LONGITUDINAL ESTABLISHMENT MICRODATA AT THE BUREAU OF LABOR STATISTICS:

DEVELOPMENT, USES, AND ACCESS

James R. Spletzer, Bureau of Labor Statistics

2 Massachusetts Avenue NE, Suite 4945, Washington DC 20212

Key Words: Longitudinal establishment microdata,

job creation and job destruction, net employment

growth.

1. Introduction

 The Bureau of Labor Statistics (BLS) is currently

constructing a longitudinal database that will contain

quarterly employment and wage data for virtually all

business establishments in the United States. This

longitudinal database will enable us to track changes in

employment and wages not only at the macro level, but

also at the micro level of the establishment. This

database, referred to as the Longitudinal Database or

LDB for short, will be used to generate high quality,

high frequency, timely and historically consistent

information regarding not only job creation and job

destruction, but also the life cycle of establishments.

The purpose of this paper is to describe this database

and to present some research results that will highlight

the uses of this database.

2. Motivation

 Although the cross-sectional or "snap-shot"

employment statistics that are published by the

government statistical agencies are invaluable for

policy-makers, researchers, and the business

community, these data are unable to completely

describe changes in employment and/or the number of

establishments over time. Comparing aggregate

employment levels at two points in time only tells us

about the net change in employment, and does not

inform us with regard to how many establishments are

either expanding or contracting, nor by how much

these establishments are either expanding or

contracting.

 Job creation is defined as the employment growth

contributed by establishments that expand or start up,

and job destruction is defined as the employment

decline resulting from establishments that contract or

shut down. The sum of job creation and job

destruction is the net change in employment. This

decomposition of net employment change is important

because it illustrates the underlying level of volatility

in the labor market. This can easily be seen by an

example.

 Assume that payroll employment in December 1996

is 120,659,000 jobs, and that payroll employment in

March 1997 is 121,344,000 jobs. Net employment

growth is 685,000 jobs during the quarter. This net

employment growth of 685,000 jobs is consistent with

many scenarios, including either of the following three:

1) 685,000 jobs created and 0 jobs destroyed, 2)

10,022,620 jobs created and 9,337,620 jobs destroyed,

3) 121,344,000 jobs created and 120,659,000 jobs

destroyed. Scenario #1 illustrates a labor market where

no employer decreased the size of his establishment.

Scenario #3 illustrates a labor market where all

establishments in the previous quarter shut down and

all establishments in the current quarter started up.

The true underlying labor market is, of course,

somewhere in between these two extreme cases.

Scenario #2 illustrates an intermediate case, where the

job creation rates and job destruction rates are roughly

8 percent.

 Since a simple comparison of two cross-sectional

aggregate employment levels does not provide any

information regarding the underlying level of volatility

in the labor market, it is clear that longitudinal

microdata at the establishment level is required to

decompose net employment change into its

components of job creation and job destruction. This

decomposition is one of the primary purposes of the

LDB currently being developed.

 Much of what we know about job creation and job

destruction comes from research conducted at the

Center for Economic Studies at the U.S. Bureau of the

Census using the Longitudinal Research Database (the

LRD); see, in particular, Davis, Haltiwanger and

Schuh (1996). Despite all that has been learned about

the labor market from the LRD, the conclusions that

can be drawn from these data are somewhat limited

since they only cover the manufacturing sector of the

economy. Manufacturing in 1995 accounted for only

19 percent of private sector employment. Recent work

by Anderson and Meyer (1994), Lane, Stevens, and

Burgess (1996), and Spletzer (1995) has illustrated

how job creation and job destruction in manufacturing

may not be representative of the entire economy. Since

the longitudinal database currently under construction

at the BLS will essentially be a quarterly census of

establishments and will thus encompass all industries,

the job creation and job destruction statistics derived
from the LDB have the potential to be among the most

important economic indicators published by the

statistical agencies of the U.S. government.

