college and

university systems, the public schools, the state

corrections system, and the federal government,

including the Department of Defense. Employers

are also surveyed to determine the occupation of

all students tracked in the system. This provides

a common set of educational enrollment and

employment outcomes for measuring the results

of education, training, and employment

programs in the state. The outcomes include

quarterly earnings, weeks worked, occupation,

and further enrollment in either the adult

education, community college, or higher

education systems in the state. The employment

and educational activities of a total of 1.8 million

people are currently being tracked.

A number of statewide accountability reports

required by the state legislature are regularly

produced using data from FETPIP. One report

shows the employment and educational outcomes

of all secondary and postsecondary vocational

programs in the state by program and institution.

New performance measures for vocational

education will include the placement rate, a

productivity rate, and two earnings measures.

Each measure will include rates for special

populations. FETPIP has emphasized making

these reports and files of all the outcome data in

the system available to local institutions to use

for their own planning and other purposes. Some

institutions are beginning to ask for data from

the system and request information that goes

beyond what is required in the accountability

reports, such as longitudinal data on the outcomes of programs. By law, all secondary and

postsecondary vocational programs in the state

must achieve a placement rate of 70 percent.

Recently, a “hot ticket” bill has been passed by

the state legislative to build a system of incentive

bonus funds for completions and placements in

certain occupational areas. Additional bonuses

will be provided for enrolling, completing, and

placing certain targeted groups of people. 14

Over the years, FETPIP has worked to

steadily broaden the number of program areas

where participants are being tracked, and is

becoming a factor in statewide processes of

strategic planning and budgeting for education

and training programs. These procedures require

“conferences” between an educational sector

(e.g., the community colleges) and the executive

and legislative branches of government to agree

on estimates of revenues and enrollments prior to

the process of determining a budget for the

sector in the state legislature. Recently, the

purpose of these conferences has been expanded

to include occupational forecasting. Data from

FETPIP are combined with data from the state’s

Bureau of Labor Market Information to support

the discussions of the conferees.

A second process of “occupational forecasting” for program planning has also been

established in Florida, where the governor, the

commissioner of education, the secretaries of

labor and commerce, and the legislature must

periodically come together and agree on the 30

or so occupations with the best “potential for

employ merit.” Data from FETPIP provides the

basis for these discussions and agreement among

the parties on the occupations. States funds are

then directed to those occupational areas and

away from others. This process will also be

repeated in 28 regions of vocational education in

the state.

FETPIP is now providing outcome

information to the JOBS program and is working

with the SJTCC to determine how followup

could be provided for JTPA. JTPA is currently

comparing the wage record data they are getting

from FETPIP with their own information from

questionnaire surveys as part of the OSPPD

project mentioned above. In addition,

employment and further education reports are

provided to all the public schools in the state, the

university system, the Adult Migrant Education

Program, the General Educational Development

(GED) program, an Exceptional Students

Program, a study of Associate of Arts degree

holders, and a study of training and recidivism

for the prison system.

1A private communication with Jay Pfeiffer.
Other Experience With Wage Records in

Vocational Education. There is other state

experience with wage record information systems

in vocational education, but generally not as

much as in JTPA. According to a study

conducted by the National Governors’ Association (NGA) for NOICC, 20 state vocational

education agencies use or have explored the use

of wage record data for various purposes. 15 In

1992, there was “sustained” use of the wage

record data in 12 of these states, “planned use”

in two states, and a “short-term effort” in the

remaining six states. A recent survey of state

plans for implementing performance standards

under the 1990 Amendments to the Perkins Act

shows that only one state (Illinois) is planning to

utilize wage record information. 16

NGA found that in nearly all cases where

wage record information is being used in

vocational education, there is a history of prior

use by a JTPA agency. 17

One of the other states where wage records

have been used in vocational education is

Washington. Staff in the Washington State Board

for Community College Education have combined wage record data from five states

(Washington, Alaska, California, Idaho, and

Oregon) with enrollment data from the state university system and employment information from

the Department of Defense. Eighteen data

elements describing the background characteristics (e.g., age and sex), vocational program

completed, and employment histories of

approximately 15,000 former students have been

tracked for several years. 18

15 ~ico, op. cit., table 2.

