EV and Eff of Auditing Public Schools

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The Economic Value of Auditing and Its Effectiveness

in Public School Operations*

YOSHIE SAITO, Eastern Illinois University

CHRISTOPHER S. MCINTOSH, University of Idaho

1. Introduction

Numerous studies have documented the important role auditing plays in the

public sector (Baber 1983, 1994; Bendor 1990; Deis and Giroux 1994).

Because governance mechanisms are less intense than in private sector capi-tal markets, auditors provide a control function in monitoring public sector

entities. However, it is unclear whether auditing ultimately produces eco-

nomic value for stakeholders in this setting. In theory, if the costs of audit-

ing exceed the benefits, there is no justification to allocate public resources

to auditing activities. This is especially true in the public sector where

resources must be allocated among different governmental operations, all of

which compete for a share of the budget.

This paper sheds light on this issue and analyzes the economic impact

of the auditing of public school operations from the stakeholders point of

view. We define stakeholders as educational and political interest groups

and taxpayers. The objective of the study is to examine whether auditing

provides a monitoring function to enhance optimal resource allocation and

whether the layers of complexity for an audit create a roadblock that can

hinder its effectiveness. Our results indicate that auditing improves the effi-

cient use of public resources by reducing nonproductive use of tax dollars

as illustrated in Figure 1. However, we also find that the complex new

regulation reduces auditing effectiveness.

Governmental auditing provides two types of services that enhance fiscal

monitoring of public education. The first is financial auditing that provides

attestation of the veracity of financial statements. The second is performance

auditing that assesses the quality of internal controls. Because auditors assess

* Accepted by Raffi Indjejikian. The authors would like to thank C. A. Knox Lovell,

Shawna Grosskopf, Tim Park, John Penson, William Baber, Ehsan Feroz, Teresa Gordon,

Richard A. Lord, William Kinney, and seminar participants at the University of Georgia,

Utah State University, and the University of Idaho for constructive comments on earlierdrafts of this manuscript. The authors also would like to thank Graham Lynch (at the

Georgia Department of Audits and Accounts) who provided us with auditing time data,


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the reliability of financial reporting, the attestation by auditors should reduce

agency costs between elected officials and political interest groups by enhanc-

ing the credibility of financial statements (e.g., Baber and Sen 1984; Baber

1983, 1994). Public officials, legislators and citizens need to know whether

government funds are handled properly and in compliance with laws and

regulations (U.S. General Accounting Office [USGAO] 1994a, 2007a,b). We

investigate the governance role played by auditing because prior studies

have extensively documented the inefficient use of educational resources

(Hanushek 1986, 1996; Grosskopf, Hayes, Taylor, and Weber 1997).

To assess the impact of elected officials incentives to supply auditing

and their effectiveness, this study examines the period following the passage

of the Quality Basic Education (QBE) Act in the state of Georgia in 1985.

1

The QBE Act was intended to ensure that Georgia students receive a

y2

Efficient Frontier

ID(w/c, y)

yo

y*Audit Effect

IP(w/c)

y1

Figure 1 Output distance function and nonproductive use of taxes

Notes:

yo is school performance without auditing. y* is actual performance. ID(wc, y) is

the cost distance function. IP(wc) is the budget constrained production possi-

bilities set. y1 and y2 are educational outputs such as grade 12 reading and

mathematics average school districts student test scores.

640 Contemporary Accounting Research


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quality K-12 education. Along with this mission, elected officials introduced

new state funding schemes to enhance the quality of public education. Thus,

QBE funds are directly tied to political promises made by elected public

officials who need to demonstrate the veracity of their commitments and

have a strong incentive to make good on their pledges. An audit does notdirectly influence the allocation of school resources. However, according to

Georgia State Law (O.C. G.A. 20-2-67 (b)), state auditors have oversight

responsibility over local school districts financial reports and the obligation

to assure the proper use of QBE funds, and hence provide a governance

mechanism to mitigate inefficient use of resources.2

We define audit efficiency as the ability of auditing to reduce school dis-

tricts nonproductive use of tax dollars, which can manifest as perquisite

consumption by educational officials. In Figure 1, we describe how we esti-

mate the effect of auditing on inefficient employment of resources by schooldistricts. Our definition of audit efficiency is different from that of Dopuch,

Gupta, Simunic, and Stein 2003, because we consider auditing in a competi-

tive budgetary environment and examine the incremental effect of auditing

on public resource use. They define efficiency as the optimal utilization of

labor resources to produce an audit.

Our research contributes to the accounting literature in several ways.

First, the results provide compelling evidence that state auditors enhance

efficiency in the use of school resources. There is a significant positive rela-

tionship between auditing effort and the efficiency of school districts opera-

tions. Assuming that specific funds do not affect audit efficiency, we show

that the Georgia State auditing contributes to the reduction of school dis-

tricts nonproductive use of taxes by approximately $144.9 million (state-

wide annual average) during the two-year period between the 19951996 and

19961997 academic years. This result indicates that auditing plays a crucial

role in enhancing optimal resource allocation in government operations.

Second, this paper shows that the benefits of auditing diminish as the

complexity of the procedures increase. The distribution of QBE funds is

based on complex formulae, which increases the demand for auditing. Our

result suggests that the budget allocation for auditing is insufficient. The

complexity associated with the implementation of QBE funding results in

inefficiencies totaling $180 million. Such inefficiency can be interpreted as

an increase in costs of auditing as regulators impose an extra burden on the

auditors. The results also show that the auditing of federal funds, where the

rules have been more stable and long standing, is more effective, suggesting

that experience helps to improve the effectiveness of auditing.

Third, in terms of a policy evaluation, our analysis supports the notionthat auditing is important to establish governance mechanisms and suggests

The Economic Value of Auditing and Its Effectiveness 641


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that disclosure about auditing costs is vital to adequately evaluate a new

policy. The lack of disclosure of auditing costs hinders the ability to con-

duct a cost-benefit analysis of new requirements. The state Department of

Education voluntarily discloses student performance based on QBE funds,

but it can avoid disclosure that might raise concerns about the implementa-tion costs. This asymmetry in information disclosure may contribute to the

shortfall in resources allocated to auditing.

Fourth, this paper makes a methodological contribution. The analysis

demonstrates the applicability of the econometric method of estimating an

efficient frontier to evaluate the contribution of auditing. Prior studies have

introduced frontier estimation techniques as a relative performance evalua-

tion in several different settings. The two most widely used methods are

Data Envelopment Analysis (DEA) (Banker 1989; Mensah and Li 1993;

Dopuch et al. 2003) and Stochastic Frontier Estimation (SFE) (Dopuch andGupta 1997; Banker, Change, and Cunningham 2003; Dopuch et al. 2003).

Here, both approaches are employed to evaluate the economic benefits of

auditing. We note that our results for both the DEA and stochastic meth-

ods are consistent. While Dopuch et. al. (2003) find these two methods pro-

duce different results, we suggest that their finding may be due to the model

specifications that impose restrictions on cost behavior.3

2. Background, schools, and auditing

Governmental accounting differs from that of the private sector in several

respects. In this section, we summarize the structure of budgetary allocation,

characteristics, and environmental factors that might affect efficiency and

effectiveness of auditing of public school operations in the state of Georgia.

