Does Accounting Quality Influence Product Market Contracting? Evidence
from Government Contract Awards
Kai Wai Hui
Department of Accounting
Hong Kong University of Science and Technology
Clear Water Bay, Kowloon, Hong Kong
ackw@ust.hk
Joseph Pacelli
Samuel Curtis Johnson Graduate School of Management
Cornell University
Ithaca, NY 14850
jp792@cornell.edu
P. Eric Yeung*
Samuel Curtis Johnson Graduate School of Management
Cornell University
Ithaca, NY 14850
eric.yeung@cornell.edu
March 2014
Abstract: This paper examines the potential impact of accounting information quality on
product market contracting by studying government contracts awarded to public firms. We
posit that low-quality internal accounting information increases the noise in estimating
project costs, which dampens a firm’s competitiveness in winning government contracts.
Empirically, we find that firms with internal control weaknesses are less likely to win longterm contracts, specialized products contracts fixed-price contracts and contracts for which
cost or pricing data are requested by the government.
Corresponding author. We thank workshop participants at Cornell University and the 2014 AAA
Financial Accounting and Reporting Section Midyear Meeting for their helpful comments. We also
appreciate the research assistance from Esterina Frangaj and Chuchu Liang.
*
Does Accounting Quality Influence Product Market Contracting? Evidence
from Government Contract Awards
1. Introduction
The economic impact of high accounting quality on the firm is of great interest. The
literature has so far focused largely on whether firms with high accounting quality benefit
economically from capital providers. 1 Although product market stakeholders such as
customers and suppliers are often cited as important external users of financial information
(e.g., Spiceland, Sepe and Nelson 2012, Libby, Libby and Short 2012), there has not been
much research on how accounting information quality may affect product market
contracting. Given the importance of contracting with product market stakeholders in
determining a firm’s survival and growth, the impact of accounting quality perhaps
deserves better understanding. In particular, one basic question is whether high accounting
quality helps a firm to contract more effectively with customers and suppliers. In this
study, we examine U.S. government contracts awarded to public firms and test whether the
quality of a firm's internal accounting reports affects its ability to compete for and win
contracts from customers.
The U.S. government is the largest single entity that contracts periodically with
millions of business entities, and the size of federal government procurement has increased
substantially in recent years.2 Our focus on government contracts is motivated by three
considerations. First, this is a relatively clean setting of price competition, and government
contract awards depend crucially on firms’ ability to price the project accurately and offer a
competitive bid, which in turn hinges on the quality of internal accounting reports. Second,
the government is often at a monopolistic bargaining position against many firms in the
contracting process, which does not generally exist in other product market settings as both
suppliers and customers often share bargaining power (e.g., Hui, Klasa and Yeung 2012).
See literature review by Dechow, Ge and Schrand (2010) on earnings quality. More recent studies
include Costello and Wittenberg-Moerman (2011), Dhaliwal, Hogan, Trezevant and Wilkins (2011),
Ashbaugh-Skaife, Collins, Kinney and LaFond (2009), and Kim, Song and Zhang (2011).
2 For example, federal spending on contracts increased from approximately $200 billion in 2000 to
over $540 billion in 2011 (www.USASpending.gov).
1
1
This provides us a clean setting for testing the economic role of accounting quality in the
contracting process. Third, many of the terms of government contracts are observable,
which enables contract-level analysis that complements prior work at industry- and firmlevel.
We posit that high noise in internal accounting reports hinders the management's
ability to accurately estimate the costs of certain projects (i.e., long-term, complex and
specialized projects). Higher noise in internal accounting reports increases the variance of
forecasted costs, which leads to a lower probability of winning contracts for at least two
potential reasons. First, while over-estimation of costs obviously hurts the firm’s chances,
under-estimation does not necessarily help because there is a non-trivial probability that
the government detects the estimation errors by examination of bidders’ accounting
records. Consequently, the average probability of winning is lower when internal reports
are of low-quality.
Empirically, we expect that firms with low-quality internal reports are less likely to win
contracts for which the government summons cost or pricing data in the process of choosing
a contractor. Low-quality internal reports likely result in incomplete and inconsistent
historical accounting records, which become transparent when the government inspects and
compares financials of the bidders. We thus predict a lower probability for firms with lowquality internal accounting reports to win a contract that requires the submission of cost or
pricing data to the government.
The second reason why a firm with less precise accounting information may experience
lower probability of winning contracts is that the management’s risk-aversion results in a
less competitive price. Knowing accounting information quality is low, a risk-averse
manager (who maximizes expected utility under uncertainty) is likely to demand a
relatively higher mark-up when determining the bidding price. Ceteris paribus, this
additional layer of price protection (i.e., risk-premium) reduces the competitiveness of the
bid.
Empirically, we expect that the risk-premium associated with poor information quality
should be higher when it is more difficult to accurately estimate project costs or when the
2
risk is less diversifiable. Specifically, the cost of a long-term contract should be more
uncertain than a short-term contract because the variance is proportional to the contract
duration. It is also more difficult to estimate the costs and set the prices of specialized
products that lack commercial markets, as the firm may be less experienced in their
production and less able to observe their market prices. Lastly, we expect that the
management’s risk-premium is higher for fixed-price contracts relative to cost-plus
contracts because the firm bears the full risk of any cost overrun.
We obtain data about recent federal procurement contracts awarded to contractors,
which provide information about the purchaser (e.g., agency), selected contractor, amount,
as well as many important contract terms. We focus on contracts awarded to publicly
traded firms (or their subsidiaries) involving at the minimum one million dollars. Setting a
contract amount threshold ensures that the errors in cost estimates and pricing risk are
economically significant. To ensure a competitive bidding process, we require a contract to
have received at least two bids. Our final sample includes 49,079 contracts during the 20032012 period. 3
We use internal control weaknesses over financial reporting as the main proxy for lowquality internal accounting reports (e.g., Feng, Li, and McVay 2009), because poor internal
controls likely result in erroneous and stale unaudited accounting information.4 We find
that firms that had disclosed internal control weaknesses are less likely to win contracts for
which the government requires submission of cost or pricing data in the contracting
process. We also find that firms with internal control weaknesses are less likely to win
contracts lasting longer than three years or producing specialized products, consistent with
the expectation that these contracts involve high uncertainty. Finally, firms with internal
control weaknesses are less likely to win fixed-price contracts. Overall, firms with internal
Note that this data only contains firms that win government contracts. To the extent the
government does not award contracts to firms due to direct concerns on accounting issues (e.g.,
fraud), variation among winners is less likely to be attributed to government’s direct concern on
accounting problems.
4 Accrual-based accounting quality measurers, on the other hand, are extracted from financial
statements for external users, which have been audited or analyzed by audit committees.
3
3
control weaknesses are less likely to win government contracts because they do not have
advantages of winning other types of contracts.
In supplemental analyses, we also use management earnings forecast accuracy as an
alternative proxy for internal accounting reports quality, because inaccurate or incomplete
internal accounting reports lead to inaccurate earnings estimates. We find that firms that
issue more accurate management earnings forecasts are more likely to win contracts that
require submission of accounting data, long-term contracts, and specialized products. To
the extent that management forecast accuracy reflects the quality of internal accounting
reports, this evidence corroborates our main findings based on internal control weaknesses.
This study contributes to our understanding of the economic benefits of accounting
quality in product market settings. Instead of focusing on product market consequences of
accounting quality, prior literature examines how supplier-customer contracting may affect
accounting policies. Raman and Shahrur (2008) take long-term customer-supplier
relationships as given, and argue and find that such relationships encourage earnings
management, consistent with the earlier findings by Bowen, Ducharme, and Shores (1995).
Hui et al. (2012) argue and provide evidence that firms anticipate the demand from
customers and suppliers for accounting conservatism. Costello (2013) provides empirical
evidence that long-term supply contracts are less likely to rely on financial covenants when
accounting quality is low. As a departure from prior work, we examine how accounting
quality affects firms’ real activities in product markets.
Our study also extends the literature examining the economic impact of internal control
quality. While the majority of the studies focus on internal control over financial reporting
and its effects on capital suppliers, Feng et al. (2009) link internal control weaknesses over
financial reporting to the quality of internal accounting reports and find that internal
control weaknesses are associated with less accurate management earnings guidance.
Consistent with the notion that internal control quality affects the quality of internal
accounting reports, we find that internal control weaknesses are associated with a firm’s
product market competitiveness.
4
Finally, our study is also related to the literature on earnings management in the
context of government contracts. Prior accounting studies focus on cost padding in
government incentive contracts and find mixed results on whether contractors shift costs
from commercial segments to government contracting segments (Lichtenberg 1992,
Rogerson 1992, Thomas and Tung 1992, Demski and Magee 1992, McGowan and Vendrzyk
2002). For instance, while Thomas and Tung (1992) find evidence supporting cost shifting,
McGowan and Vendrzyk (2002) find no such evidence. The results of our study suggest that
a competitive bidding process helps to filter out firms with poor internal control. If highquality internal control is associated with a lower degree of earnings management, our
results suggest that a competitive bidding process alleviates the concern of future earnings
management.
The remainder of this paper proceeds as follows. Section 2 reviews the related literature
and develops our hypotheses. Section 3 explains the data. Section 4 discusses the research
design and reports the empirical results of main analyses. Sections 5 presents additional
analyses. Section 6 concludes and discusses the limitations of our study.
2. Literature Review and Hypotheses
2.1. Prior Studies On Government Contracting
Economics and accounting literature has long recognized the importance of the
government in the economy and has analyzed the government procurement process from an
efficient contracting perspective. In analytical studies in economics, researchers’ primary
interest has been to model the government’s objectives in the contracting process and
examine how contract design may facilitate the achievement of these objectives (Agapos
and Dunlap 1970, Rogerson 1989, Reichelstein 1992). 5
For example, Agapos and Dunlap (1970) suggest that while the government's primary objective is
to minimize the cost of public projects, other considerations also play a role in the contracting process
(e.g., spurring innovation, political considerations and legal constraints). Rogerson (1989) provides a
detailed elaboration of how government contracting should encourage innovation. In these models,
bargaining power of contracting parties is an important factor determining the equilibrium outcome.
Reichelstein (1992) models the contracting process in an agency setting and proposes contract
designs that overcome the information asymmetry between the government and the contractor.
5
5
Early empirical studies on government contracting have mainly focused on the firm’s
profit margin related to government procurements and find mixed evidence on whether
government contracts provide higher profit margins than the commercial contracts.
Weidenbaum (1968) finds that large defense contractors earn a higher rate of return on
capital than comparable industrial firms. On the other hand, Bohi (1973) finds that returns
on equity of defense firms are not significantly different from those of commercial firms.