16 Rahn, Mikala, E. Gareth Hoachlander, and men

Levesque, “State Systems for Accountability in

Vocational Education,” MDS-491, National Center

for Research in Vocational Education, December

1992, appendix. Recently, a decision has also been

made in Florida to utilize wage record information in

the implementation of performance standards.

17 Amico, p. Vii.

18 pro~es for wm~on and seven additional states

can be found in Jarosik, op. cit., pp. 22-26.

One of the major problems in developing

wage record information systems in vocational

education is defining who is a vocational

student. 19 Students may take one course or

complete an entire program of studies. Programs

of study differ among institutions even within a

particular occupational area. Without consistency

among vocational programs and institutions,

there is no basis for making comparisons among

vocational programs or between vocational

education and other programs. Substantial efforts

within states and among regions will be needed

to reach agreements on common definitions of

vocational programs before unemployment

insurance wage record data can be very useful

for evaluating vocational programs or conducting

policy-oriented research. 20

Another serious problem in a substantial

number of states is lack of access to the Social

Security numbers of vocational students. Some

states tie vocational education funding to the

collection of Social Security numbers from

students, while in others obtaining the social

security numbers of students at the state level for

any reason is considered an invasion of privacy.

This is especially true at the secondary level.

Only 15 of the 32 state vocational programs at

the secondary level that responded to NGA’s

survey of wage record use in vocational

education are currently collecting Social Security

numbers from students. Seven of the 13 state

postsecondary agencies that responded to the

survey do so.

21 Overcoming this problem would

be very difficult.

Costs of Development and Operation. States

have also gained experience with the costs of

developing and operating wage record

information systems. Estimates for the annual

19 Stevem, David Peggy l?ichmon~ Joseph F. Haenn,

and Joan S. Michie, “Measuring Employment

Outcomes Using Unemployment Insurance Wage

Records,” prepared for the OffIce of Plannin g and

Evaluation by Research and Evaluation Associates,

Inc., Washington DC: U.S. Department of Education,

December 1992, p. 100; and Jarosik, op. cit., p. 71.

20 Ibid., p. 83.

21 ~ico, op. cit., table 5; and JMOSik, op. cit”l P“ 82”

9
costs of operation are in the range of one to ten

dollars per person tracked, but there is a great

deal of uncertainty in these numbers and a lack

of documentation concerning what costs are

included. 22 NCEP estimates the average

operating cost in JTPA to be $1.75,23 while

Stevens estimates it to be $3.00 per person in 24 Sometimes only the costs vocational education.

of paying the SESA agency for computer runs to

match a list of Social Security numbers with

wage records are included, not the costs of

staffing the information system, preparing lists

of program participants, conducting analyses,

and providing services. Startup costs are likely

to be higher than long run average costs. One

study of JTPA estimates startup costs of $20,000

per state and $2.00 per program participant for

list preparation and paying the SESA agency for 25 Only $.20 of this was for the record matching.

actual costs of matching. No estimates of how

the annual costs of data collection compare with

the total costs of the reporting system were

provided.

On important issue is how the costs of

obtaining earnings and employment data from

wage records compares with collecting the same

data using questionnaire survey or other

methods. The NCEP study is one of the few

sources. NCEP estimates that the annual costs of

obtaining program outcomes information for

JTPA performance standards using a telephone

survey is $19.00 per completed interview. 26

Whether the questionnaire methods also provided

data on outcomes other than quarterly earnings

or other information was not revealed.

