Tax Administration
IRS Can Improve Its Productivity Measures by Using Alternative Methods Gao ID: GAO-05-671 July 11, 2005In the past, the Internal Revenue Service (IRS) has experienced declines in enforcement productivity as measured by cases closed per Full Time Equivalent. Increasing enforcement productivity through a variety of enforcement improvement projects is one strategy being pursued by IRS. Evaluating the benefits of different projects requires good measures of productivity. In addition, IRS's ability to correctly measure its productivity has important budget implications. GAO was asked to illustrate available methods to better measure productivity at IRS. Specifically, our objectives were to (1) describe challenges that IRS faces when measuring productivity, (2) describe alternative methods that IRS can use to improve its productivity measures, and (3) assess the feasibility of using these alternative methods by illustrating their use with existing IRS data.
Measuring IRS's productivity, the efficiency with which inputs are used to produce outputs, is challenging. IRS's output could be measured in terms of impact on taxpayers or the activities it performs. IRS's impacts on taxpayers, such as compliance and perceptions of fairness, are intangible and costly to measure. IRS's activities, such as exams or audits conducted, are easier to count but must be adjusted for complexity and quality. An increase in exams closed per employee would not indicate an increase in productivity if IRS had shifted to less complex exams or if quality declined. IRS can improve its productivity measures by using a variety of methods for calculating productivity that adjust for complexity and quality. These methods range from ratios using a single output and input to methods that combine multiple outputs and inputs into composite indexes. Which method is appropriate depends on the purpose for which the productivity measure is being calculated. For example, a single ratio may be useful for examining the productivity of a single simple activity, while composite indexes can be used to measure the productivity of resources across an entire organization, where many different activities are being performed. Two examples show that existing data, even though they have limitations, can be used to produce a more complete picture of productivity. For individual exams, composite indexes controlling for exam complexity show a larger productivity decline than the single ratio method. On the other hand, for exams performed in the Large and Mid-Size Business (LSMB) division, the single ratio understates the productivity increase shown, after again controlling for complexity. By using alternative methods for measuring productivity, managers would be better able to isolate sources of productivity change and manage resources more effectively. More complete productivity measures would provide better information about IRS effectiveness, budget needs, and efforts to improve efficiency.
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Report to the Chairman and Ranking Minority Member, Committee on
Finance, U.S. Senate:
July 2005:
Tax Administration:
IRS Can Improve Its Productivity Measures by Using Alternative Methods:
[Hyperlink, http://www.gao.gov/cgi-bin/getrpt?GAO-05-671]:
GAO Highlights:
Highlights of GAO-05-671, a report to the Committee on Finance, U.S.
Senate:
Why GAO Did This Study:
In the past, the Internal Revenue Service (IRS) has experienced
declines in enforcement productivity as measured by cases closed per
Full Time Equivalent. Increasing enforcement productivity through a
variety of enforcement improvement projects is one strategy being
pursued by IRS. Evaluating the benefits of different projects requires
good measures of productivity. In addition, IRS‘s ability to correctly
measure its productivity has important budget implications.
GAO was asked to illustrate available methods to better measure
productivity at IRS. Specifically, our objectives were to (1) describe
challenges that IRS faces when measuring productivity, (2) describe
alternative methods that IRS can use to improve its productivity
measures, and (3) assess the feasibility of using these alternative
methods by illustrating their use with existing IRS data.
What GAO Found:
Measuring IRS‘s productivity, the efficiency with which inputs are used
to produce outputs, is challenging. IRS‘s output could be measured in
terms of impact on taxpayers or the activities it performs. IRS‘s
impacts on taxpayers, such as compliance and perceptions of fairness,
are intangible and costly to measure. IRS‘s activities, such as exams
or audits conducted, are easier to count but must be adjusted for
complexity and quality. An increase in exams closed per employee would
not indicate an increase in productivity if IRS had shifted to less
complex exams or if quality declined.
IRS can improve its productivity measures by using a variety of methods
for calculating productivity that adjust for complexity and quality.
These methods range from ratios using a single output and input to
methods that combine multiple outputs and inputs into composite
indexes. Which method is appropriate depends on the purpose for which
the productivity measure is being calculated. For example, a single
ratio may be useful for examining the productivity of a single simple
activity, while composite indexes can be used to measure the
productivity of resources across an entire organization, where many
different activities are being performed.
Two examples show that existing data, even though they have
limitations, can be used to produce a more complete picture of
productivity. For individual exams, composite indexes controlling for
exam complexity show a larger productivity decline than the single
ratio method. On the other hand, for exams performed in the Large and
Mid-Size Business (LSMB) division, the single ratio understates the
productivity increase shown, after again controlling for complexity. By
using alternative methods for measuring productivity, managers would be
better able to isolate sources of productivity change and manage
resources more effectively. More complete productivity measures would
provide better information about IRS effectiveness, budget needs, and
efforts to improve efficiency.
Illustrations of Exam Productivity Indexes before and after Controlling
for Complexity:
[See PDF for image]
Source: GAO analysis of IRS data.
[End of figure]
What GAO Recommends:
GAO recommends that the Commissioner of Internal Revenue put in place a
plan for introducing wider use of alternative methods of measuring
productivity, such as those illustrated in this report, taking account
of the costs of implementing the new methods. The Commissioner of
Internal Revenue agreed with our recommendation and assigned
responsibility for considering alternative methods of measuring
productivity.
www.gao.gov/cgi-bin/getrpt?GAO-05-671.
To view the full product, including the scope and methodology, click on
the link above. For more information, contact James White at (202) 512-
9110 or whitej@gao.gov.