3. Data Sources and Definitions

 The source of the establishment microdata used for

the LDB will be the ES-202 program, which is a

cooperative endeavor of BLS and the states. All

employers subject to state unemployment insurance

(UI) laws are required to submit quarterly contribution

reports detailing their monthly employment and

quarterly wages to the State Employment Security

Agencies. After the microdata are edited and, if

necessary, corrected by the State Labor Market

Information staff, the states submit the data to the

Bureau of Labor Statistics as part of the Covered

Employment and Wages program (ES-202). The data

gathered in the ES-202 program are a comprehensive

and accurate source of employment and wages, and

provide a virtual census (98 percent) of employees on

nonfarm payrolls. According to Employment and

Wages, employers in private industry in 1996 provided

state employment security agencies with quarterly UI

tax reports for an average of 99.3 million wage and

salary workers in approximately 6.9 million business

establishments. For more information on the ES-202

program, see U.S. Bureau of Labor Statistics (1997)

and Farmer and Searson (1995).

 Several definitions deserve mention. An

establishment is an economic unit, such as a factory or

store, which produces goods or provides services. An

establishment is usually a physical location and

engaged in one or predominantly one type of economic

activity for which a Standard Industrial Classification

(SIC) code is applicable. The industry code of an

establishment is assigned based on its primary activity,

which is determined by the primary product or groups

of products produced or distributed (or services

rendered) by the establishment.

 Employers report employment and wages on an

individual establishment basis. Multiple Worksite

Reports are used to collect separate employment and

wage data for each establishment owned by employers

with multiple locations within a state. The Multiple

Worksite Reports were instituted as part of the

Business Establishment List Improvement Project

(BEL breakouts), which was a major initiative

conducted jointly by the states and the Bureau of Labor

Statistics in 1990 and 1991. The purpose of the BEL

breakouts was to get businesses to report their

employment and wages at the establishment level

rather than the reporting unit level that was state

specific prior to the first quarter of 1991. Since the

first quarter of 1991 (with the exception of two states

that implemented the BEL breakouts in 1992), every

multi-establishment employer with ten or more

employees in secondary physical locations covered

under one UI account has been requested to provide

establishment level data.

 Employment for a given month is the number of

covered workers who earned wages during the pay

period which includes the 12th of the month. The

employment count includes all corporation officials,

executives, other supervisory personnel, clerical

workers, wage earners, persons on paid vacations or

paid sick leave, pieceworkers, part-time workers, and

workers earning wages which are nontaxable under UI

because the taxable wage limit has been exceeded. The

employment count excludes workers who were on leave

without pay or who earned no wages during the

applicable pay period because of strikes, work

stoppages, or temporary layoffs.

 The quarterly UI microdata have information on

monthly employment. The LDB publications will use

employment in the third month of the quarter as the

measure of the establishment’s quarterly employment.

This policy was selected because comparisons between

specific points in time are easier to interpret than are

comparisons of quarterly averages. Averaging distorts

the timing of when changes in employment occurred,

especially changes in employment that occur when an

establishment shuts down. Furthermore, monthly

employment flows constructed from data reported

quarterly might be affected by unknown problems such

as quarterly seam effects and other forms of recall bias.

4. Construction of Longitudinal Microdata

 There are two pieces of information in the ES-202

microdata that allow for matching establishments

across quarters. The first is the SESA ID, which is the

UI account number in combination with the

establishment's reporting unit (RU) number. The

SESA ID is the establishment’s unique identifier in the

data that the state transmits to BLS. Although the RU

number is not used for administration of the

unemployment insurance system, the RU number is

assigned by the state for BLS purposes of identifying

establishments within a multi-establishment employer

in that state. The SESA ID is establishment specific

rather than location specific, which implies that if an

establishment moves across the street in search of a

bigger or better location, its SESA ID stays the same.

 The second piece of information in the UI

microdata used for longitudinal linking is the use of

predecessor and successor numbers. The predecessor

number is the SESA ID of the establishment that

previously owned the establishment in the event of

either a change in ownership or a change in reporting
configuration (i.e. a breakout of units). The successor

number is the SESA ID of the establishment that will

take over the establishment in the event of either a

change in ownership or a change in reporting

configuration (i.e. a consolidation of units). The term

“breakout” refers to a transition from a single

establishment employer to a multi-establishment

employer, and the term “consolidation” refers to a

transition from a multi-establishment employer to a

single establishment employer. These breakouts and

consolidations may be actual economic events

representing business expansions and contractions, or

merely administrative reporting changes due to

whether or not the business completes the Multiple

Worksite Report.