Similarly, Agapos and Galloway (1970) conclude that aerospace defense firms do not earn
excessive profits from defense sales. Stigler and Friedland (1971) find that investments in
defense firms earn relatively higher stock returns than the market in the 1950s but not in
the 1960s.
Agapos and Gallaway (1970) also test the impact of bargaining power of the government
in the procurement process by examining the margin received by the contractors. They
hypothesize that the high war-time demand decreases the government’s bargaining power,
which leads to higher return for suppliers. However, they find no evidence that the profit
margin is higher when the government’s bargaining power is low.
The conjecture of higher profit margin in government contracts leads to research on the
source of the high profit margin and whether firms manipulate accounting to take
advantage of the cost reimbursement schemes. Specifically, under the cost plus contracts,
the revenues of contractors are based on total production costs. Because firms allocate
overhead based on direct labor costs, one dollar increase of labor costs brings in more than
one dollar revenue through reimbursement. Analytical studies model whether contract
design could be improved to mitigate cost padding (McCall 1970, Reichelstein 1992). 6
Several accounting studies have examined empirically whether contractors shift costs
from commercial segments to government contracting segments (Pownall 1986, Lichtenberg
McCall (1970) studies government contracting efficiency by modeling the incentives of contractors
to bias costs. In this model, contractors have incentives to bias the historical costs and the direction
of bias depends on the operating efficiency of the contractors. He argues that incentive contracts with
high cost-sharing can improve the contracting efficiency. Along the same line, Reichelstein (1992)
analyzes several ways to improve the efficiency of government procurement contracting with the
presence of cost padding. He suggests a budget-based scheme to set a realistic cost target.
Specifically, in addition to the actual costs, an incentive fee proportional to the budget variance
should be provided to the contractor.
6
6
1992, Rogerson 1992, Thomas and Tung 1992, Demski and Magee 1992, McGowan and
Vendrzyk 2002). 7
More recent studies focus on frauds and political connections in
government contracting. Karpoff, Lee and Vendrzyk (1999) provide empirical evidence on
the consequence of procurement fraud discovery. They find that influential contractors are
penalized much more lightly than less influential contractors, who experience decline in
market value and subsequent loss of government contracts. Lander, Kimball, and Martyn
(2008) provide more recent statistics regarding the procurement frauds in the U.S. and
show that contractors are likely to be penalized if fraudulences are discovered in the
procumbent process. Goldman, So, and Rocholl (2012) find that politically connected firms
are more likely to receive government procurement contracts.
To sum up, prior accounting studies have taken the government contracts as given and
have not explicitly examined the role of accounting quality in the contracting process. As
pointed out by Demski and Magee (1992), the government procurement process sets up a
highly unusual institutional arrangement in which the federal government is the dominant
player in the product market and there is uncertainty inherent in the contractor
performance and production process. The question of “what role does accounting play?”
remains largely unanswered.
2.2. Government Contracting Process
Pownall (1986) studies the stock returns of contractors for the period 1968 to 1970, during which
the formation of the Cost Accounting Standard Board (CASB) is heavily debated. CASB is finally
established in 1970 and is charged with setting uniform cost accounting standards to deter firms’
incentives to overstate costs for reimbursement. She finds that defense contracting firms’ stock
returns decline during the debates, consistent with the market viewing the CASB as a deterrent to
firms’ ability to extract excessive reimbursements from the government. Lichtenberg (1992) finds
that return on assets (ROA) of industry segments with government contracts is 68 to 82 percent
higher than the ROA of commercial segments without any defense sales. He interprets the results as
evidence suggesting that excessive profitability of defense contractors could be, at least partially,
attributed to cost shifting. Thomas and Tung (1992) show that defense contractors operating under
costs reimbursement shift the pension costs to defense contracts. McGowan and Vendrzyk (2002),
however, provide an alternative explanation for the excessive profitability of defense contractors
during the 1980s. They find that pure government segments had significantly higher profitability
than mixed and pure commercial segments, while the latter do not differ in profitability. They
interpret the results as evidence suggesting that excessive profitability of defense contractors is
caused by high profitability of defense contracts rather than cost shifting.
7
7
The government procurement process begins with identification of a requirement of a
specific federal activity. A contracting officer in a government agency is appointed to
oversee the contracting process. This officer decides what is to be achieved and whether the
acquisition is really in the best interest of the government. After identifying the needs and
the objective of the purchases, the contracting officer publicizes a request for proposal
(RFP) to potential contractors, indicating the government’s desire for the procurement. The
RFP explains what the government desires to procure (i.e., all specifications and criteria),
contract type, length of the project and requests a proposal from prospective contractors. It
also identifies the selection method to be used to evaluate offers (i.e., competitiveness of the
bid, detailed specifications, etc.), and includes a deadline for the submission of bids or
proposals. Based on the RFP, interested contractors submit their proposals. The proposals
include what techniques and approaches suppliers will use to accomplish the project and at
what price.
To evaluate the proposals, contracting officers consider relevant factors that have an
impact on the source selection decision (e.g., Keyes 2004). The overall objective of proposal
evaluation is to assess the competitiveness of the bid, quality of the proposed products, and
the bidders' ability to successfully accomplish the project. Because the primary factor under
consideration is the attractiveness of the price, contracting officers carefully evaluate the
reasonableness of cost and pricing estimates through price and cost analysis (e.g., Federal
Acquisition Regulation “FAR” 15.8). Contractors rely on comparison with the government’s
own estimates, fair values in the market, bids of competitors and prices in prior
procurements (Keyes 2004, p251-257.) Contracting officers may also request historical
accounting data of bidders, which are used to support the pricing and cost estimates. The
officers may also require certification of the data provided and may also seek help from
auditors to verify the reliability of the data.
The second category of factors relates to the performance of the contract, in particular,
is the potential contractor’s ability to carry out its duties effectively. The relevant factors in
this category include past performance, technical, managerial capability, personnel
qualifications, and schedule of compliance. This evaluation usually calls for a risk
8
assessment of the proposed management or technical plans. Overall, the goal of the
contracting officer is to identify the bidder whose proposal offers the greatest value to the
government in terms of price, risk management, meeting technical requirements, etc. (i.e.,
"best value").
Following the evaluation of various factors, the contracting officer determines which
proposals are within the competitive range (i.e., a reasonable chance of being selected for
award) for the purpose of written or oral discussion.8 The contracting officer then conducts
written or oral discussions with all responsible suppliers who submit proposals within the
competitive range. The content and extent of the discussions is a matter of the contracting
officer's judgment, based on the particulars of each acquisition and each bid. Upon
completion of discussions, the contracting officer issues to all prospective contractors still
within the competitive range a request for best and final offers. Following evaluation of the
best and final offers, the contracting officer selects the contractor whose final offer is most
advantageous to the government.
2.3. Hypotheses
We expect that low-quality internal accounting information can adversely affect bidding
outcomes in two ways.9 First, high noise in its internal reports increases the variance of a
firm’s bidding prices. When the firm over-estimates the costs, its bid is likely out-priced by
the competitors with high-quality internal reports. When the firm under-estimates its costs,
on the other hand, its winning probability does not necessarily increase because the
contracting officer can detect the estimation errors through several mechanisms. First, the
contracting officer can compare the low bid with both the estimates from other bidders and
the government’s own forecasts that are built on similar projects in the past. When a large
Large contracts in our sample are mostly negotiated contracts. Only 2% of the contracts in our
sample are awarded with sealed-bidding, in which the invitation for bids is prepared and publicized,
bids submitted by prospective contracts are received, evaluation of all bids is conducted openly and
the award is announced by considering only price and price-related factors. Sealed-bidding results in
a fixed-price contract.
9 As discussed in Section 2.2, although the contracting officer is not concerned primarily with
accounting quality per se, accounting quality affects the outcome through the competitiveness and
reasonableness of bidding prices.
8
9
deviation exists the contracting officer is likely to question the reasonableness of the bid,
which eventually leads to upward revision of its bid. Second, the government may require
the firm to submit internal accounting records about costs and its pricing formulation,
which help detect under-estimation errors. 10 Overall, over-estimation hurts while underestimation does not necessarily help, which leads to a lowered average winning probability
for the firm with low accounting quality. 11
Empirically, we are able to identify contracts for which the government requires cost or
pricing data from the bidding firms. With detailed accounting data and records across
multiple bidders, errors in cost estimation and pricing should be more transparent than the
cases for which the contracting officer relies solely on estimates. In addition, knowing the
probability of error detection, firms with low-quality internal reports might be deterred
from entering the race in the first place. We therefore make the following hypothesis (in
alternative form):
H1: Firms with low-quality internal accounting reports are less likely to win contracts
that require the submission of cost or pricing data.
Next, we expect that management’s risk aversion should result in a relatively higher bid
from firms with low-quality internal accounting reports because standard utilitymaximization under uncertainty implies contract pricing is higher due to higher risk
premium. Specifically, assume that all management teams require similar levels of utility
by taking on a government project, which should equal the utility derived from alternative
use of assets (i.e., producing commercial products) and that all management teams have
similar level of risk-aversion. Utility maximization under uncertainty suggests that the bid
Thus, the contracting officer has access to a company's books and accounting records related to the
project, and uses historical accounting data as factual and a part of cost or pricing data. For example,
10 United States Code 2306a(h)(1) states “….cost and pricing data are factual, not judgmental; and
are verifiable…” Historical costs and trends are thus used as the primary base to evaluate forecasted
costs or prices, or to project the prospective contractor’s future cost trends.
11 This discussion assumes that competitors propose to produce similar products (i.e., holding
everything else constant). To the extent that the proposed products vary across firms, the
government is choosing the best value instead of the lowest bids. Although our discussion is limited
to the firms that have submitted bids, the basic intuition can also explain why some “bad” firms may
be deterred from bidding certain contracts in the first place.
10
10
from the firm with low-quality accounting information will be higher than that from its
competitors with high-quality information to compensate for the information risk. This
risk-premium reduces the attractiveness of the bid from a firm with low-quality accounting
information.
Empirically, we expect that firms with low-quality internal accounting reports are less
likely to win the contracts that expose the management to a higher level of risk because of
the difficulty in estimating the project costs. Two types of projects involve higher difficulty
in estimating costs. First, the costs for a long-term contract should be more difficult to
estimate than a short-term contract, because the variance of direct costs is proportional to
the contract duration. Additionally, even fixed costs become variable in the long-run as the
firm might need to change its production capacity. We thus propose the following
hypothesis:
H2a: Firms with low-quality internal accounting reports are less likely to win long-term
contracts.