[End of section]
Contents:
Letter:
Results in Brief:
Background:
Measuring Productivity at IRS Is Challenging because Measuring the
Output of Services Is Difficult:
However Output Is Measured, IRS Can Improve Its Current Productivity
Measures by Using Alternative Methods:
Illustrations of Alternative Methods of Measuring Productivity:
Conclusion:
Recommendations for Executive Action:
Agency Comments and Our Evaluation:
Appendixes:
Appendix I: Methods for Calculating Productivity Indexes:
Productivity Indexes:
Estimation of Distance Functions:
Table:
Table 1: Summary of Output Measures:
Figures:
Figure 1: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for All Individual Returns:
Figure 2: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for Individual Returns (without EIC):
Figure 3: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for LMSB Exams:
Figure 4: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for LMSB Exams (Excluding Individual and
Corporate Exams):
Letter July 11, 2005:
The Honorable Charles E. Grassley:
Chairman:
The Honorable Max Baucus:
Ranking Minority Member:
Committee on Finance:
United States Senate:
In the past, we have reported on declines in the Internal Revenue
Service's (IRS) enforcement programs, including declining exam and
collection efforts.[Footnote 1] One factor we have cited as
contributing to these declines is decreased enforcement productivity as
measured by cases closed per staff time.[Footnote 2] Increasing
enforcement productivity through a variety of enforcement improvement
projects is one strategy being pursued by IRS that could help reverse
the declines. However, evaluating the benefits of these different
projects requires good measures of productivity. IRS's ability to
correctly measure its productivity has important budget implications.
Productivity declines may indicate that IRS is not using its resources
as efficiently as possible. Increasing the productivity of existing
resources might lessen, to some ex^tent, the need for budget increases.
Productivity is measured as a ratio of outputs to inputs. In a January
2004 report on IRS's enforcement improvement projects,[Footnote 3] we
recommended that IRS invest in enforcement productivity data that
better adjust for complexity and quality, taking into consideration the
costs and benefits of doing so. More complete productivity data--data
that adjust for complexity and quality--would give managers a clearer
picture of how effectively resources are being used. In addition,
Congress would have better information about IRS's performance and
budget needs. To better understand productivity measurement at IRS, you
asked us to illustrate methods available to better measure it.
Specifically, our objectives were to (1) describe challenges that IRS
faces when measuring productivity, (2) describe alternative methods
that IRS can use to improve its productivity measures, and (3) assess
the feasibility of using these alternative methods by illustrating
their use with existing IRS data.
In the contex^t of the productivity literature, output is a general
concept representing what is produced. However, in the performance
measurement literature, the term "output," as defined in the Government
Performance and Results Act of 1993 (GPRA)[Footnote 4] is limited to an
activity or effort, while an outcome is the result of a program
activity. Activities are typically easily measured, such as
transactions completed. Results such as the difference an activity
makes in the economy or people's lives are usually less tangible. In
this report, we use the general concept of "output" to define
productivity but then distinguish between outputs that are results and
those that are activities.
To describe the challenges IRS faces when measuring productivity and
alternative methods IRS can use to improve its productivity measures,
we reviewed the literature on the methods used to measure productivity
in the public and private sectors. We also consulted IRS officials and
reviewed IRS documentation on IRS's methods for measuring productivity.
To assess the feasibility of using these alternative methods by
illustrating their use with existing IRS data, we used currently
available IRS data to calculate alternative exam, or audit,
productivity measures. These methods included calculating unweighted
productivity indexes and weighted productivity indexes. We compared
these indexes to show how implementing different methods can provide
IRS with better measures of productivity and better ways to identify
the causes of productivity change.
For this report existing IRS examination data were used to illustrate
the feasibility of using alternative methods of productivity. The data
are from IRS's Tax Compliance Report and Automated Inventory Management
System.[Footnote 5] In prior reports we recognized that IRS's existing
examination data have limitations. For example, direct measures of
complexity were not available. We use type of exam as a proxy for
complexity. We have also recommended that IRS improve its input data by
implementing a cost accounting system. While there are reliability
issues related to the data, we are using the available IRS data for
illustrative purposes, and we will not be representing these
illustrations as complete measures of IRS productivity. Therefore, we
determined that the information contained in IRS's Tax Compliance
Report and Automated Inventory Management System databases were
sufficiently reliable for illustrative purposes.
We initiated our review in September 2003 but conducted most of our
review from August 2004 through April 2005 in accordance with generally
accepted government auditing standards.
Results in Brief:
Because IRS provides services, such as providing information to
taxpayers and enforcing the tax laws, that are intangible and complex,
measuring output--and therefore productivity--is challenging.
Productivity is the efficiency with which inputs are used to produce
outputs. IRS can use its activities or the results of its activities or
services as measures of output. IRS's results are the impacts on the
condition or behavior of taxpayers, such as compliance and compliance
burden. IRS's activities are what IRS does to achieve those results,
such as phone calls answered and exams conducted. Generally,
information about results is preferred, but measuring such results is
often difficult. Activities may be used instead to provide information
about internal efficiency--how effectively IRS is using resources to
perform a specific function--or as proxies for ultimate results to
which the activities are closely related.
IRS can improve its productivity measures by using alternative methods
for calculating productivity that adjust for complexity and intangibles
such as quality. The methods range from computing ratios of single
outputs to inputs--exams closed per Full Time Equivalent (FTE)--to
using statistical methods to combine multiple indicators of outputs and
inputs. Which method is appropriate depends on the purpose for which
the productivity measure is being calculated. For example, a single
ratio may be useful for examining the productivity of a single simple
activity, while composite indexes can be used to measure the
productivity of resources across an entire organization, where many
different activities are being performed.
Existing IRS data can be used to illustrate alternative exam
productivity measures that adjust for complexity and quality. For
example, the single ratio index, unadjusted for complexity or quality,
shows a decline in individual exam productivity (as measured by exams
closed per FTE) of 32 percent from 1997 to 2001. A composite index,
controlling for complexity, shows a larger decrease of 53 percent. The
composite measure shows a greater decline because it accounts for IRS's
shift to less complex Earned Income Credit (EIC) exams. On the other
hand, for examinations conducted by IRS's Large and Mid-Sized Business
(LMSB) division from 2002 to 2004, a single ratio index understates
productivity improvements. The single ratio index shows a productivity
gain of 4 percent. After adjusting for changes in the complexity of
exams over those years, productivity increased by 16 percent.