 After matching on SESA ID and matching on

predecessor and successor numbers both within and

across quarters, a third and final step undertaken to

link the establishment level microdata across quarters

is a probability based match that attempts to identify

two establishments with different SESA IDs as

continuous. This match is based upon comparing

births in the current quarter to deaths in the previous

quarter and looking for occurrences such as the same

name, the same address, and so forth.

 Almost all of the establishments identified as

continuous from quarter to quarter are matched by

SESA ID. Although the predecessor-successor match

and the probability based match links only a small

number of establishments, these matches have a

significant effect on the number of births and deaths.

See Robertson, Huff, Mikkelson, Pivetz, and Winkler

(1997) for a more detailed description of the matching

algorithm used for the LDB.

5. Researcher Access to the LDB Data

 The BLS plans to publish quarterly and annual

tabulations of job creation and job destruction. Current

plans involve producing these statistics for the entire

U.S. economy, by industry, by state, by size, and by

age. These tables will be put through a non-disclosure

review to insure that we consistently protect the

identity of the establishments. These tables, some of

which will be published and the others which will be

available by request, should satisfy the majority of our

customers. However, we do anticipate requests by

researchers who want to go beyond our published

tabulations and "get their hands dirty" in the

microdata.

 What is the optimal tradeoff between data

confidentiality and data access? The suppliers of the

data (the businesses), and BLS as custodians of the

data, are concerned about the sensitivity of the

employment and wage data being stored on the same

microdata record as characteristics such as location,

size, and industry that could easily identify the specific

establishment. The consumers of the data (the

researchers) are eagerly anticipating the construction

of the LDB because it will provide a wealth of data

never before available which can be used to address

important research questions relevant to economic

theory, employment and wage policy, and a general

understanding of the U.S. economy. As part of the

process of constructing the LDB, BLS has been

wrestling with the question of how to maximize access

to the microdata by legitimate researchers while

minimizing the risk of a violation of respondent

confidentiality.

 Historically, access has only been granted to

confidential BLS microdata when authorized by the

Commissioner of Labor Statistics for a statistical or

research purpose that furthers the mission and function

of BLS. Although some confidential data from the

National Longitudinal Survey of Youth (NLSY) and

the Census of Fatal Occupational Injuries (CFOI) are

made available to outside researchers under special

agreements, even these data do not contain all the

available information. With the exception of the

NLSY and the CFOI, most previous access to

confidential BLS microdata by outside researchers has

been through the ASA/NSF/BLS senior research

fellows program. Researchers in the ASA/NSF/BLS

program must be affiliated with an organization, and a

high official in the organization (a Dean of a university

or a Vice President of the organization) signs the

agreement with BLS committing the organization to

abide by BLS confidentiality policy. The researchers

sign BLS non-disclosure affidavits pledging to protect

the data and not release the data to anyone.

 With regard to the large expected demand for

access to the LDB microdata, perhaps the two most

important questions are who will be able to use the

data, and under what conditions? As of this writing in

summer 1997, no specific access policies regarding the

LDB have been implemented. Although still in the

preliminary stages, BLS is exploring various methods

that could be used to establish the Federal Reserve

Banks as regional access centers for the LDB

microdata. Whatever the details are regarding how

researchers access the data, BLS is expected to incur

costs. Unlike the Center for Economic Studies at the

Census Bureau, BLS does not currently charge user

access fees. However, as has been demonstrated in the

past both within BLS and in other federal agencies,

serious research based on our data has benefits in that

it often improves the quality of published statistics.

 Two other issues relevant to the debate between

confidentiality and access are also worth mentioning.
First, it is assumed that researchers will have access to

the actual microdata records, with no masking of data

and with no censoring of variables. Second, just as

access to the longitudinal database will require a

proposal review, the research resulting from the access

will need to be reviewed for potential breaches of

confidentiality before it is disseminated. The statistical

techniques of disclosure avoidance are an active area of

research at BLS.

6. Research results

 As part of the LDB development, I have been using

a sample of ES-202 microdata to analyze net

employment growth, job creation, and job destruction.

I report the results here, focusing on differences across

various sectors of the economy.

