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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, IRSís 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 IRSís productivity, the efficiency with which inputs are used 
to produce outputs, is challenging. IRSís output could be measured in 
terms of impact on taxpayers or the activities it performs. IRSís 
impacts on taxpayers, such as compliance and perceptions of fairness, 
are intangible and costly to measure. IRSís 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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