Melco Management Accounting Research
Discussion Paper Series
No.MDP2017-007
Cost Manipulation in Japanese Defense Procurement Contracts:
Forcusing on Opportunistic Cost-Shifting

September 2017
Takahiro Morimitsu*
Associate Professor, Japan University of Economics,
Graduate School of Business Administration
24-5 Sakuragaoka-cho, Shibuya-ku, Tokyo, 150-0031, Japane-mail: morimitsu@tk.jue.ac.jp
Yoshitaka Shirinashihama
Associate Professor, Yamagata University,
Faculty of Humanities and Social Sciences
1-4-12 Kojirakawa-machi, Yamagata City, 990-8560, Japane-mail: snh@human.kj.yamagata-u.ac.jp*Corresponding author

The Melco Foundation
Nagoya, Japan
Discussion Paper Series of the Melco Management Accounting Research, in order to promote
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Cost Manipulation in Japanese Defense Procurement Contracts:
Forcusing on Opportunistic Cost-Shifting

Morimitsu, Takahiro
Shirinashihama, Yoshitaka

Abstract
This study identifies serious problems in Japanese defense procurement from the
management accounting perspective, especially regarding cost overcharging. There isan inherent conflict of interest between the Japanese Ministry of Defense (MoD) andits contract partner firms. While the MoD tries to procure as effectively and efficiently
as possible, contracting firms attempt to maximize profit from transactions with thedefense department. Furthermore, most items in defense contracts have no market
price. Such items are priced by multiplying the profit rate by the cost. This pricing
structure incentivizes companies to execute contracts inefficiently, that is, firms may
have an incentive to overcharge by manipulating costs. This is particularly significant
due to the contract provision that requires the return of excess profit. Therefore, we
aim to detect such overcharging by analyzing data from individual contracts containing
this provision. We use a profit distribution approach to verify whether firms manipulate
costs, and a cost manipulation and logistic regression approach to test our hypothesis
about the factors that promote cost manipulation. Our result strongly suggests thatmany firms inflate costs to maximize profits, and partly support our hypotheses.

Keywords
Defense Procurement, Earnings Management, Cost manipulation, Overcharging

1

1. Introduction
Public sectors entities make various contracts to fulfill their duties, called public
contracts. Among them, a specific type of contract made to purchase goods andservices are called government procurement contracts, paid for through taxes. Because
the effective and efficient use of taxes is an important duty of the public sector, it must
execute government procurement effectively and efficiently. Furthermore, the public
sector must maintain accountability to taxpayers.
Among the various types of government procurement, defense procurement is
the most important to monitor because it represents a large portion of government
budgets. It is also significant in Japan, where the budget for defense procurement has
consistently been about 2 trillion yen in recent years (Japan Ministry of Defense
Equipment Procurement and Construction Office, 2014). Furthermore, reflecting
Japan’s complex defense environment, the Japanese defense budget tends to increaseeach year. Thus, defense procurement contracts receive great scrutiny in Japan.
Therefore, we focus primarily on defense contracts in this study.
From management accounting perspective, we can identify serious problems
with defense contracts. First, there is an inherent conflict of interest between thegovernment and its contract partner companies. While the government tries to execute
procurement as effectively and efficiently as possible, contracting firms attempt tomaximize profit from transactions with the defense department.
Second, most items in defense contracts have no market price. Therefore, suchitems must be priced based on the accumulation of appropriate costs plus profits to thecontract firms, calculated by multiplying the profit rate by the cost. Under this profit
structure, if contracting firms produce defense items at lower cost, they obtain lower
profits. This pricing structure essentially incentivizes companies to manipulate costs(i.e., overcharging) and execute contracts inefficiently.
Although some previous studies indicate the presence of overcharging, noprevious research discusses this theme using directly related empirical data. A contract
provision specific to Japanese defense contracts requires the return of excess profit,
and thus provides a good example to verify the presence of overcharging. This
provision stipulates that if actual prices (actual costs plus profits) are lower than theinitial prices determined in the contracts, firms must return the excess profits (initial
prices minus actual prices). Thus, firms may try to maintain high costs, even if they
can produce the contract items at a lower cost (i.e., firms may have a strong incentive
to overcharge). The Acquisition, Technology, and Logistics Agency (ATLA) in Japan
provided us with Defense Department procurement contract data for 2009–2014, which2we analyze to verify the presence of overcharging directly.
In addition to investigating the presence of overcharging with a focus on
Japanese defense contract data, we also identify the factors that promote cost
manipulation. For this purpose, we use the CM ratio and logistic regression approaches
to test several hypotheses about cost manipulation factors.
The remainder of this paper proceeds as follows. The second section introduces
the contract provision requiring the return of excess profit. The third section reviews
the previous literature on the subject. The fourth section describes the researchmethodology. The fifth section presents the sample selection and descriptive statistics.
The sixth section reports the analysis and results. Finally, the last section discusses thefindings, contributions, limitations, and suggestions for future research.