Second, it is difficult to estimate the costs of specialized products. Specialized products
are custom-made for the government and generally lack commercial markets (e.g., spacetechnology related products). The difficulty of estimating the costs comes from at least two
sources. First, because these are specialized products, the firm is less experienced in their
production and is unfamiliar with their costs. Second, the firm is less able to infer the costs
from their prices in commercial markets. Because of the higher uncertainty in estimating
the costs for specialized products, we propose the following hypothesis:
H2b: Firms with low-quality internal accounting reports are less likely to win
specialized products contracts.
Not only does the difficulty of estimating costs reduce the bad firm's competitiveness,
but the lack of risk sharing in some contracts also pushes the bad firm's bidding price
higher. Notably, a fixed-price contract binds the firm to take on the project at a
predetermined fixed price. Thus, a firm bears all the entire risk of any cost overrun. On the
11
other hand, cost-plus contracts allow risk sharing between the firm and the government
because the firm is reimbursed for actual costs incurred (plus a certain level of mark-up).
Because cost uncertainty is higher for the bad firm, the lack of risk diversification in fixedprice contracts leads to a higher bidding price than for the good firm. We thus make the
following prediction:
H2c: Firms with low-quality internal accounting reports are less likely to win a fixedprice contracts.
3. Data and Research Design
3.1 Government Contract
We
begin
by
obtaining
a
comprehensive
sample
of
all
government
contracts
from www.usaspending.gov. The Federal Funding Accountability and Transparency Act of 2006
(FFATA) requires that the Office of Management and Budget (OMB) maintain a single website
that contains important data related to all federal procurement contracts awarded to
contractors across approximately 65 departments and agencies. This data includes information
about the purchaser (e.g., agency), contractor, contract terms, etc. Therefore, our sample
comprises firms that have won government contracts.
Because we use internal control weaknesses as our primary proxy for low-quality internal
accounting reports (See section 4.1), our sample period starts in 2003 as firms began to disclose
internal control weakness following the Sarbanes-Oxley Act 2002 (SOX). We impose three major
filters on the contract data. First, we focus on contracts worth at least one million dollars. We
impose this threshold to ensure that the errors in cost estimates and pricing risk for
contracts above this level are economically significant. 12 Second, to ensure a relatively
competitive bidding process, we require that a contract must have received at least two
bids. Third, we remove contracts awarded to non-public firms who do have a public parent.
We identify parent companies by searching Hoover’s and matching them with Compustat.
These three filters result in 62,010 contracts for the ten-year period 2003-2012.
12
Results are similar when we use $5 million as an alternative threshold.
12
Table 1 lists the rest of our sample selection procedures. We delete 2,659 contracts with
missing contract effective date, expected completion date, or errors in the dates (e.g., the
completion date is before the effective date). We eliminate 7,848 contracts due to missing
data on Compustat to measure control variables in our multiple regression analysis. We
further lose 2,424 observations when we match the sample with Audit Analytics, which
provide data on internal control weaknesses. Our final sample includes 49,079 contracts.
Table 2 Panel A shows the distribution of yearly number and amounts of the contracts
in our sample. We observe U-shaped trends in both the total number of contracts and the
total amount of contracts over the sample period − relatively high during the early 20032005 period, dropped to a flat level in the period of 2006-2011, and then quadrupled in
2012, the last year of our sample. Because contracts in our sample are large (i.e., have an
amount over $1 million and multiple bids), the initial war-time efforts in Iraq during the
early period should have contributed to the higher numbers between 2003 and 2005.
Average contract size in these three years, on the other hand, does not differ from that in
subsequent years. The average contract size across the sample period is about $9.04
million.
Panel B provides statistics on how many contracts a firm is awarded in a given year.
The figures indicate that about 60% of the firms (i.e., 1,424 ÷ 2,337) receive between one
and five contracts per year. It is somewhat surprising to observe that about 13% of the
firms receive more than 25 contracts per year (i.e., over two contracts per month). The data
also indicates that contract size tends to be smaller for firms that are awarded contracts
least and most frequently by the government.
Table 2 Panel C shows the distribution of contracts across major industry sectors,
defined as firms in the same two-digit North American Industry Classification System
(NAICS). The statistics show that 60% of the contracts in our sample are awarded to firms
in manufacturing industries. Professional, scientific, and technical services is a distant
second (23%). The third largest sector is construction, which receives a little over 5% of all
contracts. In terms of average contract size, the health care sector receives the largest
13
contracts, averaging about $60 million per contract. Finance and insurance ranks the
second, with average contract values of about $53 million.
Panel D shows the number of contracts by government agency. Perhaps not
surprisingly, about 74% of the contracts come from Department of Defense alone, which
constitute about 75% of the total value of all contracts. The frequency of contracts is
distributed fairly evenly across the rest of the government agencies. Contract size, on the
other hand, does not appear to be much larger for Department of Defense ($9.2m) compared
with other agencies. The Department of Energy tends to award the largest contracts,
averaging over $40 million per contract.
3.2 Characteristics of Contracts
Table 3 shows some key characteristics of the contracts in the sample. Panel A shows
the frequency of contracts by $5 million increments in contract amount. We observe that
the number of contracts declines exponentially as the contract amount increases, down
from 34,678 contracts in the $1m ‒ $5m range (70.66% of our sample) to only 144 contracts
in the $45m ‒ $50m range (0.29% of our sample). There are also 1,210 contracts with
amount greater than $50m (2.47% of our sample), which constitute over 44% of total
contracts awarded. Overall, the pattern in Panel A indicates that mega contracts are rare.
Panel B shows the number of bids per contract. Again, we observe that the number of
contracts declines exponentially as the number of bids increases, down from 17,045
contracts with two bids (34.73%) to 731 contracts with ten bids each (1.49%). There are a
total of 6,005 contracts (12.24%) that have received over ten bids. Somewhat surprisingly,
as reported in Table 5, the number of bids and contract amount is negatively correlated (= ‒
0.071), which does not support the conjecture that large contracts attract more bids.
Panel C shows the frequency of contracts by duration, which is defined as the expected
contract completion date minus contract effective date. We find that 13,017 contracts
(26.53%) are expected to be finished in six months, and 29,810 contracts (61%) within a
year. There are 9,615 contracts (19.59%) that will be completed in the second year, and
14
3,468 contracts (7.07%) in the third year. Finally, long-term contracts (i.e., those lasting
longer than three years) represent about 12.6% of our sample.
Table 3 Panel D shows the distribution of types of contract pricing. Broadly speaking,
contracts are either fixed-price contracts (52.24%) or cost-based contracts (44.89%). Within
fixed-price contracts, the strictly fixed-price contract is most popular, constituting about
89% of all fixed-price contracts (i.e., 22,783 ÷ 25,525). The rest involve fixed-price plus some
adjustments. Within cost-based contracts, cost plus contracts (i.e., cost plus award fee, fixed
fee, and incentive) are popular, and they represent about 75% of all cost-based contracts
(i.e., 16,477 ÷ 22,034). We also observe that 4,719 cost-based contracts (9.62%) are based on
time and materials, and 838 contracts (1.71%) are based on labor hours alone.
The last panel of Table 3 tabulates contracts that are of national interest. Surprisingly,
contract amounts are not substantially larger than average ($9.04m). For instance, the
average size of contracts related to Hurricane Katrina is $17.15m and for Operation
Enduring Freedom (i.e., war in Afghanistan) are only $5.01m. 13
4. Research Design, Descriptive Statistics And Regression Results
4.1 Proxy for Low-Quality Internal Accounting Reports
Our measure of low-quality internal reports is the disclosure of internal control
weaknesses over financial reporting by management. Feng et al. (2009) argue that internal
control weaknesses are driven by underlying low-quality internal management reports.
They suggest that internal control weaknesses affect the quality of internal reports in two
meaningful ways. While some types of internal control weaknesses are likely to result in
erroneous internal reports, other kinds lead to stale financial information (Feng et al. 2009,
p. 193). They provide empirical evidence that internal control weaknesses in financial
reporting reduce the accuracy of management earnings forecasts, suggesting that the
quality of internal control over financial reporting is strongly correlated with the quality of
internal management reports.
It is not clear to us, however, what qualifies a contract as having national interest in the first
place.
13
15
One major advantage of internal control weaknesses over accrual-based accounting
quality measurers in our setting is that internal control weaknesses should reduce the
quality of unaudited accounting reports for decision making. Accrual-based accounting
quality measures, on the other hand, are derived based on financial statements for external
users, which have been analyzed by audit committees and audited by external auditors. In
a supplemental analysis, we also use management forecast accuracy as an alternative
proxy for the quality of internal accounting information.
We focus more specifically on internal control weakness disclosed under SOX 302 for
two reasons. First, SOX 302 weaknesses are particularly amenable to our setting because
they represent instances in which managers are likely to be aware of the limitations of
their internal accounting systems. In order for a firm to disclose a material weakness under
SOX 302, managers must be aware of the deficiency and choose to disclose it (e.g.,
Ashbaugh-Skaife, Collins, and Kinney 2007). This feature is compatible with our setting as
in some of the tests we assume the management requires a risk premium when pricing the
projects given the awareness of the less reliable accounting information. Second, prior
literature finds that SOX 302 material weaknesses affect accounting outputs as they are
significantly associated with lower accruals quality (Doyle, Ge and McVay 2007a).14
4.2 Regression Models
Because we only observe firms that have won the contracts, we rely on the following
Probit regression model to estimate the associations between different types of firms (i.e.,
with or without internal control weaknesses) and types of contracts that involve more
difficulties in estimating the costs or have less diversifiable risk:
Pr (ICWFirm) it = a 0 + a 1 AccData ijt + a 2 LongTerm ijt + a 3 Specialized ijt + a 4 Fixed ijt
+ ∑ a m XContract ijt + ∑ a n XFirm ijt-1 + ε ijt
(1)
We focus on internal control material weaknesses instead of internal control deficiencies because
the latter are generally less severe and are not required to be publicly disclosed (Doyle, Ge and
McVay 2007b).
14
16
where 𝑖 indexes firms, 𝑗 indexes contracts, and 𝑡 indexes year. Dependent variable,
ICWFirm, is defined as one if a firm reports a material weakness under SOX 302 in the
prior year and zero otherwise. Our main variables of interest are AccData, LongTerm,
Specialized, and Fixed, each of which indicates a type of contract that is less likely to be
awarded to the firms with low-quality internal accounting reports.