 The microdata used here are from the state of West

Virginia from the first quarter of 1990 to the first

quarter of 1995. As mentioned previously, the BEL

breakouts were introduced in the first quarter of 1991,

with two states being developed in 1992. However,

eight states were reporting at the establishment level as

of the first quarter of 1990, and using data from any of

these eight states adds eight extra quarters to the

research data used in this project, thus maximizing the

“T” in a panel of size NxT. Of these eight states,

several were too small for meaningful analysis, and

several were too large for my computing capabilities.

West Virginia was the largest of the eight states that

would fit within my computing capabilities.

 Before linking the data across quarters, I excluded

private household (SIC 8811), government, and

agricultural establishments. I then matched the 21

consecutive quarters of West Virginia UI microdata

(1990:1 to 1995:1) by SESA ID. I have also performed

a match by UI account (ignoring the RU number) in

order to identify breakouts and consolidations not

reported by predecessor or successor numbers. On

average, 93.4 percent of establishments in two

consecutive quarters matched in this first step. Any

establishment that did not match on SESA ID was then

matched by predecessor and successor numbers. This

second step matched approximately 8.3 percent of

those eligible, thus resulting in an average quarterly

match rate of 93.9 percent for establishments

appearing in two consecutive quarters. Finally, I

removed all occurrences of zero and imputed

employment at birth and death, and I have defined four

consecutive quarters of zero or imputed employment as

a death.

 The reason for removing all occurrences of zero and

imputed employment at birth and death is motivated by

the following question: should births and deaths be

defined by the first and last filing of the UI tax form, or

should births and deaths be defined by the first and last

appearance of positive employment? Births and deaths

are not identifiable events, but rather are processes of

discrete steps such as the gestation period when the

idea for a new business is formed, the formal

application for UI eligibility, and the actual hiring of

employees. Defining births and deaths as the first and

last quarter of positive employment is necessary when

analyzing job flows, because we want to identify

changes in employment corresponding to specific

points of the establishment's life cycle, rather than the

administrative process of activating or de-activating

the UI account which is often independent of any job

flows. In the longitudinal West Virginia data, 21.9

percent of new UI accounts have zero or imputed third

month employment their first quarter of reporting, and

81.4 percent of deactivated UI accounts have zero or

imputed third month employment in their final quarter

of reporting. The reason that a zero employment value

appears when a new UI account is first reported is that

business owners probably apply for UI eligibility before

actually hiring their first employee. Similarly,

reporting a zero employment value when the UI

account disappears undoubtedly occurs when a

business owner reduces his employment levels to zero

but keeps his UI account active by reporting zero

employment because he anticipates starting up the

business again when economic conditions improve.

 The motivation for defining four consecutive

quarters of zero or imputed employment as a death is

to help define births for those establishments that have

a left censored stream of reported zero employment

levels, and to help define deaths for those

establishments that have a right censored stream of

reported zero employment levels. For example, in the

West Virginia microdata, we observe 1123

establishments (1.7 percent of the total sample) with at

least four quarters of zero employment before the panel

is right censored following the first quarter of 1995. In

order to implement this four quarter rule, we must

delete the first and the last three quarters of the panel.

The final longitudinal West Virginia dataset has

approximately 35,000 operating establishments in each

quarter from 1990:4 to 1994:2.

 The research reported in this section is an integral

part of the development of the LDB, but should in no

way be considered a sneak-preview of the future

published numbers. I have not matched predecessor

and successor numbers within, rather than across,

quarters, and I have not made use of the probability

match that would attempt to further match births and

deaths based upon characteristics such as name and

address. Because of this, the longitudinal dataset used

in the research reported here will not duplicate the data
for West Virginia in the forthcoming LDB. However,

the research of Robertson, Huff, Mikkelson, Pivetz, and

Winkler (1997) suggests that any discrepancies will be

small.

 We now turn to empirical estimates of the

decomposition of net employment growth into job

creation and job destruction. Notationally, let Et

denote aggregate employment in quarter t, let e index

establishments, define S+ as the sector of expanding

and opening establishments, and define S- as the sector

of contracting and closing establishments. The

average quarterly net employment growth rate is

written as

1

14 2

1

911 1

 94 2











−

+













−

= −

∑

Et Et

t : {Et Et } /

:

,

the average quarterly job creation rate is defined as

1

14 2

1

911 1

 94 2











−

+

























−

∈ −

∑

=

∑

+

t Ee

t

Ee

t : e S {Et Et } /

:

,

and the average quarterly job destruction rate is

defined as

1

14 2

1

911 1

 94 2











−

+





























−

∈ −

∑

=

∑

−

t Ee

t

Ee

t : e S {Et Et } /

:

.