2. Contract Provisions Requiring Firms to Return Excess Profit
In this study, we focus on the contract provision requiring the return of excess
profit, which the Japanese government adopts when there is high uncertainty about
future costs in defense procurement contracts. This provision is used in fixed-pricecontracts1 and stipulates that a contract firm’s excess profit must be returned to Japan
Ministry of Defense (MoD) when it generates excess profits (Japan MoD, 2010; JapanMoD Equipment Procurement and Construction Office, 2014). In this type of contract,
the Japan MoD audits the firm’s actual costs. In the course of this audit, if the actual
costs are less than the initial costs based on the initial contract, then the firm must
return a portion of the contract payment, representing excess profit. Figure 1 illustrates
this contract structure.

1

IFRS (International Financial Reporting Standards) define a fixed-price contract as“a construction contract in which the contractor agrees to a fixed contract price, or a
fixed rate per unit of output, which in some cases is subject to cost escalation clauses”(IAS 11).
Based on this definition and the features of defense contracts, we define a
fixed-price contract as one in which the contracted firm agrees to a fixed contract price,which in some cases is subject to cost revisiting procedures.
3

Figure 1:
profitStructure of the contract provision requiring the return of excess

Note: In this example, we assume that both profit rates are 10%.
Source: Japan MoD (2010)

The problem with this contract structure is that it gives no incentive to thecontracted firms to work toward cost reduction and may, in fact, inhibit cost reduction.
In this type of contract, if firms achieve a cost reduction, they must return excess profit.
Furthermore, as Figure 1 shows, cost reduction reduces the firm’s profit. In thiscontractual arrangement, we presume that contract firms attempt to maximize theirprofit by maintaining the initial cost determined at the time the contract was executed(i.e., contract firms have an incentive to overcharge).
The Board of Audit of Japan (2012) noted several incidents of this type ofovercharging in which firms manipulated their product cost, particularly manufacturing
overhead, to gain a larger profit than was actually earned. We believe that these cases
provide evidence of a negative performance incentive.

3. Literature Review and Hypothesis Development
3.1. Literature Review
Although numerous studies address the relationships and controls between
buyers and suppliers, the number of studies of this type of overcharging is limited. In
this section, we provide an overview of the literature.
Under the condition of information asymmetry, firms have suspicions about theopportunistic behavior of their counterparts. Accordingly, many firms develop various
interfirm control mechanisms (Anderson & Dekker 2005; Dekker & Abeele, 2010) andgovernance designs (Dekker, 2008).
Some studies argue for cost manipulations in defense contracts as anopportunistic behavior. Rogerson (1992) utilizes a mathematical model to describe4