AccData is an indicator variable that takes the value of one if the government requires
cost or pricing data from firms when choosing contractors and zero otherwise.15 LongTerm
takes the value of one if the contract duration is greater than three years and zero
otherwise. Specialized is an indicator variable that takes the value of one if the products or
services produced under the contract are specially made and lack commercial markets and
zero otherwise. Fixed is an indicator variable that takes the value of one if the type of
contract pricing is fixed-price and zero otherwise. Under the null that accounting quality
does not influence the winning probabilities across contract types, we would expect
insignificant coefficients for these indicator variables. To the extent that firms with internal
control weaknesses are less likely to win contracts that require accounting data, are longterm in nature, produce specialized products, or are fixed-price, we predict negative
coefficients for these indicator variables (i.e., negative coefficients for a 1 to a 4 ).
We control for a vector of four contract-level variables (XContract) that may be
associated with the type of contractors selected by the government and may also be
correlated with our main variable of interest. First, we include an indicator variable
NationalInt in the regression, which is defined as one if the contract is related to national
interest and zero otherwise. If the contracting officer is aware of firms’ internal control
problems and thus is less likely to award the contracts with national interest to these firms
(e.g., for political reasons), we expect a negative coefficient for NationalInt. Second, we
include a variable logNumBid as a control, which is the natural log of the number of bids on
the contract. Contracting officers may be less willing to award a contract to firm with
To keep a constant sample, we assume the cost and pricing data are not requested by the
government when this field is blank in the government contract dataset (35% of the observations). To
the extent that some of these contracts require submission of cost or pricing data, our results are
biased toward the null. We obtain similar results when we exclude these observations.
15
17
internal control weaknesses when they have a larger pool of candidates to choose from. We
therefore expect a negative coefficient for logNumBid. Third, we include logAmount in the
model, which is the natural log of the dollar amount (in million) of the contract. We expect a
negative coefficient for logAmount if the contracting officer is more reluctant to award large
contracts to firms with internal control weaknesses. Our last contract-level variable is
Defense because, as noted earlier, 74% of the contracts are awarded by the Department of
Defense and these contracts may be awarded to different type of firms.
We also control for a number of firm characteristics that are associated with material
weakness disclosures (XFirm). Prior literature finds that firms that disclose material
weaknesses are, on average, smaller, younger, financially weaker, more complex, growing
rapidly, and more likely to be undergoing restructuring (Ge and McVay 2005; Doyle et al.
2007b). We control for firm size using variable logAssets, which is the natural log of total
assets. We control for firm age by variable logAge, which is the log of number of years for
which a firm has data available on Compustat. We expect negative coefficients for logAsset
and logAge.
We include two controls that proxy for the financial health of the firm. First, Loss is
measured as the proportion of loss years in the preceding five years. Second, Z-Score is
Alman’s Z score on bankruptcy risk. As poorly performing firms may not have the resources
for adequate internal control systems, we expect positive coefficients for Loss and a
negative coefficient for Z-Score. To control for operating complexity, we include two
variables. First, logNumSeg is measured as the natural log of the sum of the number of
operating and geographic segments as reported in the Compustat segment files. Second,
Foreign captures foreign operations, defined as one if the firm has non-zero foreign
currency translation on Compustat and zero otherwise.
Firms experiencing rapid growth may also be more prone to internal control weaknesses
since it generally takes time for the firm to adapt and modify its internal controls. We thus
include change in sales growth (𝛥 SGrowth) and acquisition activities (ACQ) as control
variables. 𝛥SGrowth is measured as the change in sales growth from year 𝑡 − 1 to year 𝑡,
and ACQ is an indicator variable that takes the value of one if the firm reports sales from
18
acquisitions and zero otherwise. Prior research argues that firms undergoing restructuring
may also have poorer internal controls. We therefore include an indicator variable
(Restruct) that takes the value of one if the firm reports restructuring charges and zero
otherwise. 16
We expect that prior contracting experience with a firm should reduce information
asymmetry between the government and the firm. We therefore control for whether a firm
is awarded a contract in the prior period by PriorContract, which takes the value of one if
the firm have received contract in the prior year and zero otherwise. We expect a positive
coefficient for PriorContract. We also control for the political connection between the firm
and the government (Political), defined as one if a board member of the firm holds a
position such as a Senator, Member of the House of Representatives, Member of the
Administration, or is a Director of a government organization during that year and zero
otherwise (Goldman et al. 2012). We adjust standard errors for clustering by firm and year
to account for multiple contracts won by the same firm. 17
4.3 Descriptive Statistics
Table 4 provides descriptive statistics of the variables in the regression model (1). We
observe that only 4.9% of the firms in the sample had disclosed internal control
weaknesses, which is significantly lower than the unconditional mean of 14% for our
sample period. In other words, firms that win government contracts, on average, have
relatively high quality internal reports. This is hardly surprising as firms that are awarded
tend to be larger and more mature. While a sample with relatively low frequencies of
internal control weaknesses likely weakens the power of our tests, our research design
overcomes this issue by focusing on contract-level variations.
The mean of AccData is 12.5%, indicating that a relatively small proportion of contracts
require the firms to submit cost or pricing data to the contracting officer. On the other
hand, the majority of the contracts in our sample (i.e., 83.6%) involve specialized products.
In our sample, 99% of the firms are audited by Big Four auditors, so we do not include this
variable in the model. Our results are similar if we include it in the model.
17 All continuous variables are winsorized at the 1st and 99th percentile.
16
19
We define long-term contracts (LongTerm) as the ones that last beyond three years (12.6%
of our sample), for which the costs are relatively more difficult to estimate. Following the
classification in Panel D of Table 3, we define Fixed as pure fixed-price contracts and fixedprice contracts with adjustment (52% of our sample).
Descriptive statistics of other firm-level variables indicate that firms in our sample are
large. Average assets are over $11 billion, significantly higher than the Compustat average
for the same period. Not surprisingly, firms in the sample are also older and rarely report
losses. Firms on average have about three segments (including both geographic and
operating segments), and about 21% of the firms in the sample have foreign operations. We
find that over a quarter of the firms in our sample are politically connected. In addition,
82.2% of the firms have received a contract in the previous year.
Table 5 Panel A shows the pairwise Pearson correlations between ICWFirm and
contract variables, as well as the correlations among contract variables. Consistent with
our hypotheses, we find that ICWFirm is negatively correlated with AccData, LongTerm,
Specialized, and Fixed. The magnitude of these correlations is comparable to the magnitude
of correlations between ICWFirm and many of the firm-level determinants of internal
control weakness identified in prior research.
Contracts that require cost or pricing data (AccData) are positively correlated with
LongTerm (= 0.053) and Specialized (= 0.155), indicating that the contracting officer is more
likely to examine accounting records for long-term projects and projects that produce
specialized products. This is consistent with the notion that it is more difficult to estimate
the cost in such cases. Likewise, AccData is positively correlated with National and
logAmount, indicating that the contracting officer is more likely to examine accounting
records when the projects are related to national interest and when the amount involved is
large. In contrast, AccData is negatively associated with Fixed (= ‒0.125), consistent with
our argument that fixed-price contracts are less risky to the government. Finally, AccData
is positively correlated with Defense, indicating that defense contracts are more likely to be
subject to the examination of cost or pricing data.
20
Variable LongTerm is positively correlated with Specialized (= 0.061), indicating that
custom-made products without commercial markets are more complex and tend to last
longer than three years. It is, however, negatively correlated with Fixed (= ‒0.118),
indicating that contracts that last more than three years are more likely to be cost-plus
contracts. Not surprisingly, LongTerm is positively correlated with logAmount (= 0.127), as
these projects tend to be more costly. We also find that long term contracts tend to receive
fewer bids (Corr = -0.060), suggesting that fewer firms are interested in projects that last
over three years. Variable Specialized is negatively associated with Fixed and logNumBid,
indicating that projects involving specialized projects are less likely to be fixed-price
contracts and receive relatively fewer bids.
Overall, the correlations among these contract-level variables support our arguments
about the nature of these contract terms in Section 2. Panel B of Table 5 presents the
correlations among firm-level variables. We observe that firm age (logAge) and foreign
operation (Foreign) have the highest correlations with ICWFirm, and the signs of these
correlations are consistently in tune with expectations. Interestingly, the correlation
between political connection (Political) and internal control weakness is negative. In
general, no high correlations among other variables in Panel B are observed, indicating
multicollinearity is unlikely to be a problem in our regression analysis.
4.4 Results From Probit Model
Table 6 presents the results of estimating Probit model (1). In Columns (1) through (4),
we enter the four main variables of interest in the regression one at a time, and in Column
(5), we include all of them in the same regression. For ease of interpreting the coefficients
in Probit models, we report marginal effects. Consistent with our hypothesis H1, Columns
(1) and (5) show negative coefficients for AccData (t = ‒4.82 and ‒4.21), indicating that firms
with internal control weaknesses are less likely to be rewarded with contracts that require
submission of cost or pricing data. The estimated coefficient in Column (5) is ‒3.87%,
indicating that contracts that require accounting and pricing data are about four-percent
less likely to be received by a firm with internal control weaknesses. Considering the fact
21
that only less than 5% of the firms in the sample have internal control weaknesses, this
figure is economically significant. These results support our hypothesis H1.
In Columns (2) and (5), we find negative coefficients for LongTerm (t = ‒3.27 and ‒2.03),
consistent with hypothesis H2 that firms with internal control weaknesses are less likely to
receive contracts that last longer than three years. Results are similar when we use two
years as the cutoff alternatively. The estimated coefficient for LongTerm in Column (5) is 0.0147, indicating that long-term contracts are about 1.5% less likely to be received by a
firm with internal control weaknesses.
We find relatively weaker results regarding Specialized and Fixed dummy variables.
Specifically, the negative coefficients for Specialized and Fixed are significant only when
other testing variables are also included in Column (5). The magnitudes of estimated
coefficients are in the neighborhood of one to two percent, similar to that for LongTerm.
Overall, the results in Table 6 are generally consistent with hypotheses H2a to H2c.
Regarding contract-level control variables, we find that Defense carries significantly
positive coefficients, indicating that firms with internal control weaknesses are more likely
to receive defense contracts. For firm-level contract variables, we find that internal control
weaknesses are negatively associated with firm age (logAge) and positively associated with
foreign operations (Foreign). These results are consistent with prior research. Finally, we
find that Political tends to be negatively associated with ICWFirm, indicating that firms
with political connections in our sample are less likely to disclose internal control
weaknesses.
5. Additional analyses
5.1 Do Firms with Low-Quality Internal Accounting Reports Win Fewer Contracts?
The evidence in Section 4 is consistent with the hypotheses that firms with low-quality
internal accounting reports are less likely to win contracts that require the examination of
accounting data or impose higher pricing risk on the firm’s management. Assuming firms
with low-quality internal accounting reports do not have advantages in winning other types
22
of government contracts, we expect them to win fewer government contracts on average.
This section provides evidence on this issue by conducting an out-of-sample firm-level
analysis.
