Improvements in Record Linkage Processes for the Bureau of Labor Statistics’October 1997

 Business Establishment List

by: Kenneth Robertson, Larry Huff, Gordon Mikkelson, Timothy Pivetz, and Alice Winkler

2 Massachusetts Ave. NE, Washington, DC 20212

Introduction

In preparation for the building of a new longitudinally-linked establishment database, the Bureau of

Labor Statistics decided to review its current system for linking business establishments across time.

Because the new database will be used to produce statistics on business births and deaths and job

creation and destruction, we had to ensure that the linkage procedures used in building the database

would yield the most accurate results possible. Since the current linkage system was built for different

purposes than the new system, there were areas where we could potentially improve the process. This

paper provides an explanation of the current linkage procedures, details of the work completed to date,

and areas of research that need to be explored in the future.

Background

Quarterly Unemployment Insurance Address File

The Bureau of Labor Statistics oversees the Covered Employment and Wages, or ES-202 program,

that provides a quarterly census of information on employers covered under the State Unemployment

Insurance (UI) laws. These data are compiled into a data file, the Quarterly Unemployment Insurance

(QUI) Address File.

The QUI file includes the following information for each active employer subject to UI coverage

during the reported quarter: State UI Account Number, Establishment Reporting Unit Number (RUN),

federal Employer Identification Number (EIN), four-digit Standard Industrial Classification (SIC) code,

county/township codes, monthly employment during the quarter, total quarterly wages, and the

establishment's name(s), address and telephone number. Known predecessor and successor

relationships are also identified by UI Account Number and Establishment Reporting Unit Number

(UI/RUN). These numbers are used as administrative codes for matching records from one quarter to

the next. The State code, EIN, and UI/RUN allow establishments to be uniquely identified. Imputed

employment and wage data are assigned specific codes to distinguish them from reported data. Codes

are placed on the records to identify the type of address (i.e., physical location, mailing address,

corporate headquarters, address on UI tax file, or “unknown”).

The Universe Database

The State QUI files are loaded to a database, the Universe Database (UDB), for access by users for

survey sampling and research purposes. The UDB is composed primarily of data elements drawn from

the QUI files. In addition, there are a few system-assigned and derived data elements, as well as

information on SIC code changes merged from other sources. An important system-assigned field is

the UDB Number, a unique number identifying continuous business establishments.

UDB Record Linkage

When considering the linkage of these records, the reader should understand that we are linking files

which have the same structure across time. These files are linked to a new iteration of themselves each

quarter. This linkage allows us to identify business establishments which may have gone out of
business; establishments which remain in business for both periods; and, new establishments. The

quality of the administrative codes are very good, so we expect that we correctly link most records

which should be linked. We follow the administrative code match with a probability-based match. This

procedure is followed to identify the small percentage of links which are missing the appropriate

administrative codes.

Each quarter prior to loading the QUI files to the UDB, a matching procedure is performed to link

businesses. By default, all units that do not link are identified as either new establishments or closed

establishments. In order to have accurate data on business births and deaths, it is critical that the

matching system accurately link establishments. The intent of the original linkage system was to

minimize the number of invalid matches. Unfortunately, however, this causes some good matches to

be missed. Because statistics on business births and deaths were not being produced from these linked

data and only a small percentage of the total number of records was affected, this situation was

acceptable.

The match system was composed of four main components. The first component identified the most

obvious continuous establishments - those with the same State code-UI/RUN combination. These are

establishments that from one quarter to the next did not change their UI reporting - no change of

ownership, reorganization, etc. The second component matched units that States submitted with codes

identifying predecessor/successor relationships. Given that State personnel have access to the

information needed to determine these relationships, they are assumed to be correct.