Consistent with our 2004 report on IRS's enforcement improvement
projects, IRS officials said they generally use single ratios as
measures of productivity. More complete productivity measures would
provide better information about the effectiveness of IRS resources,
IRS's budget needs, and IRS's efforts to improve efficiency.
We are making a recommendation to investigate the use of alternative
methods of measuring productivity.
Background:
Productivity is defined as the efficiency with which inputs are used to
produce outputs. It is measured as the ratio of outputs to inputs.
Productivity and cost are inversely related--as productivity increases,
average costs decrease. Consequently, information about productivity
can inform budget debates as a factor that explains the level or
changes in the cost of carrying out different types of activities.
Improvements in productivity either allow more of an activity to be
carried out at the same cost or the same level of activity to be
carried out at a lower cost.
IRS currently relies on output-to-input ratios such as cases closed per
FTE to measure productivity and productivity indexes. A productivity
change is measured as an index which compares productivity in a given
year to productivity in a base year. Measuring productivity trends
requires choosing both output and input measures, and the methods for
calculating productivity indexes.
In the past we have reported on declining enforcement trends, finding
in 2002 that there were large and pervasive declines in six of eight
major compliance and collection programs we reviewed. In addition to
reporting these declines, we reported on the large and growing gap
between collection workload and collection work completed and the
resultant increase in the number of cases where IRS has deferred
collection action on delinquent accounts.[Footnote 6] In 2003, we
reported on the declining percentage of individual income tax returns
that IRS was able to examine or audit each year, with this rate falling
from 0.92 percent to 0.57 percent between 1993 and 2002.[Footnote 7]
Since 2000, the audit rate has increased slightly but not returned to
previous levels. IRS conducts two types of audits: field exams that
involve complex tax issues and usually face-to-face contact with the
taxpayer, and, correspondence exams that cover simpler issues and are
done through the mail. We also reported on enforcement productivity
measured by cases closed per FTE employee, finding that IRS's telephone
and field collection productivity declined by about 25 percent from
1996 through 2001 and productivity in IRS's three exam programs--
individual, corporate, and other audit--declined by 31 to 48
percent.[Footnote 8]
In January 2004 we reported on the ex^tent to which IRS's Small
Business and Self-Employed (SB/SE) division followed steps consistent
with both GAO guidance and the experience of private sector and
government organizations when planning its enforcement process
improvement projects. We reported on how the use of a framework would
increase the likelihood that projects target the right processes for
improvement and lead to the most fruitful improvements. In that report,
we also reported that more complete productivity data--input and output
measures adjusted for the complexity and quality of cases worked--would
give SB/SE managers a more informed basis for decisions on how to
identify processes that need improvement, improve processes, and assess
the success of process improvement efforts. This report elaborates on
that recommendation, providing more information about the challenges of
obtaining complete productivity data.
Improving productivity by changing processes is a strategy SB/SE is
using to address these declining trends. However, the data available to
SB/SE managers to assess the productivity of their enforcement
activities, identify processes that need improvement, and assess the
success of their process improvement efforts are only partially
adjusted for complexity and quality of cases worked. This problem of
adjusting for quality and complexity is not unique to SB/SE process
improvement projects--the data available to process improvement project
managers are the same data used throughout SB/SE to measure
productivity and otherwise manage enforcement operations.
Measuring Productivity at IRS Is Challenging because Measuring the
Output of Services Is Difficult:
Because IRS provides services, such as providing information to
taxpayers and enforcing the tax laws, that are intangible and complex,
measuring output--and therefore productivity--is challenging. Like
other providers of intangible and complex services, IRS has a choice of
measuring activities or the results of its services. Generally,
information about results is preferred, but measuring results is often
difficult. In the absence of direct measures of results, activities
that are closely related to the results of the service can be used as
proxies.
Measuring productivity in services is difficult. Unlike manufacturing,
which lends itself to objective measurement because output can be
measured in terms of units produced, services, which involve changes in
the condition of people receiving the service, often have intangible
characteristics. Thus, the output of an assembly line is easier to
measure than the output of a teacher, doctor, or lawyer. Services may
also be complex bundles of individual services, making it difficult to
specify the different elements of the service. For example, financial
services provide a range of individual services, such as financial
advice, accounts management and processing, and facilitating financial
transactions.
IRS provides a service. IRS's mission, to help taxpayers understand and
meet their tax responsibilities and to apply the tax law with integrity
and fairness, requires IRS to provide a variety of services ranging
from collecting taxes to taxpayer education. IRS, like other service
providers, could measure output in terms of its results--its impact on
taxpayers--or in terms of activities. The results of IRS's service are
the impacts on the condition or behavior of taxpayers. These taxpayer
conditions or behaviors include their compliance with the tax laws,
their compliance burden (the time and money cost of complying with tax
laws), and their perception of how fairly taxpayers are treated. IRS's
activities are what IRS does to achieve those results. These activities
include phone calls answered, notices sent to taxpayers, and exams
conducted.
Generally, information about results is preferred, but measuring such
results is often difficult. In the case of the public sector, this
preference is reflected in GPRA, which requires that federal agencies
measure performance, whenever possible, in terms of results or outcomes
for people receiving the agencies' services. However, results such as
compliance and fairness have intangible characteristics that are
difficult to measure. In addition, results are produced in complicated
and interrelated ways. For example, a transaction or activity may
affect a number of results: IRS's exams may affect taxpayers'
compliance, compliance burden, and perceptions of the fairness of the
tax system. In addition, a result may be influenced by a number of
transactions or activities: A taxpayer's compliance may be influenced
by all IRS exams (through their effect on the probability of an exam)
as well as by other IRS activities such as taxpayer assistance
services.
