COMMERCIAL PAYROLL SOFTWARE AND THE QUALITY OF EMPLOYMENT DATA

Karen L. Goldenberg, Susan E. Moore, Richard J. Rosen, U.S. Bureau of Labor Statistics

Susan Moore, BLS, Suite 4840, 2 Massachusetts Avenue, N.E., Washington, DC 20212

KEY WORDS: Measurement error; Response

error; Establishment data; Payroll records.

I. Introduction and Background

The Bureau of Labor Statistics (BLS) measures

monthly job fluctuations by tabulating employee

counts supplied to two different statistical programs:

the Covered Employment and Wages Program (ES202), which receives data from State Unemployment

Insurance (UI) systems, and the Current Employment Statistics (CES) Program. Employers submit

employee counts for UI purposes on State Unemployment Insurance Quarterly Contribution Reports

(QCRs), and to CES by means of the BLS monthly

Report on Employment, Payroll, and Hours. Data

for these employee counts frequently come from

reports associated with payroll records. Since the

1980s, growing numbers of payrolls have been produced by standardized software (Werking et al.,

1993), either supplied by commercial payroll processing firms (PPFs) or generated for the commercial

market by payroll software developers (PSDs). The

quality of employment data in both statistical

programs is thus increasingly subject to the way

PPFs and PSDs define and calculate employment.

The importance of the payroll processing industry came to light in June, 1992, following the release

of a much larger-than-usual adjustment from the

1991 sample-based CES data to the universe-based

ES-202 data. Research eventually attributed the

situation to the payroll processing industry's introduction of improved reporting procedures early in

1991 (Werking et al., 1993). In an attempt to better

understand the overall importance and influence of

the industry on the quality of employment data, BLS

launched the PPF Response Analysis Survey (RAS).

II. General Survey Plan

The PPF RAS studied the payroll procedures and

reporting practices used by the payroll processing

industry to prepare data for UI purposes. (Preliminary study showed that PPFs did not usually prepare

data for CES.) RAS results are helping BLS to

understand the extent of the industry and the

limitations of employment data prepared by it.

The PPF RAS was conducted in two distinct

phases. The first phase was a pilot study and consisted of personal visit interviews with the larger

firms in the payroll processing industry. These firms

were known to provide services covering a substantial portion of U.S. employment. Twenty-four firms

were targeted for interview, and 21 of these were

actually interviewed by personal visit.

The next phase of the RAS involved telephone

interviews with the remaining firms in the payroll

processing industry.1 There were two primary goals

for the telephone portion of the RAS.

• To continue the research begun in the pilot

study, ultimately establishing a reporting profile

for the entire industry.

• To determine the magnitude and direction of

response error in the data produced by the

payroll processing industry, and to assess the

impact of this error on employment data

reported to the statistical programs.

The BLS Atlanta Data Collection Center (DCC)

conducted the telephone interviews between June

and September, 1993. Survey procedures included

the use of advance letters, a Computer Assisted

Telephone Interview (CATI) instrument, up to eight

attempts to reach a sample unit, and thank you

letters to respondents for completed interviews.

III. Employment Definitions

The central focus for this study was the reporting

of employment. The BLS definition of employment

is "the number of employees who worked during

or received pay for the pay period including the

12th day of the calendar month." Correct reporting

depends on a number of factors, including conformity to this definition, availability of information,

and correct use of data.

There are three separate components to the BLS

definition of employment: method, timing, and content. If the reported employment count is to meet

BLS definitions for the two programs, all three

components must be handled correctly.

• The method by which the employment counts are

derived can vary widely, and is probably

determined by the content of the payroll system's

standard outputs. The count most consistent with

the BLS definition is an unduplicated count of

individuals working or receiving a check or other

form of payment, which prevents the double

counting of employees receiving more that one

check. However, reporters may use counts of

active employees, employee records, employees

who received checks, or counts of checks issued.

Most of the incorrect reporting methods tend to

overstate employment.