The introduction of quality basic education funds and the structure of the

organization

In Georgia, the Quality Basic Education (QBE) Act was signed in 1985.

The QBE Act provided new funds to local school districts with a stated

goal of improving the quality of public education. Because funding must

satisfy three conflicting goals (equity of opportunity for all children, tax-

payer equity, and reduction of political waste)4 QBE funds are based on a

3. In their DEA approach, Dopuch et.al. (2003) use a constant return-to-scale model (their

equation 4), which is known to impose a priori restrictions. We use a variable return-to-

scale model. For SFE, they modify a Cobb-Douglas cost function by including interac-

tions between log of assets and client characteristics. They choose to include clients assets

but no other clients operating activities. There is no explanation of a choice of this func-

tional form. We use a translog functional form which imposes fewer a priori restrictionson the outcome of the estimation than other second order polynomial forms.

4. Taxpayer equity means that no taxpayer has to pay a disproportionate share of tax



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complex formula (QBE formula). QBE specifies funds to be used for speci-

fied educational programs, such as handicapped, gifted, and limited-Eng-

lishspeaking students.5 These funds are distributed on a per student basis

and include the training of educators, equipment, and materials for use in

the classroom. In addition, some parts of QBE funds are designated forsmall size schools or districts to satisfy an equalization policy. Each fund

allocation has a specific weight, and hence, extensive data gathering is

required to calculate the allocated amounts (FitzGerald 1991).

The county board of education is the lowest level government authority

exercising oversight responsibility. The board prepares a financial budget

plan for each fiscal year to control overall activities related to public educa-

tion in the county.6 In accordance with provisions of the QBE Act, the

Georgia Department of Education allocates sufficient funds for local school

operations (O.C.G.A. Section 20-2-167). Because all unexpended budgetauthority lapses at the end of each fiscal year, school district administrators

are often depicted as budget maximizers who exhaust all budgets in order

to maintain the current level of funding for the subsequent year (Deis and

Giroux 1994). Consistent with this budget maximization behavior, the data

indicate that almost all allocated funds are expended within a year.

There are three sources of funds for public education: local, state and

federal. About 90 percent of state funding in Georgia comes from QBE

funds. As shown in Table 1, QBE funds (QBES and QBER) are the largest

source of funds, followed by local funds (LOCS and LOCR) and federal

funds (FEDS and FEDR), respectively.7

The role of state auditors

Auditors responsibilities in the public sector are different from those in

the private sector. The basic accounting entity for governmental opera-

tions is the fund, which requires separate budget-based accounting and

self-balancing (Zimmerman 1977; Ingram 1984). Expenditures denote the

use of governmental resources, and financial statements report financial

position, comparison of revenue and expenditure, and also changes in

fund balances.

The Government Auditing Standards issued by the Comptroller

General of the United States is the primary guideline for the performance

5. These programs include support for handicapped students, gifted students, special

instructional assistance, remedial education and limited English-speaking students. For

example, the programs for handicapped students are divided into four levels, Mild-re-

sourced, Moderate-resourced, Moderate-self-contained, and Severe-self-contained. Dif-ferent weights are assigned to each of these in the QBE formula.

6. The board is not included in any other governmental reporting entity as defined by



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TABLE 1

Descriptive statistics for auditing time, funds allocation, and student enrollment

(excludes school districts with private auditors)

Variables Year Mean Std Median Minimum Maximum

AUDT 1996 509.276 239.104 464.000 179.000 1789.000

1997 619.428 294.853 571.000 201.000 2173.000

1998 575.117 300.178 524.000 173.000 2474.000

QBES 1994 $2755.388 $305.700 $2795.488 $847.863 $3253.002

1995 $2908.034 $310.230 $2947.273 $1037.345 $3749.528

1996 $3096.509 $319.648 $3140.178 $1230.029 $4262.168

FEDS 1994 $488.118 $173.846 $480.448 $115.011 $1346.793

1995 $537.525 $172.831 $523.172 $197.376 $956.204

1996 $547.533 $172.792 $544.472 $173.994 $981.148

LOCS 1994 $1407.059 $659.185 $1243.232 $336.871 $3884.546

1995 $1493.622 $688.527 $1309.090 $484.750 $4072.394

1996 $1554.017 $673.317 $1371.437 $391.719 $4015.931

TENR 1994 5078.77 7951.59 3118.00 496.00 83722.00

1995 5228.84 8263.68 3123.00 475.00 87291.00

1996 5358.34 8536.32 3253.00 494.00 90311.00

QBER 1994 0.5495 0.0923 0.5656 0.1442 0.6374

1995 0.5374 0.0940 0.5578 0.1756 0.6582

1996 0.5511 0.1043 0.5767 0.2148 0.6486

FEDR 1994 0.0962 0.0338 0.0950 0.0194 0.2812

1995 0.0983 0.0312 0.0981 0.0296 0.1689

1996 0.0967 0.0321 0.0949 0.0305 0.1611

LOCR 1994 0.2719 0.1050 0.2436 0.0786 0.7644

1995 0.2670 0.1028 0.2458 0.1068 0.6582

1996 0.2668 0.0978 0.2527 0.0881 0.7705

Notes:

AUDT is auditing time (hours) for the school, data from the academic years from

199697 to 199899.

QBES is the dollar value of QBE funds allocated to school districts per student for

the academic years from199495 to 199697.

LOCS is the dollar value of LOCAL funds allocated to school districts per student

for the academic years from 199495 to 199697.

FEDS is the dollar value of FEDERAL funds allocated to school districts per

student for the academic years from 199495 to 199697.

TENR is the total enrolment for a school district for the academic years from



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of government audits. These standards are often referred to as Generally

Accepted Auditing Standards (GAAS).8 Under governmental auditing stan-

dards, auditors not only provide opinions on financial statements, but are

also required to provide written reports on an entitys internal financial con-

trol systems.

OMB Circulars A-128 and A-133 require that government auditors

perform tests of compliance with the rules of specific funds, laws, and regula-

tions to determine whether an audited entity has utilized governmental

resources according to statutes and specific legislative appropriations

(Raman and Wilson 1994; Gauthier 1991; Banker, Cooper, and Potter

1992).

9

The Single Auditing Act of 1984 (USGAO 1994b, 1996) promotesuniform entity-wide audit coverage, and thus ensures that financial informa-

tion in this sector is a homogeneous audit product (Deis and Giroux 1992;

Dopuch et al. 2003; Keating, Fisher, Gordon, and Greenlee 2005).10 Accord-

ingly, accounting rules and regulations are uniformly applied to all school

districts in Georgia, 90 percent of which are audited by state auditors. This

provides an ideal setting to utilize the second stage approach to examine the

economic contribution of auditing to public school operations (Battese and

Coelli 1995).

Accountability increases demands for information about governmentprograms and services. Accordingly, an auditor examines the design of

TABLE 1 (Continued)

QBER is the percentage of QBE funds in total budget allocated to a school district,

which includes all of state budget.

FEDR is the percentage of federal funds in total budget allocated to a schooldistrict, which includes all of state budget.

LOCR is the percentage of local funds in total budget allocated to a school district,

which includes all of state budget.