One good benchmark for the longer term total

costs of operating a wage record information

system is provided by FETPIP. FETPIP’s total

annual budget for collecting data, preparing

reports, and providing user services is currently

22 * mnwxs&n with -of the National Govemm’

Awciatioq Washing$cq DC.

23 NCEP, Op. cit., p. 65.

24 Stevens, Op. cit, P. ‘ii”

~ NCEP, p. 65.

26 NCEp, op. cit., p. 2.

$310,000 for the total of 1.8 million people

being tracked, or about 16 cents per person. The

main product of the program has been the annual

report to the state legislature on the employment

outcomes of vocational education, but now

several other reports are produced for the new

client agencies. The vocational education report

covers about 200,000 secondary and

postsecondary students27 and was produced when

the budget for the program was somewhat higher

than it is now. The recent increase in the total

number of program participants being tracked

from 200,000 to 1.8 million was therefore

accomplished with no increase in the total costs

of the program. This indicates large economies

of scale in the development and operation of

wage record information systems. 28

The cost advantages of wage record information systems compared to surveys may be

especially great for time periods long past the

end of participation in a program. These lower

costs are inherent in the nearly universal

coverage of the wage record data and the

quarterly requirement for data submission by

employers. In surveys, finding a high proportion

of the original group of respondents usually

becomes extremely difficult as the length of time

since the last interview or after leaving a

program increases. In experimenting with the

collection of followup data 18 months after

program completion, it has been found in JTPA

that usable data cannot be obtained with surveys

because of low response rates. 29 NCEP estimated

the ongoing costs of a wage record information

system for JTPA as one-fifth the cost using

surveys. 

30

In conclusion, the costs of tracking the

employment and earnings of program participants appear to be significantly lower using wage

records than with survey or other methods; howZT JUo& op. cit., table 1.

28 see ~so Rij, op. cit., figure 5-1”

29 private conversation with Karen Greene, OffIce of

Strategic Planning and Policy Development, U.S.

Department of Labor, Washington, DC.

m NCEP, op. cit., p. 43.

10
ever, the size of the cost advantage is very uncertain because of the lack of documentation in

existing studies. The greatest cost advantages are

likely to be in situations where long term followups

or longitudinal outcome data are needed.

QUALITY OF STUDIES DONE WITH

WAGE RECORDS

While the examples above indicate the

potential value and operating costs of wage

record information systems, they do not show

the factors affecting the quality of studies. The

fact that wage record data come from

administrative sources can make them much

more difficult to use and interpret than the

results of surveys, where the categories of

response are under the control of the analyst and

samples of respondents can be randomly

selected.

General Problems With Data From

Administrative Systems. Data from administrative sources can be subject to several kinds of

systematic errors. One is when incentives may

be operating that cause respondents to report

differently to the administrative system than they

otherwise would, in order to minimize or avoid

certain consequences. In the case of wage record

systems, these incentives exist because of the tax

liabilities of employers involved in unemployment

compensation. For example, some employers may

classify themselves in reporting as belonging to

industries or jurisdictions with the lowest tax rates,

irrespective of where the employees whose

wages are being reported actually work. 31 Also,

some groups of employers may not report nonwage payments to workers as accurately as they

do wages and salaries. If so, the wage record

data for people who go into industries or

occupations where earnings are systematically

under-reported will be biased downward.

A second reason is that policies may change

in the administrative system so that what was

originally included in reports may no longer be

included, and vice versa. If analysts fail to keep

31 Jarosik, op. Cit., P. 82”

up the changes, the comparison of outcomes at

one point in time to another point in time using

data could be too high or too low. In a highly

decentralized system such as unemployment

insurance, keeping up with all the changes that

occur and figuring out whether they are

important or not for the purpose of analysis

could involve a great deal of effort.

A third problem can be that details of the

reporting system that are not known to the

analyst may affect the results of analyses. For

example, in studies of displaced workers

earnings estimated from wage record data can be

distorted because employers typically report

severance payments after the termination of

employment. 32

None of these problems are fatal, but they do

indicate why caution is warranted and substantial

efforts are needed to understand the specific

factors affecting the quality of studies done with

wage record data.