Specifically, we select a sample of firms from Audit Analytics that have government
sales in prior three years (based on Compustat Segment data). Focusing on firms with
government sales helps ensure that firms in the sample actively compete for government
contracts. After merging Audit Analytics with Compustat Segment files, there remain 5,910
firm-year observations with requisite data for the following regression analyses:
logContract or logAmountit = β 0 + β 1 ICWFirm it + ∑ β n XFirm ijt-1 + ε ijt
(2)
where logContract it is the natural log of the total number of government contracts given to
firm i in year t, logAmountit is the natural log of the total contract amounts for firm i in
year t, and other variables are defined previously. We include firm-level control variables to
ensure that our results are not driven by firm characteristics that are correlated with
ICWFirm. Because we predict that firms with low-quality internal accounting reports are
less likely to win government contract on average, we expect a negative coefficient for
ICWFirm.
Columns (1) and (2) of Table 7 present the results of the regressions. We find negative
coefficients for ICWFirm in the regression of logContract (t = ‒2.27) and in the regression of
logAmount (t = ‒2.56). Consistent with expectations, we also find larger and older firms
tend to win more government contracts. Interestingly, politically connected firms also win
more government contracts. To ensure that our results are not driven by some timeinsensitive omitted variables, we also estimate a change version of Equation (2) in which all
variables are first-differenced. Columns (3) and (4) of Table 7 report the regression results
of this change model. We continue to find significantly negative coefficients for ICWFirm.
Overall, results from these firm-level analyses support our contract-level results.
5.2 Management Forecast Accuracy as an Alternative Proxy for Information Quality
In our main analysis, we use SOX 302 internal control weaknesses over financial
reporting as our main proxy for the quality of internal accounting reports. In this section,
23
we present results when we use management earnings forecast accuracy as an alternative
proxy for the quality of internal accounting reports. We expect that firms with more
accurate earnings forecasts are more likely to be awarded with contracts that require
accounting data, long-term contracts, specialized products contracts, and fixed-priced
contracts.
We run the following regression to test these predictions:
ForecastError it = γ 0 + γ 1 AccData ijt + γ 2 LongTerm ijt + γ 3 Specialized ijt + γ 4 Fixed ijt
+ ∑ γ m XContract ijt + ∑ γ n YFirm ijt-1 + ε ijt
(3)
where ForecastError is defined as the absolute difference between the last annual earnings
forecast issued prior to the contract date and its corresponding actual realization, scaled by
assets per share. 18 We expect that γ 1 to γ 4 are negative.
Following Ajinkya, Bhojraj and Sengupta (2005), we include a vector of firm-level
control variables (YFirm ijt-1) that explain forecast accuracy. InstHold is the percentage of
common shares held by institutions at the beginning of the period. LogAsset is the natural
log of total assets during the period. Analyst is the number of analysts providing a forecast
for a firm prior to the managers’ forecast. Litigate is a dummy equal to one if a firm
operates in an industry with high litigation risk and zero otherwise.19 MKBK is the marketto-book ratio, calculated as the market value of assets divided by the book value of assets.
NegEarn is an indicator variable equal to one if the firm reports net losses in the period,
and zero otherwise. EarnVol is the standard deviation of quarterly net income over the
seven preceding years.20 Beta is equity beta for the fiscal year, obtained from the Center for
Research in Security Prices (CRSP) decile portfolio files. Disp is the standard deviation of
analyst forecasts at the beginning of the year. Age is the natural log of the firm’s age.
AbsChgROA is the absolute value of the change in return on assets from the prior period.
StdRet is the standard deviation of returns over the 120 days prior to the forecast date.
Horizon is the number of days between the fiscal period end and the announcement date,
We exclude preannouncements in this analysis (i.e., forecasts issued after year-end).
This includes firms operating in industries with SIC codes 2833-2836, 8731-8734, 3570-3577, 36003674, 7371-7379, 5200-5961, 4812-4813, 4833, 4841, 4899, 4911, 4922-4924, 4931.
20 We require at minimum three observations over this time period.
18
19
24
divided by 100. Optimism is analyst forecast optimism at the beginning of the year, defined
as the difference between the mean forecast estimate and actual realized earnings, scaled
by the absolute value of actual earnings. Surprise is the absolute value of the forecast
guidance less the last available consensus analyst forecast, scaled by assets per share. We
also include industry and year fixed effects in this regression. Standard errors are double
clustered by firm and year.
Table 8 presents the results of the regressions. We find that AccData is negatively
associated with forecast errors in Columns (1), suggesting that firms with more accurate
management forecast are more likely to win contracts requiring accounting data. We also
find in Columns (2) and (3) that firms with more accurate forecasts are more likely to win
long-term contracts and contracts involving specialized products. On the other hand, we do
not find that firms with more accurate management forecast are more likely to win fixedprice contracts. When we enter all variables at once in the same regression, results in
Column (5) are consistent with these results. On balance, we interpret the results in Table
8 as being supportive of our main hypothesis that firms with low-quality internal
accounting reports are less likely to win certain types of government contracts.
6. Conclusion and Limitations
In this study, we examine U.S. government contracts awarded to public firms and test
whether a firm's internal accounting reports quality affects its ability to compete against
other firms in winning contracts that require high-quality accounting information to price
the project. We examine federal procurement contracts awarded to public contractors
during 2003-2012 that has received at least two bids and is worth at least one million
dollars. We find that firms that had disclosed internal control weakness are less likely to
win contracts for which the government requires submission of cost or pricing data in the
contracting process. We also find that firms with internal control weaknesses are less likely
to win contracts longer than three years or require specialized-product contracts. Finally,
firms with internal control weaknesses are less likely to win fixed-price contracts for which
the firm bears relatively higher pricing risk. These results support our proposition that low25
quality internal accounting reports hinder management's ability to accurately estimate the
cost of certain types of contracts. The results from our management earnings forecast
accuracy tests also corroborate our main results.
We acknowledge several limitations of our study. First, only the winners of contracts
are observed (i.e., firms that compete but fail to win the contract are not disclosed).
Therefore, our analyses are limited to examining the variation in the winners associated
with different contract types. One possible future extension is to identify the competitors on
a contract (as well as their bids), and conduct a deeper analysis of the impact of accounting
quality. Second, although we follow the literature and use internal control weaknesses and
management earnings forecast accuracy as alternative proxies for quality of internal
accounting reports, the underlying construct is ultimately unobservable in our context.
Future research in this area could develop more powerful measures of the quality of
internal accounting reports using publicly observable data or through proprietary sources.
26
References
Agapos, A. M., and Paul R. Dunlap. "The theory of price determination in governmentindustry relationships." The Quarterly Journal of Economics (1970): 85-99.
Agapos, A. M., and Lowell E. Gallaway. "Defense profits and the renegotiation board in the
aerospace industry." The Journal of Political Economy 78.5 (1970): 1093-1105.
Ajinkya, Bipin, Sanjeev Bhojraj, and Partha Sengupta. "The association between outside
directors, institutional investors and the properties of management earnings
forecasts." Journal of accounting Research 43.3 (2005): 343-376.
Ashbaugh-Skaife, Hollis, Daniel W. Collins, and William R. Kinney Jr. "The discovery and
reporting of internal control deficiencies prior to SOX-mandated audits." Journal of
Accounting and Economics 44.1 (2007): 166-192.
Ashbaugh‐Skaife, Hollis, Daniel W. Collins, and Ryan LaFond. "The effect of SOX internal
control deficiencies on firm risk and cost of equity."Journal of Accounting Research 47.1
(2009): 1-43.
Bohi, Douglas R. "Profit performance in the defense industry." The Journal of Political
Economy 81.3 (1973): 721-728.
Bowen, Robert M., Larry DuCharme, and David Shores. "Stakeholders' implicit claims and
accounting method choice." Journal of Accounting and Economics20.3 (1995): 255-295.
Costello, Anna M. "Mitigating Incentive Conflicts in Inter-Firm Relationships: Evidence
from Long-Term Supply Contracts." Journal of Accounting and Economics (2013).
Costello, Anna M. "The impact of financial reporting quality on debt contracting: Evidence
from internal control weakness reports." Journal of Accounting Research 49.1 (2011):
97-136.
Dechow, Patricia, Weili Ge, and Catherine Schrand. "Understanding earnings quality: A
review of the proxies, their determinants and their consequences."Journal of Accounting
and Economics 50.2 (2010): 344-401.
Demski, Joel S., and Robert P. Magee. "A perspective on accounting for defense
contracts." The Accounting Review 67.4 (1992): 732-740.
Dhaliwal, Dan, Chris Hogan, Robert Trezevant, and Michael Wilkins. "Internal control
disclosures, monitoring, and the cost of debt." The Accounting Review 86.4 (2011): 11311156.
Doyle, Jeffrey T., Weili Ge, and Sarah McVay. "Accruals quality and internal control over
financial reporting." The Accounting Review 82.5 (2007): 1141-1170.
Doyle, Jeffrey, Weili Ge, and Sarah McVay. "Determinants of weaknesses in internal
control over financial reporting." Journal of Accounting and Economics44.1 (2007): 193223.
Feng, Mei, Chan Li, and Sarah McVay. "Internal control and
guidance." Journal of Accounting and Economics 48.2 (2009): 190-209.
management
Ge W, and Sarah McVay. “The disclosure of material weaknesses in internal control after
the Sarbanes-Oxley Act.” Accounting Horizons, 19.3 (2005): 137-158.
27
Goldman, Eitan, Jongil So, and Joerg Rocholl. "Politically connected boards of directors and
the allocation of procurement contracts." Forthcoming Review of Finance (2012).
Hui, Kai Wai, Sandy Klasa, and P. Eric Yeung. "Corporate suppliers and customers and
accounting conservatism." Journal of Accounting and Economics 53.1 (2012): 115-135.
Karpoff, Jonathan M., D. Scott Lee, and Valaria P. Vendrzyk. "Defense procurement fraud,
penalties, and contractor influence." Journal of Political Economy 107.4 (1999): 809-842.
Keyes, W. N. 2004. Government contracts In a Nut Shell. West Academic Publishing.
Kim, Jeong-Bon, Byron Y. Song, and Liandong Zhang. "Internal control weakness and bank
loan contracting: Evidence from SOX Section 404 disclosures." The Accounting
Review 86.4 (2011): 1157-1188.
Lander, G. H., V. J. Kimball, and K. A. Martyn. "Government procurement fraud." CPA
JOURNAL 78.2 (2008): 16.
Lichtenberg, Frank R. "A perspective on accounting for defense contracts." The Accounting
Review 67.4 (1992): 741-752.