overhead allocation in U.S. defense procurement, concluding that defense contract
firms have an incentive to shift the cost of non-defense items to defense items. Thomas
and Tung (1992) note a similar cost-shifting incentive under a cost compensation
contract using quantitative evidence and conclude that defense contract firms could
reduce the cost of non-defense items by overfunding pension plans when employees
work on defense contracts and withdrawing excess pension assets when employees
work on non-defense business. Lichtenberg (1992) demonstrates the relative
profitability of defense and non-defense contracts using an empirical analysis of 9,300
annual segment data points from 1983–1989. His results show that the return on assets(ROA) of defense segment contracts as a whole is 68%–82% higher than the ROA of
non-defense segment contracts.
In contrast, McGowan and Vendrzyk (2002) conclude that there is no evidence
that defense contract firms manipulate costs. To compare ROA, they divide allsegments into three types: defense segments, non-defense segments, and segments that
produce both items (i.e., mixed segments). Using data from 1984–89 and 1994–1998,
they find that although there are significant differences between the ROA of defense
segments and the other segments, and inconsistent with the hypothesis, defense
segments have the highest. They explain that firms gain the high profitability indefense segments from factors beyond the accounting context.
In Japan, Morimitsu (2012) suggests that the current defense contract pricing
structure yields little incentive for firms to reduce costs. However, he provides noempirical data collection, so his suggestion is limited. Morimitsu (2013) argues thatcost transparency is the responsibility of the Cost Audit Agency in Japanese defense
procurement. He demonstrates that the primary function of the Cost Audit Agency is
the false check, so verification of cost manipulation is limited. Ohta (2014) argues anappropriate institutional design of cost auditing and a competitive bidding process forJapanese defense procurement contracts. Although he suggests the existence ofovercharging, he uses a mathematical model analysis, and therefore also has noempirical data. This literature review illustrates that there is no consensus about cost
manipulation in defense procurement contracts in Japan.
While previous studies make use of segment data, few prior studies attempt toassess overcharging using empirical contract data. To analyze overcharging inJapanese defense contracts, we focus attention on the contract itself and, in particular,
on the provision requiring the return of excess profit. We hypothesize that this contract
provision strongly promotes overcharging and therefore believe that this contract
provision is an appropriate focus for examining cost manipulation.
We can precisely verify whether contract firms manipulate costs using contract
5

data from Japanese defense contracts that require the return of excess profit. This data
allows us to examine whether the actual costs accrued during contract fulfillment
exceed the anticipated contract costs. In addition, the distribution of the actual/contract
ratio will indicate the seriousness of the cost manipulation problem.

3.2. Hypothesis Development
Prior studies suggest some common views and indications. Rogerson (1992),
Thomas and Tung (1992), and Lichtenberg (1992) assert the existence of opportunistic
cost-padding, mainly focusing on overhead allocation (i.e., a cost-shifting hypothesis).
Their assertions support the existence of cost manipulation, and we believe that
cost-shifting is a method of cost manipulation.
Although McGowan and Vendrzyk (2002) disagree, we argue that they overlook
cost-shifting between defense businesses. In practice, cost-shifting between defense
contracts has occurred in Japan (Board of Audit of Japan, 2012). Thus, we predict that
cost-shifting behaviors exist in defense contract firms as a cost manipulation method.
Morimitsu (2012, 2013) and Ohta (2014) suggest that current defense contract
structures provide contract firms with a disincentive to reduce costs. Although they do
not identify the manipulation method specifically, they indicate the existence of cost
manipulation implicitly. Therefore, we propose that defense contract firms manipulate
their costs.
H1: Defense contract firms manipulate their costs to maximize their own gain.
In addition, we believe there are several cost manipulation methods and factors
with positive effects on the probability of cost manipulation.

3.2.1.

Contract Scale

Based on the cost-shifting hypothesis, the contract value is a simple but
important factor. The higher the value of the contract is, the greater the manufacturing
overhead tends to become. Consequently, a higher manufacturing overhead makes it
easier for firms to shift costs. Furthermore, with a larger contract value, the price will
become larger even with the same ratio. Thus, firms have a stronger incentive tomanipulate costs in larger contracts than in smaller ones. Accordingly, we predict that ahigher contract value has a positive effect on cost manipulation.
H2: As the contract value increases, the probability of cost manipulation increases.
6