Overall Matching Rate. One of the most

significant variables affecting the quality of

studies is the number of sources from which data

are obtained on the employment and other

activities of individuals. The choices made about

the number of sources can greatly affect the

overall matching rate achieved. This matching

rate is the percentage of all persons being

tracked for whom some positive identification of

employment, unemployment, or other activity

appears in the outcome data. 33 This rate is

analogous to the response rate in questionnaire

surveys and is the linch pin of wage record

information systems.

32 Decker, Paul T., “Systematic Bias in Earnings

Data Derived From Unemployment Insurance Wage

Records and Implications for Evaluation the Impact

of Unemployment Insurance Policy on Earnings,”

Unpublished paper, Princeton, NJ: Mathematical

Policy Research, April 1989, p. 5.

33 In gener~, the over~ matching rate could be

obtained by averaging the quarterly matching rates

over the time individuals are traced in wage record

information systems.

11
Generally, the higher the overall matching

rate, the more confidence that can be placed in

the results of studies. The administrative origins

of the data and the non-random processes by

which individuals are selected into programs are

two reasons for this. For both reasons, the

results of studies done with wage record data are

vulnerable to being systematically biased too

high or too low compared to the actual values.

Short of having done the studies necessary to

determine the consequences of low matching

rates, the only way of guarding against such

problems in wage record data is having positive

identification of all the activities of the complete

population of interest.

At present, there are no firm rules for

deciding what the overall matching rate needs to

be in what circumstances to have confidence in

the results of analyses. Accumulating this

knowledge will require: (a) time, (b) substantial

efforts to summarize the experience of state

agencies with different configurations of data and

purposes, and (c) specific studies comparing the

results of analyses using wage record

information with data from other sources.

Adequate levels of the matching rate using

wage records alone may be very different among

population groups for several different reasons.

Three of the most important are differences in

the rates of unemployment among the groups,

where they tend to be employed and how they

earn their income, and their likelihood of

engaging in other activities, such as education.

Rates of unemployment are important because

positive identification of whether a person is

actually not employed is generally not possible

from the data that are available from state unemployment insurance systems. The reason for

this is that only about one third of all people

who are unemployed (according to the standard

definitions of unemployment) receive compensation from the unemployment system.34 People

M private conversation with Paula @WUI, NOrtheast Midwest Institute, Washington, DC.; and Lynne

who receive compensation from the system can

be positively identified, but those who do not

cannot be.

Among JTPA participants, it has generally

found that matching rates of 70 percent or so can

be obtained using the wage records from only

one state, while for vocational education

graduates the rate is typically somewhat lower--

about 60 percent. The primary reason for this is

that more vocational education graduates

continue on for additional education than do

JTPA completers. 35 For populations of former

prisoners, the matching rates in Florida have

been in the neighborhood of 30 percent.

The basic way of increasing the matching rate

for any population group is to increase the

number of sources from which outcome data are

obtained. In one study, Stevens has shown the

possibility of increasing the matching rate for

vocational program participants to 90 percent or

more by obtaining wage records from four

neighboring states, the home state’s state

university system, the home state’s community

college system, and the federal government. 36

Many analysts would argue that for most

populations, overall matching rates of 90 percent

or more are needed for studies done with wage

record data.

The main criterion for deciding how high the

matching rate should be is how it affects the

results of studies. If the results change

significantly when the matching rate is increased,

then the first matching rate is too low. In one

study of 14 community colleges in two states,

the observed rates of employment changed by 20

percent by state depending upon whether the

wage record data came from only the state where

each of the colleges was located, or the state and

three other neighboring states. 37 A performance

funding formula based on a wage record data

Webb, unemployment Imuran@ -Uli Department

of Labor, Washington, DC.

35 p~vate Converwtion with Karen Levesque of MPR

Associates, Berkeley, California.