IRS's activities are easier to measure than results but still present
challenges. Activities are easier to measure because they are often
service transactions such as exams, levies issued, or calls answered
that can be easily counted. However, unlike measures of results, more
informative measurement of activities requires that they be adjusted
for quality and complexity, as we noted in our report on IRS's
enforcement and improvement projects.[Footnote 9] A productivity
measure based on activities such as cases closed per FTE may be
misleading if such adjustments are not made. For example, an increase
in exam cases closed per FTE would not indicate an increase in true
productivity if the increase occurred because FTEs were shifted to less
complex cases or the examiner allowed the quality of the case review to
decline to close cases more quickly.
Activities-based productivity measures can provide IRS with useful
information on the efficiency of IRS operations. Measuring output, and
therefore productivity, in terms of activities provides IRS with
measures of how efficiently it is using resources to perform specific
functions or transactions. However, activities do not constitute--and
should not be mistaken for--measures of IRS's productivity in terms of
ultimate results.
While the productivity measures we have examined are based on
activities, IRS has efforts under way to measure results such as
compliance and compliance burden. Recently, we reported on IRS's
National Research Program (NRP) to measure voluntary compliance and
efforts to measure compliance burden.[Footnote 10] As we mentioned
previously, measuring these results is difficult. For some results,
such as compliance, measurement is also costly and intrusive because
taxpayers must be contacted and questioned in detail. Despite these
difficulties, IRS can improve its productivity measurement by
continuing its efforts to get measures of results. These efforts would
give Congress and the general public a better idea of what is being
achieved by the resources invested in IRS.
In the absence of direct measures of results, activities that are
closely related to the results of the service are used as proxies. The
value of these proxies depends on the ex^tent to which they are
correlated with results. By carefully choosing these measures, IRS may
gain some information about the effect of its activities on ultimate
results. Because activities may affect a number of results and a single
result may be affected by a number of activities, a single activity
likely will not be a sufficient proxy for the results of the service.
Therefore, a variety of activities would likely be necessary as proxies
for the results of the service.
Both types of output measures, those that reflect the results of IRS's
service and those that use activities to measure internal efficiency,
should be accurate and consistent over time. In addition, both output
measures should be reliably linked to inputs. Linking the results of
IRS's service to inputs may be difficult because of outside factors
that may also affect measured results. For example, an increase in
compliance could result both from IRS actions such as exams and from
changes in tax laws. Another challenge is that IRS currently has
difficulties linking inputs to activities, as we note in a previous
report, where we reported IRS's lack of a cost accounting system. In
particular, IRS only recently implemented a cost accounting system, and
has not yet determined the full range of its cost information needs.
Table 1 summarizes some of the key differences between activities and
results measures. Table 1 also indicates some general criteria that
apply to both types of measures.
Table 1: Summary of Output Measures:
Type of measure: Activities;
Purpose:
* Measure internal efficiency;
* Serve as a proxy for results;
Criteria: Activities measures should;
* reflect the work performed;
* adjust for quality and complexity;
* be accurate and consistent over time and reliably linked to inputs.
Type of measure: Results;
Purpose:
* Measure impact on taxpayers;
Criteria: Results measures should;
* reflect the effects of the service;
* be accurate and consistent over time and reliably linked to inputs.
Source: GAO analysis.
[End of table]
Because inputs are more easily measured and identifiable than outputs,
measuring them is more straightforward. IRS, as a government agency,
may be able more often to use labor costs or hours as a single input in
its productivity measures because it relies heavily on labor. However,
it may be particularly important for IRS to use a multifactor measure
that includes capital along with labor during periods of modernization
that involve increased or high levels of capital investment. As with
outputs, inputs should be measured accurately and consistently over
time. Measuring inputs consistently over time may require adjusting for
changes in the quality of the labor, which has been done using proxies
such as education level or years of experience. Also, as mentioned
previously, inputs should be reliably linked to outputs.
However Output Is Measured, IRS Can Improve Its Current Productivity
Measures by Using Alternative Methods:
The appropriate method for calculating productivity depends on the
purpose for which the productivity measure is being calculated. The
alternative methods that can be used for calculating productivity range
from computing single ratios--exams closed per FTE--to using complex
statistical methods to form composite indexes that combine multiple
indicators of outputs and inputs. While single ratios may be adequate
for certain purposes, the composite indexes based on statistical
methods may be more useful because they provide information about the
sources of productivity change.[Footnote 11]
Comparing the ratios of outputs to inputs at different points in time
defines a productivity index that measures the percentage increase or
decrease in productivity over time. The ratios that form the index may
be single, comparing a single output to a single input or composite,
where multiple outputs and inputs are compared. The single ratios may
be useful for evaluating the efficiency of a single noncomplex
activity. Composite indexes can measure the productivity of more
complicated activities, controlling for complexity and quality.
Composite indexes can also be used to measure productivity of resources
across an entire organization, where many different activities are
being performed.[Footnote 12]
One method of producing composite indexes is to use weights to combine
such disparate activities as telephone calls answered and exams closed.
One common weighting method, used by the Bureau of Labor Statistics
(BLS), is a labor weight. Weighting outputs by their share of labor in
a baseline period controls for how resources are allocated between
different types of outputs. If the productivity of two activities is
unchanged but resources are reallocated between the activities, the
composite measure of productivity would change unless these weights are
employed. For example, if IRS reallocates exam resources so that it
does more simple exams and fewer complex exams, the number of total
exams might increase. Consequently, a single productivity ratio
comparing total exams to inputs would show an increase. Labor weighting
deals with this issue. The weights allow any gains from resource
reallocation to be distinguished from gains in the productivity of the
underlying activities. When types of activities can be distinguished by
their quality of complexity, labor weighting can also be used to
control for quality and complexity differences when resources are
shifted between types of outputs.