 

1 The RAS was planned as a census of the entire industry. However,

the number of firms in the payroll processing industry was larger than

expected. Therefore, we selected a sample of about 250 firms.
• The time period for which employment is

reported is the second dimension. The pay period

including the 12th of the month is consistent

with the BLS definition. Again, most incorrect

reporting results in an overcount. Typical errors

include reporting a count for the entire month, or

reporting the total quarterly employment each

month. In rare cases, undercounting results from

using an incorrect time reference period, such as

an employment figure that counts the number of

employees on a specific day of the month.

• The content (i.e., who is counted) is the third

component and can introduce another type of

response error. This occurs if the concept of

employment for CES and UI is misunderstood.

Content error also tends to produce overcounts,

because reporters are more likely to include employees who are not covered than to exclude

covered employees. Of the three sources, content

error has the smallest impact on overall counts.

Other variable aspects of employment reporting

relate to how PPFs and payroll software products

present the data to the user, as well as how the

employer uses the data made available by the PPF.

These variables clearly affect the quality of employment counts submitted.

IV. The Payroll Processing Industry

This section describes the payroll processing

industry and its contributions to monthly employment counts. We deal here with two types of businesses. PPFs collect information on hours worked,

pay rates, deductions, etc. from their clients and use

that information to generate paychecks and payroll

reports. PSDs write and sell computer software to

accept information on hours worked, pay rates, and

deductions and to generate paychecks and payroll

reports. Some firms are engaged in both payroll

processing and software development. The industry

also includes payroll tax filing services, businesses

which accept data from clients and use those data to

prepare and submit payroll taxes. The tax filing services do not generate employment counts, and we

excluded them as out of scope.

This report emphasizes the effects of the payroll

processing industry on employment counts compiled

by BLS. The relationship between the payroll industry and those counts is not necessarily a direct one.

Although many PPFs offer services that can provide

a direct input to State employment reports, clients

are not obligated to purchase those services. Payroll

software systems and packages may contain functions that streamline reporting, if the licensee or purchaser chooses to use them. An employer who prepares his or her own UI documents may obtain data

from a payroll report or develop the figures from

another source. Even if the data have been computed

correctly on the payroll report or other source document, the employer must find and extract the figures

that correspond to BLS definitions.

For purposes of the analysis and discussion that

follow, all results are presented as percentages

weighted by probability of selection.2 The resulting

estimates are representative of the entire industry.

When indicated, results for the PPFs may be shown

as percentages weighted by the number of employees

ultimately receiving services from those PPFs. The

weighted data illustrate the effect of these firms on

reported employment counts, and therefore the extent to which measurement error is a problem.

NOTE: Information on the distribution of the

errors was subject to the extent of the knowledge of

the respondents and was difficult to verify. In some

cases, responses were inconsistent with other data

collected. The team edited inconsistent responses if

the data could not be verified.

A. Descriptive Profile of Industry

For purposes of this survey, a firm provides payroll processing services if it collects basic wage and

hour inputs each pay period, issues pay checks or

direct deposits, and/or produces payroll summaries

for clients. A firm supplies payroll software if it develops a software product that generates pay checks

or payroll summaries, and sells, licenses, or leases

that product directly to clients or end users.

While these definitions were helpful in identifying respondents, the pilot test showed that we can

not draw a clear line between PPFs and PSDs. PPFs

can and do sell, lease, or license their software as

well as offer payroll processing services. In addition,

a small number of PSDs have ventured into payroll

processing as a business activity. These fuzzy boundaries between groups are important because the influence of the industry goes beyond that of its direct

clients. The extent of that influence depends on the

purchasers or licensees of the software and their size,

information which is beyond the scope of this study.

Using the above definitions, we obtained data on

216 firms.3 Of these, 44 provide only payroll processing services, 146 develop software and sell,

lease, or license it, and 26 do both.

The questionnaire contained parallel sets of questions for PPFs and PSDs. When a firm supplied both

types of services, we asked whether the company's

primary business was payroll processing or software

development. Two respondents said both areas were

equal in importance, while the rest placed themselves into one category or the other. Based on these

responses, and to avoid confusion in looking at the

 

2 While PPFs were selected into the sample with certainty, some

firms identified as PSDs were reclassified as PPFs based on their

responses to a question on their primary activity.