36 Stevens, op. cit., chapter 6“

37 mid., p. 103 and 106.

12
system using data from only one state could

therefore be very unstable.

Stevens showed in another study that the

employment rate for the former students of one

community college was 64 percent compared to

42 percent for another community college when

wage record data only from the state in which

the colleges were located was utilized. On basis

of this information, institution “b” was

performing much more poorly than institution

“a.” However, when wage records were also

obtained from adjacent states, as well from the

federal government and the military, the

positions of these two institutions changed

completely. The employment rate for institution

“b” increased to 76 percent, while the rate for

institution “a” increased to only 66 percent. On

this basis, institution “b” appears to be a

considerably better place to go than institution

“a.” This provides a clear example of how the

matching rate can be related to the accuracy of

study results.

Variations like this are caused by the highly

localized patterns of migration and labor market

mobility that exist among localities, schools,

industries, and occupations. These localized

patterns can strongly affect the results of

analyses when comparisons are made among

specific institutions or programs rather than

among statistically selected samples of

individuals or institutions covering much larger

areas. The analyses done by Stevens did not

include any data on subsequent enrollments in

postsecondary education or training programs of

any kind, which could have changed the picture

even more. 

38 His comparisons also included no

adjustment for economic conditions in the

counties surrounding the two institutions, which

could also have recast the relative performance

of the two institutions.

Differences in Earnings Among Quarters.

Another factor that affects the results of these

studies is the quarter selected as the basis for

computing the earnings of individuals. In order

to simplify data processing, some states have

elected to compute annualized earnings by

multiplying the earnings from one quarter by

four. Stevens has shown that annual earnings

computed from earnings in the first quarter of

the year will overstate actual earnings by an

average of $2,000 per year, or 10 percent of

total earnings.

39 Annualized earnings based on

earnings in the fourth quarter are nearly correct.

This means that in systems of performance

management where earnings are estimated from

one quarter’s worth of earnings, data will tend to

punish programs with larger proportions of their

graduates in the fourth quarter compared to the

first quarter. Estimating earnings from four

quarters of data or compensating for the quarter

used could result in a different picture.

Demographic Variables. Another potential

problem in using wage record data is lack of

means for identifying comparison groups and

controlling for program effects due to differences

in the background characteristics of individuals

(e.g., age, sex, race, and social status). Wage

record data by themselves do not contain

information about gender, race, social

background, age, or years of experience in the

labor market, which are clearly related to

employment and earnings. Identification of

characteristics such as these must come from

data on program participation. However, this

would tend to limit wage record studies to

populations of individuals for whom there is data

from program participation. The problem is that

without good information about the composition

of study populations, observed trends of

employment and earnings in the labor market

may be due more to changes in the composition

of the labor force than to any other factors. Likewise, differences in the outcomes of programs

may be due more to differences in the populations being served than the effectiveness of the

programs. Particularly where wage record data

are being used in strategies of performance

management, not knowing the composition of

populations could result in the creation of large

incentives for creaming. In many situations of

38 ~id., figure 39.

13

39 mid., figure 14.
performance management, these characteristics on quarterly earnings from interviews and the

may not be known.

Full-time and Part-time Employment.

Another source of error in wage record studies is

lack of means for distinguishing between

individuals who are employed full-time and parttime. The wage records for the graduates of

individual programs typically will include a mix

of people who worked only a few weeks within a

quarter (and often only 1 or 2 weeks) compared

to others who worked the full 13 weeks. As a

result, there can be large differences in the

estimates of average earnings from one quarter

to the next for a given program due to very small

fluctuations in the proportions of individuals

who are working part-time and full-time. In

labor market studies, this problem is normally

handled by restricting the data used to compute

earnings only to fill-time workers. This problem

has occurred in FETPIP. In order to obtain

stable estimates of earnings, FETPIP’s policy is

to include only full-time workers, and only

workers who are making more than the minimum

wage fill-time. Florida is able to do this because

of the fact that weeks worked is a data element in

their unemployment insurance system. Only

seven other states collect this information, so

they are the only ones currently able to reduce

this large source of variability in earnings estimates in this way. 40

Accuracy of Wage Records Compared to

Surveys. While many people have asserted that

wage records are “more accurate” than survey

data, there have been few efforts to rigorously

compare the results of studies using the two different sources. One study where this comparison