The third component matched units based upon certain shared characteristics. Prespecified weights

were assigned based on data element values that the units had in common. This weighted match

routine processed the data in three steps (or blocks). All three blocks limited potential matches to those

units coded in the same 4 digit SIC code and county. (The New England States also use township

codes.) The first block included all units that also matched on a key constructed from the Trade Name

field. This “Name Search Key” was composed of the first seven consonants of the Trade Name. The

second block included all units that also matched on the first 15 positions of the Street Address field.

The third block included all units that also contained identical phone numbers. Two matched units

were considered a valid match when they exceeded a cutoff weight. A limitation stemming from this

three-block structure is that units that had a valid relationship but had different 4 digit SIC or county

codes are missed by the linkage system.

The fourth component of the matching routine attempted to capture changes that occurred within a

quarter. It first linked units that had State-identified predecessor relationships already coded. It next

performed a within-quarter weighted match to capture relationships not previously identified by the

States. A significant restriction placed upon both parts of the within-quarter matching was that the

potential predecessors had to contain zero employment in the third month of the quarter while the

potential successor units had to contain zero employment in the first month of the quarter. Because of

inconsistencies in reporting by some employers, valid relationships could exist that did not meet this

criteria, and were not matched.

Reasons for Modifying the UDB Record Linkage Process

The UDB record linkage process effectively linked over 96 percent of all the records received each

quarter. Nevertheless, because its methodology was designed to limit the number of false matches, the

original linkage system may not have been the most effective at identifying all valid relationships that
existed between the remaining four percent of establishments. The result was a potential undercounting of continuous businesses and over-counting of business births and deaths. It is for that reason

that the research described in this paper was undertaken.

Furthermore, experience with the previous matching process had highlighted specific areas of the

process that needed improvement or enhancement. Although these areas affect only the four percent of

the records mentioned above, the net effect on the number of births and deaths identified could be

significant.

New Approach

The matching process consists of the two major procedures described below - an administrative code

match and a probability-based weighted match.

Administrative Code Match

Imputed Records

The first step in our new linkage process is to identify the imputed records (i.e., non-reporting records

that are assumed to remain in business), and flag the corresponding record in the preceding quarter of

the match. We then temporarily remove the imputed records from the current quarter file. Rather than

assume that these units are delinquent, we attempt to identify the units that actually may have been

reported under new ownership. At the end of all of the other match processes, we identify the

unmatched flagged records on the past quarter file. These records have their matching imputed record

restored to the current quarter file, and the link is made between them.

Within-quarter Matches

Establishments that experience a reporting change within a quarter are generally assigned either a

predecessor code or successor code pointing to another record within the same quarter. We determined

that these within-quarter links were legitimate, so we included a process to find them.

Remove Breakouts and Consolidations

After the within-quarter matches were identified, we examined situations where multi-establishment

reporters changed the way they reported. States encourage these reporters to supply data for each

worksite. When a reporter changes from reporting all worksites on one report to supplying multiple

reports, there is a possibility of failing to capture this as a non-economic event. If we were just

counting records, it would look like we have a lot more establishments in the current quarter than we

did in the past quarter. The reverse situation is also possible.

We were interested in identifying these links in order to exclude them in the counts as business

openings or closings. The limited number of situations found were sent to a data editing routine, where

the employment values were checked for reasonableness. If the match failed the edits, it was not

counted as a breakout or a consolidation, and not included in counts of establishments increasing or

decreasing in employment. Those cases failing the edits were still linked. However, since there is some

type of economic change occurring along with the reporting change, the units failing the edits are

included in counts of establishments increasing or decreasing in employment.
All Other Administrative Code Matches

The files were then linked by UI/RUN. These administrative codes linked most of the records.

Additional links were identified using Predecessor and Successor codes. In general these administrative

code match processes link over 96 percent of the current quarter file, depending on economic

conditions.

Probability-based Match

The probability-based weighted match process involves only the unmatched records from the

administrative code match process. In this process we generally expect to match less than one-half of

one percent of the current quarter records. This can also be expressed as linking less than ten percent of

the current quarter residuals. While this is not a large portion of the overall records, it is still an

important part of the overall process. The more accurate we can make the overall linkage process, the

more useful the database will be in identifying economic occurrences.