More complicated statistical methods can be used for calculating
composite indexes that allow for greater flexibility in how weights are
chosen to combine different outputs and for a wider range of output
measures that include both qualitative and quantitative outputs. Data
Envelopment Analysis (DEA), which has been widely used to measure the
productivity of private industries and public sector services, is an
example of such methods DEA estimates an efficiency score for each
producing unit, such as the firms in an industry or the schools in a
school district, or for IRS, the separately managed areas and
territories composing its business units. DEA estimates the relative
efficiency of each producing unit by identifying those units with the
best practice--those making the most efficient use of inputs, under
current technology, to produce outputs--and measuring how far other
units are from this best practice combination of inputs used to produce
outputs. DEA estimates provide managers with information on how
efficient they are relative to other units and the costs of making
individual units more efficient.
These efficiency scores are used to form a composite productivity index
called a Malmquist index. An advantage of the Malmquist index is that
IRS managers can restrict the weights to adjust for managerial or
congressional preferences to investigate the effect on productivity of
a shift, for example, from an organization that emphasizes enforcement
to one that emphasizes service. IRS can also include many different
types of outputs and inputs, control for complexity and quality, and
isolate the effects of certain historical changes, such as the IRS
Restructuring and Reform Act of 1998 (RRA98).[Footnote 13]
Another advantage of the Malmquist index is that productivity changes
can be separated into their components, such as efficiency and
technology changes. In this contex^t, efficiency can be measured
holding technology constant, and technology can be measured holding
efficiency constant. Holding technology constant, IRS might improve
productivity by improving the management of its existing resources. On
the other hand, technology changes might improve productivity even if
the management of resources has not changed. Thus, the productivity
change of a given IRS unit is determined by both changes in its
efficiency relative to the current best-practice IRS units and changes
in the best practices or technology.
Illustrations of Alternative Methods of Measuring Productivity:
Currently available IRS data can be used to produce productivity
indexes that control for complexity and quality. The examples that
follow focus on productivity indexes that use exams closed as outputs
and FTEs as inputs. The data on examinations cover individual returns
across IRS and IRS's LMSB division. For both individuals and LMSB, the
complexity and quality of exams can vary over time. For example, the
proportion of exams that are correspondence versus field, business
versus nonbusiness, and EIC versus non-EIC can vary over time. As
already discussed, failing to take account of such variation can give a
misleading picture of productivity change.
While these examples do not encompass all the methods, data, and
adjustments that may be used, they illustrate the benefits of the
additional analysis that IRS can perform using current data. In
addition, as we pointed out in our 2004 report, IRS can improve its
productivity measurement by investing in better data, taking into
account the costs and benefits of doing so. These better data include
measures of complexity, improved measures of quality, and additional
measures of output.
Figures 1 through 4 illustrate, using currently available data between
fiscal years 1997 and 2004, the difference between weighted indexes
that make an adjustment for complexity and unweighted indexes that make
no adjustments.[Footnote 14] In the illustrations, a labor-weighted
composite index, which can control for complexity, is contrasted with a
single unweighted index to show how the simpler method may be
misleading. (See app. I for a fuller description of the labor-weighted
index.) In each case, complexity is proxied by type of exam. Although
the data were limited (for example, the measure of complexity was
crude), the illustrations show that making the adjustments that are
possible provides a different picture of productivity than would
otherwise be available.[Footnote 15]
The advantage of weighted indexes is that they allow changes in the mix
of exams to be separated from changes in the productivity of performing
those exams. In the examples that follow, an unweighted measure could
be picking up two effects. One effect is the change in the number of
exams that an auditor can complete if the complexity or quality of the
exam changes. The second effect is the change in the number of exams an
auditor can complete if the time an auditor requires to complete an
exam changes, holding the quality and complexity of exams constant. By
isolating the latter effect, the weighted index more closely measures
productivity, or the efficiency with which the auditor is working the
exams.
For individual exams, the comparison of productivity indexes shows that
the unweighted index understates the decline in productivity. As figure
1 shows, between fiscal years 1997 and 2001, the unweighted
productivity index declined by 32 percent while the weighted index
declined by 53 percent. The difference is due largely to the increase
in EIC exams during the period. Over the period between fiscal years
1997 and 2001, exams were declining. However, the mix of exams was
changing, with increases in the number of EIC exams. EIC exams are
disproportionately correspondence exams, and IRS can do these exams
faster than field exams. IRS shifted to "easier" exams, and that shift
caused the unweighted index to give an incomplete picture of
productivity. The shift masked the larger productivity decline shown by
the weighted index.[Footnote 16]
Figure 1: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for All Individual Returns:
[See PDF for image]
[End of figure]
Figure 2 provides additional evidence to support the conclusion that
the shift to more EIC exams is the reason for the difference in
productivity shown in figure 1. Between fiscal years 1997 and 2001, the
weighted and unweighted indexes track each other very closely when the
EIC exams are removed. Both show a decline in productivity of about 50
percent over this period. The available data were not sufficient to
control for other factors that may have influenced exam productivity.
For example, RRA98 imposed additional requirements on IRS's auditors,
such as certifications that they had verified that past taxes were due.
Figure 2: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for Individual Returns (without EIC):
[See PDF for image]
[End of figure]
Figure 3 compares unweighted and weighted productivity indexes for
exams done in LMSB division. As figure 3 shows, between fiscal years
2002 and 2004, the unweighted productivity index increased by 4
percent, while the weighted index increased by 16 percent. This
difference appears largely due to the individual exams and small
corporate exams done in LMSB. Over the period, total exams were
declining but the mix of exams was changing. LMSB was shifting away
from less labor-intensive individual returns and small corporation
returns to more complex business industry and coordinated industry
return exams.[Footnote 17] This shift caused the unweighted index to
give an incomplete picture of productivity. Here, the shift masked the
larger productivity increase as shown by the weighted index.
Figure 3: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for LMSB Exams:
[See PDF for image]
[End of figure]
Figure 4 provides additional evidence to support the conclusion that
the shift away from individual and small corporate exams is the reason
for the difference in productivity shown in figure 3. Between fiscal
years 2002 and 2004, when individual and corporate exams are excluded,
the two indexes track more closely, with the unweighted index
increasing by 15 percent and the weighted index by 17 percent.