3.2.2

Defense Contract Firms
Rogerson (1992) concludes that defense contract firms purposely overuse direct
labor to shift more overhead costs to defense contracts. Thus, he indicates that defense
contract firms tend to be labor-intensive (i.e., the more firms employ labor in the
defense business, the higher the opportunity to manipulate costs is). Thus, we propose
the same prediction under the Japanese defense contract structure.
H3a: The ratio of the firm’s employees to total assets at the end of the fiscal year
(i.e., labor-intensity) has a positive effect on the probability of cost manipulation.
Based on this hypothesis, the number of contracts may also be an importantfactor. In simple terms, as the number of a firm’s defense contracts in the fiscal year
increases, manufacturing overhead increases. Thus, the scope for cost manipulation
increases.
H3b: As the number of contracts increase, the probability of cost manipulation
increases.
Additionally, we predict that a firm’s total sales under defense contracts has apositive effect because higher sales will tend to increase a firm’s manufacturing
overhead, which makes it easier for firms to shift costs from other defense contracts.
Consequently, the higher defense contract sales become, the greater the opportunities
for cost manipulation become.
H3c: As a firm’s total sales under defense contracts increases, the probability
that it will manipulate costs increases.

7

4. Research Design
To test our hypotheses, we use three approaches: (1) profit distribution, (2) cost
manipulation ratio, and (3) logistic regression.
4.1. Profit Distribution Approach
To verify cost manipulation, we apply the profit distribution approach 2(Burgstahler & Chuk, 2015; Burgstahler & Dichev, 1997; Degeorge et al., 1999) toanalyze the data. Prior studies used the profit distribution approach to detect earnings
management that exceeds thresholds.
We anticipate that if firms manipulate costs to maximize profit, the frequency of
the ratio of actual prices to contract prices on a histogram will be extraordinarily
weighted to the right of 100%. Because firms know the actual costs, if they are below
the contract costs, they may try to manipulate the actual costs to obtain the full profit.
If this occurs, then the frequency of data points to the left of 100% on a histogram
should decrease and the frequency to the right of 100% should increase. Thus, if firms
manipulate costs, the histogram of the ratios will show discontinuation around 100%.
By using significance tests of standardized differences in the interval (Burgstahler &Dichev, 1997), we can confirm whether this discontinuation is statistically significant.
In creating the histogram, the width of the bin determines the shape of the
distribution and the result of the statistical tests. To avoid arbitrarily deciding the width,
we use Degeorge et al.’s (1999) proposed method, in which the width of the bin should
be positively related to data variability and negatively related to sample size. They
recommend using a bin width of 2 (IQR) n -1/3, where IQR is the interquartile range ofthe interest variable and n is the sample size.
4.2. Cost Manipulation Ratio Approach
To test for differences in cost manipulation between smaller and larger contracts(H2), we report the cost manipulation ratio (CM ratios). The CM ratio is the ratio of
the number of observations in the interval to the immediate right of (and including)
100% to the numbers to the immediate left of 100%. Some studies report similar ratios
2Some researchers criticize the profit distribution approach (Durtschi & Easton 2005;
Durtschi & Easton 2009, hereafter DE). DE assert that scaling and sample selection
explain discontinuities. However, in a recent study, Burgstahler and Chuk (2015) assertthat DE’s studies provide no evidence that this is the case. In addition, they show thepresence of discontinuities using the same data that DE use in their studies. Based on
Burgstahler and Chuk (2015), we use a profit distribution approach to show evidenceof cost manipulation.
8

as evidence of earnings management (Brown & Caylor, 2005; Dechow et al., 2003;
Shuto, 2009). We split our sample into larger and smaller contracts based on the
median of the initial contract prices and compare their CM ratios. Per H2, firms withlarge contracts can manipulate defense contract costs easier than firms with smaller
contracts can. Therefore, the CM ratio based on larger contracts is larger than that ofsmaller contracts is.
4.3. Logistic Regression Approach
To test H2 and H3, we estimate the following logistic regression model3:

where:
indicator variable that takes a value of one if the firm scaled the ratioof the actual price to contract price in the interval between 100% (inclusive) and102.8% (exclusive), and 0 otherwise;
natural logarithm of the initial contract value;
ratio of the firm’s employees to total assets at the end of the fiscal year;
the number of the firm’s defense contracts at the end of the fiscal year;
ratio of the firm’s sales from defense contracts to total sales;
indicator variable for the fiscal year.