Libby, Robert, Patricia A. Libby, and Daniel G. Short. Financial Accounting. Boston:
McGraw-Hill/Irwin, 2012.
McCall, John J. "The simple economics of incentive contracting." The American Economic
Review 60.5 (1970): 837-846.
McGowan, Annie S., and Valaria P. Vendrzyk. "The relation between cost shifting and
segment profitability in the defense-contracting industry." The Accounting Review 77.4
(2002): 949-969.
Pownall, Grace. "An empirical analysis of the regulation of the defense contracting
industry: The Cost Accounting Standards Board." Journal of Accounting Research 24.2
(1986): 291-315.
Raman, Kartik, and Husayn Shahrur. "Relationship-specific investments and earnings
management: Evidence on corporate suppliers and customers." The Accounting
Review 83.4 (2008): 1041-1081.
Reichelstein, Stefan. "Constructing incentive schemes for government contracts: An
application of agency theory." Accounting Review (1992): 712-731.
Rogerson, William P. "Profit regulation of defense contractors and prizes for
innovation." The Journal of Political Economy (1989): 1284-1305.
Rogerson, William P. "Overhead allocation and incentives for cost minimization in defense
procurement." Accounting Review (1992): 671-690.
Spiceland, J. David, James F. Sepe, Mark W. Nelson. "Intermediate Accounting” McGrawHill/Irwin (2012).
Stigler, George J., and Claire Friedland. "Profits of defense contractors." The American
Economic Review 61.4 (1971): 692-694.
Thomas, Jacob K., and Samuel Tung. "Cost manipulation incentives under cost
reimbursement: Pension costs for defense contracts." Accounting Review(1992): 691-711.
Weidenbaum, Murray L. "Arms and the American economy: A domestic convergence
hypothesis." The American Economic Review 58.2 (1968): 428-437.
28
Table 1
Sample Selection
This table presents the sample selection procedure. Government contract data is obtained
from the USA Spending database (http://www.usaspending.gov/). We apply three data
filters to the government contract data to construct our sample. First, contracts must be
awarded to publicly traded firms (or their subsidiaries) that have an amount above one
million dollars. Second, a contract must receive at least two bids. Third, a firm (or its
parent) that receives the contract is identifiable on Compustat.
Contracts > $1 million, with at least two bids, and with available
GV KEY (2003-2012)
Less: Obs. with missing contract date data
Less: Obs. with missing Compustat data for control variables
Less: Obs. with missing Audit Analytics data
62,010
(2,659)
(7,848)
(2,424)
49,079
29
Table 2
Contract Distribution
This table presents the distribution of our government contract sample. Panel A shows the
distribution of contracts by year. Panel B shows the distribution of firms by number of contracts
awarded. Panel C shows the distribution of contracts by NAICS two-digit industry sector. Panel
D shows the distribution of contracts by government agency.
Panel A: Distribution by Year
Year
Number of
Contracts
% of Total
Contracts
2003
2004
2005
2006
2007
2008
2009
2010
2011
2012
7,535
7,218
7,775
1,752
2,639
3,551
2,243
3,108
2,787
10,471
15.35
14.71
15.84
3.57
5.38
7.24
4.57
6.33
5.68
21.33
Total
Award
($Millions)
66,230.06
65,356.91
74,718.45
16,632.35
22,257.59
33,939.91
18,684.09
25,895.94
24,632.34
95,155.89
Total
49,079
100.00
443,503.54
Panel B: Distribution by Firm-Year
Number of Contracts
Number of Firm-Years
0-5
5-10
10-15
15-20
20-25
>25
1,424
340
137
82
46
308
Total
2,337
30
% of Total
Award
Avg. Award
($Millions)
14.93
14.74
16.85
3.75
5.02
7.65
4.21
5.84
5.55
21.46
8.79
9.05
9.61
9.49
8.43
9.56
8.33
8.33
8.84
9.09
100.00
9.04
Avg. Amount
($Millions)
8.61
11.13
14.98
12.57
15.56
8.43
9.04
Panel C: Distribution by Industry
NAICS Code
(2-Digit)
11
21
22
23
31-33
42
44-45
48-49
51
52
53
54
56
61
62
72
81
99
Total
Industry Sector
Agriculture, Forestry, Fishing and Hunting
Mining, Quarrying, and Oil and Gas Extraction
Utilities
Construction
Manufacturing
Wholesale Trade
Retail Trade
Transportation and Warehousing
Information
Finance and Insurance
Real Estate and Rental and Leasing
Professional, Scientific and Technical Services
Admin. and Support and Waste Mgmt. and Remediation Services
Educational Services
Health Care and Social Assistance
Accommodation and Food Services
Other Services, except Public Administration
Unclassified
31
Number of
Contracts
% of Total
Contracts
24
43
269
2,539
29,346
1,374
90
1,878
1,076
339
47
11,101
413
26
219
4
95
196
49,079
0.05
0.09
0.55
5.17
59.79
2.80
0.18
3.83
2.19
0.69
0.10
22.62
0.84
0.05
0.45
0.01
0.19
0.40
100.00
Total
Award
($Millions)
85.11
430.73
1,881.43
24,581.23
291,538.23
16,074.22
222.95
8,606.39
4,807.91
18,131.99
124.07
59,491.21
3,266.84
90.17
13,035.48
7.81
357.99
769.78
443,503.54
% of Total
Award
Avg. Award
($Millions)
0.02
0.10
0.42
5.54
65.74
3.62
0.05
1.94
1.08
4.09
0.03
13.41
0.74
0.02
2.94
0.00
0.08
0.17
100.00
3.55
10.02
6.99
9.68
9.93
11.70
2.48
4.58
4.47
53.49
2.64
5.36
7.91
3.47
59.52
1.95
3.77
3.93
9.04
Panel D: Distribution by Government Agency
Number of
Contracts
37
621
131
163
441
92
316
693
55
4
72
8
19
2
1
1,054
3
291
36
51
16
1
163
529
766
168
7
926
1,060
72
17
503
94
36,076
4,591
49,079
Agency
1100: Executive Office of the President
1200: Department of Agriculture
1300: Department of Commerce
1400: Department of the Interior
1500: Department of Justice
1600: Department of Labor
1900: Department of State
2000: Department of the Treasury
2400: Office of Personnel Management
2700: Federal Communications Commission
2800: Social Security Administration
2900: Federal Trade Commission
3100: Nuclear Regulatory Commission
3300: Smithsonian Institution
3400: International Trade Commission
3600: Department of V eterans Affairs
4500: Equal Employment Opportunity Commission
4700: General Services Administration
4900: National Science Foundation
5000: Securities and Exchange Commission
5800: Federal Emergency Management Agency
6000: Railroad Retirement Board
6800: Environmental Protection Agency
6900: Department of Transportation
7000: Department of Homeland Security
7200: Agency for International Development
7300: Small Business Administration
7500: Department of Health and Human Services
8000: National Aeronautics and Space Administration
8600: Department of Housing and Urban Development
8800: National Archive and Records Administration
8900: Department of Energy
9100: Department of Education
9700: Department of Defense
Other
Total
32
% of Total
Contracts
0.08
1.27
0.27
0.33
0.90
0.19
0.64
1.41
0.11
0.01
0.15
0.02
0.04
0.00
0.00
2.15
0.01
0.59
0.07
0.10
0.03
0.00
0.33
1.08
1.56
0.34
0.01
1.89
2.16
0.15
0.03
1.02
0.19
73.51
9.35
100.00
Total Award
($Millions)
858.94
1,918.66
1,229.41
810.16
2,880.86
340.11
2,055.14
3,068.48
182.19
5.07
334.90
24.25
66.76
2.80
1.77
10,592.08
4.95
1,089.20
360.44
223.90
63.71
1.73
759.08
3,572.77
5,074.35
923.57
20.24
10,211.50
7,967.63
850.89
53.99
20,263.68
795.64
332,068.66
34,826.02
443,503.54
% of Total
Award
0.19
0.43
0.28
0.18
0.65
0.08
0.46
0.69
0.04
0.00
0.08
0.01
0.02
0.00
0.00
2.39
0.00
0.25
0.08
0.05
0.01
0.00
0.17
0.81
1.14
0.21
0.00
2.30
1.80
0.19
0.01
4.57
0.18
74.87
7.85
100.00
Avg. Award
($Millions)
23.21
3.09
9.38
4.97
6.53
3.70
6.50
4.43
3.31
1.27
4.65
3.03
3.51
1.40
1.77
10.05
1.65
3.74
10.01
4.39
3.98
1.73
4.66
6.75
6.62
5.50
2.89
11.03
7.52
11.82
3.18
40.29
8.46
9.20
7.59
9.04
Table 3
Contract Characteristics
This table presents the characteristics of our government contract sample. Panel A shows the distribution of contracts by
intervals of contract amount. Panel B shows the distribution of contracts by the number of bids. Panel C shows the distribution of
contracts by duration. Panel D shows the distribution of contracts by type of contract pricing. Panel E shows the distribution of
contracts by type of national interest.