Theoretical Basis for Weighted Matching

The weighted match process is accomplished using the software packages AutoStan and Automatch,

from Matchware Technologies Incorporated. The first is a software package used to standardize names

and addresses for linking. The second package uses a record linkage methodology based on the work of

Ivan P. Felligi and Alan B. Sunter. Automatch uses the frequency of occurrence of selected variable

values to calculate the probability that a variable’s values agree at random within a given block. The

probability that the variable’s values agree given that the record is a match can also be calculated by the

software. These match and non-match probabilities form the basis of the weight assigned to the

variable in the match process. The sum of these variable weights are assigned as the overall weight for

a given record pair. The distribution of these summed weights, along with a manual review of selected

cases, allows us to determine an appropriate region where we find mostly matches. The lower bound of

this region is set as the match cutoff value. We expect that above this cutoff will be mostly good

matches, and that below this cutoff will be mostly bad matches.

These theoretical constructs are the foundation of probability-based record linkage. However, the

nature of the data, in combination with software, hardware, and resource limitations, sometimes

requires that additional steps be taken to fine-tune this process. Fortunately, Automatch provides some

capabilities in this direction. The weights assigned to a matched or non-matched variable can be

overwritten or augmented as needed. This allows the user to augment the weight of important

variables, as well as to penalize certain combinations of variable values, so that a record pair will not

match.

Weighted Matches

Blockings

While the UDB Record Linkage system only utilized three basic blocks (trade name, address, and

phone number), the new system, using Automatch, provides the option to use as many blockings as

needed to match records. Based on empirical studies using California data, we constructed 21 blocks

for the new system. All blocks match on two to four data elements. Within these 21 blocks, there are

three groups which block on certain data elements. The first group contains blocks that include either

exact name or exact street address. The second group blocks on phone number, and the third group

blocks on various other data elements, such as ZIP code and EIN.
Adjustments to Blockings

After the first few runs of Automatch, we adjusted the blockings and their probability weights to

enhance their matching potential. One weight adjustment we made was to records with similar street

addresses. If the street addresses contained different suite numbers, we reduced the weight. Similarly,

we reduced the weight if primary names contained different unit numbers. If one data element was

unknown or blank, we increased the weight because these data elements did not necessarily disagree.

However, if both data elements were unknown or blank, we deducted weight because there was a

greater possibility that they would disagree. Finally, we deducted weight if both records were part of a

multi-establishment employer.

We also made adjustments based on the address types. Some accountants submit data for many

companies. Therefore, more than one record could have the same accountant’s address and telephone

number. If two records contained the same physical location address, they were considered a good

match and we gave them more weight. If one record contained a physical location address and the

other record contained an unknown or tax address, it is possible that it would be a good match, so we

gave it slightly more weight.

Subjective Results and Cutoffs

Although these records contained some common data elements, frequently it was difficult for us to

decide whether the records were good matches. We subjectively identified matches as being “good,”

“bad,” or “questionable.” We reviewed these data to determine the quality of each matched pair. Then

we set the cutoff weights for each of the 21 blocks, approximately in the middle of the questionable

records.

RESULTS

California data files were linked forward from the first quarter of 1994 (1/94) through the first quarter

of 1995 (1/95). We evaluated the matches resulting from the final two quarters (4/94 to 1/95). These

results are shown in Tables 1 though 4. Additionally, two quarters of data were matched for three

other States -- West Virginia, Georgia, and Florida -- using preliminary match parameters developed

for California. The results were evaluated by the four analysts using the same rules used in evaluating

the California results. Although the results are not tabulated, they are approximately equivalent to

those obtained for California. This finding is significant since there are insufficient resources to

manually review cases which fall close to the match cutoff parameter. It is, therefore, important that

we find match cutoff parameters for each block which produce satisfactory results in all States.