Figure 4: Base Year Labor-Weighted (Adjusted for Type of Exam) and
Unweighted Productivity Index for LMSB Exams (Excluding Individual and
Corporate Exams):
[See PDF for image]
[End of figure]
There is evidence that adjusting for quality would show that LMSB's
productivity increased more than is apparent in figures 3 and 4 for the
years 2002 to 2004. Average quality scores available for selected types
of LMSB exams show quality increasing over the 2-year period.[Footnote
18] Adjusting for this increase in quality, in addition to adjusting
for complexity, would show a productivity increase for these types of
exams of 28 percent over the period.[Footnote 19]
While labor-weighted and other more sophisticated productivity indexes
can provide a more complete picture of productivity changes, they do
not identify the causes of the changes. These productivity indexes
would be the starting point for any analysis to determine the causes of
productivity changes.
Another example of the advantages of weighted productivity indexes is
provided by IRS. As noted earlier, IRS has developed a weighted
submission processing productivity measure. The measure adjusts for
differences in the complexity of processing various types of tax
returns. In an internal analysis, IRS showed how productivity
comparisons over time and across the 10 processing centers depended on
whether or not the measure was adjusted for complexity. For example,
the ranking of the processing centers in terms of productivity changed
when the measure was adjusted for the complexity of the returns being
processed.
The more sophisticated methods for measuring productivity can provide
IRS and Congress with better information about IRS's performance. By
controlling for complexity and quality, IRS managers would have more
complete information about the true productivity of activities, such as
exams, that can differ in these dimensions. In addition, the weighted
measures can be used to measure productivity for the organization,
where many different activities are being performed. More complete
information about the productivity of IRS resources should be useful to
both IRS managers and Congress. More complete productivity measures
would provide better information about the effectiveness of IRS
resources, IRS's budget needs, and IRS's efforts to improve efficiency.
Although there are examples, such as the submission processing
productivity measures, of IRS using weighted measures of productivity,
IRS officials said they generally use single ratios as measures of
productivity. That is consistent with our 2004 report on IRS's
enforcement improvement projects, where we reported on SB/SE's lack of
productivity measures that adjust for complexity and quality.
While there would be start-up costs associated with any new
methodology, the long-term costs to IRS for developing more
sophisticated measures of productivity may be modest. The examples so
far in this section demonstrate the feasibility of developing weighted
productivity indexes using existing data. Relying on existing data
avoids the cost of having to collect new data. The fact that IRS
already has some experience implementing weighted productivity measures
could reduce the cost of introducing more such measures.
As we stated previously, IRS could also improve its productivity
measurement by getting better data on quality and complexity. These
improved data could be integrated with the methods for calculating
productivity illustrated in this report to further improve IRS's
productivity measurement. However, as we acknowledged in our prior
report, collecting additional data on quality and complexity may
require long-term planning and an investment of additional resources.
Any such investment, we noted, must take account of the costs and
benefits of acquiring the data.
Conclusion:
Using more sophisticated methods, such as those summarized in this
report, for tracking productivity could produce a much richer picture
of how IRS manages its resources. This is important not only because of
the size of IRS--it will spend about $11 billion in 2005 and employ
about 100,000 FTEs--but also because we are entering an era of tight
budgets. A more sophisticated understanding of the level of
productivity at IRS and the reasons for productivity change would
better position IRS managers to make decisions about how to effectively
manage their resources. Such information would also better enable
Congress and the public to assess the performance of IRS.
As we illustrate, more can be done to measure IRS's productivity using
current data. However, another advantage of using more sophisticated
methods to track productivity is that the methods will highlight the
value of better data. Better information about the quality and
complexity of IRS's activities would enable the methods illustrated in
this report to provide even richer information about IRS's overall
productivity.
Recommendations for Executive Action:
We recommend that the Commissioner of Internal Revenue put in place a
plan for introducing wider use of alternative methods of measuring
productivity, such as those illustrated in this report, taking account
of the costs of implementing the new methods.
Agency Comments and Our Evaluation:
The Commissioner of Internal Revenue provided written comments on a
draft for this report in a June 23, 2005, letter. The Commissioner
agreed with our recommendation to work on introducing wider use of
alternative measure of productivity. Although expressing some caution,
he has asked his Deputy Commissioner for Services and Enforcement to
work with IRS's Research, Analysis, and Statistics office to assess the
possible use of alternative methods of measuring productivity. The
Commissioner recognized that a richer understanding of organizational
performance is crucial for effective program delivery.
As agreed with your office, unless you publicly release its contents
earlier we plan no further distribution of this report until 30 days
from the date of this letter. At that time, we will send copies to
interested congressional committees, the Secetary of the Treasury, the
Commissioner of Internal Revenue, and other interested parties. We will
also make copies available to others on request.
If you or your staff have any questions, please contact me at (202) 512-
9110. I can also be reached by e-mail at [Hyperlink, whitej@gao.gov].
Key contributors to this assignment were Kevin Daly, Assistant
Director, and Jennifer Gravelle.
Signed by:
James R. White:
Director, Tax Issues:
Strategic Issues Team:
[End of section]
Appendixes:
Appendix I: Methods for Calculating Productivity Indexes:
Productivity Indexes:
Methods for calculating productivity range from computing single ratios
to using statistical methods. In its simplest form, a productivity
index is the change in the productivity ratio over time relative to a
chosen year. However, this type of productivity index allows for only a
single output and a single input. To account for more than one output,
the outputs must be combined to produce a productivity index.