Panel A: Frequency by Contract Amount Intervals
Contract Amount
Number of
Contracts
% of Total
Contracts
1M-5M
5M-10M
10M-15M
15M-20M
20M-25M
25M-30M
30M-35M
35M-40M
40M-45M
45M-50M
>50M
34,678
7,412
2,464
1,203
739
448
320
265
196
144
1,210
70.66
15.10
5.02
2.45
1.51
0.91
0.65
0.54
0.40
0.29
2.47
Total
Award
($Millions)
81,154.67
50,877.51
29,797.11
20,614.80
16,486.86
12,241.26
10,357.98
9,877.17
8,287.99
6,821.31
196,986.88
Total
49,079
100.00
443,503.54
Panel B: Frequency by Number of Bids
Number of Bids
Number of
Contracts
% of Total
Contracts
2
3
4
5
6
7
8
9
10
>10
17,045
9,554
6,231
3,273
2,504
1,576
1,395
765
731
6,005
34.73
19.47
12.70
6.67
5.10
3.21
2.84
1.56
1.49
12.24
Total
Award
($Millions)
188,158.78
82,620.55
51,276.24
24,966.37
15,125.68
7,883.17
10,258.98
3,025.10
10,436.81
49,751.85
Total
49,079
100.00
443,503.54
Contract Length
Number of
Contracts
% of Total
Contracts
1 month
3 month
6 month
1 year
2 years
3 years
More than 3 years
3,583
3,861
5,573
16,793
9,615
3,468
6,186
7.30
7.87
11.36
34.22
19.59
7.07
12.60
Total
Award
($Millions)
28,667.39
19,637.97
41,659.68
121,312.21
88,908.78
39,526.26
103,791.24
Total
49,079
100.00
443,503.54
Panel C: Frequency by Contract Length
33
% of Total
Award
18.30
11.47
6.72
4.65
3.72
2.76
2.34
2.23
1.87
1.54
44.42
Avg. Award
($Millions)
2.34
6.86
12.09
17.14
22.31
27.32
32.37
37.27
42.29
47.37
162.80
100.00
9.04
% of Total
Award
Avg. Award
($Millions)
42.43
18.63
11.56
5.63
3.41
1.78
2.31
0.68
2.35
11.22
11.04
8.65
8.23
7.63
6.04
5.00
7.35
3.95
14.28
8.29
100.00
9.04
% of Total
Award
Avg. Award
($Millions)
6.46
4.43
9.39
27.35
20.05
8.91
23.40
8.00
5.09
7.48
7.22
9.25
11.40
16.78
100.00
9.04
Panel D: Frequency by Type of Contract Pricing
Type of Contract Pricing
Fixed-Price:
Fixed Price Level of Effort
Firm Fixed Price
Price with Economic Price Adjust
Fixed Price Incentive
Fixed Price Award Fee
Fixed Price Redetermination
Total:
Cost-Based:
Cost Plus Award Fee
Cost No Fee
Cost Sharing
Cost Plus Fixed Fee
Cost Plus Incentive
Time and Materials
Labor Hours
Total:
Combination (two or more)
Other (none of the above)
Not Reported
Total
Number of
Contracts
% of Total
Contracts
Total
Award
($Millions)
% of Total
Award
81
22,783
1,366
704
501
90
25,525
0.17
46.42
2.78
1.43
1.02
0.18
52.01
303.30
164,531.46
32,787.06
14,960.36
3,010.88
417.39
216,010.45
0.07
37.10
7.39
3.37
0.68
0.09
48.71
3.74
7.22
24.00
21.25
6.01
4.64
8.46
7,056
640
37
7,188
1,556
4,719
838
22,034
14.38
1.30
0.08
14.65
3.17
9.62
1.71
44.89
115,544.12
5,453.10
172.35
39,359.42
32,054.83
21,655.16
3,559.68
217,798.68
26.05
1.23
0.04
8.87
7.23
4.88
0.80
49.11
16.38
8.52
4.66
5.48
20.60
4.59
4.25
9.88
1,315
93
112
2.68
0.19
0.23
8,044.82
511.91
1,137.68
1.81
0.12
0.26
6.12
5.50
10.16
49,079
100.00
100.00
9.04
% of Total
Award
Avg. Award
($Millions)
Panel E: Frequency by National Interest
443,503.54
National Interest Action
Number of
Contracts
Midwest Storms
Hurricane Katrina
Hurricane Wilma
Hurricane Ike
Hurricane Rita
Hurricane Ernesto
Hurricane Irene
Gulf Oil Spill
Operation Enduring Freedom
California Wildfires
None
8
47
7
7
3
2
2
4
314
454
48,231
0.02
0.10
0.01
0.01
0.01
0.00
0.00
0.01
0.64
0.93
98.27
Total
Award
($Millions)
122.79
806.09
96.78
30.96
6.75
3.65
6.04
21.64
1,571.95
9,909.21
430,927.68
Total
49,079
100.00
443,503.54
% of Total
Contracts
34
Avg. Award
($Millions)
0.03
0.18
0.02
0.01
0.00
0.00
0.00
0.00
0.35
2.23
97.16
15.35
17.15
13.83
4.42
2.25
1.83
3.02
5.41
5.01
21.83
8.93
100.00
9.04
Table 4
Summary Statistics
This table presents the descriptive statistics for our sample. ICWFirm is defined as one if a firm reports a material
weakness under SOX 302 in the prior year and zero otherwise. AccData is an indicator variable that takes the value
of one if the government requires cost or pricing data and zero otherwise. LongTerm takes the value of one if the
contract duration is greater than three years and zero otherwise. Specialized is an indicator variable that takes the
value of one if the products or services produced under the contract are specially made and lack commercial markets
and zero otherwise. Fixed is an indicator variable that takes the value of one if the type of contract pricing is fixedprice and zero otherwise. NationalInt is defined as one if the contract is related to a national interest and zero
otherwise. logNumBid is the natural log of the number of bids on the contract. logAmount is the natural log of the
dollar amount (in million) of the contract. Defense is an indicator variable that takes the value of one if the contract is
awarded by the Department of Defense, or zero otherwise. Asset is the dollar value of assets. Age is the number of
years that a firm has data available on Compustat. Loss is measured as the proportion of loss years over the prior
five-year period. Z-Score is the Altman’s Z-Score on bankruptcy risk. NumSeg is the sum of the total number of
operating and geographic segments, as reported in the Compustat segments database. Foreign is defined as one if the
firm has non-zero foreign currency translation data on Compustat and zero otherwise. 𝛥SGrowth is measured as the
change in sales growth between years 𝑡 and 𝑡 − 1. ACQ is an indicator variable that takes the value of one if the firm
reports sales from acquisitions and zero otherwise. Restruct takes the value of one if the firm reports restructuring
charges and zero otherwise. PriorContract takes the value of one if the firm receives contract in the prior year and
zero otherwise. Political is defined as one if a board member of the firm holds a position such as a Senator, Member of
the House of Representatives, Member of the Administration, or is a Director of a government organization during
that year and zero otherwise.
Variable
ICWFirm
AccData
LongTerm
Specialized
Fixed
NationalInt
logNumBid
logAmount
Defense
Asset
Age
Loss
Z-Score
NumSeg
Foreign
∆Sgrowth
ACQ
Restruct
Political
PriorContract
Mean
0.049
0.125
0.126
0.836
0.520
0.017
1.668
15.090
0.735
11,020
31.952
0.063
1.963
2.825
0.210
-0.007
0.166
0.286
0.255
0.822
STD
0.216
0.331
0.332
0.371
0.500
0.130
0.694
0.978
0.441
4.628
2.151
0.146
0.856
2.231
0.407
0.148
0.372
0.452
0.436
0.382
5%
0
0
0
0
0
0
1.099
14.006
0
481
6.000
0
0.673
1.000
0
-0.269
0
0
0
0
25%
0
0
0
1
0
0
1.099
14.344
0
5,048
24.000
0
1.451
1.000
0
-0.061
0
0
0
1
35
50%
0
0
0
1
1
0
1.386
14.844
1
16,183
47.000
0
1.863
3.000
0
-0.001
0
0
0
1
75%
0
0
0
1
1
0
1.946
15.577
1
31,152
54.000
0
2.329
6.000
0
0.067
0
1
1
1
95%
0
1
1
1
1
0
3.434
17.082
1
79,986
61.000
0.4
3.744
9.000
1
0.228
1
1
1
1
Table 5
Correlation Matrix
This table presents Pearson correlation coefficients among the variables in the sample. Panel A presents the correlation among contract characteristics. Panel B presents
the correlation among firm characteristics. All variables are as defined in Table 4.
Panel A: Correlation among Contract Characteristics
AccData
LongTerm
Specialized
Fixed
NationalInt
logNumBid
logAmount
Defense
ICWFirm
-0.054
-0.023
-0.022
-0.048
0.013
-0.019
-0.018
0.007
AccData
LongTerm
Specialized
Fixed
NationalInt
logNumBid
logAmount
0.053
0.155
-0.125
0.082
-0.005
0.042
0.170
0.061
-0.085
-0.038
-0.060
0.140
-0.035
-0.334
0.018
-0.153
0.029
0.032
0.070
0.121
-0.032
-0.009
0.007
0.013
0.064
-0.071
-0.016
-0.012
Panel B: Correlation among Firm Characteristics
logAsset
logAge
Loss
Z-Score
logNumSeg
Foreign
∆Sgrowth
ACQ
Restruct
Political
PriorContract
ICWFirm
logAsset
logAge
-0.072
-0.160
0.031
0.502
-0.185
-0.065
-0.014
0.010
0.152
-0.009
0.000
0.029
-0.095
0.089
-0.258
0.065
0.064
-0.022
-0.198
0.127
0.175
0.093
-0.146
0.021
0.014
-0.047
-0.233
0.116
0.036
0.072
Loss
Z-Score
-0.226
-0.157
0.042
0.190
0.038
-0.112
0.098
-0.127
-0.103
-0.148
0.070
-0.116
-0.080
-0.112
0.088
36
log
NumSeg
Foreign
∆Sgrowth
0.231
0.043
0.063
0.062
-0.033
0.041
0.022
-0.112
0.029
0.351
-0.118
0.045
-0.017
-0.064
-0.040
ACQ
Restruct
-0.026
0.052
-0.112
-0.147
0.010
Political
-0.037
Table 6
Probit Regressions of Contract Awards to Firms with Internal Control Weaknesses
This table presents results from Probit regressions of ICWFirm on contract characteristics: AccData, LongTerm, Specialized,
and Fixed. ICWFirm is defined as one if a firm reports a material weakness under SOX 302 in the prior year and zero
otherwise. AccData is an indicator variable that takes the value of one if the government requires cost or pricing data and zero
otherwise. LongTerm takes the value of one if the contract duration is greater than three years and zero otherwise.
Specialized is an indicator variable that takes the value of one if the products or services produced under the contract are
specially made and lack commercial markets and zero otherwise. Fixed is an indicator variable that takes the value of one if
the type of contract pricing is fixed-price and zero otherwise. All other variables are as defined in Table IV. Standard errors
are clustered by firm and year. ***,**, and * denote 1%, 5% and 10% level of significance respectively.