3 Actual interview counts are 19 from the pilot test, 7 from the

pretest, and 190 from the DCC. We excluded 2 pilot study

interviews with payroll tax filing services since they were out of

scope.
effect of the industry on employment, we designated

all companies as being either PPFs or PSDs, depending on their primary activity. For the companies that

said that they were equally payroll processors and

software developers, we separated PPF clients and

covered employment from that of software users to

avoid double counting the impact of these firms.

However, we tabulated them in both places. As a

result, the analysis is based on 218 companies, and

describes the entire industry.4

Following these actions, the distribution by primary activity is 50 payroll processing firms and 168

payroll software developers.

1. PPF Coverage

Coverage is a measure of the extent to which the

payroll processing industry influences overall employment counts compiled by BLS. For payroll processing firms, we looked at two different indicators

of coverage: the number of client firms to which a

PPF provides services, and the number of employees

in those client companies. In addition, for both PPFs

and PSDs, we looked at the concentration of clients

in specific industries.

Number of PPF client firms. The RAS asked

participating PPFs for the approximate number of

client firms they supplied with payroll processing

services. The majority of payroll processors each

handle a small number of clients. Just under 19 percent of PPFs have fewer than 25 clients, and 52

percent have 250 or fewer clients. Seventeen percent

report more than 2,500 clients, while only a very few

firms (3 percent) handle more than 22,000.

Number of employees in PPF client firms. We

estimated that approximately 24 million employees

are covered by PPFs. Table 1 shows the distribution

of this employment. Nearly 38 percent of PPFs provide payroll services covering no more than 5,000

employees, while another 45 percent of these processing companies cover from 5,000 to 100,000

employees. Only 17 percent of PPFs generate payrolls for more than 100,000 employees.

2. PSD Coverage

Coverage of payroll software was more difficult

to determine. Most of the PSDs we visited leased or

licensed their software, and so had information about

their users. The PSDs we telephoned tended to sell

their software, often at retail, and did not have this

information.

The questionnaire for the telephone phase of the

study asked about the number of copies of a product

in use instead of the number of employees covered.

In many cases, respondents were able to report only

the total number of copies that had ever been sold.

 

4 Several firms that provide both types of services do so in extremely

unequal numbers. For example, one of the largest software firms had

recently started a payroll processing business using its own software.

The impact of this firm is generally through the sale of its software,

not through the clients for which it prepares paychecks.

We used this figure to arrive at an estimated 25 to 40

million employees covered by PSD products.5

Table 1. PPFs by Client Firm Employment

Approximate number of

employees covered

Percent of

PPFs

1,000 or fewer 18

1,001 to 5,000 20

5,001 to 10,000 14

10,001 to 50,000 17

50,001 to 100,000 14

100,001 to 300,000 12

More than 300,000 5

Total 100

3. Length of pay period

The length of an employer's pay period is important for employment counts because those counts are

supposed to refer to the pay period including the

12th of the month. However, that pay period can be

anywhere from 7 to 31 days long, as most employers

pay their employees weekly, biweekly, semi-monthly,

or monthly. In addition, in some companies, different groups of employees are paid on different time

schedules. One important question is whether the

software permits the client or user to associate payrolls and numbers of employees with specific pay

period dates. Table 2 shows how PPFs and PSDs

maintain the length of pay period information.

Table 2. Length of Pay Period in Payroll Software

Length of pay period indicator Percent

PPFs

Percent

PSDs

Start or end dates 15 18

Code for length of time period 56 42

Date and length of time period 27 26

Other (includes up to client) 2 14

The fact that fewer than half of these payroll

systems only have a length of pay period indicator

rather than specific dates is of some concern to BLS.

Although employers submit QCRs only once a quarter, the employment count is based on a single pay

period each month. If the software does not identify

start or end dates for the pay period, the proper reference period becomes more difficult to determine.6

4. Data Availability for Reporting Employment

QCR. The most important question is whether

the software can supply the PPF, the client, or the

system user with the employment counts needed to

 

5 The estimate ranges are based on an average of 10 employees and

20 employees per user company. The average size of a firm in the UI

program is 20 employees.