As is evident in the above equations, I follow Davis,

Haltiwanger, and Schuh (1996) by using the mean of

employment in the current and the previous quarter as

the measure of employment in the denominator when

converting employment levels into rates.

 Measured on a quarterly basis, net employment

growth and job creation and job destruction rates are

presented in Table 1 for the entire state of West

Virginia (excluding private household, government,

and agricultural establishments) and for each major

industry division. Note that whereas job creation refers

to opening and expanding establishments and job

destruction refers to closing and contracting

establishments, net employment growth also includes

the net effects of breakouts and consolidations.

Therefore, the job creation and job destruction rates in

Table 1 may not precisely add to the net employment

growth rate (for example, the net employment effect of

breakouts and consolidations when measured on a

quarterly basis is 0.1 percent).

 For the state of West Virginia as a whole during the

early 1990s, employment in the average quarter is

growing by 2,341 jobs. Converted into percentage

terms, employment is growing on average at the rate of

one-half of one percent per quarter (the 0.5% statistic

in the upper right corner of table 1). This average net

quarterly employment growth can be decomposed into

a quarterly job creation rate of 8.4 percent and a

quarterly job destruction rate of 8.0 percent. These

statistics imply that the average expanding

establishment (including births) is growing by 8.4

percent per quarter, and the average contracting

establishment (including deaths) is declining by 8.0

percent per quarter.

 Looking at the industry statistics in Table 1,

quarterly job creation and job destruction rates are

lowest in manufacturing, at 4.9 and 5.8 percent

respectively. These quarterly job creation and job

destruction rates in manufacturing are quite similar to

those reported by Davis, Haltiwanger, and Schuh

(1996) -- 5.2% and 5.5% respectively, and are also

quite similar to those reported by Anderson and Meyer

(1995) -- 5.8% and 6.2% respectively. While we

should not expect to replicate the job creation and job

destruction rates found by these other studies, I find the

similarity amazing in light of the different samples and

the different time periods used in these various studies.

Table 1: Net Employment Growth Rate

 Job Creation and Job Destruction Rates

 Quarterly Averages

 West Virginia 1990:4 - 1994:2

%Emp J.C. J.D. N.E.G.

Total

8.4% 8.0%

 0.5%

Mining 6.2%

8.8%

 10.8% -1.9%

Construction 5.9% 23.3% 21.0% 2.2%

Manufacture 16.9%

4.9% 5.8%

 -0.4%

TPU 7.1%

6.4% 6.0%

 0.3%

Whole Trade 6.1%

6.4% 6.3%

 0.0%

Retail Trade 24.1%

8.7% 8.5%

 0.3%

FIRE 5.0%

6.6% 6.4%

 0.2%

Services 28.5%

8.2% 6.8%

 1.4%

%Emp = Percent of total employment in the industry (averaged over

1990:4 to 1994:2). Job Creation (J.C.) - Job Destruction (J.D.) = Net

Employment Growth (N.E.G.). Statistics in table may not add precisely

because of rounding, because the industry "Not Elsewhere Classified" is
not reported, and because employment effects of breakouts and

consolidations are not reported.

 The job creation and job destruction statistics in

manufacturing are slightly below the quarterly job

creation and job destruction rates of 6.0 to 6.6 percent

observed in TPU, wholesale trade, and FIRE. Services

and retail trade have somewhat higher quarterly job

creation and job destruction rates, and mining has the

highest quarterly job creation and job destruction rates,

at 8.8 and 10.8 percent respectively, of any industry

except construction.

 We see immediately in Table 1 that the job creation

and job destruction rates in construction are outliers.

This was also found by Anderson and Meyer (1995).

The explanation is the large seasonality of employment

in the construction industry. In the first and fourth

quarters when construction employment declines, the

quarterly job creation rate is roughly 16.6 percent and

the quarterly job destruction rate is roughly 27.3

percent. In the second and third quarter when

construction employment expands, the quarterly job

creation rate is roughly 30.1 percent and the quarterly

job destruction rate is roughly 14.7 percent. Many of

the other industries exhibit seasonal variation in their

quarterly employment levels, but not nearly as large (in

percentage terms) as the seasonal variation in quarterly

construction employment.