Since QBE is a large proportion of state budget but not all, the summation of above

do not add up one.

8. The full text of the Yellow Book can be found on the USGAOs website, at http://

www.gao.gov/govaud/ybk01.htm (last accessed June 2007).

9. The Governmental Accounting Standards Board (GASB) was established by the Finan-

cial Accounting Foundation in 1984. The GASB is the successor of the National Coun-

cil on Governmental Accounting (NCGA), and its overall goal is to establish standard

accounting and reporting procedures for state and local governments. Its mission

includes: (a) comparing actual financial results with legally adopted budgets; (b) assess-ing the financial condition and results of operations; (c) assisting in determining compli-

ance with the rules of specific funds, finance-related laws and regulations; (d) assessing



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relevant internal control policies and procedures, assesses reportable

deficiencies, and verifies the effectiveness or efficiency of off-balance sheet

wealth transfers (Baber 1994). These various levels of responsibility make

governmental auditing complex. However, at the same time, they provide

an opportunity for auditing to play a critical role in enhancing both exter-nal and internal monitoring systems in the governmental operations.

The Georgia state constitution defines education as a primary responsi-

bility of the state. Georgia Law O.C.G.A. 20-2-67(b) specifies that local

school systems should be in a form to be specified and prescribed by the

state auditor. Under this law, the responsibility of the Department of

Audits and Accounts includes conducting performance audits on the effi-

ciency and effectiveness of state programs (e.g., QBE funds) and activities.11

Auditing fees are not separately charged to local school districts unless a

private auditing firm is employed.12 Thus, auditing efforts do not directlyaffect the allocation of resources.

3. Hypotheses development

The state support of public education through QBE funding significantly

increased the amount of financial resources devoted to public education

(Dayton and Matthews 1995). As a result, elected public officials reputa-

tions are tied to the success of the program. There is a potential penalty to

be paid if the goals of QBE are unfulfilled. Thus, elected public officials

have an incentive to implement an effective monitoring system if they are

interested in enhancing their personal objective functions (e.g., reelection

and reputation).

The QBE funding systems redistributed tax revenues across the state.

These changes in distribution of tax revenues generated risks for elected

officials because the failure of QBE to improve educational outcomes would

damage the reputations of the politicians. Increased calls for improving effi-

ciency in the use of school resources arise from the increases in real expen-

ditures accompanied by the decline in performance of American schools

(Hanushek 1986, 1996; Grosskopf et al. 1997). Public officials, legislators

and citizens want to know that government funds are handled in compli-

ance with laws and regulations (USGAO 1994a, 1:11:9). These phenomena

are comparative to the political environment discussed by Baber 1983, 1994

and Baber and Sen 1984. Baber (1994) summarizes prior studies and con-

cludes that political competition affects accounting and auditing practices in

various ways at the national, state, or local level. In our case, elected public

officials (may) use accounting and auditing to ensure the QBE funds are

allocated and spent according to specification. Although there is no direct



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link between performance of QBE funds and elected officials compensation,

the state politicians have staked their reputations on the QBE funding

process. Thus, we contend that the risk faced by elected officials is similar

to that faced by the managers of firms who must relay credible information

about their operations to current and potential investors.Datar, Feltham, and Hughes (1991) and Feltham, Hughes, and Simu-

nic (1991) argue that when managers face high risks in disseminating

information, the demand for high quality audits increases. We expect the

same to hold for state politicians, and hence the demand for high quality

governmental audits should increase after the implementation of the QBE

Act.

From a public economy point of view, the theory of administrative-

based controls discussed by Bendor (1990) suggests that because

bureaucrats may not always fulfill elected officials desires, legislaturesuse accounting and auditing to constrain bureaucratic initiatives on the

use of public resources. There have been seven major changes in QBE

formulas in the twelve fiscal years since its implementation. These contin-

ual legislative changes in the complex QBE formulas are indicative of

such behavior.

Anecdotal evidence obtained via telephone conversations with a senior

state auditor confirmed that QBE funds demanded significant auditing

time, partially due to the complexity of frequent changes in the QBE for-

mulas. These complex compliance requirements come at a cost by creating

additional demand for audit time. However, budget allocation is a politi-

cal process (Baber 1983, 1994; Deis and Giroux 1994). If risk imposed to

elected public officials is too low to ensure adequate governance, the

amount of financial resources allocated to auditing may be insufficient

(Deis and Giroux 1994). Lack of an adequate budget creates roadblocks

for auditors to assure that the audited financial statements are not materi-

ally misstated and that an internal financial control system is effectively

implemented.

Moreover, it is difficult for elected public officials or outsiders to under-

stand the costs of QBE because information about auditing fees and audit-

ing hours are not disclosed in public. However, the benefits of QBE funds

can be measured since the state department of education discloses the

school districts performances.

Summing the above, the political environment in budgeting process, risk

imposed on elected officials, and the difference in the level of information

disclosures provide a unique opportunity for us to closely examine the eco-

nomic consequences of the implementation of QBE funds on audit effective-ness. Because there is no information about auditing fees charged to public

schools and auditors effort specifically exerted on QBE funds we analyze



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The monitoring effect of auditing Hypothesis 1

Auditors provide documentation when significant deficiencies exist in the

design and operation of internal control structures. Such assessment should

enhance internal monitoring and discourage inefficient use of public

resources and potential perquisite consumption. According to GovernmentAuditing Standards (USGAO 1994a), the responsibility of the Department

of Audits and Accounts includes conducting performance audits, which

determines whether public resources are acquired and used economi-

callyefficiently. Any misallocation of QBE funds could result in a wealth

transfer from taxpayers to public school officials at any level. An internal

control system should constrain local school officials from indulging in inef-

ficient activities, unauthorized transactions or the misuse of government

resources, thereby enhancing resource allocation in the interest of society,

as discussed by Liebenstein 1975, Lev 1988, and Penno 1990.

Audits also play an external monitoring role. Auditors attestation in

accordance with the pronouncements issued by GASB provides credibility

in financial reports. Financial information attested by auditors enables the

elected public officials to communicate with outsiders about school districts

performance under the QBE Act and to reduce contracting costs (Baber

1983, 1994; Baber and Sen 1984).

We investigate the ability of audits to ensure the efficient use of school

resources by examining the relationship between auditing effort and the

unexplained portion of governmental expenditures that school districts

could have used to increase students educational performance. Auditors

increase the possibility of detecting misstatements and inefficient use of pub-

lic resources. Therefore, if audits play a monitoring role, although they are

not direct inputs to school district operations, they should reduce unex-

plained expenditures.13

To test this proposition, we utilize the relative performance evaluation

technique. We estimate the relative nonproductive use of tax dollars, which

measures the amount of student performance that school districts shouldhave achieved if a district had used their budget purely to produce educa-

tional outcomes. If auditing helps to enhance the efficient use of school

recourses, auditing should reduce nonproductive use of tax dollars, which

is known to exist in public school district operations (Hanushek 1986;

Grosskopf et al. 1997). Thus, our first hypothesis is:

13. It is possible to create scenarios under which the implied causality in the relationship

between audit effort and financial efficiency could flow in either direction. That is,



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Hypothsis 1. Auditing effort does not increase efficient use of tax dollars.