6 One possibility we did not explore is that the software may contain

no length of pay period designator. A microcomputer payroll package purchased for use by one company doesn't really need one, since

the user generates a payroll each pay period and knows exactly how

long the pay period is.
prepare State and BLS forms. For the QCR, the

answer is encouraging. Ninety-one percent of

respondents said that their software produces the

specific employment counts needed for the QCR,

with no difference between PPFs and PSDs.7 The

percentage rose to 98 for PPFs and to 94 for PSDs

when we asked if the system produced any reports

that contained employee counts.

CES. During the interview we gave respondents

the CES definition of employment and asked if they

produced a specific count that conforms to it. If so,

we asked PPF respondents if that count was made

available each pay period, which it must be if

respondents are to use the information for the CES.

Roughly 42 percent of the respondents said they

produced the CES count, with no difference between

PPFs and PSDs. Approximately 68 percent of the

PPFs who produce the correct count also do so each

pay period. If the count is available from a software

product, the user can generate it each pay period.

5. Services Supporting Employment Counts

PPFs provide their clients with two important

types of services that facilitate the submission of

employment counts on State employment reports.

These include filing a client's payroll taxes directly

with the State, and preparing forms that contain

employment counts. Payroll software products from

PSDs may also produce government forms, but they

do not offer direct tax filing services.

Filing a client's payroll taxes directly with the

State. Employers make quarterly submissions of

State payroll taxes using the State's QCR. Some

PPFs use an employer's payroll to prepare the QCR

and then submit the form and associated taxes directly to the State, assuming legal responsibility for the

UI taxes. Direct filing of payroll taxes works well if

the PPF includes employment counts on the QCRs

and uses the correct definitions for these employment

counts. If not, the effect of the resulting errors is a

function of the size of the PPF's client base and the

number of employees who work for those clients.

Sixty-two percent of PPFs file payroll taxes for at

least some of their clients. The number of clients for

whom they pay taxes ranges from 1 to greater than

100,000, with a median of around 100. This represents slightly more than half of the total number of

client firms reported in this study. Of the PPFs that

file payroll taxes, 15 percent do so for 90 percent or

more of their clients. At the other end of the scale,

around 20 percent of the PPFs file taxes for a small

portion of their clients (10 percent or fewer). On

average (median), PPFs that file payroll taxes do so

for about 40 percent of their client firms.

 

7 On the other hand, there appears to be a difference between what

respondents think their clients/users need for the QCR and what BLS

requires for the same reports. See Reporting Picture/Error Profile,

below.

Preparing Forms that Contain Employment

Counts. Whether or not a PPF files its clients' payroll

taxes, many PPF and commercial software products

have the capability to generate the QCR and the CES

form. The data show striking differences between

PPFs and software packages, especially for the QCR.

While 86 percent of PPFs prepare QCRs for their

clients, only 61 percent of PSDs offer the capability

in their payroll packages. Additionally, software

products are nearly twice as likely as PPFs to

produce the CES forms, although the percentage in

both cases is much lower than that for the QCR.

As pointed out earlier, making a service or capability available does not ensure that clients use it. If

a PPF prepares the QCR, we asked whether they did

so for all clients, or if some clients completed the

form themselves. (This is a moot point for PSDs,

because the user decides whether or not to have the

software generate the form.) Of the PPFs that file

the QCR for some clients, nearly half (49 percent) do

so for all their clients. Small PPFs were somewhat

more likely than large PPFs to complete the QCR for

all their clients (51 versus 39 percent).

B. Reporting Picture/Error Profile

1. Analysis of Measurement Errors

The consequences of measurement error in the

three aspects of employment reporting (method,

timing, and content) vary widely in severity. Seemingly incorrect reporting merely provides the potential for varying degrees of error in employment data.

For example, if a PPF uses a "check count" method

and employees always receive all wages (bonuses,

overtime, etc.) on one check, the count is correct.

The "worst" errors in record-keeping practices contain the most potential for measurement error. The

actual extent of the error depends upon many factors,

including individual record-keeping practices, client

need for the data, State tax laws, Federal tax laws,

and knowledge of the BLS definitions.