The larger the contract cost is, the more the opportunities for cost manipulation
increase and the stronger the motivation to manipulate costs (). We also
expect that firms with labor-intensive production processes can manipulate costs moreeasily than capital-intensive firms () can. Firms with more defense contracts
can shift actual costs among contracts more easily than other firms () can.
Finally, our hypotheses predict that the larger the firms’ total contract values are, themore the opportunities for cost manipulation will increase ().
Following McGowan and Vendrzyk (2002), we introduce a year effect() dummy in our model to control for the effect of annual changes in theprices of raw materials and other environmental effects.
3

We considered a multilevel logistic regression because our contract data is nested infirms. However, the interclass correlation coefficient is close to 0. Thus, we do not use
this method.
9

5. Sample Selection and Descriptive Statistics
5.1. Sample Selection
The ATLA provided us with Defense Department procurement contract data for
2009–2014. This data consists of contact year, false names of firms, contract prices,
actual prices, and contracts terms. These data do not include cost data, but do include
price data (costs plus profits). We cannot compare actual costs to contract costs, though
we can use this data to calculate the ratio of actual prices to contract prices to verify
the existence of cost manipulation in defense procurement contracts. The ATLA
recently showed that some firms paid penalties for disguising costs.4 The ATLA data
eliminates observations of firms that engaged in disguising costs. The sample size of
the ATLA dataset is 361 contracts (n = 361). In addition to this data, ATLA provides
processed financial data and contract related data. Both types of data are connected tocontract data so we cannot identify the real names of the contract firms.

5.2. Descriptive Statistics
Table 1 reports the descriptive statistics of the ATLA contract data. The mean of
the actual/contract ratio is 110.1% and the median is 104.8%, which indicate that the
average actual contract costs are larger than the projected contract costs.

Table 1 Descriptive statistics: ATLA contract data
variables
contract price (thousand yen)actual price (thousand yen)actual/contract ratio (%)

n
361
361
361

mean
1,715,339
1,807,455
110.1

std. dev.
4,238,073
4,354,023
22.38

median572,000
649,989
104.8

mini
1,156
858
45.8

max
43,837,600
46,917,320
286.8

Table 2 reports the descriptive statistics and correlations for the variables used in
the logistic regression. Panel B shows a high correlation between LINTENS andDSALES. When we introduce both variables in the logistic equation, the varianceinflation factors (VIF) of these variables are higher than 5.0. Thus, we introduce thesetwo variables in the logistic regression models separately.

4

We define cost disguise as illegally manipulating costs (e.g., inflating the man-hours
spent on a project).
10Table 2 Descriptive statistics and correlations among the variables in the logistic
regression
Panel A: Descriptive statistics for the variables used in the logistic regression approach
variablesnmean
std. dev.
medianmini
OVER100%
96
0.72
0.45
1.00
0.00
AMMOUNT
96
20.72
1.50
20.73
15.85
LINTENS
96
0.03
0.01
0.02
0.01
CNUMBER
96
184.5279.80
214.00
1.00
DSALES
96
11.52
1.43
12.21
5.30

max
1.00
24.25
0.06
301.0012.67

Panel B: Correlations ofr the variables used in the logistic regression approach
OVER100% AMMOUNT LINTENS CNUMBER DSALESOVER100%
1.00
AMMOUNT
0.07
1.00
LINTENS
-0.11
-0.13
1.00
CNUMBER
-0.06
0.06
-0.27
1.00
DSALES
0.09
0.09
-0.72
0.71
1.00

6. Results
6.1. Profit Distribution
Figure 2 shows the histogram of the ratios of actual costs to projected contract
costs, revealing a discontinuation and skew to the right of 100%. The frequency of theratios to the right of 100% is very high, the frequency to the left is relatively low, andthe frequency of values in the 2nd interval to the left of 100% shows a dramatic
decrease. In addition, if we assume a distribution peak to the right of 100%, thefrequency of values in the right half of the distribution is relatively higher than that in
the left half of the distribution. These results indicate that a certain amount of cost
manipulation occurred.