has been made is the New Jersey Unemployment

Insurance Re-employment Demonstration Project. This research and demonstration project is

testing whether displaced workers can be successfully identified early in their unemployment

spells and provided with alternative forms of job

search assistance and training to accelerate their

return to work. Comparable data were collected

40 NCEP, op. cit., table 1. Hours worked are

collected in one other state.

New Jersey unemployment insurance system.

The study showed that estimates of the subsequent quarterly earnings of displaced workers

were $344 dollars higher in the first quarter after

becoming unemployed using data from the unemployment insurance than they were from interviews, $130 lower in the second quarter, and

$374 lower in the fourth quarter. These figures

were about 10 percent of quarterly earnings. Investigation of the reasons for these differences

showed that the higher figures from the unemployment insurance data in the first quarter were

due to severance payments received after first

becoming unemployed, while the lower figures

in the later quarters were due to growing numbers of the study population holding jobs out-ofstate. Dropping the people who moved out of

state from the study population and subtracting

the severance payments eliminates all differences

in the estimates of earnings between the interview and wage record data.

One other direct comparison of the accuracy

of unemployment insurance wage record data

with data from other sources is the National

Study of JTPA. In this study, estimates of the

earnings of the low income individuals studied

differed greatly between wage record

information and survey data. Among the four

population groups considered, the average

earnings of individuals were 35 to 80 percent

higher using the survey data compared to the

wage record data. No attempt was made to

obtain out of state wage records but the

employment rate for JTPA sites near state

borders were not systematically different from

other sites. Other possible sources of the

discrepancies were also investigated but no

conclusions could be drawn about the reasons for

the observed differences. 41

41 US, -dd*, hpbymd d ‘rrtig

Administration, “Comparison of Earnings,

Employment, and Impact Estimates Based on Data

From the First Follow-Up Survey and From State

Unemployment Insurance Agencies.” In Z+e National JTPA Study: Title II-A Impacts on Earnings

and Employment at 18 Months, Research Report

14
Differences Among Population Groups. 130th

of these studies may be correct--that is, for

displaced workers wage records may be

accurate, while for low income, disadvantaged

populations they may not be. One possible

reason for the differences found in these two

studies is that low income, disadvantaged people

apparently have more earnings from sources that

are not reported to the unemployment insurance

system than do displaced workers. These

earnings could be from casual work, the

underground economy, or tips from reported

jobs. 42 The results of these two studies point out

that systematic differences in the accuracy of

wage records may exist among different

population groups. This provides a clear example

of the importance of avoiding statements that

wage record data are more or less accurate than

data from other sources, until more studies can

be done to find out what the differences are and

how the data can be responsibly used.

It is important to point out that both studies

described above were major research efforts in

which substantial investments were made in

questionnaire development and the careful

collection data from respondents. Comparisons

of the accuracy of wage record data with the

collection of earnings data obtained from

interviews conducted in the context of state or

local program operations could result in very

different conclusions.

Accuracy of SESA Recordkeeping. Finally,

another potential source of error in wage record

studies is inaccuracy in the actual wage records

collected and kept by SESA agencies. The extent

of error is currently not known because no largescale audits of the wage records in state systems

have ever been done. The first major audit of

wage records is being carried out by the BLS as

part of their planning effort. BLS is concentrating their audit on the accuracy of the

Social Security numbers and SIC codes in the

wage records of all SESA agencies in the

Philadelphia region of the Department of Labor.