The questionnaire contained items asking about

each of the three components of employment counts

(method, timing, and content). The "method" question asked how employees were counted and offered

these options: number of active employees, number

of employees in personnel data file, count of time

cards, count of checks, unduplicated count of individuals receiving checks, or some other method. The

timing question determined the time period the

employment count represented, e.g., current pay

period, a specific day during the month, cumulative

for the quarter, and so forth. The content question

looked specifically at whether groups of employees

were included or excluded from the employment

count. If the respondent could not answer the

question, the interviewer recorded "don't know,"

noting cases where the respondent volunteered that

the decision was "up to the client."

The results for each of the components were

analyzed separately for the PPFs and the PSDs to see
the impact each group has on employment counts.

For the PPFs, the predominant finding is that 95

percent of the employment reported is determined

with the correct method for counting employees. In

addition, both the correct method and timing are

used for 64 percent of the employment reported by

PPFs. Results for the PSDs suggest that 37 percent

of the firms use the correct method. As explained,

the distribution of the correct method could be much

higher since actual error is a function of the structure

and content of client record-keeping systems. For

PSDs, 9 percent utilize the correct time reference

period. Keeping in mind the built-in flexibility of

commercial payroll software, a combined 60 percent

use either the current pay period, or leave the choice

up to the client. These two responses could very well

reflect the correct timing for the employment count,

if used correctly by the client.

2. Payroll Processing Firms

Method. The correct method for the employment

count is an unduplicated count of employees

receiving pay. Of the PPFs responding to the question, 47 percent use this method. Results for all

methods are given in Table 3. While the other

methods do not adhere to the exact BLS definition,

they should be fairly consistent over time. These

methods might overstate actual employment, lose

seasonality effects, or even be correct if they result in

an unduplicated count of employees.

Nearly 95 percent of employment reported by

PPFs is reported using the correct method. This

suggests that the larger PPFs count employees in a

way that is consistent with the BLS definition.

Table 3. PPF Employment Count Method

Method

Percent

of PPFs

Percent of

Employment

Reported by

PPFs

Undup Count Employees 47 95

Active Employees 12 0

Number of Checks 19 2

Nr. Employee Records 8 1

Number of Time Cards 2 0

Don't Know 9 1

Other 3 1

Total 100 100

Timing. The correct timing for the "all employment" count is the pay period including the 12th of

the month. Thirty-one percent of the PPFs used this

reference period. Results for all timing options

appear in Table 4. As noted previously, the fact that

some firms' responses do not adhere to the BLS definition does not necessarily mean that those firms

produce incorrect counts. The current pay period

will produce correct counts if the report is completed

for the pay period of the 12th. This count, along

with the count for one day during the month and the

end of the quarter count, should produce fairly small

errors which are consistent over time. The two

cumulative counts, cumulative for month and any

time in quarter/cumulative for quarter, would overstate actual employment to some degree.

Two-thirds of employment reported by PPFs (66

percent) is reported using the pay period of the 12th.

Including the 1 percent that use the current pay

period brings the total to 67 percent.

Table 4. PPF Employment Count Timing

Timing

Percent

of

PPFs

Percent of

Employment

Reported by

PPFs

Pay Period Including the 12th 31 66

Current Pay Period 27 1

As of Any Day in the Month 9 1

Cumulative for the Month 5 0

Any Time During the Quarter 2 0

End of the Quarter 9 1

Unknown a 14 31

Other 3 0

Total 100 100

a Includes timing up to the client

Content. The content component of the BLS

employment definition is the least likely to affect the

employment count. Overall, 24 percent of the PPFs

perform all eight aspects of the content component

correctly. If the responses "don't know" (DK) and

"up to the client" (UC) are considered as potentially

correct, an additional 24 percent of the PPFs include

and exclude all categories of employees correctly.

Approximately 54 percent of employment covered by PPFs is reported using all content groups

correctly. If the responses DK and UC are recognized as potentially correct, an additional 10 percent

of employment is reported using the correct content.

To measure the content of employee counts, the

questionnaire listed eight separate groups or situations. Respondents indicated whether the employment counts they produced included or excluded

employees in each group or situation.