The rejection of this null hypothesis suggests that auditing enhances the effi-

cient use of public resources, and hence increases government accountability

to the public.

The roadblock effect Hypothesis 2

According to Government Auditing Standards (USGAO 1994a), auditing

should determine whether an audited entity is acquiring, protecting, and

using its resources economically and efficiently. Accordingly, school audit-

ing should improve efficiency and generate economic benefits for stakehold-

ers. Auditing procedures in public school operations can, however, become

very complex. QBE funds increases complexity due in part to legislators

desires to produce taxpayers equity and equity of educational opportuni-ties.14 But there is no requirement for the disclosure of auditing fees or

auditing hours. This means that there is no information regarding a third

party assessment of operational complexity. This makes it difficult for out-

siders to determine sufficient budgets for auditing. Deis and Giroux (1994)

suggest that an accounting system can be designed to include roadblocks in

order for bureaucrats to control their operation. They found a negative

association between educational expenditures and audit quality, supporting

their prediction of roadblocks.

We expand their notion of roadblocks by investigating whether theauditing complexity of QBE funds has an impact on audit effectiveness and

by examining the effect of QBE and federal funds on the nonproductive use

of tax dollars. The complexities created by QBE funds can be thought of as

a roadblock that hinders auditors, preventing the assessment of how eco-

nomically and efficiently school districts utilize their resources. We choose

QBE and federal funds as benchmarks for comparison because auditors

level of experience may help to resolve weakening audit effectiveness arising

from fund complexity. The distinct differences between QBE and federal

funds are the dollar value in size per student and the auditors experience incomplying with rules and regulations. Although federal funds require com-

plex audit procedures, auditors have built up experience in compliance with

these rules. In contrast, QBE funds presented a new and complicated chal-

lenge to auditors. These challenges are exacerbated by the constant changes

to the rules over the short history of the legislation. Our second null

hypothesis is:

Hypothesis 2. There is no difference in the effects of QBE and federal

funds on auditing effectiveness in the reduction of nonproductive useof tax dollars.



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4. Data description

The Georgia Department of Education began to publish precise educa-

tional data at the school district level in the 199495 academic year in

their annual Georgia Public Education Report Card. We collect three

years of data (from the 199495 to the 199697 academic years) from thissource.

Because the Georgia Department of Education changed definitions and

classification of variables over these three years, we had to reconstruct a

consistent data set based on the definitions provided by the Department of

Education (199697 Academic Year Georgia Public Education Report

Cards).15 During this time period, GASB statements 25 through 31 were

implemented. These statements require finer assessment of financial risk and

investment activities for governmental operations, thereby increasing the

complexity of the auditing procedures.16

Georgia has a total of 181 school districts. Seven counties do not have

a high school, and six of these seven do not have a junior high school. The

students from these counties attend junior high or high schools in nearby

counties. As sufficient data for these merged school districts are not avail-

able, these fourteen counties and one outlier are excluded from our analy-

sis.17 A final sample of 166 school district observations is available for each

year (comprising a total of 498 observations). We use these school districts

to estimate nonproductive use of tax dollars.

Auditing time data (the hours spent on auditing) were collected from

the Georgia Department of Audits and Accounts. Current regulations do

not require auditors to keep a record of their auditing work, so data were

only available for the 199697, 199798 and 199899 academic years. No

auditing fee data were available because the Department of Audits and

15. One measure that changed was the test scores reported in the Report Cards. Despite

criticism surrounding this change, the scores for a test given exclusively to students in

the state of Georgia (GHSGT) are now reported. These test scores have no comparative

measure with nationwide test scores (TAP), which were used in the report prior to thechange. Along with others, we argue that the new test is sensitive to grade inflation so

that students in rural areas can receive HOPE scholarship to universities or colleges in

the state of Georgia. This change makes it difficult for us to extend our data collection

period. The TAP data are only available for 199495 and 199596 academic years.

Therefore, we approximated a 199697 TAP score using the GHSGT. We calculated the

ratio of TAP to GHSGT for every county using the average of the data for 199495

and 199596. Then, an approximation of the county TAP score for 199495 was com-

puted as the product of the county GHSGT score and the ratio estimated above. The

ratio of the 199495 TAP score to this estimated TAP score was reasonably close to

one, which provides a reasonable estimate of TAP scores for the counties.16. For example, Statement 30 requires a risk financing omnibus method to calculate defi-

ciencies for financial risk, and Statement 31 requires reporting certain investments for



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Accounts does not charge auditing fees separately to each school district.

Each year, approximately 20 school districts use private auditors, and no

auditing data are available for these school districts. Therefore, these school

districts were dropped from our sample. This results in a total of 145 school

districts for each year.18

School performance data are based on the academic years 199495 to

199697. Prior studies suggest that public school operations are budget-based

(e.g., Baber 1983, 1994; Chalos 1994) and the governmental budget process

follows a similar pattern from year to year. This means that in the current

period (t) school operations are based on a budget from time (t ) 1). The

budget set at time (t ) 1) is audited in time period (t). It is then logically

matched time (t ) 1) school data with time (t) audit data.

Occasionally unexpended budget lapses will occur at the end of a fiscal

year. This suggests that there is a very high correlation between budgets intime period (t ) 1) and current expenditure in time (t), which is confirmed

by the data. For each budget (QBE, Federal, and Local), untabulated per

student results indicate that all of the coefficient estimates for a first order

autoregressive model cannot reject the null hypotheses of the estimated

coefficient equal to one, at the a level 0.01. This result indicates that (a) the

current budget for a school district is based on prior periods budget and

(b) year-to-year changes in student characteristics have a relatively small

impact on auditing and budget allocations. This is consistent with the

notion that once the school budget is allocated to each district, it will use

up all available moneys.

The year-by-year descriptive statistics for audit time, budgets, and stu-

dent enrollment data are presented in Table 1. The statistics show that

auditing time increased between the first and second years but decreased

between second and third years. The average dollar values of the funds per

student and the average number of student enrollment increased over the

three years. The proportion of QBE funds to the total budget is about 55

percent while the proportion of federal funds to total budget is about 10

percent and of local funds is approximately 30 percent. Each of those

proportions is stable over the three years of data collected.

5. Research Design

The selection of efficiency estimation techniques

Frontier estimation techniques employed by Grosskopf el at. 1997 and

Dopuch et al. 2003 are used here to estimate inefficiency and therefore the

nonproductive use of tax dollars. Estimating the relative efficiency of school

district operations enables us to test whether auditing reduces inefficiency inthe use of school resources. We define this type of auditors contribution as



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efficiency in auditing, which is different from that of Dopuch et al. 2003.

However, resource allocation in the public sector is a political process. If

auditing does not produce benefits to interest groups (or taxpayers), there is

no justification for allocating budgets to auditing activities as each aspect of

governmental operations must compete for a share of the general budget.Thus, our definition is consistent with auditing under this competitive envi-

ronment. It is well known that there is little incentive for bureaucrats to be

efficient unless they are monitored (Hanushek 1986, 1996; Grosskopf et al.,

1997).