Number of Units Matched

Table 1 provides a summary of the matches in California which were obtained from the current

matching procedures and the new procedures as tested. Both procedures produce the same number of

matches on administrative number identifiers (79.58% of the file matched on UI/RUN). The first

improvement in the matching process appears among those “delinquent” reporters which are assumed

to remain in business. In the new procedures being tested, these imputed records are not generated

until after all other matching processes are completed. The rationale for this change is that these nonreporting records may represent administrative business changes such as a change in ownership and

may be reporting with a new UI/RUN. These units were matched with new UI/RUNs in 342 cases in

California (0.04 percent of the file). These cases represent Type II errors (erroneous matches) for the

previous matching process. The remaining 154,143 delinquent reporters were later matched to a new

imputed record, as in current procedures.
The second improvement in the matching process appears in the within-quarter administrative match.

These within-quarter matches represent units which have undergone some administrative change such

as a change in ownership in a quarter and appear twice in the quarter with different UI/RUNs. It has

become apparent during study of these files over several years that these units do not always cease

reporting in one month during the quarter and begin reporting as a new entity in the next month of the

quarter. The previous match procedures restricted these within-quarter matches to those reporters

which report in a very precise manner. The new procedures allow for some reporting discrepancies in

the monthly employment in matching these cases. The new procedures obtained approximately 1,110

(0.12%) additional matches for these situations.

Finally, the third improvement in the matching process is in the weighted matching for all units in both

quarters which do not match during any of the administrative matching procedures. The new

procedures make use of many additional block structures which make possible incremental increases

in the number of matches without significantly increasing the number of Type II errors. This is

accomplished by tailoring the match cutoff parameter for each block so that most of the good matches

fall above the match cutoff parameter without including a large number of Type II errors. The good

matches falling below the parameter in one block are captured as matches in other blocks without

picking up significant numbers of additional errors. The number of weighted matches went from 686

to 1,513 for an increase of 827 (0.09%). The total number of additional matches from the new

procedures over the current procedures is 1,642. This reduces the number of business births (and

business deaths) by 1,642 per quarter.

Table 1 Results: Match Comparison for CA 95 Qtr. 1

Current Method New Method

Match Type Count % Count %

UI/RUN to UI/RUN 739,442 79.58 739,442 79.58

Correct “Delinquent” Matches 154,143 16.59 154,143 16.59

Incorrect “Delinquent” Matches (Type II Errors) 342 0.04 0 0

Pred/Succ. Codes / Non-Economic Reporting

Changes /Within-Quarter Administrative Matches 821 0.09 1,978 0.21

Weighted 686 0.07 1,513 0.16

Births 33,723 3.63 32,081 3.45

Total (Records = 929,157) 100.0 100.0

 Note that the new system identifies 1,642 more links than the old system.

Although the results of the new linkage procedures do not appear dramatically different from the

results of the current linkage procedures, the marginal improvements are significant in terms of the

uses of the linkages. As stated earlier, one of the principal uses of the linked data files is to estimate

the number and characteristics of business births and deaths and to track business births over time to

determine when they increase or decrease their employment and how long they continue in business. It

is easy to see that even though a large portion of the units match through the administrative codes, it is

the remainder of the units which are considered business births and business deaths. Marginal

improvements in matching these other units can have a relatively large impact on the number of

business births and deaths and the ability to track them over time.
Quality of Matched Units

Tables 2 and 3 compare the quality of the weighted matches resulting from each procedure. There are

two conclusions of interest from these tables. First, there are many more good matches resulting from

the new procedures and fewer Type II matching errors. Also, there are approximately 150 to 300 good

or questionable weighted matches obtained from the current matching procedures which are not being

identified during the new weighted matching procedures. There are two possible explanations. The

first is that we are missing these matches with the new procedures and we must find methods which

will identify the good matches. The second is that although we are not identifying these matches

during the weighted match, they may be identified in the enhanced administrative matching procedures

which would preclude them from the weighted matching process. The truth may lie somewhere

between these possibilities and will be one focus of our future research efforts.