3. Payroll Software Developers

Method. Of the PSDs responding to this question, 37 percent based the count on an unduplicated

count of employees receiving checks. Results for all

methods appear in Table 5. While the other methods

do not adhere to our exact definition, they should be

fairly consistent over time. Again, these methods

might overstate actual employment, lose seasonality

effects or even be correct if they actually result in an

unduplicated count of employees.

Timing. Results for all timing options appear in

Table 6. An estimated 9 percent of PSDs use the pay

period including the 12th of the month as the time
Table 5. PSD Employment Count Method

Method Percent of

PSDs

Unduplicated Count of Employees 37

Active Employees 28

Number of Checks 6

Number of Employee Records 18

Number of Time Cards 1

Don't Know 0

Other 10

Total 100

reference period for counting employees. A larger

percentage of PSDs (26 percent) use the current pay

period as their timing option. This is correct if the

report is prepared for the pay period including the

12th of the month. As stated earlier, this count,

along with the one day during the month and the end

of the quarter count, should produce fairly small

errors which are consistent over time.

Another large group of PSDs (31 percent) leaves

the timing aspect up to the client, which may also

lead to correct results if the client knows what pay

period to choose. All three cumulative counts tend

to overstate actual employment.

Table 6. PSD Employment Count Timing

Timing Percent of

PSDs

Pay Period Including the 12th 9

Current Pay Period 26

As of Any Day in the Month 2

Cumulative for the Month 6

Any Time During the Quarter 3

End of the Quarter 1

Cumulative for the Year 1

Up to the Client 31

Don't Know 14

Other 7

Total 100

Content. An estimated 26 percent of PSDs handle

each of the eight content issues correctly. If all

responses of "don't know" (DK) and "up to the

client" (UC) are interpreted as being correct, an

additional 25 percent of the PSDs may count the

appropriate categories of employees.

One big difference between PPFs and PSDs is the

number of content items that PSDs leave to the user

to implement, or about which respondents had no

information. Only two PPF content items were left

up to the client (or the respondent didn't know how

an item was treated) more than 10 percent of the

time. This contrasts with PSDs, where the smallest

percentage of firms leaving the decision to include or

exclude a group up to the user was 15 percent,

and the comparable portion for four different

employee groups was about a third of PSDs.

V. Discussion

BLS statistical programs that depend on employment counts increasingly obtain information produced by or in conjunction with standardized payroll

software. The software is produced by two components of the payroll processing industry, PPFs and

PSDs. Deviations from BLS employment count definitions built into in this software can have a noticeable effect on reported employment data.

BLS conducted a RAS of firms in the payroll processing industry to assess the industry's employment

coverage and to evaluate measurement error in data

produced by the industry. Survey results show that,

overall, PPFs and PSDs prepare employment counts

consistent with the BLS definition of employment.

In many cases, the correct employees will be counted

regardless of deviations from those definitions.

Also, content "errors" may reflect extremely rare situations (e.g., strikes) or a very small percentage of

employees (e.g., on leave without pay), and are often

subject to interpretation by the client or software

user.

Where measurement error does exist, the effect of

it depends greatly on the number of employees

covered by the PPF or the software product. Based

on the percent of employment covered by PPFs, 95

percent of employment counts are derived using the

correct method, 66 percent are based on the correct

timing, and 54 percent use all 8 content items properly. Comparable percentages of employment for

software firm products are not available, in part

because of user-specified options in the software.

In sum, the payroll processing industry has the

potential to introduce measurement error into BLS

employment count data. These errors are relatively

minimal for clients of payroll processing firms, and

may be somewhat greater for employers who

purchase commercial payroll software products to

generate their payrolls. In both cases, improvements

and corrections are possible. The BLS has established direct contact with members of the industry

that provide incorrect employment counts and will

encourage these firms to modify their reporting

practices in accordance with BLS definitions.

REFERENCE

Werking, George S., Patricia M. Getz, and Richard

L. Clayton. 1993. "New Approaches to Controlling

Response Error in Establishment Universe and

Sample Data." Presented at the Annual Meeting of

the American Statistical Association, August 1993.