The relative efficiency in school operations is estimated as a function of

two types of observable input information. One is a vector of average stu-

dent test scores at the school district level reported by the Department of

Education, and the other is financial information attested by the Depart-

ment of Audits and Accounts. Following Grosskopf et al. (1997) andGrosskopf and Moutray (2001), we use a translog form of the cost distance

function (CDF) to estimate efficiency scores, using an ordinary least squares

(OLS) error adjusted approach.

While a few papers in the accounting literature have employed DEA

analysis, the use of SFE in this body of literature is rare. The approach

taken here is a modification of an OLS estimation technique suggested by

Greene (1980). The OLS method is well understood. The modification used

here explains efficiency as a decomposition of the OLS error term into ran-

dom and inefficiency components. In the section on empirical results, sev-

eral different approaches to efficiency estimations are shown to be highly

correlated.19

The CDF has a built-in interpretation as a performance measure, which

consists of two components of errors, the random and inefficiency compo-

nents.20 It is also well-suited to the use of a flexible functional form. This is

a desirable property because production or cost functions for school dis-

tricts are known to be nonlinear (e.g., Grosskopf et al. 1997). Moreover,

Bauer, Berger, Ferrier, and Humphrey (1998) compare the results of several

different frontier estimation techniques and suggest that the SFE approach

provides results that more closely match a traditional accounting research

method than do the results from DEA. Thus, the selection of OLS error

adjusted approach maximizes our contribution to this area of accounting

literature.

19. These estimations include an ordinary least squares (OLS) error-adjusted approach,

SFE of error decomposition method, and DEA assuming both constant and variablereturns to scale. Two different OLS error-adjusted approaches are specified using Cobb-

Douglas and translog functional forms. The SFE is conducted only with a Cobb-Doug-



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The empirical model for efficiency

School operations are based on and constrained by budgeted revenue. We

incorporate this budget constraint by creating a vector of budget-deflated

input prices. The CDF is used to identify the benchmark school districts

that produce the highest student value-added outcome given allocated bud-gets. In our setting, inefficiency (departures from the benchmark) is a joint

distribution of a school districts average student test scores and financial

information accredited by the Department of Education and the Depart-

ment of Audits and Accounts, respectively. Since this efficiency is estimated

by the same model used by Grosskopf et al. (1997) for public school opera-

tion, we do not repeat the explicit illustration in this paper. But interested

readers should refer to the above paper. The model is

1

jjystjj IDo wst

cst;zst; ys

jjystjj; a; b

est 1;

where IDo is CDF with a translog functional form, || yst || is the outputs

measure, yst is the vector of educational outputs (seven different kinds of

student test scores), wistcst is the vector of budget deflated input prices.

Indices are for school districts (s = 1,, n) and for time (t = 1, 2, and 3).

zst is an environmental variable, the student-to-teacher ratio. The est is the

non-normally distributed error term composed of random error, m, and one-

sided error, l , defined as est = ms - lst. The educational output is estimated

using a value-added method that controls for students socioeconomic fac-

tors, described in Grosskopf et al. (1997). It is well known that socioeco-

nomic factors affect test scores, so we control for these factors. Our model

yields 82 parameters with 36 restrictions, resulting in a total of 46 free

parameters to be estimated. About 40 percent of the estimated parameters

are significant at a = 0.10. Our estimation also shows that all share equa-

tions are positive, indicating that the demand functions are positive when

evaluated at the mean points.

Built-in inefficiency is captured by the negative non-normal componentof error terms l.21 We illustrate how the efficiency score is related to the

nonproductive use of tax dollars using Figure 1, and show how the impact

21. OLS estimation is based on average observations and the underlying assumption that

residuals from the regression model are normally distributed with zero mean and con-

stant variance. This implies that there is no inefficiency captured in regression residuals.

In contrast, SFE tests for potential inefficiency captured in residuals by estimating the

frontier as indicated by the behavior of the best practicing school districts. Any devia-

tions from the frontiers are potential inefficiencies that are captured by additional com-ponent of residuals. SFE allows for a random component of errors in the estimation of

inefficiency while DEA assumes all deviations are inefficient. An adjusted OLS method



16/30

of auditing on efficiency scores (audit efficiency) is estimated in output

space. All school districts on the curved frontier are efficient and have a

benchmark efficiency score of one. School districts that fall inside of this

frontier (y* and yo) do not utilize school budgets fully to increase student

performance. The radial distance between the observed school district per-formance and the frontier measures inefficiency. We directly estimate the

impact of audits on this inefficiency by estimating the corresponding reduc-

tion of radial distance. Since an audit is an independent outside-governance

mechanism, it is not an input of efficiency. However, for an audit to serve

an effective monitoring function, it should reduce the inefficiency as an

outside governance mechanism.

Empirical models to test the hypotheses

If auditing plays a monitoring role, there should be an inverse relationshipbetween auditing and inefficiency. Our empirical model to test Hypothesis 1

is to regress auditing effort on efficiency scores and examine whether an

inverse relationship between auditing effort and nonproductive use of tax

dollars (as measured by inefficiency) exists. Our proxy for auditing effort is

auditing hours. Based on prior studies (Deis and Giroux, 1994; Baber 1994;

and Dopuch et al. 2003), we include two control variables, both of which

are thought to significantly affect the utilization of educational resources.

The first control variable is student test scores, specifically the grade 11

mathematical test scores (TM). This variable should control for student

characteristics (e.g., difficulties in teaching) that influence the performance

of a specific program.22 The second is a proxy for size of the organization,

which is the log form of student enrollment (LGTEN). The LGTEN is the

most widely used school size proxy in educational studies and likely reflects

the size of local bureaucrats political constituency.23

Model A is:

EFFi a0 a1AUDTi a2QBER a3TMi a4LGTENi ei 2;

where EFF is efficiency scores, AUDT is auditing time and QBER is the per-

centage of QBE funds in the total budget allocated to a school district.

Because EFF lies between zero and one, we estimate the model above using

Tobit. A positive coefficient on a1 results in the rejection of null Hypothesis 1.

We also examine the dollar value of auditing contribution to the

improvement of efficiency scores, which is a measure of the direct economic

impact of monitoring. Inefficiency can be interpreted as welfare loss for tax-

payersinterest groups. The rejection of null Hypothesis 1 suggests that

22. We also use grade 11 reading test scores, instead of and in addition to TM. But the



17/30

auditing has a significant impact on the reduction of nonproductive use of

tax dollars and provides evidence of successful monitoring.

Hypothesis 2 tests whether complexity in audit procedures reduces audit

effectiveness. QBE and federal funds are both complex funds. The auditing

treatment of federal school funding has a long history and its rules havebeen relatively stable through the years. But QBE funding is more recent

and fund allocation rules under QBE have been constantly changing. In this

situation, auditors should have built-up skill in monitoring federal funds,

but lack such expertise with QBE funds. This allows us to analyze two

issues. First, if complexity in auditing reduces audit effectiveness, we should

observe that the monitoring power of audits weakens as the high proportion

of QBE funds in the total budget increases. Second, because of the expertise

that auditors have for federal funds, the effect of federal funds on audit

effectiveness should be minimal. Thus, by including an interaction term, wetest Hypothesis 2 using Model B:

EFFi a0 a1AUDTi a2AUDTi QBERi a3AUDTi FEDRi

a4QBERi a5TMi a6LGTENi ei3:

Complexity in auditing is reflected by the proportion of QBE funds in

the total budget (QBER), the proportion of federal funds (FEDR), and

their interaction with auditing time (AUDT). A negative coefficient on

either interaction term indicates that the effect of the complexity in auditingis to reduce school districts efficiency in the use of resources. As the pro-

portion of funds increases this negative effect amplifies.