Table 2 A Comparison of Weighted Match Counts and Quality

Current Method New Method

Match Quality Count % Count %

Good 262 49.1 1,317 87.0

Questionable 198 37.1 173 11.4

Bad 74 13.9 23 1.5

Total 534 1,513

 Note that all weighted match results are based on a manual review of linked records, and are

 based on the subjective opinions of several reviewers.

Table 3 continues the comparison of the quality of matches obtained from the current and new match

procedures. It is obvious from this table that, although, the new match procedures apparently miss

some good and questionable matches at or near the cutoff parameters for a match, the new procedures

identify many additional good matches which are missed by the current weighted match procedures.

This is accomplished by the new procedures while picking up fewer questionable and bad matches than

the current procedures.

Table 3 Weighted Matches

Current

Method Only

New Method

 Only

Both

Methods

Match Quality Count % Count % Count %

Good 156 38.4 1,211 87.4 76 91.6

Questionable 178 43.8 153 11.0 7 8.4

Bad 72 17.7 21 1.5 0 0

Total 406 1,385 83

Finally, Table 4 provides an analysis of the overall quality of the weighted matches obtained from the

new procedures. Those units above the match cutoff parameter are identified as matches while the

units below the match cutoff parameter are not identified as matches. There are at least 23 Type II

errors while there are at least 51 Type I errors. This rough balance in these error Types seems a

reasonable one for the purposes for which we are matching the files. Since there are only 142 good or

questionable matches which fall below the match cutoff parameter, it seems that a substantial portion

of the weighted matches identified only by the current weighted match procedures are identified during

the enhanced new administrative match procedures.
Table 4 New Weighted Match Distribution and Quality

Group

Match Quality Above

Cutoff

% Below

Cutoff

%

Good 1,317 12.2 51 0.5

Questionable 173 1.6 91 0.8

Bad 23 0.2 9,098 84.6

Total 10,753

Non-Matches are only counted within the twenty-one designated blocks, and with a match weight greater than or equal to

zero.

FUTURE AREAS OF RESEARCH

The results shown in Tables 1 through 4 are based on the research completed to date. As we are now

aware from this preliminary effort, the matching procedures used here can be improved and there are

more areas of study which may yield further improvement. In addition, there is additional testing

which will be necessary to complete an initial assessment of the quality of the matching process.

1. Since the files which make up the UDB are the product of each of the State Employment Security

Agencies, it is important that the new match procedures be tested on data files from each of the States.

This is the only way to insure that anomalies in any of the State files will not adversely affect the match

results. The short time available for completing each of the quarterly matches and the size of the files

does not allow for a manual review of the quarterly results. This initial review of the matching process

using the final parameter values will provide some measure of the quality of matches obtained. It may

also be advantageous to tailor the match cutoff parameters independently for each State.

2. It is apparent from our initial analysis that additional analysis of the results of the current and new

match procedures is necessary to determine how many good matches are being missed by the new

procedures and how many of these are being identified by the new enhanced administrative match

procedures. Once it is determined how many of these matches are being missed by the new procedures

and their characteristics, the new match procedures must be modified to identify these matches.

 3. Intra-quarter weighted matching procedures should be tested to determine if such a procedure

should be added to the new match procedures and its impact on overall results.

4. Once the new procedures are enacted, an ongoing review of selected States may be recommended to

insure that the match results do not deteriorate over time.

Acknowledgments

The authors would like to acknowledge the contributions of Larry Lie and James Spletzer of Bureau of

Labor Statistics and Catherine Armington of Westat.

Disclaimer

Any opinions expressed in this paper are those of the authors and are not to be considered the policy of

the Bureau of Labor Statistics.