11

Figure 2:

S Histogram of actual/contract price ratio

Table3 shows the results of the standardized differences tests. In the interval to
the left5 of 100%, the standardized difference is significant (p < 0.05). Thus, thefrequency of observations in the intervals to the left of 100% is extraordinarily low.
These results again suggest that firms manipulated costs in their contracts.

Table 3 Standardized differences test results
standardized differences
around 100%in left of 100% in right of 100%
-1.69 *
2.60 **

standardized differencesin other intervals
mean median mini max-0.30 -0.54 -2.08 1.74

* p < 0.05 (2-sided), ** p < 0.01(2-sided)

6.2. Cost Manipulation Ratio
To test H2, we calculate the CM ratios for smaller and larger contracts according
to the median value. Figure 3 shows the histograms of actual/contract ratios for smaller
5

According to Shuto (2010, p. 89), when the interval to the right of 100% is the modal
class of the histogram, we cannot properly estimate the expected number ofobservations in the interval. In this case, Shuto (2010) recommends that we interpretthe result of the standardized difference test based on only the left side of the result.
12and larger contracts in Panels A and B, respectively. Panel A resembles the histogramof the full sample. On the other hand, the peak in Panel B is not between 100% and102.8%, but between 102.8% and 105.6%.

Figure 3: Comparison histograms of actual/contract price ratios

Table 4 shows the CM ratios of Panel A and Panel B and the result of thetest.
H2 predicted that Panel B would have a larger CM ratio than Panel A. However, theCM ratio of Panel A is not so different from that of Panel B (2.53 vs. 2.30). Thus, wefail to disprove our null hypothesis (= 0.00, df = 1, p = 1).

Table 4 The CM ratio and results of the
Figure 3. Panel A
Figure 3. Panel B

CM ratioc 2 -value a

2.53 (48/19)
2.30 (23/10)

0.00

test

(Figure 3. Panel A vs. Figure 3. Panel B)

Notes:a

The chis-square statistics for the CM ratio differencese are caliculated using the 2 by 2 table.

136.3. Logistic Regression

Table 5 Logistic model of cost manipulation for firms with small earnings changes

Variable

Coeff.

Std. Err.

z

p

16.39
0.06
-26.080.00

1384.00
0.16
20.88
0.00

0.01
0.39
-1.25
-1.03

0.99
0.70
0.21
0.30

11.27
0.06

1374.00
0.16

0.01
0.40

0.99
0.69

-0.01
0.45

0.00
0.24

-1.83
1.87

0.07
0.06

Panel A: LINTENS
Constant
AMMOUNT
LINTENSCNUMBERDSALES
Log-likelihood
2
McFadem RN-53.470.06
96

Panel B: DSALES
Constant
AMMOUNT
LINTENSCNUMBERDSALES
Log-likelihood
McFadem R 2-52.490.08
N96
Note: The estimations sample includes the contracts data with only the absolutevalue of actual/contract ratio between 97.19 to 102.81.

OVER100%=

AMMOUNT=

LINTENS=

indicator variable that takes the value of one if the firm
has scaled the ratio of actual price to contract price in
the interval between 100% (inclusive) and 102.8%(exclusive), and 0 otherwise;
natural logarithm of the initial contract amount;ratio of the firm’s employees to total asset at the endof the fiscal year;
CNUMBER=the number of defense contracts of the firm at the
fiscal year;
DSALES=
ratio of the firm’s sales amounts of defense contract tototalsalesvariable
amounts;
year dummy=
indicator
for the fiscal year.
Indicator variables for year ( year dummy ) are inculuded but not reported.