One method is to weight the outputs by their share of inputs used in
the chosen base year. In a case where only labor input is used,
following this method provides a labor-weighted output index, which,
when divided by the input index, produces the labor-weighted
productivity index. The use of the share of labor used in each output
effectively controls for the allocation of labor across the outputs
over time. For example, if productivity in producing two outputs
remained fixed over time, a single productivity index may show changes
in productivity if resources are reallocated to produce more of one of
the outputs.[Footnote 20]
The Bureau of Labor Statistics (BLS) has also used labor-weighted
indexes. BLS published, under the Federal Productivity Measurement
Program, data on labor productivity in the federal government for more
than two decades (1967-94). Due to budgetary constraints, the program
is now terminated. BLS's measures used the "final outputs" of a federal
program, which correspond generally to what we have called intermediate
outputs in this report, as opposed to the outcomes or results of the
program. BLS used labor weights because of their availability and their
close link to cost weights. In particular, as with the labor weights in
our illustrations, BLS used base year labor weights and updated the
weights every 5 years. It relied only on labor and labor compensation,
and acknowledges that the indexes did not reflect changes in the
quality of labor. BLS measured productivity for a number of federal
programs, ranging from social and information services to corrections.
However, BLS did not produce productivity measures for IRS.
In addition to weighted productivity indexes, there are a number of
composite productivity indexes designed to include all the inputs and
outputs involved in production. This group of indexes is called Total
Factor Productivity (TFP) indexes.[Footnote 21] They are called total
because they include all the inputs and outputs, as opposed to Partial
Factor Productivity indexes, which relate only one input to one output.
Many of the main TFP indexes, including Tornqvist, Fisher, Divisia, and
Paache, require reliable estimates of input and output prices, data not
available for industries in the public sector. Therefore we use the
Malmquist index, which does not require that data.
Malmquist indexes are TFP indexes based on changes in the distance from
the production frontier, or distance functions. These distance
functions are estimated using Data Envelopment Analysis (DEA).
Productivity change is represented by the ratio of two different period
distance functions. The Malmquist index is the geometric average of
these productivity changes (evaluated at the two different
periods).[Footnote 22] This index can be further decomposed into
efficiency and technology changes.[Footnote 23] From the decomposition
of the Malmquist index, productivity change can be shown to equal the
efficiency change times the technology change.
The interpretation of changes in productivity, in terms of distance
functions, depends on relative distances between periods. For
simplicity, assume there was no change in technology between two
periods, than the productivity change equals efficiency change. In this
case, when the productivity index is less than one, the distance
function in the second period is smaller than the distance function in
the first period. Since the distance functions are less than one, this
corresponds to a distance function in the second period that is a
smaller fraction than the distance function in the first period. Since
movements away from one show declining productivity, a smaller fraction
in the second period, with a larger fraction in the first, indicates a
movement away from one over time and thus declining productivity. Thus,
a productivity change less than one indicates declining productivity
and therefore an efficiency change less than one also indicates
declining efficiency.
Alternatively, if the efficiency change was one, then the productivity
change equals the technology change. Following previous analysis, a
productivity change less than one indicates declining productivity.
Therefore, a technology change less than one indicates an inward shift
of the production frontier. If the technology change is less than one,
it must be that the distance function in the first period is less than
the distance function in the nex^t period. Thus, the distance in the
first period is farther away from one than is the distance in the nex^t
period, and the distance from the frontier decreased from the first
period to the second period. Since the output and input bundles did not
change, the frontier must shift in to produce the decrease in distance.
The Internal Revenue Service (IRS) can follow this method to generate
indexes for the areas and territories and then focus on the average for
an estimate of overall IRS productivity.
Estimation of Distance Functions:
DEA is a nonparametric method for calculating distances from an
estimated best practice production frontier. These distance functions
are used to calculate malmquist indexes. Output distance functions are
based on changes in output holding the amount of inputs
constant.[Footnote 24] The output distance functions are estimated by a
linear programming method which finds the scalar value that expands
output as far as possible such that that output is still producible
with the fixed level of inputs.[Footnote 25] Thus, a scalar value equal
to one means that output could not be expanded any more without
increasing the level of inputs. This situation indicates a firm that is
efficient, producing the maximum amount of output with a given level of
inputs and technology. Thus, firms with scalar values equal to one
define the estimated best practice production frontier. However, a
scalar value that is greater than one means that the firm could have
more output then is currently produced with the same level of inputs. A
firm in this situation is, therefore, inefficient relative to firms
with a scalar value of one. Thus, output distance functions are less
than one. IRS can use this method, treating territories and areas as
firms. The weights used in the linear program are designed to make each
firm look its best; they represent best case scenarios.
While DEA is a nonparametric method, there is also a parametric method
available called stochastic frontier analysis. Stochastic frontier
analysis (regression) uses a regression model to estimate cost or
production efficiency. After running the regression of performance and
input data, the frontier is found by decomposing the residuals into a
stochastic (statistical noise) part and a systematic portion attributed
to some form of inefficiency. Stochastic frontier analysis thus
requires specifying the distributional form of the errors and the
functional form of the cost (or production) function. Its merits
include a specific treatment of noise. While DEA's use of nonparametric
methods eliminates the need to specify functional forms, one drawback
is a susceptibility to outliers.
(450267):
FOOTNOTES
[1] GAO, Compliance and Collection: Challenges for IRS in Reversing
Trends and Implementing New Initiatives, GAO-03-732T (Washington, D.C.:
May 7, 2003), and IRS Modernization: Continued Progress Necessary for
Improving Service to Taxpayers and Ensuring Compliance, GAO-03-796T
(Washington, D.C.: May 20, 2003).
[2] GAO, Tax Administration: Impact of Compliance and Collection
Program Declines on Taxpayers, GAO-02-674 (Washington, D.C.: May 22,
2002).
[3] GAO, Tax Administration: Planning for IRS's Enforcement Process
Changes Included Many Key Steps but Can Be Improved, GAO-04-287
(Washington, D.C.: Jan. 20, 2004).
[4] P. L. No. 103-62 (1993).
[5] IRS, Tax Compliance Activities Report, June 24, 2002, prepared in
response to a directive in the House Report accompanying the
legislation (P.L. 107-67).
[6] GAO-02-674.