7. Discussion and Extensions

 The research presented in this paper has only

scratched the surface regarding the usefulness of job

creation and job destruction statistics. While I have

presented the quarterly data by industry, there are

many more dimensions by which it is possible to

present the data. For example, we can analyze job

creation and job destruction at other frequencies such

as annual comparisons. Tables by age will shed light

on how establishments grow and decline based upon

where they are in their life cycle, and tables by size will

clarify the current debate about the (net) magnitude of

jobs created by small businesses. The job creation and

job destruction statistics presented here are means from

the distribution of jobs created at expanding

establishments and the distribution of jobs lost at

contracting establishments -- summarizing these

distributions by statistics other than the mean (such as

the variance or by using quartiles) will inform us as to

whether job growth and job loss are concentrated at

just a few establishments or spread evenly across many

establishments. The job creation and job destruction

statistics can also be decomposed into the jobs gained

by births versus expansions and the jobs lost by deaths

versus contractions -- Spletzer (1995) concludes that

the amount of job creation and job destruction

attributable to births and deaths is non-negligible in

the short run and is quite high in the long run.

 Davis, Haltiwanger, and Schuh (1996) have shown

that job destruction rates in manufacturing exhibit

greater cyclical variation than job creation rates. In

particular, recessions are characterized by a sharp

increase in manufacturing job destruction accompanied

by a relatively mild slowdown in job creation. As the

LDB becomes a part of the regular production cycle of

BLS, we will be able to assess the cyclicality of job

creation and job destruction rates in all industries.

This information should help economists, policymakers, and the business community develop a more

complete understanding of the business cycle.

 Finally, the longitudinal database that the BLS is

constructing from the UI establishment microdata

might provide a first step towards the ultimate goal of a

longitudinal linked employer-employee dataset. Along

with the total employment and wages that each

business files on its UI tax forms, the business also

provides quarterly wages for every employee. These

employee level data are known as the "wage records."

The tremendous potential uses of a longitudinal linked

employer-employee dataset are explained in Lane,

Burgess, and Theeuwes (1997). Although the BLS

does
not currently maintain any wage records, the

experiences gained while constructing the LDB will

teach us about development, uses and access relevant to

a large confidential database should a longitudinal

linked employer-employee dataset ever come to

fruition.

Disclaimer

 All empirical work in this paper is based on the

author’s calculations and does not necessarily reflect

the official position of the BLS. Any views expressed

in this paper are those of the author and do not

necessarily reflect the policies of the BLS or the views

of other BLS staff members.

References

Anderson, Patricia M. and Bruce D. Meyer. 1994.

 "The Extent and Consequences of Job Turnover.”

 Brookings Papers on Economic Activity, pp.

 177-236.

Davis, Steven J., John C. Haltiwanger, and Scott

 Schuh. 1996. Job Creation and Destruction.

 MIT Press.

Farmer, Tracy E. and Michael A. Searson. 1995.

 “Use of Administrative Records in the Bureau of

 Labor Statistics’ Covered Employment and

 Wages (ES-202) Program.” Proceedings of the

 Bureau of the Census 1995 Annual Research

 Conference, pp. 198-235.

Lane, Julia, Simon Burgess and Jules Theeuwes.

 1997. "The Uses of Longitudinal Matched

 Worker/Employer Data in Labor Market

 Analysis." Paper presented at the August 1997

 American Statistical Association Meetings.

Lane, Julia, David Stevens, and Simon Burgess.

 1996. “Worker and Job Flows.” Economics

 Letters, pp. 109-114.

Robertson, Kenneth, Larry Huff, Gordon Mikkelson,

 Timothy Pivetz, and Alice Winkler. 1997.

 "Improvements in Record Linkage Processes for

 the Bureau of Labor Statistics' Business

 Establishment List." Forthcoming, Proceedings

 for the 1997 Record Linkage Workshop and

 Exposition.

Spletzer, James R. 1995. "The Contribution of

 Establishment Births and Deaths to Employment

 Growth." Paper presented at the January 1996

 Econometric Society Meetings.

U.S. Bureau of Labor Statistics, 1997. BLS

 Handbook of Methods, Bulletin #2490.