Names and Social Security numbers will be

checked by the Social Security Administration.

The Social Security number error rates for the

first two states checked are seven percent each.43

A problem for BLS in developing a NWRD is

that the quality of wage record data may vary

significantly among the states. This means that

the inclusion of any states in the NWRD without

carefully auditing their entire system of wage

records could contaminate the national data.

Conclusion. More experience with wage

record systems and studies of that experience is

urgently needed so that guidelines and rules of

thumb can be developed regarding the design of

studies and wage record systems in which trust

can be placed in the results. High quality

research on the factors affecting the validity of

studies done with wage record data should have

a very high priority in future developments.

Studies of the relationships between data

quality and system design are just as important

for consumer information and local uses of wage

record information as they are for purposes of

state level program evaluation and national

policy. Even for local uses, bad data may be

worse than no data at all.

POSSIBILITIES FOR THE

DEVELOPMENT OF WAGE RECORD

INFORMATION SYSTEMS

The submission of BLS's plan for the creation

of a NWRD in June 1994 will set the stage for

considering what additional steps, if any, could

be taken to encourage or support state

capabilities for using wage record information

for policy research, program evaluation, or

consumer information. The BLS plan is

important because it could become a driving

force in the development of wage record

information systems in the states.

Series, 93-C, Washington, DC: U.S. Superintendent

of Documents, 1993, Appendix E.

42 Ibid., p. 346. 43 Private conversation with Brian MacDonald.

15
Design choices made by BLS could affect the

usefulness of the NWRD and incentives for

improving the quality and usefulness of studies

done at the state level with wage record

information.

. For example, one major choice facing BLS

is whether a NWRD should be based on

only a sample of individuals in the

working population of the U.S. or the

entire universe of people covered by

unemployment compensation. The sample

could be defined for the U.S. as a whole,

regions of the United States, within states,

by industry, or in many different ways.

. A national database consisting of a sample

of wage records could be perfectly

adequate for purposes of nationally

oriented policy-oriented research on issues

of trends in the labor market and broadly

defined program participation, but most

likely would not be very useful for

purposes of program evaluation and

monitoring at the state level. Most

program evaluation at the state level is

likely to involve comparison among

specific institutions and programs within

those institutions. A national database

containing only a sample of individuals,

even if the sample is selected within states,

would not provide sufficient numbers of

individuals for many institutions and

programs within a state to be able to allow

the conduct of such program evaluation

studies or to monitor the outcomes of

programs.

. On the other hand, creating a national

wage record database containing employment and earnings records for all

workers in the U.S. will be an expensive

proposition for the states and the federal

government, and require dealing

effectively with important problems of

maintaining confidentiality and privacy. At

the same time, the availability of wage

record data from a national database could

eliminate many of the needs for

negotiating state-by-state data sharing

agreements, which could significantly

reduce their costs of constructing user

systems. These reduced costs of obtaining

multi-state data could help to stimulate

both major expansion of user systems and

improvements in the quality of studies.

Option #1: The development of wage record

information systems has reached the point where

many states have experimented with user systems

and have actually begun to use or are poised to

begin using wage record systems for purposes of

program evaluation and policy analysis. (There

are few examples of consumer information so

far.) This progress coupled with the potential

impetus for expansion following from BLS's

submission of a plan for a national database calls

for a deliberate review by the Congress and

the consideration of other steps that may be

needed to improve the quality and usefulness

of studies done at the state level using wage

record data and uses of the data for purposes

of performance management. These other steps

are important to consider even if plans for the

NWRD do not go forward.

Option #2. The broad review that is needed

should include but go beyond questions of the

NWRD to issues of how the federal government can be most helpful to state user agencies

in all program areas. Many states and state user

agencies have invested substantial amounts of

time, effort, and dollars in data-sharing agreements and other elements of capacity for using

wage records that could be greatly helped or

seriously impeded by new federal initiatives. The

review should seek input from the states and

federal program offices in all areas, as well as

from individuals with the background and experience necessary to raise issues of improving the

quality and usefulness of wage record information systems.