Table 5 shows the results of the logistic regressions. Due to the high correlation
14

between LINTENS and DSALES, we conduct two logistic regressions (Panel A with
LINTENS; Panel B with DSALES). The results in Panel A do not support our
hypothesis. On the other hand, Panel B indicates that DSALES has a significant
positive effect on OVER100%, and is consistent with H3c. CNUMBER also has asignificant relationship with OVER100%, but its sign is contrary to our hypothesis(H3b).
Overall, the profit distribution results strongly suggest that firms manipulated
contract costs to obtain full profit in contracts that require that they return excess profit.
However, the results of our CM ratio test do not support our hypothesis about the effectof contract values on cost manipulation (H2). Our logistic regression approach
supports one of our hypotheses, that firms with large total contract values have more
opportunities for cost manipulation (H3c), but does not support the other hypotheses(H2, H3a, and H3b).

7. Discussion and Conclusion
In this study, we focused on defense procurement contracts that require firms toreturn excess profit using a profit distribution approach to verify whether firms
manipulate costs, and CM ratio and logistic regression approaches to test our
hypothesis about the factors that contribute to cost manipulation. Previous research
results on the subject are mixed. Some note that firms manipulate costs by shifting
overhead costs from commercial projects to defense contracts (Lichtenberg, 1992;
Rogerson, 1992), while others suggest that the high profitability in defense segments is
not due to cost manipulation (McGowan & Vendryzk, 2002). However, prior studies
rely on firms’ profitability rather than using specific contract data. We argue that themixed results are due to both insufficient data and poor research methods. In this study,
we use contract data to obtain more accurate results, and our results strongly suggestthat many firms inflate costs to maximize profit.
Some previous studies suggest that several factors encourage defense contractors
to manipulate actual costs (Lichtenberg, 1992; Rogerson, 1992; Thomas & Tung,
1992). Firms with high-value contracts have incentives and opportunities to manipulate
their actual costs, such as by manipulating man-hours. Thus, firms with labor-intensive
production processes may easily manipulate their actual costs, as might firms withmany contracts with the Japanese MoD and high total contract values. The CM costand logistic regression approaches partially support our hypotheses.
Our result suggests that the larger the total value of a defense contract is, thegreater the opportunities to control actual costs are. Controlling single contract values
15and contract numbers means that firms with larger average defense contract values canshift costs from other defense contracts or commercial products to contracts requiringthat the firm return excess profits. On the other hand, our result suggests that firms
with many contracts cannot shift their costs to such contracts. Firms with many
contracts may face difficulty attaining an over 100% actual/contract ratio in eachcontract by shifting costs because as the number of contracts increases, thecomplexities of the interdependencies of cost allocation among contracts increases.
Furthermore, the costs that firms can shift to defense contracts are not infinite.
Therefore, we predicted that firms can shift limited costs, as those firms having many
contracts cannot shift enough costs to all contracts.
The primary contribution of this study to the literature is on cost manipulation in
the defense segment. Using a profit distribution approach, our study yields highly
accurate analysis results about firms’ costs manipulation. In addition, our logistic
regression results could partly support the hypotheses about the factors that encouragefirms to manipulate those costs. These results give new insights and strong evidence
for the existence of cost manipulation in defense contracts and its exacerbating factors.
Our research has important implications for policymakers. Our results suggestthat contract provisions requiring that firms return excess profit will encourage them tomanipulate costs. Thus, it is important for policymakers to consider the side effects ofthis kind of contract provision and a revision of the incentive mechanism of thecontract system.
Our study has some limitations. Our results only partly support our hypotheses,
in part due to data limitations. We use firms’ consolidated financial information;
however, the Board of Audit of Japan reports some cases in which divisional managers
disguised their actual costs (Board of Audit of Japan, 2012). If we can specify adivision of a firm that makes contracts with Japan’s MoD, we can use segment
financial information (e.g., the labor intensiveness of the division, total divisional sales,or number of defense contracts).
Notwithstanding this limitation, our research is the first to use defense
procurement contract data and provides strong evidence for the existence of cost
manipulation. Future research using contracting firms’ segment data will advance ourunderstanding of cost manipulation in defense contracts.

16

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