A negative coefficient for QBER and a nonnegative coefficient for

FEDR would suggest that the complexity of auditing procedures inherent in

QBE funds reduces audit effectiveness, but the complexity of auditing proce-

dures inherent in federal funds does not reduce audit effectiveness. If the

coefficient is negative in both cases, it indicates the reduction in effectiveness

from both sources of funding. A third alternative is that the estimated coeffi-

cients are positive on both variables. In this case, both funds help electedofficials to attain their goal despite the extra burden for auditors.

6. The empirical results

Descriptive statistics

The descriptive statistics for the 489 observations of school district variables

are shown in panel A of Table 2. The average expenditure for teaching and

student-related activity is approximately $4,000 per student. The average

enrollment is about 7,600, which is much larger than reported in Table 1.

This indicates that school districts that hire private auditors have large

enrollments. The proportion of students who participated in a free lunch



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TABLE 2

Descriptive statistics and correlation among estimated efficiency scores

Panel A: Descriptive statistics for all 498 observations

Variable Mean Std Median Minimum Maximum

TOT1R $4,064.81 $426.948 $4,035.90 $2,594.43 $5,677.81

SPSAL $38,265.03 $3,343.23 $38,117.40 $29,539.50 $49,856.50

TEASAL $32,300.59 $2,426.81 $32,083.50 $26,673.40 $39,995.02

CPI $154.607 $3.474 $154.50 $150.410 $158.910

TENR 7602.03 13459.91 348.35 4750.00 90311.00

LUNP 0.4869 0.1631 0.4928 0.0627 0.8971

TM 53.9016 10.4716 54.00 21.00 87.00

STUTEA 16.1504 1.2533 16.1421 8.1121 21.9483EFF 0.9129 0.0187 0.9116 0.8622 1.0000

Notes:

Variable definitions are as follows: TOT1R is the teaching and student related expenses per

student; SPSAL is the average of support staff salaries; TEASAL is the average of

teachers salaries; CPIis the price for materials related to teaching and students;

TENR is the total student enrolment; EFFis the estimated nonproductive use of tax

dollars (efficiency score), LUNP is the proportion of students participating in the free

lunch program in total population, TMis student mathematical test scores at grade

11, and STUTEA is the student-to-teacher ratio.

Panel B: Correlation in efficiency scores among different estimation techniques

VRS Cobb_OLS Cobb_SFE Translog_OLS

VRS 1.00000 0.77658 0.72913 0.58814

Cobb_OLS 0.77459 1.00000 0.95890 0.72622

Cobb_SFE 0.79175 0.99475 1.00000 0.69129

Translog_OLS 0.59721 0.77458 0.76596 1.00000

Notes:

VRS is the efficiency score employing the variable return to scale model in DEA.

Cobb_OLS is the efficiency score employing a Cobb-Douglas function in OLS error-

adjusted-method.

Cobb_SFE is the efficiency score employing a Cobb-Douglas function in the error

decomposition method.

Translog_OLS is the efficiency score employing a Translog function in the OLS error-

adjusted method.



19/30

scores for grade 11, TM, and student-to-teacher ratios, STUTEA, vary

widely across school districts but not as widely as that of parentfamily

wealth level. Although not reported in the tables, the salary for teachers is

the largest portion of expenditure, which represents about 38 percent of the

total budget accounted for our analysis.The three-year average efficiency score (EFF) is approximately 91 per-

cent, which is higher than that reported for Texas school districts examined

by Grosskopf et al. (1997). They found a mean efficiency score of 70.8 per-

cent. This difference reflects differences in characteristics of the data. Gross-

kopf et al. restrict their samples by school size (measured by student

enrollment to be between 1,000 and 5,000 students).24

Panel B of Table 2 shows the correlation among the efficiency scores

estimated by four different frontier estimation techniques: variable returns

to scale (VRS) is an efficiency score estimated by DEA; two stochasticmethods are SFE and the OLS error-adjusted method, within which two

functional forms are estimated, the Cobb-Douglas (Cobb_SFE, Cobb_OLS)

and Translog (Translog_OLS).25 The four efficiency scores are very highly

correlated, especially the efficiency scores from the two stochastic methods

(Cobb_SFE, Cobb_OLS). These correlations provide evidence that the

estimated efficiency scores are robust to different estimation techniques. In

presenting our results we will use the Translog_OLS efficiency scores.

According to Bauer et al. (1998), the stochastic approach provides results

that more closely match a traditional accounting research method than do

the results from DEA.

Panel A in Table 3 presents the mean efficiency scores for all school dis-

tricts that have audit data. The variable EFFAUD (= 0.9119) is the mean

efficiency score for the 435 school districts over three years. The variable

EFF9596 (= 0.9118) is the mean efficiency score for the 294 school dis-

tricts over a two-year period (the 19951996 and 19961997 academic

years). The mean efficiency score increases between the 199495 (EFF94)

and 199596 (EFF95) academic years from 0.9121 to 0.9142, but it

decreases between the 199596 and 199697 (EFF96) academic years from

0.9142 to 0.9096. To better understand the impact of inefficiency on the use

of public resources, we calculate the opportunity costs arising from school

districts operating at less than efficient levels. This gives us a means of

quantifying the nonproductive use of tax dollars. Among all school districts,

the average expense per student is $4,064.81 and the three-year average effi-

ciency score is 0.9119. This means that the average level of inefficiency is

24. As shown in Table 1, the sample used here has an average enrollment of 7,602 with astandard deviation of 13,460. The Georgia data cover all school districts in the state

and hence, contain more variations in student and school-related factors.



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TABLE 3

Descriptive statistics for estimated efficiency scores and related variables

Panel A: Estimated efficiency scores (Translog _OLS)

Variable Obs Mean STD Minimum Maximum

EFFAUD 435 0.9119 0.0169 0.8622 0.9725

EFF9596 294 0.9118 0.0162 0.8622 0.9725

EFFA94 145 0.9121 0.0183 0.8698 0.9713

EFFA95 145 0.9142 0.0169 0.8703 0.9725

EFFA96 145 0.9096 0.0152 0.8622 0.9613

Notes:

EFFAUD is the efficiency scores for school districts that have used state auditors forall three years.

EFF9596 is the efficiency scores for school districts in the 199596 and 199697

academic years, which are used to analyze the hypotheses.

EFFA94 is the efficiency scores for school districts in the 199495 academic year.

EFFA95 is the efficiency scores for school districts in the 199596 academic year.

EFFA96 is the efficiency scores for school districts in the199697 academic year.