V ARIABLES
AccData
LongTerm
(1)
-0.0372***
(-4.82)
Specialized
(2)
-0.0145***
(-3.26)
Fixed
NationalInt
logNumBid
logAmount
Defense
logAsset
logAge
Loss
Z-Score
logNumSeg
Foreign
∆Sgrowth
ACQ
Restruct
Political
PriorContract
Constant
Observations
Pseudo R-squared
0.0527
(1.32)
-0.0040
(-1.14)
-0.0016
(-0.80)
0.0120*
(1.82)
-0.0021
(-0.30)
-0.0364*
(-1.93)
-0.0107
(-0.23)
-0.0063
(-0.67)
-0.0080
(-0.36)
0.0683**
(2.08)
-0.0281
(-0.79)
-0.0107
(-0.47)
0.0060
(0.22)
-0.0586***
(-2.67)
0.0842***
(3.55)
-0.5345
(-0.49)
49,079
19.59%
0.0449
(1.02)
-0.0045
(-1.24)
-0.0012
(-0.57)
0.0090
(1.28)
-0.0022
(-0.32)
-0.0364*
(-1.90)
-0.0090
(-0.19)
-0.0064
(-0.67)
-0.0079
(-0.36)
0.0680**
(2.09)
-0.0282
(-0.80)
-0.0099
(-0.44)
0.0068
(0.25)
-0.0598***
(-2.78)
0.0853***
(3.58)
-0.5825
(-0.53)
49,079
19.17%
37
(3)
-0.0078
(-0.94)
0.0464
(1.04)
-0.0048
(-1.49)
-0.0016
(-0.84)
0.0093
(1.28)
-0.0023
(-0.34)
-0.0363*
(-1.91)
-0.0110
(-0.24)
-0.0063
(-0.67)
-0.0078
(-0.35)
0.0676**
(2.06)
-0.0296
(-0.84)
-0.0100
(-0.44)
0.0065
(0.24)
-0.0597***
(-2.78)
0.0867***
(3.60)
-0.4437
(-0.41)
49,079
19.10%
(4)
-0.0163
(-1.47)
0.0487
(1.14)
-0.0035
(-1.01)
-0.0018
(-0.98)
0.0101
(1.52)
-0.0016
(-0.24)
-0.0368**
(-1.96)
-0.0019
(-0.04)
-0.0054
(-0.54)
-0.0072
(-0.33)
0.0680**
(2.05)
-0.0282
(-0.78)
-0.0111
(-0.50)
0.0083
(0.31)
-0.0584***
(-2.77)
0.0829***
(3.43)
-0.4787
(-0.44)
49,079
19.44%
(5)
-0.0387***
(-4.21)
-0.0147***
(-3.21)
-0.0128*
(-1.90)
-0.0222**
(-2.02)
0.0568
(1.48)
-0.0038
(-1.34)
-0.0011
(-0.53)
0.0114*
(1.73)
-0.0021
(-0.31)
-0.0358*
(-1.95)
-0.0028
(-0.06)
-0.0060
(-0.61)
-0.0069
(-0.32)
0.0678**
(2.06)
-0.0261
(-0.72)
-0.0128
(-0.59)
0.0074
(0.28)
-0.0571***
(-2.74)
0.0820***
(3.41)
-0.3474
(-0.33)
49,079
20.36%
Table 7
OLS Level and Change Regressions of Number of Contracts and Contract Amounts
This table presents results from OLS regressions of total number of contract (logContract) and total contract amounts
(logAmount) received by a firm in a year for a sample of firms in Audit Analytics that have government sales in prior three
years. Columns (1) and (2) present the results from level regressions, and Columns (3) and (4) present results when all
variables are first-differenced. logContract is the natural log of the number of government contracts received by a firm in a
year. logAmount is the natural log of number of the total contract amounts received by a firm in a year. ICWFirm is defined
as one if a firm reports a material weakness under SOX 302 in the prior year and zero otherwise. All other variables are as
defined in Table IV. Coefficients on industry fixed-effects are omitted. Standard errors are clustered by firm and year. ***,**,
and * denote 1%, 5% and 10% level of significance respectively.
(1)
V ARIABLES
ICWFirm
logAsset
logAge
Loss
Z-Score
logNumSeg
Foreign
∆Sgrowth
ACQ
Restruct
Political
Constant
Observations
R-Squared
Levels Analysis
Number of
Contract
-0.1396**
(-2.27)
0.2330***
(8.18)
0.1284***
(2.67)
-0.0711
(-1.19)
-0.0112***
(-5.06)
-0.0245
(-0.35)
-0.1675**
(-2.53)
-0.0005
(-0.04)
-0.0072
(-0.12)
-0.1338**
(-2.46)
0.9355***
(5.31)
-0.7583***
(-3.24)
(2)
Contract
Amounts
Number of
Contract
5,910
50.77%
4,399
0.05
-0.9413**
(-2.56)
1.4827***
(10.34)
0.6819**
(2.33)
-1.3177***
(-2.93)
-0.0379***
(-2.77)
0.3347
(0.90)
-0.5207
(-1.40)
0.0904
(0.78)
-0.1733
(-0.49)
0.2736
(0.91)
5.9969***
(5.81)
-2.8261**
(-1.98)
5,910
46.65%
38
Change Analysis
(3)
(4)
-0.0385*
(-1.92)
0.0644*
(1.68)
0.2941
(0.41)
-0.1045
(-1.28)
-0.0016**
(-2.31)
-0.0952**
(-2.26)
0.0937
(1.21)
-0.0202
(-1.05)
0.0779**
(2.18)
0.0099
(0.27)
0.5826
(1.50)
-0.1418
(-1.22)
Contract
Amounts
-0.3518*
(-1.73)
0.3363
(1.16)
1.7007
(0.50)
-0.8473***
(-3.30)
0.0115
(0.99)
-0.4974
(-1.35)
0.5033
(0.78)
-0.0667
(-0.61)
0.2182
(1.25)
0.1698
(0.53)
5.6291***
(3.06)
0.0589
(0.06)
4,399
0.06
Table 8
OLS Regressions of Management Earnings Forecast Errors
This table presents results from OLS regressions of ForecastError on contract characteristics: AccData, LongTerm,
Specialized, and Fixed. ForecastError is defined as the absolute difference between the most recent annual forecast and actual
earnings, scaled by total assets per share (divided by 100). AccData is an indicator variable that takes the value of one if the
government requires cost or pricing data and zero otherwise. LongTerm takes the value of one if the contract duration is
greater than three years and zero otherwise. Specialized is an indicator variable that takes the value of one if the products or
services produced under the contract are specially made and lack commercial markets and zero otherwise. Fixed is an
indicator variable that takes the value of one if the type of contract pricing is fixed-price and zero otherwise. InstHold is the
percentage of common shares held by institutions at the beginning of the period. LogAsset is the natural log of total assets
during the period. Analyst is the number of analysts providing a forecast for a firm prior to the managers’ forecast. Litigate is
a dummy equal to one if a firm operates in an industry with high litigation risk and zero otherwise. MKBK is the market-tobook ratio, calculated as the market value of total assets divided by the book value of total assets. NegEarn is an indicator
variable equal to one if the firm reports net losses in the period, and zero otherwise. EarnVol is the standard deviation of
quarterly net income over the prior 7 years (requiring at least 3 observations). Beta is equity beta for the fiscal year, obtained
from the Center for Research in Security Prices (CRSP) decile portfolio files. Disp is the standard deviation of analyst
forecasts in the beginning of the year. logAge is the natural log of the firm’s age. AbsChgROA is the absolute value of the
change in ROA from the prior period. StdRet is the standard deviation of returns over the 120 days prior to the forecast date.
Horizon is the number of days between the fiscal period end and the announcement date, divided by 100. Optimism is analyst
forecast optimism at the beginning of the year, defined as the difference between the mean forecast estimate and actual
realized earnings, scaled by the absolute value of actual earnings. Surprise is the absolute value of the forecast guidance less
the last available consensus analyst forecast, scaled by assets per share. All other variables are as defined in Table IV.
Standard errors are clustered by firm and year. ***,**, and * denote 1%, 5% and 10% level of significance respectively.
V ARIABLES
AccData
LongTerm
(1)
-0.0541**
(-2.16)
Specialized
(2)
-0.0683***
(-2.76)
Fixed
NationalInt
logNumBid
logAmount
Defense
InstHold
logAsset
Analyst
Litigate
MKBK
NegEarn
EarnV ol
Beta
0.2527***
(3.42)
0.0083
(0.55)
0.0050
(0.69)
0.0244
(1.28)
0.0027
(0.77)
0.0137
(0.51)
-0.1238
(-1.34)
-0.0694
(-1.00)
0.2572**
(2.57)
0.3927
(.95)
8.6254*
(1.77)
0.1109
0.2242***
(3.76)
0.0074
(0.50)
0.0076
(1.02)
0.0136
(0.79)
0.0027
(0.77)
0.0123
(0.46)
-0.1237
(-1.34)
-0.0654
(-0.93)
0.2562**
(2.54)
0.3953
(.95)
8.6964*
(1.79)
0.1120
39
(3)
-0.0522**
(-2.04)
0.2373***
(3.62)
0.0053
(0.34)
0.0045
(0.62)
0.0182
(1.03)
0.0028
(0.78)
0.0111
(0.42)
-0.1208
(-1.31)
-0.0684
(-0.97)
0.2548**
(2.53)
0.3980
(.96)
8.5297*
(1.74)
0.1082
(4)
0.0072
(0.28)
0.2450***
(3.11)
0.0088
(0.58)
0.0043
(0.57)
0.0181
(1.06)
0.0027
(0.78)
0.0128
(0.48)
-0.1235
(-1.33)
-0.0651
(-0.92)
0.2549**
(2.52)
0.3955
(.95)
8.6170*
(1.76)
0.1103
(5)
-0.0476**
(-2.01)
-0.0645***
(-2.75)
-0.0464**
(-2.05)
-0.0098
(-0.42)
0.2261***
(2.99)
0.0038
(0.25)
0.0083
(1.12)
0.0195
(1.07)
0.0027
(0.76)
0.0116
(0.43)
-0.1215
(-1.31)
-0.0721
(-1.03)
0.2579***
(2.58)
0.3951
(.95)
8.6212*
(1.76)
0.1102
Disp
logAge
AbsChgROA
StdRet
Horizon
Optimism
Surprise
Constant
Observations
R-squared
(0.54)
2.7637**
(2.40)
0.0171
(0.64)
3.1749
(1.27)
-13.4391**
(-2.29)
0.1040***
(3.19)
3.0245***
(2.66)
70.4257***
(5.51)
-1.8859**
(-2.52)
(0.54)
2.7569**
(2.39)
0.0197
(0.72)
3.1239
(1.26)
-13.4846**
(-2.32)
0.1044***
(3.16)
3.0248***
(2.66)
70.2818***
(5.50)
-1.8882**
(-2.53)
(0.53)
2.7551**
(2.39)
0.0196
(0.73)
3.1306
(1.25)
-13.5997**
(-2.34)
0.1031***
(3.13)
3.0308***
(2.67)
70.5035***
(5.52)
-1.8122**
(-2.47)
(0.54)
2.7544**
(2.39)
0.0182
(0.67)
3.1510
(1.26)
-13.5886**
(-2.33)
0.1036***
(3.16)
3.0275***
(2.67)
70.4481***
(5.51)
-1.8616**
(-2.41)
(0.54)
2.7654**
(2.40)
0.0198
(0.73)
3.1284
(1.25)
-13.3711**
(-2.29)
0.1043***
(3.17)
3.0251***
(2.66)
70.3139***
(5.50)
-1.8682**
(-2.42)
33,882
62.94%
33,882
62.96%
33,882
62.94%
33,882
62.92%
33,882
63.00%
40