Useful advice might be obtained from the:

● National Occupational Information Coordinating Committee, which provides a

16
forum for 10 federal agencies on issues of

occupational information. Many of these

agencies have strong interests in program

followup and career development information. NOICC is also responsible for coordinating the State Occupational Information

Coordinating Committee (SOICC) network

of state occupational information systems.

. National Commission on Employment

Policy, which has important responsibilities for

helping to coordinate and support the

development of national policy on employment and training across federal

agencies.

. Office of Vocational and Adult Education,

which is the federal agency responsible for

the implementation of performance standards under the Carl D. Perkins Vocational and Applied Technology Education

Act.

● National Center for Research in

Vocational Education, which is funded

under the Perkins Act.

Other organizations in other program areas

that could be asked to comment on the plan

include: (a) program offices of the federal

government in areas other than vocational

education, (b) a selection of state user agencies,

and (c) the National Governor’s Association.

The reviewers should be asked to comment on:

. The implication of the BLS design and

plan for a NWRD for the development of

user systems at the state level for purposes

of: (a) conducting policy-oriented research,

(b) performance management, and/or (c)

providing consumer information.

. Steps that should be taken beyond the BLS

plan to strengthen the capacity of state

user agencies for organizing wage record

information systems and using the data

well.

. Actions to improve the quality of studies

done with wage record information by

state user agencies (and researchers).

. Specific steps to encourage the use of

wage record information systems at the

local level by policy makers in vocational

education and other program areas for

understanding how programs are working,

and developing their own policies and

strategies.

BLS is also likely to propose strategies for

providing technical assistance to the state offices

where access to the NWRD will be provided, if

a decision to proceed with such a national

database is made.

. The reviewers should be asked specifically

to comment on the needs for technical

assistance to state user agencies for

purposes of policy making or performance

management, and improving the quality of

studies. [One possibility is that this

technical assistance could be provided

through federal support for the sharing of

information among the states themselves

rather than through more direct efforts. ]

Option #3: A small program of cooperative

demonstration projects could be supported to

encourage the development of regional consortia on using wage record information for

policy development, performance management, and/or consumer information.

. The projects should concentrate on the

development of data sharing agreements, the

investigation of issues of data quality,

demonstration of how program evaluation

results can actually be used to improve

programs at the local level, and

demonstration of the kinds of broader

questions of program strategy and

dynamics of the labor market that can be

17
effectively addressed using wage record

data.

. The project should also be encouraged to

experiment with ways of supporting the

local use of wage record information.

. Each consortium should be required to

produce a report summarizing the results

of their efforts that would be publicly

available and distributed.

. Funding in the neighborhood of $100,000

apiece should be provided for these

consortia.

The clear purpose of this cooperative program should be to foster the regional development of state-level agencies as effective users

of wage record information.

Option #4: Federal agencies could be directed

to conduct systematic research on the sound

use of wage record information in policy studies

and systems of performance management. 

. More studies like the current 16-state

demonstration in JTPA funded by the

Employment and Training Administration to

compare the impacts of switching to wage

records from survey methods are needed.

. A similar demonstration and systematic

analysis of the use of wage record information for performance management in

comparison to followup surveys is needed.

. As in the New Jersey Unemployment

Insurance Re-employment Demonstration

Project, federal studies should be encouraged to utilize wage record information in major evaluations of state or

federal programs where wage records can

be rigorously compared with other sources

of earnings and employment information.
— — — ..-...—

OTHER REFERENCES

Bishop, John, “Policy Evaluation and Archived Wage Record Data: Limitations of Existing Data

Sets, ” Washington, DC: Northeast-Midwest Institute, 1989.

Crosslin, Robert L., “The Feasibility of a National Wage Record Data Base: A Conference Synopsis

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