Panel B: Variables in the model for hypothesis tests

Obs Mean STD Minimum Median Maximum

AUDT 294 599.1599 298.9268 173 545 2474

TOT1R 294 $4144.86 $376.98 $3248.99 $4125.30 $5657.00

QBER 294 0.5433 0.0993 0.1756 0.5653 0.7705

FEDR 294 0.0977 0.0319 0.0296 0.0970 0.1689

QBES 294 $3002.88 $326.39 $1037.35 $3033.84 $4262.17FEDS 294 $544.60 $174.73 $173.99 $539.34 $981.15

LGTEN 294 8.1609 0.8147 6.1633 8.0863 11.4110

TENR 294 5296.65 8346.56 475.00 3249.50 90311.00

TM 294 53.8061 10.1187 24 54 87

Notes:

AUDTt is the auditing time.

TOT1R is the teaching and student related expenses per student.

QBER is the percentage of QBE funds in total budget allocated to a school district



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about 8.8 percent. Since the efficiency scores are scale free measures, 8.8

percent inefficiency can be converted into a dollar value, which is equivalent

to the nonproductive use of tax dollars of approximately $360 per student

each year. At the statewide level, the nonproductive use of tax dollars is

equivalent to a total cost of $271 million to taxpayers each year.26 Thus,although inefficiency of less than 10 percent sounds small, it is quite large

when put into statewide dollar terms.

Panel B presents descriptive statistics for the characteristics of school

districts and audit hours, both of which are included in the model to test

the hypotheses. We use two years of school data, 199596 and 199697, and

audit hours for the 199697 and 199798 academic years to conduct the

hypothesis tests.27 There are a total of 294 observations. The average audit-

ing time is about 600 hours. The two-year average teaching and student-

related expenses per student are about $4,145 and approximately 75 percent

of these expenses are from QBE funds. The average federal funds allocated

per student are $545. The average proportion of QBE funds in all budgets

allocated to school districts is 54 percent, with federal funds averaging 9

percent of the budget. An average total student enrollment is 5,297.

The results of the hypotheses tests

Table 4 presents the results for the two models, A and B, one with no inter-

action terms (Model A) and the other including interactions of auditingtime with both the percentage of QBE funds and the percentage of federal

funds in the total budget (Model B). Based on the results in Table 4, we

reject null Hypothesis 1. The fundamental relationship between auditing

TABLE 3 (Continued)

LGTEN is the log form of the total enrollment for a school district.

QBES is the dollar value of QBE funds allocated to school districts per student.

FEDS is the dollar value of FEDERAL funds allocated to school districts perstudent.

TM is mathematics test scores for grade 11.

26. The dollar value of inefficiency is calculated as follows: (1 ) 0.9119)*4,061.81 =

357.845 per student, and in terms of an annual average across the 145 schools,

357.845*5,222*145 = 270,957,004. Note that the total student enrollment is 5,222 in

this calculation. This is because the efficiency score 0.9119 is the three-year average and

the corresponding student enrollment is 5,222, not 5,297 as reported in Table 3. Thesample for this analysis consists of 145 school districts rather than the 166 analyzed in

the previous section. Because 21 school districts used private auditors, and thus data on



22/30

TABLE 4

Tobit models used to test hypotheses.

EFFi = a0 + a1AUDTi + a2QBER + a3TMi + a4LGTENi + ei

EFFi a0 a1AUDTi a2AUDTi QBERi a3AUDTi FEDRi a4QBERi a5TMi a6LGTENi ei

Panel A: The coefficient estimations

Parameter Model A (SE) Model B (SE)

INTER 92.9127*** (1.2377)a 85.9662*** (1.6331)

AUDT 0.0012** (0.0004) 0.0076*** (0.0015)

AUDTQBER )0.0174*** (0.0031)

AUDTFEDR 0.0206*** (0.0049)

QBER )0.1939 (0.9425) 9.2323*** (2.0086)

TM 0.0535*** (0.0092) 0.0711*** (0.0094)

LGTEN )0.6399*** (0.1448) )0.4686*** (0.1450)

Log likelihood )536.9900 )528.1949

Notes:

EFF is the efficiency score multiplied by 100. AUDT is the auditing time, hours

spend on auditing. AUDTQBER is the interaction of auditing time and the

percentage of QBE funds to total budget, AUDTFEDR is the interaction of

auditing time and the percentage of federal funds to total budget, TM is the

grade 11 student mathematical test scores, and LGTEN is the log form of the

total enrollment for a school district. QBER is the percentage of QBE funds in

total budget allocated to a school district.

a The values in parentheses are standard errors.

SE = standard error.

There are 294 total observations for the 199556 and 199697 academic years.

*,**, *** Significant at the a 0.1, 0.05, 0.001 levels (two-tailed).

Panel B: Marginal economic effects of complexity on audit effectiveness.

Variables Calculation Total effect Dollar value

AUDTQBER_lower

quartile

(0.0076 ) 0.0174*0.49

+ 0.0206*0.0977)

0.001087 $34,600

AUDTQBER_Median (0.0076 ) 0.0174*0.5653

+ 0.0206*0.0977)

)0.000224 )$7,100

AUDTQBER Upper (0.0076 ) 0.0174*0.6154 )0.001095 )$34,900



23/30

and efficiency scores is positive and significant (at the 0.001 percent level) as

shown by the estimated coefficient on auditing time a1 in Model A. This

positive coefficient indicates that, independent of the impacts on audit effec-

tiveness from either QBE or federal funds, auditing provides economic ben-

efits for taxpayers and interests groups by reducing nonproductive use oftax dollars.

Next, in Hypothesis 2 we test whether the complexity of each fund

becomes a roadblock for auditors and reduces the economic benefits of

auditing. When both interaction terms are included (Model B), the coeffi-

cient on AUDT a1 is still positive and significant (0.0076). The test of

Hypothesis 2 is shown in the estimated coefficient on the interaction term

AUDTQBER, which is negative ()0.0174). This is in contrast to the sign on

an interaction term AUDTFEDR, which is positive (0.0206). In addition,

once interaction terms are included in the model, the coefficient on QBERbecomes significantly positive (9.2323). These results indicate that a direct

effect of QBER is to increase utilization of school resources to improve stu-

dent performance. However, QBER reduces auditing effectiveness.

As the proportion of QBE funds in the total budget increases, the effect

of auditing on nonproductive use of tax dollars decreases. On the other

hand, as the proportion of federal funds increases, the effect of auditing

increases. Since QBE funds are new for auditors, this result suggests that

complexity in QBE funds reduces auditing effectiveness.28

The marginal effect of auditing is 0.0076)

0.0174 QBER + 0.0206

FEDR. Evaluated at the means, the effect of auditing on efficiency scores is

positive, 0.00016.29 QBER lies between 17 percent and 77 percent of the

school budgets, and the coefficient is negative, )0.0174. This means that if a

school district has a large proportion of QBE funds (relative to the mean),

this can reduce audit effectiveness. These results serve to reject null Hypoth-

esis 2. Given the level of auditing time, as the proportion of QBE funds in

the total budget increases, the nonproductive use of tax dollars also

increases. However, the complexity in auditing procedures for federal funds

does not reduce audit effectiveness.

The coefficient on student test scores (TM) is positive for both models,

indicating that school districts with students starting at a higher level of

achievement utilize school resources more efficiently. The coefficient on stu-

dent enrollment is negative, indicating that larger districts are less efficient

in their use of educational resources.