[7] GAO, Tax Administration: IRS Should Continue to Expand Reporting on
Its Enforcement Efforts, GAO-03-378 (Washington, D.C.: Jan. 31, 2003).
[8] GAO-02-674.
[9] By measuring the actual impact on taxpayers, measures of results
incorporate the quality and complexity of the service.
[10] GAO, Tax Administration: IRS Is Implementing the National Research
Program as Planned, GAO-03-614 (Washington, D.C.: June 16, 2003), and
Tax Administration: New Compliance Research Effort Is on Track, but
Important Work Remains, GAO-02-769 (Washington, D.C.: June 27, 2002)
look at IRS's research on compliance, and Tax Administration: IRS Is
Working to Improve Its Estimates of Compliance Burden, GAO/GGD-00-11
(Washington, D.C.: May 22, 2000) reported on IRS's measures of
compliance burden.
[11] For a more technical description of these methods, see app. I.
[12] For example, in GAO, Tax Administration: IRS Needs to Further
Refine Its Tax Filing Season Performance Measures, GAO-03-143
(Washington, D.C.: Nov. 22, 2002), we distinguished between the
information provided by a productivity measure of individual returns
processing functions and IRS's submission processing composite
productivity measure of several different functions, including
processing returns, remittances and refunds, and issuing notices and
letters.
[13] P. L. No. 105-206 (1998).
[14] In addition to using labor weighting and similar methods for
adjusting for complexity and quality, IRS may be able to use Malmquist
indexes estimated using statistical methods such as DEA.
[15] We used the type of exam as a proxy for complexity based on the
availability of data. Other proxies or direct measures might be used,
although direct measures might be difficult to define and calculate. We
included limited quality adjustments for the LMSB illustration only
because, given that the purpose of the analysis is to illustrate
methods, we determined it was not worthwhile to fully investigate the
ex^tent to which quality data currently available at IRS could be
integrated with the exam-level data that we used for our analysis. Due
to a lack of readily available data, capital inputs were not included.
[16] In figures 1 and 2, the exam types are correspondence and field
exams, business and individual exams, and EIC exams. More specifically,
the types for the weighted index are combinations of the following
return categories: EIC and non-EIC; business and nonbusiness; low,
medium, and high income; and correspondence and field exams. An example
of an output type would be correspondence exams of non-EIC, nonbusiness
high-income filers. The output types are meant to reflect differences
in degrees of audit difficulty. Altogether, there are 13 output types
used in the BLS index for individual returns.
[17] In figures 3 and 4 the exams are distinguished by size and
complexity of the business and whether they are individual or corporate
exams. More specifically, the types for the weighted BLS index are
combinations of the following return categories under LMSB: coordinated
industry (large and more complex businesses); low income (under $10
million) corporate exams; low (under $100,000) and high (above
$100,000) income individual exams; and business industry exams (smaller
or less complex business). The output types are meant to reflect
differences in degree of audit difficulty. Altogether there are five
output types in this illustration. While LMSB generally serves
corporations, subchapter S corporations, and partnerships with assets
greater than $10 million, it also examines all the individual officers
associated with corporations as well as any individual returns that
cannot be done by the other divisions or that need the particular
expertise of LMSB. LMSB will also examine small corporations that are
associated with larger corporations, including those related to tax
shelters.
[18] Our use of these IRS exam quality scores is to illustrate how a
quality adjustment can be made and does not mean that we endorse them
as adequate measures of quality. We have indicated that the methodology
for computing these scores could be improved by better adjusting for
the new higher level of quality implied by the new standards imposed by
RRA98. See GAO-04-287.
[19] We included quality adjustments for the coordinated industry exam
and business industry exam and therefore the productivity measure is
for those exams. No quality measures were available for the corporate
and individual exams.
[20] In a simple example of one input and two outputs over 2 years,
Qa^1= A^1*La^1, Qa^2= A^2*La^2, Qb^1= B^1*Lb^1, Qb^2= B^2*Lb^2, and
labor-weighted productivity change would be equal to x * A^2/ A^1 + (1-
x) * B^2/ B^1, where x = La^2/ (La^2+Lb^2) then 1-x = Lb^2/
(La^2+Lb^2). However, assuming additive outputs, a nonweighted
productivity change would be equal to [x*A^2 + (1-x)*B^2]]/ [y*a^1 + (1-
y)*B^1], where x is defined as above and y = La^1/ (La^1+Lb^1) then 1-y
= Lb^1/ (La^1+Lb^1).
[21] BLS regularly produces multifactor productivity measures, another
term for TFP indexes, that reflect both labor and capital inputs.
[22] Mathematically, the Malquist index is defined as:
{[D^t(x^t+1,y^t+1)/
D^t(x^t,y^t)]*[D^t+1(x^t+1,y^t+1)/D^t+1(x^t,y^t)]}^1/2, where x^t,
x^t+1 denote the vector of inputs at time t and t+1, and y^t, and y^t+1
denote the vector of outputs in time t and t+1 and D^t and D^t+1 are
distance functions relative to the technology in time t and t+1.
[23] Malmquist index, M =
{[D^t(x^t+1,y^t+1)/D^t(x^t,y^t)]*[D^t+1(x^t+1,y^t+1)/
DD^t+1x^t,y^t)]}^1/2 = [D^tT+1x^t+1 y^t+1/ DD(x^t,y^t)]*{[
D^t(x^t+1y^t+1/ D^tT+1x^t+1y^t+1]*[D^t(x^t,y^t)/
D^tT+1x^t,y^t)]}^1/2=E*T, the efficiency change, E, times the
technology change, T.
[24] Mathematically, the distance function can be defined as:
D^t(x^t,y^t)= [max { f | (x^K, phi y^K) eta T}]^-1 and phi * =
(D^t(x^t,y^t))^-1 with phi * >1 and D^t(x^t,y^t)< 1, where phi denotes
the value to scale output.
[25] The linear programming problem is to max phi subject to lambda x
less than or equal to x, lambda y greater than or equal to phi y,
lambda greater than 0.
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