28. We note that when we include FEDR in the model 3, the coefficient on the interaction

term AUDTFEDR became insignificant and the coefficient on FEDR is significantly



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Calculation of dollar value impacts

We calculate the dollar value of the contribution of auditing by measuring

the reduction in the nonproductive use of tax dollars. Previously, we showed

that inefficiency of 8.8 percent in the use of tax dollars is equivalent to a

total annual cost of $271 million to taxpayers. Similarly, we calculate theimpact of the estimated coefficients on efficiency scores in dollar value terms.

The estimated coefficients on AUDT shown in Model B are 0.0076

AUDT ) 0.0174 AUDTQBER + 0.0206 AUDTFEDR. Because the aver-

age auditing hours are 599 for the two academic years between 199697 and

199798, the auditing effect on the efficiency score is 0.095 percent.30

Although this percentage may seem small, when converted to dollar values

it appears more substantial. Since the two-year average expenditure per stu-

dent is $4,144.86 and average student enrollment is 5,297 for each of the

145 school districts, the total reduction in the nonproductive use of tax dol-

lars per year for the state becomes approximately $3 million.31 This amount

is equivalent to the reduction of nonproductive use of tax dollars illustrated

in Figure 1. Ceteris paribus, the distance between y* and y0 is the audit

effect (audit efficiency) that results in the reduction of nonproductive use of

tax dollars.

More importantly, the effect of auditing on the efficiency score is small

because of the negative coefficient of AUDTQBER. This negative effect on

efficiency scores is 5.66 percent that is to reduce the efficient use of tax dol-

lars. Converting this into dollar value yields $180 million increase in the

nonproductive use of tax dollars due to the reduction of auditing effective-

ness.32

We also examine the marginal effect of the negative coefficient of AU-

DTQBER on audit effectiveness. We calculate the impact of the negative

coefficient on the interaction term on efficiency scores for three groups of

school districts. Since the proportion of QBE funds is our proxy for com-

plexity in audit procedure arising from QBE funds, by varying the level of

QBE funds in total budget (QBER), we can assess the effect of complexity

30. This is calculated as 0.0076*599 ) 0.0174*325.5 + 0.0206*58.5 = 0.095, where 599 is

the two-year average auditing time in hours, 325.5 is the average of AUDTQBER, and

58.5 is the average of AUDTFEDR.

31. The average nonproductive use of tax dollars per student year for the 145 school dis-

tricts is 573.276. The calculation for the total dollar amount is

0.00095*4144.86*5297*145 = 3,036,442, the auditing effect * an average expenditure

per student * the average number of students per school district* the numbers of school

districts, respectively.

32. The calculation is)

0.0174*325.5 =)

5.66, and converted into dollar terms becomes)5.66100*4,144.86*5,297*145 = )180,306,273. Assuming that specific funds do not

affect audit efficiency, auditing effect in dollar value is 145 million,



25/30

for different groups of school districts. The first group contains the school

districts in the lowest quartile with respect to QBER (49 percent), the sec-

ond contains those in the median QBER (57 percent), and the third con-

tains those in the upper quartile (64 percent).

The impact of a one-hour increase of auditing on efficiency scores anddollar value is reported in panel B of Table 4. This panel shows the economic

effect of complexity in QBE funds on the use of tax dollars, assuming all

variables are evaluated at their means except for QBER. The positive effect

on efficiency for a school in the lowest quartile QBE fund districts is

0.001087, which translates to an average $34,600 increase in the economic

benefit of auditing.33 For the median school districts, on the other hand, the

complexity in QBE funds increases the nonproductive use of tax dollars by

$7,10034 and for the upper quartile school districts by $34,900.35 These results

indicate a significant effect of complexity in auditing procedures on the useof tax dollars. It shows that the impact of a roadblock on school district oper-

ations varies according to the size of the QBE receipts of each school district.

Sensitivity analysis using the DEA technique and political factors implication

To test the robustness of our results, we reestimate Models A and B using

efficiency scores based on the variable returns to scale (VRS) of the DEA

model. The efficiency scores estimated by DEA have smaller variation

among school districts than those of OLS error-adjusted methods. About

10 percent of the school districts (24 observations) are deemed to be

efficient. However, as shown in Table 2, the correlation among efficiency

scores from the different estimation approaches is high. The lowest corre-

lation is 59 percent, between VRS DEA and the Translog form of OLS

error-adjusted method.

As expected, the fundamental results remain similar to those reported in

Table 4. The estimated coefficient on auditing is positive, supporting our pre-

vious conclusion that auditing reduces nonproductive use of tax dollars and

thereby has a real monitoring effect. There is also a negative coefficient on

the interaction terms with QBER and a positive coefficient on the interaction

terms with FEDR, indicating that complexity together with a lower level of

auditor experience leads to reduce auditing effectiveness. Our results are in

contrast to Dopuch et al. 2003 who find inconsistent results between stochas-

tic and DEA models. We suspect that their difference arises from the use of a

constant return-to-scale DEA model that assumes a proportional increase in

all inputs results in the same proportional increase in outputs.

We also consider the potential influence of political factors on audit

effectiveness and efficiency scores. If political factors affect efficiency scoresand are correlated with audit effectiveness, there is an omitted variable



26/30

problem. To examine the influence of political factors, we further re-

estimate Models A and B using the dollar value of the school districts

budget per student. Baber (1983, 1994) and Deis and Giroux (1994) suggest

that the dollar value of budget captures the influence of the political envi-

ronment in government operations. However, the signs of the estimatedcoefficients of AUDT, AUDTQBER and AUDTFEDR do not change.

7. Conclusions

In this study, we examine the effect of auditing on public school resources

used in the period subsequent to the implementation of the QBE Act in the

State of Georgia. We utilize a unique educational reform opportunity where

elected officials pledged to improve the quality of the public school system. As

a result of this promise, various new monitoring structures were implemented.

This provides an ideal setting to examine the economic benefits of auditingand the effect of new complex compliance rules on audit effectiveness.

We provide direct evidence that auditing produces economic benefits

for stakeholders as indicated by reductions in the nonproductive use of tax

dollars. Our method of estimating audit efficiency enables us to calculate

the economic value of auditing to taxpayers andor interest groups, which

is an annual average of approximately $145 million.

We also show that complexity in audit procedures reduces audit effec-

tiveness and hence increases the nonproductive use of tax dollars. In partic-

ular, our results show that when compliance rules are new and continuously

changing (i.g., complexity is increasing), auditing effectiveness drops; when

compliance rules are static and auditors have built up experience with these

rules, auditing effectiveness is stable. We find that complex compliance rules

can become a roadblock and reduce the monitoring effect of audits. This

research thus implies that it is important to consider potential roadblocks

for auditors when evaluating auditing effectiveness, the economic value of

auditing and when new regulation alters existing auditing practices.

Auditing clearly contributes to greater efficiency in the use of public

school budgets. Our results reinforce the notion that auditing is important

for government accountability for public education funding. The efficiency

of school resource allocation is often overlooked in the politically charged

environment of public school funding. Our results indicate that greater

transparency and reduced complexity in the use of funds and continued

auditing efforts will improve overall efficiency in the use of public education

budgets.

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